diff --git a/Ch11-surv-lab.ipynb b/Ch11-surv-lab.ipynb
index a8a6182..efbc233 100644
--- a/Ch11-surv-lab.ipynb
+++ b/Ch11-surv-lab.ipynb
@@ -5,7 +5,9 @@
    "id": "c7f4eb5a",
    "metadata": {},
    "source": [
-    "# Chapter 11"
+    "\n",
+    "# Chapter 11\n",
+    "\n"
    ]
   },
   {
@@ -32,10 +34,10 @@
    "id": "91ac40fd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:36.910686Z",
-     "iopub.status.busy": "2023-07-31T02:15:36.910197Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.043363Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.043019Z"
+     "iopub.execute_input": "2023-08-06T17:35:46.303006Z",
+     "iopub.status.busy": "2023-08-06T17:35:46.302426Z",
+     "iopub.status.idle": "2023-08-06T17:35:47.388674Z",
+     "shell.execute_reply": "2023-08-06T17:35:47.388193Z"
     }
    },
    "outputs": [],
@@ -44,7 +46,7 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "from ISLP.models import ModelSpec as MS\n",
-    "from ISLP import load_data"
+    "from ISLP import load_data\n"
    ]
   },
   {
@@ -62,10 +64,10 @@
    "id": "99782418",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.045310Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.045152Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.137179Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.136883Z"
+     "iopub.execute_input": "2023-08-06T17:35:47.390741Z",
+     "iopub.status.busy": "2023-08-06T17:35:47.390555Z",
+     "iopub.status.idle": "2023-08-06T17:35:47.484777Z",
+     "shell.execute_reply": "2023-08-06T17:35:47.484422Z"
     }
    },
    "outputs": [],
@@ -76,7 +78,7 @@
     "from lifelines.statistics import \\\n",
     "     (logrank_test,\n",
     "      multivariate_logrank_test)\n",
-    "from ISLP.survival import sim_time"
+    "from ISLP.survival import sim_time\n"
    ]
   },
   {
@@ -95,10 +97,10 @@
    "id": "3137149a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.138950Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.138844Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.145075Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.144817Z"
+     "iopub.execute_input": "2023-08-06T17:35:47.486501Z",
+     "iopub.status.busy": "2023-08-06T17:35:47.486385Z",
+     "iopub.status.idle": "2023-08-06T17:35:47.493362Z",
+     "shell.execute_reply": "2023-08-06T17:35:47.493058Z"
     }
    },
    "outputs": [
@@ -115,7 +117,7 @@
    ],
    "source": [
     "BrainCancer = load_data('BrainCancer')\n",
-    "BrainCancer.columns"
+    "BrainCancer.columns\n"
    ]
   },
   {
@@ -133,11 +135,12 @@
    "id": "45963c92",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.146568Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.146458Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.149482Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.149235Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:47.494963Z",
+     "iopub.status.busy": "2023-08-06T17:35:47.494863Z",
+     "iopub.status.idle": "2023-08-06T17:35:47.497996Z",
+     "shell.execute_reply": "2023-08-06T17:35:47.497741Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -154,7 +157,7 @@
     }
    ],
    "source": [
-    "BrainCancer['sex'].value_counts()"
+    "BrainCancer['sex'].value_counts()\n"
    ]
   },
   {
@@ -163,11 +166,12 @@
    "id": "73be61f6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.151583Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.151488Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.153999Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.153712Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:47.499414Z",
+     "iopub.status.busy": "2023-08-06T17:35:47.499312Z",
+     "iopub.status.idle": "2023-08-06T17:35:47.502029Z",
+     "shell.execute_reply": "2023-08-06T17:35:47.501779Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -186,7 +190,7 @@
     }
    ],
    "source": [
-    "BrainCancer['diagnosis'].value_counts()"
+    "BrainCancer['diagnosis'].value_counts()\n"
    ]
   },
   {
@@ -195,11 +199,12 @@
    "id": "572f0b9e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.155561Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.155460Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.158411Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.158146Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:47.503331Z",
+     "iopub.status.busy": "2023-08-06T17:35:47.503251Z",
+     "iopub.status.idle": "2023-08-06T17:35:47.506059Z",
+     "shell.execute_reply": "2023-08-06T17:35:47.505826Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -216,7 +221,7 @@
     }
    ],
    "source": [
-    "BrainCancer['status'].value_counts()"
+    "BrainCancer['status'].value_counts()\n"
    ]
   },
   {
@@ -250,10 +255,10 @@
    "id": "92c39707",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.159867Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.159768Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.465840Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.465244Z"
+     "iopub.execute_input": "2023-08-06T17:35:47.507415Z",
+     "iopub.status.busy": "2023-08-06T17:35:47.507329Z",
+     "iopub.status.idle": "2023-08-06T17:35:47.692016Z",
+     "shell.execute_reply": "2023-08-06T17:35:47.690384Z"
     }
    },
    "outputs": [
@@ -282,7 +287,7 @@
     "fig, ax = subplots(figsize=(8,8))\n",
     "km = KaplanMeierFitter()\n",
     "km_brain = km.fit(BrainCancer['time'], BrainCancer['status'])\n",
-    "km_brain.plot(label='Kaplan Meier estimate', ax=ax)"
+    "km_brain.plot(label='Kaplan Meier estimate', ax=ax)\n"
    ]
   },
   {
@@ -316,10 +321,10 @@
    "id": "3fc7848c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.468449Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.468262Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.604720Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.604313Z"
+     "iopub.execute_input": "2023-08-06T17:35:47.696231Z",
+     "iopub.status.busy": "2023-08-06T17:35:47.695950Z",
+     "iopub.status.idle": "2023-08-06T17:35:47.857113Z",
+     "shell.execute_reply": "2023-08-06T17:35:47.856731Z"
     }
    },
    "outputs": [
@@ -340,7 +345,7 @@
     "for sex, df in BrainCancer.groupby('sex'):\n",
     "    by_sex[sex] = df\n",
     "    km_sex = km.fit(df['time'], df['status'])\n",
-    "    km_sex.plot(label='Sex=%s' % sex, ax=ax)"
+    "    km_sex.plot(label='Sex=%s' % sex, ax=ax)\n"
    ]
   },
   {
@@ -361,11 +366,12 @@
    "id": "bf30d26f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.606730Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.606598Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.666348Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.665926Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:47.858891Z",
+     "iopub.status.busy": "2023-08-06T17:35:47.858766Z",
+     "iopub.status.idle": "2023-08-06T17:35:47.913319Z",
+     "shell.execute_reply": "2023-08-06T17:35:47.913028Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -451,7 +457,7 @@
     "logrank_test(by_sex['Male']['time'],\n",
     "             by_sex['Female']['time'],\n",
     "             by_sex['Male']['status'],\n",
-    "             by_sex['Female']['status'])"
+    "             by_sex['Female']['status'])\n"
    ]
   },
   {
@@ -473,10 +479,10 @@
    "id": "2ab78e07",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.668086Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.667893Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.695653Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.695352Z"
+     "iopub.execute_input": "2023-08-06T17:35:47.914969Z",
+     "iopub.status.busy": "2023-08-06T17:35:47.914826Z",
+     "iopub.status.idle": "2023-08-06T17:35:47.941528Z",
+     "shell.execute_reply": "2023-08-06T17:35:47.941277Z"
     }
    },
    "outputs": [
@@ -542,7 +548,7 @@
     "cox_fit = coxph().fit(model_df,\n",
     "                      'time',\n",
     "                      'status')\n",
-    "cox_fit.summary[['coef', 'se(coef)', 'p']]"
+    "cox_fit.summary[['coef', 'se(coef)', 'p']]\n"
    ]
   },
   {
@@ -566,10 +572,10 @@
    "id": "4716b7b0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.697533Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.697411Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.703099Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.702785Z"
+     "iopub.execute_input": "2023-08-06T17:35:47.943061Z",
+     "iopub.status.busy": "2023-08-06T17:35:47.942963Z",
+     "iopub.status.idle": "2023-08-06T17:35:47.948065Z",
+     "shell.execute_reply": "2023-08-06T17:35:47.947785Z"
     }
    },
    "outputs": [
@@ -648,7 +654,7 @@
     }
    ],
    "source": [
-    "cox_fit.log_likelihood_ratio_test()"
+    "cox_fit.log_likelihood_ratio_test()\n"
    ]
   },
   {
@@ -672,10 +678,10 @@
    "id": "c2767d88",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.704759Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.704654Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.742754Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.742458Z"
+     "iopub.execute_input": "2023-08-06T17:35:47.949725Z",
+     "iopub.status.busy": "2023-08-06T17:35:47.949641Z",
+     "iopub.status.idle": "2023-08-06T17:35:47.982167Z",
+     "shell.execute_reply": "2023-08-06T17:35:47.981821Z"
     }
    },
    "outputs": [
@@ -789,7 +795,7 @@
     "fit_all = coxph().fit(all_df,\n",
     "                      'time',\n",
     "                      'status')\n",
-    "fit_all.summary[['coef', 'se(coef)', 'p']]"
+    "fit_all.summary[['coef', 'se(coef)', 'p']]\n"
    ]
   },
   {
@@ -820,10 +826,10 @@
    "id": "ede1d219",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.744507Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.744382Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.748245Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.747960Z"
+     "iopub.execute_input": "2023-08-06T17:35:47.983958Z",
+     "iopub.status.busy": "2023-08-06T17:35:47.983832Z",
+     "iopub.status.idle": "2023-08-06T17:35:47.989895Z",
+     "shell.execute_reply": "2023-08-06T17:35:47.989591Z"
     }
    },
    "outputs": [],
@@ -834,7 +840,7 @@
     "        return pd.Series.mode(series)\n",
     "    else:\n",
     "        return series.mean()\n",
-    "modal_data = cleaned.apply(representative, axis=0)"
+    "modal_data = cleaned.apply(representative, axis=0)\n"
    ]
   },
   {
@@ -853,10 +859,10 @@
    "id": "dc032a71",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.749853Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.749753Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.755760Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.755479Z"
+     "iopub.execute_input": "2023-08-06T17:35:47.991841Z",
+     "iopub.status.busy": "2023-08-06T17:35:47.991705Z",
+     "iopub.status.idle": "2023-08-06T17:35:47.997910Z",
+     "shell.execute_reply": "2023-08-06T17:35:47.997622Z"
     }
    },
    "outputs": [
@@ -963,7 +969,7 @@
     "modal_df = pd.DataFrame(\n",
     "              [modal_data.iloc[0] for _ in range(len(levels))])\n",
     "modal_df['diagnosis'] = levels\n",
-    "modal_df"
+    "modal_df\n"
    ]
   },
   {
@@ -981,10 +987,10 @@
    "id": "e7c1fe43",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.757256Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.757143Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.764838Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.764420Z"
+     "iopub.execute_input": "2023-08-06T17:35:47.999542Z",
+     "iopub.status.busy": "2023-08-06T17:35:47.999430Z",
+     "iopub.status.idle": "2023-08-06T17:35:48.007263Z",
+     "shell.execute_reply": "2023-08-06T17:35:48.006958Z"
     }
    },
    "outputs": [
@@ -1106,7 +1112,7 @@
    "source": [
     "modal_X = all_MS.transform(modal_df)\n",
     "modal_X.index = levels\n",
-    "modal_X"
+    "modal_X\n"
    ]
   },
   {
@@ -1123,11 +1129,12 @@
    "id": "f89fbed7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.766834Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.766698Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.774286Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.774004Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:48.008740Z",
+     "iopub.status.busy": "2023-08-06T17:35:48.008640Z",
+     "iopub.status.idle": "2023-08-06T17:35:48.015006Z",
+     "shell.execute_reply": "2023-08-06T17:35:48.014745Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1264,7 +1271,7 @@
    ],
    "source": [
     "predicted_survival = fit_all.predict_survival_function(modal_X)\n",
-    "predicted_survival"
+    "predicted_survival\n"
    ]
   },
   {
@@ -1283,11 +1290,12 @@
    "id": "8f0329b4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.776165Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.776021Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.882750Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.882417Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:48.016512Z",
+     "iopub.status.busy": "2023-08-06T17:35:48.016391Z",
+     "iopub.status.idle": "2023-08-06T17:35:48.128436Z",
+     "shell.execute_reply": "2023-08-06T17:35:48.127998Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1303,7 +1311,7 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "predicted_survival.plot(ax=ax);"
+    "predicted_survival.plot(ax=ax);\n"
    ]
   },
   {
@@ -1325,10 +1333,10 @@
    "id": "3045bfc0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:38.884377Z",
-     "iopub.status.busy": "2023-07-31T02:15:38.884272Z",
-     "iopub.status.idle": "2023-07-31T02:15:38.999104Z",
-     "shell.execute_reply": "2023-07-31T02:15:38.998778Z"
+     "iopub.execute_input": "2023-08-06T17:35:48.130335Z",
+     "iopub.status.busy": "2023-08-06T17:35:48.130198Z",
+     "iopub.status.idle": "2023-08-06T17:35:48.248098Z",
+     "shell.execute_reply": "2023-08-06T17:35:48.247765Z"
     }
    },
    "outputs": [
@@ -1350,7 +1358,7 @@
     "for result, df in Publication.groupby('posres'):\n",
     "    by_result[result] = df\n",
     "    km_result = km.fit(df['time'], df['status'])\n",
-    "    km_result.plot(label='Result=%d' % result, ax=ax)"
+    "    km_result.plot(label='Result=%d' % result, ax=ax)\n"
    ]
   },
   {
@@ -1370,11 +1378,12 @@
    "id": "d070f716",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:39.000963Z",
-     "iopub.status.busy": "2023-07-31T02:15:39.000831Z",
-     "iopub.status.idle": "2023-07-31T02:15:39.031731Z",
-     "shell.execute_reply": "2023-07-31T02:15:39.031439Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:48.249785Z",
+     "iopub.status.busy": "2023-08-06T17:35:48.249668Z",
+     "iopub.status.idle": "2023-08-06T17:35:48.282954Z",
+     "shell.execute_reply": "2023-08-06T17:35:48.282630Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1439,7 +1448,7 @@
     "posres_fit = coxph().fit(posres_df,\n",
     "                         'time',\n",
     "                         'status')\n",
-    "posres_fit.summary[['coef', 'se(coef)', 'p']]"
+    "posres_fit.summary[['coef', 'se(coef)', 'p']]\n"
    ]
   },
   {
@@ -1458,10 +1467,10 @@
    "id": "2bbcdd0c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:39.033405Z",
-     "iopub.status.busy": "2023-07-31T02:15:39.033297Z",
-     "iopub.status.idle": "2023-07-31T02:15:39.071871Z",
-     "shell.execute_reply": "2023-07-31T02:15:39.071602Z"
+     "iopub.execute_input": "2023-08-06T17:35:48.284714Z",
+     "iopub.status.busy": "2023-08-06T17:35:48.284593Z",
+     "iopub.status.idle": "2023-08-06T17:35:48.323890Z",
+     "shell.execute_reply": "2023-08-06T17:35:48.323545Z"
     }
    },
    "outputs": [
@@ -1559,7 +1568,7 @@
     "           intercept=False)\n",
     "coxph().fit(model.fit_transform(Publication),\n",
     "            'time',\n",
-    "            'status').summary[['coef', 'se(coef)', 'p']]"
+    "            'status').summary[['coef', 'se(coef)', 'p']]\n"
    ]
   },
   {
@@ -1593,7 +1602,7 @@
     "`Time` of day (Morning, Afternoon, or Evening). We generate data\n",
     "for these covariates so that all possibilities are equally likely: for\n",
     "instance, morning, afternoon and evening calls are equally likely, and\n",
-    "any number of operators from $5$ to $15$ is equally likely."
+    "any number of operators from $5$ to $15$ is equally likely. "
    ]
   },
   {
@@ -1602,10 +1611,10 @@
    "id": "b8ece43a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:39.073478Z",
-     "iopub.status.busy": "2023-07-31T02:15:39.073379Z",
-     "iopub.status.idle": "2023-07-31T02:15:39.076907Z",
-     "shell.execute_reply": "2023-07-31T02:15:39.076640Z"
+     "iopub.execute_input": "2023-08-06T17:35:48.325634Z",
+     "iopub.status.busy": "2023-08-06T17:35:48.325517Z",
+     "iopub.status.idle": "2023-08-06T17:35:48.329272Z",
+     "shell.execute_reply": "2023-08-06T17:35:48.328978Z"
     }
    },
    "outputs": [],
@@ -1640,10 +1649,10 @@
    "id": "3e4f766f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:39.078347Z",
-     "iopub.status.busy": "2023-07-31T02:15:39.078266Z",
-     "iopub.status.idle": "2023-07-31T02:15:39.085129Z",
-     "shell.execute_reply": "2023-07-31T02:15:39.084865Z"
+     "iopub.execute_input": "2023-08-06T17:35:48.330871Z",
+     "iopub.status.busy": "2023-08-06T17:35:48.330782Z",
+     "iopub.status.idle": "2023-08-06T17:35:48.337958Z",
+     "shell.execute_reply": "2023-08-06T17:35:48.337672Z"
     }
    },
    "outputs": [],
@@ -1672,10 +1681,10 @@
    "id": "72f42d14",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:39.086703Z",
-     "iopub.status.busy": "2023-07-31T02:15:39.086584Z",
-     "iopub.status.idle": "2023-07-31T02:15:39.090844Z",
-     "shell.execute_reply": "2023-07-31T02:15:39.090578Z"
+     "iopub.execute_input": "2023-08-06T17:35:48.339669Z",
+     "iopub.status.busy": "2023-08-06T17:35:48.339578Z",
+     "iopub.status.idle": "2023-08-06T17:35:48.343948Z",
+     "shell.execute_reply": "2023-08-06T17:35:48.343688Z"
     }
    },
    "outputs": [
@@ -1767,7 +1776,7 @@
     }
    ],
    "source": [
-    "X[:5]"
+    "X[:5]\n"
    ]
   },
   {
@@ -1784,17 +1793,17 @@
    "id": "8b921536",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:39.092615Z",
-     "iopub.status.busy": "2023-07-31T02:15:39.092336Z",
-     "iopub.status.idle": "2023-07-31T02:15:39.103571Z",
-     "shell.execute_reply": "2023-07-31T02:15:39.101990Z"
+     "iopub.execute_input": "2023-08-06T17:35:48.345389Z",
+     "iopub.status.busy": "2023-08-06T17:35:48.345291Z",
+     "iopub.status.idle": "2023-08-06T17:35:48.350596Z",
+     "shell.execute_reply": "2023-08-06T17:35:48.349892Z"
     }
    },
    "outputs": [],
    "source": [
     "true_beta = np.array([0.04, -0.3, 0, 0.2, -0.2])\n",
     "true_linpred = X.dot(true_beta)\n",
-    "hazard = lambda t: 1e-5 * t"
+    "hazard = lambda t: 1e-5 * t\n"
    ]
   },
   {
@@ -1831,15 +1840,16 @@
    "id": "96ce0f99",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:39.107799Z",
-     "iopub.status.busy": "2023-07-31T02:15:39.107513Z",
-     "iopub.status.idle": "2023-07-31T02:15:39.111944Z",
-     "shell.execute_reply": "2023-07-31T02:15:39.110837Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:48.356110Z",
+     "iopub.status.busy": "2023-08-06T17:35:48.355787Z",
+     "iopub.status.idle": "2023-08-06T17:35:48.360120Z",
+     "shell.execute_reply": "2023-08-06T17:35:48.358812Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
-    "cum_hazard = lambda t: 1e-5 * t**2 / 2"
+    "cum_hazard = lambda t: 1e-5 * t**2 / 2\n"
    ]
   },
   {
@@ -1861,17 +1871,17 @@
    "id": "63d78ff9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:39.116637Z",
-     "iopub.status.busy": "2023-07-31T02:15:39.116086Z",
-     "iopub.status.idle": "2023-07-31T02:15:39.297425Z",
-     "shell.execute_reply": "2023-07-31T02:15:39.293868Z"
+     "iopub.execute_input": "2023-08-06T17:35:48.363547Z",
+     "iopub.status.busy": "2023-08-06T17:35:48.363232Z",
+     "iopub.status.idle": "2023-08-06T17:35:48.547724Z",
+     "shell.execute_reply": "2023-08-06T17:35:48.547018Z"
     }
    },
    "outputs": [],
    "source": [
     "W = np.array([sim_time(l, cum_hazard, rng)\n",
     "              for l in true_linpred])\n",
-    "D['Wait time'] = np.clip(W, 0, 1000)"
+    "D['Wait time'] = np.clip(W, 0, 1000)\n"
    ]
   },
   {
@@ -1890,11 +1900,12 @@
    "id": "fe008dbf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:39.312187Z",
-     "iopub.status.busy": "2023-07-31T02:15:39.311513Z",
-     "iopub.status.idle": "2023-07-31T02:15:39.327004Z",
-     "shell.execute_reply": "2023-07-31T02:15:39.326346Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:48.552971Z",
+     "iopub.status.busy": "2023-08-06T17:35:48.552635Z",
+     "iopub.status.idle": "2023-08-06T17:35:48.584979Z",
+     "shell.execute_reply": "2023-08-06T17:35:48.563731Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1988,7 +1999,7 @@
     "D['Failed'] = rng.choice([1, 0],\n",
     "                         N,\n",
     "                         p=[0.9, 0.1])\n",
-    "D[:5]"
+    "D[:5]\n"
    ]
   },
   {
@@ -1997,10 +2008,10 @@
    "id": "c3a2bec7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:39.330721Z",
-     "iopub.status.busy": "2023-07-31T02:15:39.330474Z",
-     "iopub.status.idle": "2023-07-31T02:15:39.363217Z",
-     "shell.execute_reply": "2023-07-31T02:15:39.362899Z"
+     "iopub.execute_input": "2023-08-06T17:35:48.615161Z",
+     "iopub.status.busy": "2023-08-06T17:35:48.614999Z",
+     "iopub.status.idle": "2023-08-06T17:35:48.618097Z",
+     "shell.execute_reply": "2023-08-06T17:35:48.617615Z"
     }
    },
    "outputs": [
@@ -2016,7 +2027,7 @@
     }
    ],
    "source": [
-    "D['Failed'].mean()"
+    "D['Failed'].mean()\n"
    ]
   },
   {
@@ -2033,10 +2044,10 @@
    "id": "2b27af56",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:39.364912Z",
-     "iopub.status.busy": "2023-07-31T02:15:39.364808Z",
-     "iopub.status.idle": "2023-07-31T02:15:39.567228Z",
-     "shell.execute_reply": "2023-07-31T02:15:39.566838Z"
+     "iopub.execute_input": "2023-08-06T17:35:48.620507Z",
+     "iopub.status.busy": "2023-08-06T17:35:48.620371Z",
+     "iopub.status.idle": "2023-08-06T17:35:48.790525Z",
+     "shell.execute_reply": "2023-08-06T17:35:48.790116Z"
     }
    },
    "outputs": [
@@ -2068,7 +2079,7 @@
     "    by_center[center] = df\n",
     "    km_center = km.fit(df['Wait time'], df['Failed'])\n",
     "    km_center.plot(label='Center=%s' % center, ax=ax)\n",
-    "ax.set_title(\"Probability of Still Being on Hold\")"
+    "ax.set_title(\"Probability of Still Being on Hold\")\n"
    ]
   },
   {
@@ -2085,10 +2096,10 @@
    "id": "9625598d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:39.569040Z",
-     "iopub.status.busy": "2023-07-31T02:15:39.568920Z",
-     "iopub.status.idle": "2023-07-31T02:15:39.823091Z",
-     "shell.execute_reply": "2023-07-31T02:15:39.822627Z"
+     "iopub.execute_input": "2023-08-06T17:35:48.792256Z",
+     "iopub.status.busy": "2023-08-06T17:35:48.792159Z",
+     "iopub.status.idle": "2023-08-06T17:35:49.004599Z",
+     "shell.execute_reply": "2023-08-06T17:35:49.004246Z"
     }
    },
    "outputs": [
@@ -2120,7 +2131,7 @@
     "    by_time[time] = df\n",
     "    km_time = km.fit(df['Wait time'], df['Failed'])\n",
     "    km_time.plot(label='Time=%s' % time, ax=ax)\n",
-    "ax.set_title(\"Probability of Still Being on Hold\")"
+    "ax.set_title(\"Probability of Still Being on Hold\")\n"
    ]
   },
   {
@@ -2141,11 +2152,12 @@
    "id": "75a744ef",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:39.825758Z",
-     "iopub.status.busy": "2023-07-31T02:15:39.825581Z",
-     "iopub.status.idle": "2023-07-31T02:15:39.847974Z",
-     "shell.execute_reply": "2023-07-31T02:15:39.847624Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:49.006368Z",
+     "iopub.status.busy": "2023-08-06T17:35:49.006251Z",
+     "iopub.status.idle": "2023-08-06T17:35:49.026122Z",
+     "shell.execute_reply": "2023-08-06T17:35:49.025786Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2230,7 +2242,7 @@
    "source": [
     "multivariate_logrank_test(D['Wait time'],\n",
     "                          D['Center'],\n",
-    "                          D['Failed'])"
+    "                          D['Failed'])\n"
    ]
   },
   {
@@ -2247,11 +2259,12 @@
    "id": "9badb3e3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:39.850725Z",
-     "iopub.status.busy": "2023-07-31T02:15:39.850538Z",
-     "iopub.status.idle": "2023-07-31T02:15:39.873327Z",
-     "shell.execute_reply": "2023-07-31T02:15:39.873038Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:49.027909Z",
+     "iopub.status.busy": "2023-08-06T17:35:49.027782Z",
+     "iopub.status.idle": "2023-08-06T17:35:49.046955Z",
+     "shell.execute_reply": "2023-08-06T17:35:49.046606Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2336,7 +2349,7 @@
    "source": [
     "multivariate_logrank_test(D['Wait time'],\n",
     "                          D['Time'],\n",
-    "                          D['Failed'])"
+    "                          D['Failed'])\n"
    ]
   },
   {
@@ -2356,11 +2369,12 @@
    "id": "026e9ff8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:39.875394Z",
-     "iopub.status.busy": "2023-07-31T02:15:39.875265Z",
-     "iopub.status.idle": "2023-07-31T02:15:40.004667Z",
-     "shell.execute_reply": "2023-07-31T02:15:40.004293Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:49.048485Z",
+     "iopub.status.busy": "2023-08-06T17:35:49.048378Z",
+     "iopub.status.idle": "2023-08-06T17:35:49.175538Z",
+     "shell.execute_reply": "2023-08-06T17:35:49.175268Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2443,7 +2457,7 @@
     "        'Center'],\n",
     "        intercept=False).fit_transform(D)\n",
     "F = coxph().fit(X, 'Wait time', 'Failed')\n",
-    "F.log_likelihood_ratio_test()"
+    "F.log_likelihood_ratio_test()\n"
    ]
   },
   {
@@ -2460,11 +2474,12 @@
    "id": "7cab3789",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:40.006886Z",
-     "iopub.status.busy": "2023-07-31T02:15:40.006736Z",
-     "iopub.status.idle": "2023-07-31T02:15:40.134843Z",
-     "shell.execute_reply": "2023-07-31T02:15:40.134522Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:49.177180Z",
+     "iopub.status.busy": "2023-08-06T17:35:49.177056Z",
+     "iopub.status.idle": "2023-08-06T17:35:49.306158Z",
+     "shell.execute_reply": "2023-08-06T17:35:49.305806Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2547,7 +2562,7 @@
     "        'Time'],\n",
     "       intercept=False).fit_transform(D)\n",
     "F = coxph().fit(X, 'Wait time', 'Failed')\n",
-    "F.log_likelihood_ratio_test()"
+    "F.log_likelihood_ratio_test()\n"
    ]
   },
   {
@@ -2567,11 +2582,12 @@
    "id": "5cc4b898",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:40.136769Z",
-     "iopub.status.busy": "2023-07-31T02:15:40.136659Z",
-     "iopub.status.idle": "2023-07-31T02:15:40.462485Z",
-     "shell.execute_reply": "2023-07-31T02:15:40.457034Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:49.308130Z",
+     "iopub.status.busy": "2023-08-06T17:35:49.307985Z",
+     "iopub.status.idle": "2023-08-06T17:35:49.596821Z",
+     "shell.execute_reply": "2023-08-06T17:35:49.585317Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2663,7 +2679,7 @@
     "                  X,\n",
     "                 'Wait time',\n",
     "                 'Failed')\n",
-    "fit_queuing.summary[['coef', 'se(coef)', 'p']]"
+    "fit_queuing.summary[['coef', 'se(coef)', 'p']]\n"
    ]
   },
   {
@@ -2679,15 +2695,16 @@
     " `Operators`, `Center = B`, `Center = C`, \n",
     "`Time = Even.` and `Time = Morn.`   are $0.04$, $-0.3$,\n",
     "$0$,   $0.2$, and $-0.2$, respectively. The coefficient estimates\n",
-    "from the fitted Cox model are fairly accurate."
+    "from the fitted Cox model are fairly accurate.\n",
+    "\n"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "formats": "ipynb,md:myst",
-   "main_language": "python"
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/Ch12-unsup-lab.ipynb b/Ch12-unsup-lab.ipynb
index b0f01b8..030e9f3 100644
--- a/Ch12-unsup-lab.ipynb
+++ b/Ch12-unsup-lab.ipynb
@@ -5,6 +5,7 @@
    "id": "80f16ff6",
    "metadata": {},
    "source": [
+    "\n",
     "# Chapter 12\n",
     "\n",
     " # Lab: Unsupervised Learning\n",
@@ -21,11 +22,12 @@
    "id": "24559be0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:41.879970Z",
-     "iopub.status.busy": "2023-07-31T02:15:41.879508Z",
-     "iopub.status.idle": "2023-07-31T02:15:42.856920Z",
-     "shell.execute_reply": "2023-07-31T02:15:42.856210Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:50.946303Z",
+     "iopub.status.busy": "2023-08-06T17:35:50.945891Z",
+     "iopub.status.idle": "2023-08-06T17:35:51.937962Z",
+     "shell.execute_reply": "2023-08-06T17:35:51.937388Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -35,7 +37,7 @@
     "from statsmodels.datasets import get_rdataset\n",
     "from sklearn.decomposition import PCA\n",
     "from sklearn.preprocessing import StandardScaler\n",
-    "from ISLP import load_data"
+    "from ISLP import load_data\n"
    ]
   },
   {
@@ -53,10 +55,10 @@
    "id": "06fff57d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:42.859595Z",
-     "iopub.status.busy": "2023-07-31T02:15:42.859101Z",
-     "iopub.status.idle": "2023-07-31T02:15:43.112055Z",
-     "shell.execute_reply": "2023-07-31T02:15:43.111717Z"
+     "iopub.execute_input": "2023-08-06T17:35:51.940380Z",
+     "iopub.status.busy": "2023-08-06T17:35:51.940035Z",
+     "iopub.status.idle": "2023-08-06T17:35:52.182557Z",
+     "shell.execute_reply": "2023-08-06T17:35:52.182174Z"
     }
    },
    "outputs": [],
@@ -67,7 +69,7 @@
     "from scipy.cluster.hierarchy import \\\n",
     "     (dendrogram,\n",
     "      cut_tree)\n",
-    "from ISLP.cluster import compute_linkage"
+    "from ISLP.cluster import compute_linkage\n"
    ]
   },
   {
@@ -90,10 +92,10 @@
    "id": "f425e07e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:43.113880Z",
-     "iopub.status.busy": "2023-07-31T02:15:43.113757Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.064565Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.064071Z"
+     "iopub.execute_input": "2023-08-06T17:35:52.184517Z",
+     "iopub.status.busy": "2023-08-06T17:35:52.184399Z",
+     "iopub.status.idle": "2023-08-06T17:35:53.499360Z",
+     "shell.execute_reply": "2023-08-06T17:35:53.498788Z"
     }
    },
    "outputs": [
@@ -540,7 +542,7 @@
    ],
    "source": [
     "USArrests = get_rdataset('USArrests').data\n",
-    "USArrests"
+    "USArrests\n"
    ]
   },
   {
@@ -557,10 +559,10 @@
    "id": "b127d014",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.067474Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.067264Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.071359Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.070795Z"
+     "iopub.execute_input": "2023-08-06T17:35:53.502695Z",
+     "iopub.status.busy": "2023-08-06T17:35:53.502469Z",
+     "iopub.status.idle": "2023-08-06T17:35:53.506258Z",
+     "shell.execute_reply": "2023-08-06T17:35:53.505850Z"
     }
    },
    "outputs": [
@@ -576,7 +578,7 @@
     }
    ],
    "source": [
-    "USArrests.columns"
+    "USArrests.columns\n"
    ]
   },
   {
@@ -593,11 +595,12 @@
    "id": "c7343f72",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.073723Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.073531Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.079018Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.078591Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:53.508693Z",
+     "iopub.status.busy": "2023-08-06T17:35:53.508529Z",
+     "iopub.status.idle": "2023-08-06T17:35:53.513068Z",
+     "shell.execute_reply": "2023-08-06T17:35:53.512687Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -616,7 +619,7 @@
     }
    ],
    "source": [
-    "USArrests.mean()"
+    "USArrests.mean()\n"
    ]
   },
   {
@@ -635,10 +638,10 @@
    "id": "34501140",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.081447Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.081236Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.085505Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.085102Z"
+     "iopub.execute_input": "2023-08-06T17:35:53.515314Z",
+     "iopub.status.busy": "2023-08-06T17:35:53.515139Z",
+     "iopub.status.idle": "2023-08-06T17:35:53.518912Z",
+     "shell.execute_reply": "2023-08-06T17:35:53.518569Z"
     }
    },
    "outputs": [
@@ -658,7 +661,7 @@
     }
    ],
    "source": [
-    "USArrests.var()"
+    "USArrests.var()\n"
    ]
   },
   {
@@ -680,7 +683,7 @@
     "This scaling can be done via the `StandardScaler()` transform imported above. We first `fit` the\n",
     "scaler, which computes the necessary means and standard\n",
     "deviations and then apply it to our data using the\n",
-    "`transform` method. As before, we combine these steps using the `fit_transform()` method."
+    "`transform` method. As before, we combine these steps using the `fit_transform()` method.\n"
    ]
   },
   {
@@ -689,17 +692,18 @@
    "id": "daf119e8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.088730Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.088542Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.094101Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.093712Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:53.521091Z",
+     "iopub.status.busy": "2023-08-06T17:35:53.520960Z",
+     "iopub.status.idle": "2023-08-06T17:35:53.525175Z",
+     "shell.execute_reply": "2023-08-06T17:35:53.524847Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
     "scaler = StandardScaler(with_std=True,\n",
     "                        with_mean=True)\n",
-    "USArrests_scaled = scaler.fit_transform(USArrests)"
+    "USArrests_scaled = scaler.fit_transform(USArrests)\n"
    ]
   },
   {
@@ -718,15 +722,16 @@
    "id": "a0eda7c9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.096171Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.096020Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.098050Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.097717Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:53.527052Z",
+     "iopub.status.busy": "2023-08-06T17:35:53.526916Z",
+     "iopub.status.idle": "2023-08-06T17:35:53.528807Z",
+     "shell.execute_reply": "2023-08-06T17:35:53.528507Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
-    "pcaUS = PCA()"
+    "pcaUS = PCA()\n"
    ]
   },
   {
@@ -746,10 +751,10 @@
    "id": "1430fb3c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.099875Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.099755Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.105977Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.105615Z"
+     "iopub.execute_input": "2023-08-06T17:35:53.530511Z",
+     "iopub.status.busy": "2023-08-06T17:35:53.530398Z",
+     "iopub.status.idle": "2023-08-06T17:35:53.534557Z",
+     "shell.execute_reply": "2023-08-06T17:35:53.534277Z"
     }
    },
    "outputs": [
@@ -768,7 +773,7 @@
     }
    ],
    "source": [
-    "pcaUS.fit(USArrests_scaled)"
+    "pcaUS.fit(USArrests_scaled)\n"
    ]
   },
   {
@@ -787,10 +792,10 @@
    "id": "6131d8d1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.108075Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.107938Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.111407Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.110881Z"
+     "iopub.execute_input": "2023-08-06T17:35:53.536106Z",
+     "iopub.status.busy": "2023-08-06T17:35:53.536010Z",
+     "iopub.status.idle": "2023-08-06T17:35:53.538352Z",
+     "shell.execute_reply": "2023-08-06T17:35:53.538107Z"
     }
    },
    "outputs": [
@@ -806,7 +811,7 @@
     }
    ],
    "source": [
-    "pcaUS.mean_"
+    "pcaUS.mean_\n"
    ]
   },
   {
@@ -824,15 +829,16 @@
    "id": "08246aad",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.114037Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.113916Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.116405Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.115821Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:53.539900Z",
+     "iopub.status.busy": "2023-08-06T17:35:53.539817Z",
+     "iopub.status.idle": "2023-08-06T17:35:53.541716Z",
+     "shell.execute_reply": "2023-08-06T17:35:53.541426Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
-    "scores = pcaUS.transform(USArrests_scaled)"
+    "scores = pcaUS.transform(USArrests_scaled)\n"
    ]
   },
   {
@@ -843,7 +849,7 @@
     "We will plot these scores a bit further down.\n",
     "The `components_` attribute provides the principal component loadings:\n",
     "each row of `pcaUS.components_` contains the corresponding\n",
-    "principal component loading vector."
+    "principal component loading vector.\n"
    ]
   },
   {
@@ -852,10 +858,10 @@
    "id": "b682b632",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.119132Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.118910Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.121993Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.121638Z"
+     "iopub.execute_input": "2023-08-06T17:35:53.543126Z",
+     "iopub.status.busy": "2023-08-06T17:35:53.543023Z",
+     "iopub.status.idle": "2023-08-06T17:35:53.545038Z",
+     "shell.execute_reply": "2023-08-06T17:35:53.544799Z"
     }
    },
    "outputs": [
@@ -874,7 +880,7 @@
     }
    ],
    "source": [
-    "pcaUS.components_ "
+    "pcaUS.components_ \n"
    ]
   },
   {
@@ -895,11 +901,12 @@
    "id": "c165e990",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.124021Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.123902Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.326760Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.325973Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:53.546528Z",
+     "iopub.status.busy": "2023-08-06T17:35:53.546434Z",
+     "iopub.status.idle": "2023-08-06T17:35:53.703738Z",
+     "shell.execute_reply": "2023-08-06T17:35:53.701839Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -923,7 +930,7 @@
     "    ax.arrow(0, 0, pcaUS.components_[i,k], pcaUS.components_[j,k])\n",
     "    ax.text(pcaUS.components_[i,k],\n",
     "            pcaUS.components_[j,k],\n",
-    "            USArrests.columns[k])"
+    "            USArrests.columns[k])\n"
    ]
   },
   {
@@ -944,10 +951,10 @@
    "id": "848c9f35",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.331474Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.331142Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.477006Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.476667Z"
+     "iopub.execute_input": "2023-08-06T17:35:53.707901Z",
+     "iopub.status.busy": "2023-08-06T17:35:53.707587Z",
+     "iopub.status.idle": "2023-08-06T17:35:53.873714Z",
+     "shell.execute_reply": "2023-08-06T17:35:53.873405Z"
     }
    },
    "outputs": [
@@ -974,7 +981,7 @@
     "    ax.arrow(0, 0, s_*pcaUS.components_[i,k], s_*pcaUS.components_[j,k])\n",
     "    ax.text(s_*pcaUS.components_[i,k],\n",
     "            s_*pcaUS.components_[j,k],\n",
-    "            USArrests.columns[k])"
+    "            USArrests.columns[k])\n"
    ]
   },
   {
@@ -991,11 +998,12 @@
    "id": "34fdfe21",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.478797Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.478681Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.481387Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.481086Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:53.875331Z",
+     "iopub.status.busy": "2023-08-06T17:35:53.875218Z",
+     "iopub.status.idle": "2023-08-06T17:35:53.877664Z",
+     "shell.execute_reply": "2023-08-06T17:35:53.877409Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1028,11 +1036,12 @@
    "id": "31b43c57",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.482964Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.482858Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.485137Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.484860Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:53.879208Z",
+     "iopub.status.busy": "2023-08-06T17:35:53.879107Z",
+     "iopub.status.idle": "2023-08-06T17:35:53.881162Z",
+     "shell.execute_reply": "2023-08-06T17:35:53.880912Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1047,7 +1056,7 @@
     }
    ],
    "source": [
-    "pcaUS.explained_variance_"
+    "pcaUS.explained_variance_\n"
    ]
   },
   {
@@ -1065,11 +1074,12 @@
    "id": "68e47d3a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.486657Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.486550Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.488838Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.488560Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:53.882669Z",
+     "iopub.status.busy": "2023-08-06T17:35:53.882569Z",
+     "iopub.status.idle": "2023-08-06T17:35:53.884536Z",
+     "shell.execute_reply": "2023-08-06T17:35:53.884304Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1084,7 +1094,7 @@
     }
    ],
    "source": [
-    "pcaUS.explained_variance_ratio_"
+    "pcaUS.explained_variance_ratio_\n"
    ]
   },
   {
@@ -1105,11 +1115,12 @@
    "id": "e87fe198",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.490567Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.490460Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.624499Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.624178Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:53.885937Z",
+     "iopub.status.busy": "2023-08-06T17:35:53.885846Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.017444Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.017056Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -1123,7 +1134,7 @@
     "ax.set_xlabel('Principal Component');\n",
     "ax.set_ylabel('Proportion of Variance Explained')\n",
     "ax.set_ylim([0,1])\n",
-    "ax.set_xticks(ticks)"
+    "ax.set_xticks(ticks)\n"
    ]
   },
   {
@@ -1140,11 +1151,12 @@
    "id": "409fb0c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.626407Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.626296Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.730749Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.730399Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:54.019369Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.019233Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.126102Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.125778Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1168,7 +1180,7 @@
     "ax.set_ylabel('Cumulative Proportion of Variance Explained')\n",
     "ax.set_ylim([0, 1])\n",
     "ax.set_xticks(ticks)\n",
-    "fig"
+    "fig\n"
    ]
   },
   {
@@ -1187,11 +1199,12 @@
    "id": "e563e41b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.732589Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.732464Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.735051Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.734749Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:54.128000Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.127867Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.130547Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.130247Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1207,7 +1220,7 @@
    ],
    "source": [
     "a = np.array([1,2,8,-3])\n",
-    "np.cumsum(a)"
+    "np.cumsum(a)\n"
    ]
   },
   {
@@ -1226,7 +1239,7 @@
     "components of the data.  We use our scaled\n",
     "and centered `USArrests` data as $\\bf X$ below. The *singular value decomposition* \n",
     "(SVD)  is a general algorithm for solving\n",
-    "(12.6)."
+    "(12.6). "
    ]
   },
   {
@@ -1235,11 +1248,12 @@
    "id": "f83ad0bc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.736580Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.736478Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.739101Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.738830Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:54.132102Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.131992Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.134511Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.134231Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1256,7 +1270,7 @@
    "source": [
     "X = USArrests_scaled\n",
     "U, D, V = np.linalg.svd(X, full_matrices=False)\n",
-    "U.shape, D.shape, V.shape"
+    "U.shape, D.shape, V.shape\n"
    ]
   },
   {
@@ -1275,10 +1289,10 @@
    "id": "cb9bdc46",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.740634Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.740515Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.742822Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.742562Z"
+     "iopub.execute_input": "2023-08-06T17:35:54.135963Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.135871Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.138169Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.137815Z"
     }
    },
    "outputs": [
@@ -1297,7 +1311,7 @@
     }
    ],
    "source": [
-    "V"
+    "V\n"
    ]
   },
   {
@@ -1306,11 +1320,12 @@
    "id": "f23c101e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.744201Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.744092Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.746295Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.746068Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:54.139741Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.139635Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.141804Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.141560Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1328,7 +1343,7 @@
     }
    ],
    "source": [
-    "pcaUS.components_"
+    "pcaUS.components_\n"
    ]
   },
   {
@@ -1345,10 +1360,10 @@
    "id": "4cc49622",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.747796Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.747703Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.750101Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.749842Z"
+     "iopub.execute_input": "2023-08-06T17:35:54.143256Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.143161Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.145229Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.144969Z"
     }
    },
    "outputs": [
@@ -1366,7 +1381,7 @@
     }
    ],
    "source": [
-    "(U * D[None,:])[:3]"
+    "(U * D[None,:])[:3]\n"
    ]
   },
   {
@@ -1375,11 +1390,12 @@
    "id": "c96c9fe1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.751617Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.751510Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.753664Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.753419Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:54.146759Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.146653Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.148887Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.148630Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1396,7 +1412,7 @@
     }
    ],
    "source": [
-    "scores[:3]"
+    "scores[:3]\n"
    ]
   },
   {
@@ -1419,10 +1435,10 @@
    "id": "574409d6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.755144Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.755024Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.757455Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.757198Z"
+     "iopub.execute_input": "2023-08-06T17:35:54.150348Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.150251Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.152569Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.152331Z"
     }
    },
    "outputs": [],
@@ -1436,7 +1452,7 @@
     "                         n_omit,\n",
     "                         replace=True)\n",
     "Xna = X.copy()\n",
-    "Xna[r_idx, c_idx] = np.nan"
+    "Xna[r_idx, c_idx] = np.nan\n"
    ]
   },
   {
@@ -1459,18 +1475,19 @@
    "id": "89f190ae",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.758904Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.758813Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.760627Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.760397Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:54.154068Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.153976Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.155943Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.155667Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
     "def low_rank(X, M=1):\n",
     "    U, D, V = np.linalg.svd(X)\n",
     "    L = U[:,:M] * D[None,:M]\n",
-    "    return L.dot(V[:M])"
+    "    return L.dot(V[:M])\n"
    ]
   },
   {
@@ -1491,17 +1508,17 @@
    "id": "322f339c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.762072Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.761967Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.763830Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.763614Z"
+     "iopub.execute_input": "2023-08-06T17:35:54.157547Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.157431Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.159402Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.159143Z"
     }
    },
    "outputs": [],
    "source": [
     "Xhat = Xna.copy()\n",
     "Xbar = np.nanmean(Xhat, axis=0)\n",
-    "Xhat[r_idx, c_idx] = Xbar[c_idx]"
+    "Xhat[r_idx, c_idx] = Xbar[c_idx]\n"
    ]
   },
   {
@@ -1519,11 +1536,12 @@
    "id": "7e106d1a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.765257Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.765166Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.767082Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.766829Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:54.160950Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.160834Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.162815Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.162552Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -1532,7 +1550,7 @@
     "count = 0\n",
     "ismiss = np.isnan(Xna)\n",
     "mssold = np.mean(Xhat[~ismiss]**2)\n",
-    "mss0 = np.mean(Xna[~ismiss]**2)"
+    "mss0 = np.mean(Xna[~ismiss]**2)\n"
    ]
   },
   {
@@ -1558,10 +1576,10 @@
    "id": "7cb05ce4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.768575Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.768471Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.771807Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.771537Z"
+     "iopub.execute_input": "2023-08-06T17:35:54.164360Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.164263Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.167306Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.167033Z"
     }
    },
    "outputs": [
@@ -1593,7 +1611,7 @@
     "    rel_err = (mssold - mss) / mss0\n",
     "    mssold = mss\n",
     "    print(\"Iteration: {0}, MSS:{1:.3f}, Rel.Err {2:.2e}\"\n",
-    "          .format(count, mss, rel_err))"
+    "          .format(count, mss, rel_err))\n"
    ]
   },
   {
@@ -1613,11 +1631,12 @@
    "id": "6f245188",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.773365Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.773280Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.775827Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.775546Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:54.168856Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.168772Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.171306Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.171036Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1632,7 +1651,7 @@
     }
    ],
    "source": [
-    "np.corrcoef(Xapp[ismiss], X[ismiss])[0,1]"
+    "np.corrcoef(Xapp[ismiss], X[ismiss])[0,1]\n"
    ]
   },
   {
@@ -1670,18 +1689,19 @@
    "id": "345fb41e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.777389Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.777303Z",
-     "iopub.status.idle": "2023-07-31T02:15:44.779326Z",
-     "shell.execute_reply": "2023-07-31T02:15:44.779045Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:54.172898Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.172820Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.174844Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.174573Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
     "np.random.seed(0);\n",
     "X = np.random.standard_normal((50,2));\n",
     "X[:25,0] += 3;\n",
-    "X[:25,1] -= 4;"
+    "X[:25,1] -= 4;\n"
    ]
   },
   {
@@ -1698,17 +1718,18 @@
    "id": "3a8c21a2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:44.780749Z",
-     "iopub.status.busy": "2023-07-31T02:15:44.780675Z",
-     "iopub.status.idle": "2023-07-31T02:15:45.109432Z",
-     "shell.execute_reply": "2023-07-31T02:15:45.108830Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:54.176279Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.176201Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.490075Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.489602Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
     "kmeans = KMeans(n_clusters=2,\n",
     "                random_state=2,\n",
-    "                n_init=20).fit(X)"
+    "                n_init=20).fit(X)\n"
    ]
   },
   {
@@ -1725,11 +1746,12 @@
    "id": "e3e35b5d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:45.111736Z",
-     "iopub.status.busy": "2023-07-31T02:15:45.111602Z",
-     "iopub.status.idle": "2023-07-31T02:15:45.114893Z",
-     "shell.execute_reply": "2023-07-31T02:15:45.114566Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:54.492349Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.492209Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.495557Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.495020Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1746,7 +1768,7 @@
     }
    ],
    "source": [
-    "kmeans.labels_"
+    "kmeans.labels_\n"
    ]
   },
   {
@@ -1766,10 +1788,10 @@
    "id": "d928650a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:45.116690Z",
-     "iopub.status.busy": "2023-07-31T02:15:45.116594Z",
-     "iopub.status.idle": "2023-07-31T02:15:45.227910Z",
-     "shell.execute_reply": "2023-07-31T02:15:45.227523Z"
+     "iopub.execute_input": "2023-08-06T17:35:54.497396Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.497311Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.605692Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.605294Z"
     }
    },
    "outputs": [
@@ -1787,7 +1809,7 @@
    "source": [
     "fig, ax = plt.subplots(1, 1, figsize=(8,8))\n",
     "ax.scatter(X[:,0], X[:,1], c=kmeans.labels_)\n",
-    "ax.set_title(\"K-Means Clustering Results with K=2\");"
+    "ax.set_title(\"K-Means Clustering Results with K=2\");\n"
    ]
   },
   {
@@ -1813,11 +1835,12 @@
    "id": "92e5175c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:45.230054Z",
-     "iopub.status.busy": "2023-07-31T02:15:45.229932Z",
-     "iopub.status.idle": "2023-07-31T02:15:45.343327Z",
-     "shell.execute_reply": "2023-07-31T02:15:45.342951Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:54.608086Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.607746Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.725343Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.725007Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1837,7 +1860,7 @@
     "                n_init=20).fit(X)\n",
     "fig, ax = plt.subplots(figsize=(8,8))\n",
     "ax.scatter(X[:,0], X[:,1], c=kmeans.labels_)\n",
-    "ax.set_title(\"K-Means Clustering Results with K=3\");"
+    "ax.set_title(\"K-Means Clustering Results with K=3\");\n"
    ]
   },
   {
@@ -1861,11 +1884,12 @@
    "id": "4911ecc7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:45.345458Z",
-     "iopub.status.busy": "2023-07-31T02:15:45.345341Z",
-     "iopub.status.idle": "2023-07-31T02:15:45.358652Z",
-     "shell.execute_reply": "2023-07-31T02:15:45.358313Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:54.727259Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.727141Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.742605Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.742234Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1886,7 +1910,7 @@
     "kmeans20 = KMeans(n_clusters=3,\n",
     "                  random_state=3,\n",
     "                  n_init=20).fit(X);\n",
-    "kmeans1.inertia_, kmeans20.inertia_"
+    "kmeans1.inertia_, kmeans20.inertia_\n"
    ]
   },
   {
@@ -1933,10 +1957,10 @@
    "id": "1b42a700",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:45.360651Z",
-     "iopub.status.busy": "2023-07-31T02:15:45.360541Z",
-     "iopub.status.idle": "2023-07-31T02:15:45.366056Z",
-     "shell.execute_reply": "2023-07-31T02:15:45.365739Z"
+     "iopub.execute_input": "2023-08-06T17:35:54.745207Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.744988Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.750667Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.750320Z"
     }
    },
    "outputs": [
@@ -1962,7 +1986,7 @@
     "hc_comp = HClust(distance_threshold=0,\n",
     "                 n_clusters=None,\n",
     "                 linkage='complete')\n",
-    "hc_comp.fit(X)"
+    "hc_comp.fit(X)\n"
    ]
   },
   {
@@ -1980,10 +2004,10 @@
    "id": "50ef7eea",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:45.367837Z",
-     "iopub.status.busy": "2023-07-31T02:15:45.367733Z",
-     "iopub.status.idle": "2023-07-31T02:15:45.371125Z",
-     "shell.execute_reply": "2023-07-31T02:15:45.370797Z"
+     "iopub.execute_input": "2023-08-06T17:35:54.752542Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.752422Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.756021Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.755609Z"
     }
    },
    "outputs": [],
@@ -1995,7 +2019,7 @@
     "hc_sing = HClust(distance_threshold=0,\n",
     "                 n_clusters=None,\n",
     "                 linkage='single');\n",
-    "hc_sing.fit(X);"
+    "hc_sing.fit(X);\n"
    ]
   },
   {
@@ -2013,10 +2037,10 @@
    "id": "bf7a2408",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:45.372859Z",
-     "iopub.status.busy": "2023-07-31T02:15:45.372770Z",
-     "iopub.status.idle": "2023-07-31T02:15:45.377578Z",
-     "shell.execute_reply": "2023-07-31T02:15:45.377273Z"
+     "iopub.execute_input": "2023-08-06T17:35:54.758124Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.757853Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.763112Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.762785Z"
     }
    },
    "outputs": [
@@ -2046,7 +2070,7 @@
     "                     n_clusters=None,\n",
     "                     metric='precomputed',\n",
     "                     linkage='single')\n",
-    "hc_sing_pre.fit(D)"
+    "hc_sing_pre.fit(D)\n"
    ]
   },
   {
@@ -2073,10 +2097,10 @@
    "id": "a118c0ab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:45.379309Z",
-     "iopub.status.busy": "2023-07-31T02:15:45.379196Z",
-     "iopub.status.idle": "2023-07-31T02:15:45.604526Z",
-     "shell.execute_reply": "2023-07-31T02:15:45.604141Z"
+     "iopub.execute_input": "2023-08-06T17:35:54.764945Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.764807Z",
+     "iopub.status.idle": "2023-08-06T17:35:54.996080Z",
+     "shell.execute_reply": "2023-08-06T17:35:54.995754Z"
     }
    },
    "outputs": [
@@ -2098,7 +2122,7 @@
     "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
     "dendrogram(linkage_comp,\n",
     "           ax=ax,\n",
-    "           **cargs);"
+    "           **cargs);\n"
    ]
   },
   {
@@ -2118,10 +2142,10 @@
    "id": "b1ff41c0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:45.606279Z",
-     "iopub.status.busy": "2023-07-31T02:15:45.606155Z",
-     "iopub.status.idle": "2023-07-31T02:15:45.829720Z",
-     "shell.execute_reply": "2023-07-31T02:15:45.829414Z"
+     "iopub.execute_input": "2023-08-06T17:35:54.998527Z",
+     "iopub.status.busy": "2023-08-06T17:35:54.998378Z",
+     "iopub.status.idle": "2023-08-06T17:35:55.224328Z",
+     "shell.execute_reply": "2023-08-06T17:35:55.224001Z"
     }
    },
    "outputs": [
@@ -2141,7 +2165,7 @@
     "dendrogram(linkage_comp,\n",
     "           ax=ax,\n",
     "           color_threshold=4,\n",
-    "           above_threshold_color='black');"
+    "           above_threshold_color='black');\n"
    ]
   },
   {
@@ -2160,10 +2184,10 @@
    "id": "c2752a96",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:45.831473Z",
-     "iopub.status.busy": "2023-07-31T02:15:45.831358Z",
-     "iopub.status.idle": "2023-07-31T02:15:45.835095Z",
-     "shell.execute_reply": "2023-07-31T02:15:45.834802Z"
+     "iopub.execute_input": "2023-08-06T17:35:55.226138Z",
+     "iopub.status.busy": "2023-08-06T17:35:55.226012Z",
+     "iopub.status.idle": "2023-08-06T17:35:55.229625Z",
+     "shell.execute_reply": "2023-08-06T17:35:55.229330Z"
     }
    },
    "outputs": [
@@ -2181,7 +2205,7 @@
     }
    ],
    "source": [
-    "cut_tree(linkage_comp, n_clusters=4).T"
+    "cut_tree(linkage_comp, n_clusters=4).T\n"
    ]
   },
   {
@@ -2202,10 +2226,10 @@
    "id": "1407f7a4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:45.836612Z",
-     "iopub.status.busy": "2023-07-31T02:15:45.836515Z",
-     "iopub.status.idle": "2023-07-31T02:15:45.839886Z",
-     "shell.execute_reply": "2023-07-31T02:15:45.839587Z"
+     "iopub.execute_input": "2023-08-06T17:35:55.231139Z",
+     "iopub.status.busy": "2023-08-06T17:35:55.231047Z",
+     "iopub.status.idle": "2023-08-06T17:35:55.234414Z",
+     "shell.execute_reply": "2023-08-06T17:35:55.234103Z"
     }
    },
    "outputs": [
@@ -2270,7 +2294,7 @@
     }
    ],
    "source": [
-    "cut_tree(linkage_comp, height=5)"
+    "cut_tree(linkage_comp, height=5)\n"
    ]
   },
   {
@@ -2288,10 +2312,10 @@
    "id": "2d74f224",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:45.841445Z",
-     "iopub.status.busy": "2023-07-31T02:15:45.841337Z",
-     "iopub.status.idle": "2023-07-31T02:15:46.086770Z",
-     "shell.execute_reply": "2023-07-31T02:15:46.086434Z"
+     "iopub.execute_input": "2023-08-06T17:35:55.236000Z",
+     "iopub.status.busy": "2023-08-06T17:35:55.235892Z",
+     "iopub.status.idle": "2023-08-06T17:35:55.466992Z",
+     "shell.execute_reply": "2023-08-06T17:35:55.466654Z"
     }
    },
    "outputs": [
@@ -2315,7 +2339,7 @@
     "linkage_comp_scale = compute_linkage(hc_comp_scale)\n",
     "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
     "dendrogram(linkage_comp_scale, ax=ax, **cargs)\n",
-    "ax.set_title(\"Hierarchical Clustering with Scaled Features\");"
+    "ax.set_title(\"Hierarchical Clustering with Scaled Features\");\n"
    ]
   },
   {
@@ -2343,11 +2367,12 @@
    "id": "b7f7da12",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:46.088532Z",
-     "iopub.status.busy": "2023-07-31T02:15:46.088413Z",
-     "iopub.status.idle": "2023-07-31T02:15:46.277686Z",
-     "shell.execute_reply": "2023-07-31T02:15:46.277383Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:55.468711Z",
+     "iopub.status.busy": "2023-08-06T17:35:55.468585Z",
+     "iopub.status.idle": "2023-08-06T17:35:55.650076Z",
+     "shell.execute_reply": "2023-08-06T17:35:55.649676Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2372,7 +2397,7 @@
     "linkage_cor = compute_linkage(hc_cor)\n",
     "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
     "dendrogram(linkage_cor, ax=ax, **cargs)\n",
-    "ax.set_title(\"Complete Linkage with Correlation-Based Dissimilarity\");"
+    "ax.set_title(\"Complete Linkage with Correlation-Based Dissimilarity\");\n"
    ]
   },
   {
@@ -2394,17 +2419,17 @@
    "id": "b94424fc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:46.279360Z",
-     "iopub.status.busy": "2023-07-31T02:15:46.279231Z",
-     "iopub.status.idle": "2023-07-31T02:15:46.285520Z",
-     "shell.execute_reply": "2023-07-31T02:15:46.285244Z"
+     "iopub.execute_input": "2023-08-06T17:35:55.651855Z",
+     "iopub.status.busy": "2023-08-06T17:35:55.651741Z",
+     "iopub.status.idle": "2023-08-06T17:35:55.658795Z",
+     "shell.execute_reply": "2023-08-06T17:35:55.658519Z"
     }
    },
    "outputs": [],
    "source": [
     "NCI60 = load_data('NCI60')\n",
     "nci_labs = NCI60['labels']\n",
-    "nci_data = NCI60['data']"
+    "nci_data = NCI60['data']\n"
    ]
   },
   {
@@ -2427,11 +2452,12 @@
    "id": "cea54566",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:46.287328Z",
-     "iopub.status.busy": "2023-07-31T02:15:46.287220Z",
-     "iopub.status.idle": "2023-07-31T02:15:46.289574Z",
-     "shell.execute_reply": "2023-07-31T02:15:46.289308Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:55.660436Z",
+     "iopub.status.busy": "2023-08-06T17:35:55.660347Z",
+     "iopub.status.idle": "2023-08-06T17:35:55.662725Z",
+     "shell.execute_reply": "2023-08-06T17:35:55.662438Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2446,7 +2472,7 @@
     }
    ],
    "source": [
-    "nci_data.shape"
+    "nci_data.shape\n"
    ]
   },
   {
@@ -2454,7 +2480,7 @@
    "id": "6ebf27f1",
    "metadata": {},
    "source": [
-    "We begin by examining the cancer types for the cell lines."
+    "We begin by examining the cancer types for the cell lines.\n"
    ]
   },
   {
@@ -2463,11 +2489,12 @@
    "id": "4dac41bb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:46.291057Z",
-     "iopub.status.busy": "2023-07-31T02:15:46.290960Z",
-     "iopub.status.idle": "2023-07-31T02:15:46.294539Z",
-     "shell.execute_reply": "2023-07-31T02:15:46.294249Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:55.664308Z",
+     "iopub.status.busy": "2023-08-06T17:35:55.664169Z",
+     "iopub.status.idle": "2023-08-06T17:35:55.668246Z",
+     "shell.execute_reply": "2023-08-06T17:35:55.667892Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2497,7 +2524,7 @@
     }
    ],
    "source": [
-    "nci_labs.value_counts()"
+    "nci_labs.value_counts()\n"
    ]
   },
   {
@@ -2518,10 +2545,10 @@
    "id": "d8ebadd6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:46.295958Z",
-     "iopub.status.busy": "2023-07-31T02:15:46.295878Z",
-     "iopub.status.idle": "2023-07-31T02:15:46.461104Z",
-     "shell.execute_reply": "2023-07-31T02:15:46.459544Z"
+     "iopub.execute_input": "2023-08-06T17:35:55.669892Z",
+     "iopub.status.busy": "2023-08-06T17:35:55.669796Z",
+     "iopub.status.idle": "2023-08-06T17:35:55.812837Z",
+     "shell.execute_reply": "2023-08-06T17:35:55.811884Z"
     }
    },
    "outputs": [],
@@ -2529,7 +2556,7 @@
     "scaler = StandardScaler()\n",
     "nci_scaled = scaler.fit_transform(nci_data)\n",
     "nci_pca = PCA()\n",
-    "nci_scores = nci_pca.fit_transform(nci_scaled)"
+    "nci_scores = nci_pca.fit_transform(nci_scaled)\n"
    ]
   },
   {
@@ -2541,7 +2568,7 @@
     "to visualize the data. The observations (cell lines) corresponding to\n",
     "a given cancer type will be plotted in the same color, so that we can\n",
     "see to what extent the observations within a cancer type are similar\n",
-    "to each other."
+    "to each other. "
    ]
   },
   {
@@ -2550,11 +2577,12 @@
    "id": "63b5efe3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:46.465546Z",
-     "iopub.status.busy": "2023-07-31T02:15:46.465129Z",
-     "iopub.status.idle": "2023-07-31T02:15:46.678203Z",
-     "shell.execute_reply": "2023-07-31T02:15:46.677232Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:55.818431Z",
+     "iopub.status.busy": "2023-08-06T17:35:55.818044Z",
+     "iopub.status.idle": "2023-08-06T17:35:56.078668Z",
+     "shell.execute_reply": "2023-08-06T17:35:56.078379Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2586,7 +2614,7 @@
     "           c=nci_groups,\n",
     "           marker='o',\n",
     "           s=50)\n",
-    "ax.set_xlabel('PC1'); ax.set_ylabel('PC3');"
+    "ax.set_xlabel('PC1'); ax.set_ylabel('PC3');\n"
    ]
   },
   {
@@ -2597,7 +2625,10 @@
     "On the whole, cell lines corresponding to a single cancer type do tend to\n",
     "have similar values on the first few principal component score\n",
     "vectors. This indicates that cell lines from the same cancer type tend\n",
-    "to have pretty similar gene expression levels."
+    "to have pretty similar gene expression levels.\n",
+    "\n",
+    "\n",
+    "    "
    ]
   },
   {
@@ -2616,11 +2647,12 @@
    "id": "e20c3cc1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:46.684464Z",
-     "iopub.status.busy": "2023-07-31T02:15:46.683187Z",
-     "iopub.status.idle": "2023-07-31T02:15:46.840162Z",
-     "shell.execute_reply": "2023-07-31T02:15:46.839815Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:56.080337Z",
+     "iopub.status.busy": "2023-08-06T17:35:56.080250Z",
+     "iopub.status.idle": "2023-08-06T17:35:56.227591Z",
+     "shell.execute_reply": "2023-08-06T17:35:56.227252Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2648,7 +2680,7 @@
     "        nci_pca.explained_variance_ratio_.cumsum(),\n",
     "        marker='o');\n",
     "ax.set_xlabel('Principal Component')\n",
-    "ax.set_ylabel('Cumulative PVE');"
+    "ax.set_ylabel('Cumulative PVE');\n"
    ]
   },
   {
@@ -2688,10 +2720,10 @@
    "id": "622de805",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:46.841987Z",
-     "iopub.status.busy": "2023-07-31T02:15:46.841869Z",
-     "iopub.status.idle": "2023-07-31T02:15:46.844428Z",
-     "shell.execute_reply": "2023-07-31T02:15:46.844159Z"
+     "iopub.execute_input": "2023-08-06T17:35:56.229339Z",
+     "iopub.status.busy": "2023-08-06T17:35:56.229217Z",
+     "iopub.status.idle": "2023-08-06T17:35:56.231787Z",
+     "shell.execute_reply": "2023-08-06T17:35:56.231485Z"
     }
    },
    "outputs": [],
@@ -2709,7 +2741,7 @@
     "               leaf_font_size=10,\n",
     "               **cargs)\n",
     "    ax.set_title('%s Linkage' % linkage)\n",
-    "    return hc"
+    "    return hc\n"
    ]
   },
   {
@@ -2726,11 +2758,12 @@
    "id": "54d40449",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:46.845994Z",
-     "iopub.status.busy": "2023-07-31T02:15:46.845893Z",
-     "iopub.status.idle": "2023-07-31T02:15:48.160927Z",
-     "shell.execute_reply": "2023-07-31T02:15:48.160624Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:56.233590Z",
+     "iopub.status.busy": "2023-08-06T17:35:56.233477Z",
+     "iopub.status.idle": "2023-08-06T17:35:57.584097Z",
+     "shell.execute_reply": "2023-08-06T17:35:57.583812Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2748,7 +2781,7 @@
     "fig, axes = plt.subplots(3, 1, figsize=(15,30))      \n",
     "ax = axes[0]; hc_comp = plot_nci('Complete', ax)\n",
     "ax = axes[1]; hc_avg = plot_nci('Average', ax)\n",
-    "ax = axes[2]; hc_sing = plot_nci('Single', ax)"
+    "ax = axes[2]; hc_sing = plot_nci('Single', ax)\n"
    ]
   },
   {
@@ -2778,11 +2811,12 @@
    "id": "dc80afc8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:48.162702Z",
-     "iopub.status.busy": "2023-07-31T02:15:48.162595Z",
-     "iopub.status.idle": "2023-07-31T02:15:48.173866Z",
-     "shell.execute_reply": "2023-07-31T02:15:48.173467Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:57.585856Z",
+     "iopub.status.busy": "2023-08-06T17:35:57.585741Z",
+     "iopub.status.idle": "2023-08-06T17:35:57.597998Z",
+     "shell.execute_reply": "2023-08-06T17:35:57.597730Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2950,7 +2984,7 @@
     "linkage_comp = compute_linkage(hc_comp)\n",
     "comp_cut = cut_tree(linkage_comp, n_clusters=4).reshape(-1)\n",
     "pd.crosstab(nci_labs['label'],\n",
-    "            pd.Series(comp_cut.reshape(-1), name='Complete'))"
+    "            pd.Series(comp_cut.reshape(-1), name='Complete'))\n"
    ]
   },
   {
@@ -2971,10 +3005,10 @@
    "id": "40ff59f9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:48.175506Z",
-     "iopub.status.busy": "2023-07-31T02:15:48.175383Z",
-     "iopub.status.idle": "2023-07-31T02:15:48.588803Z",
-     "shell.execute_reply": "2023-07-31T02:15:48.588494Z"
+     "iopub.execute_input": "2023-08-06T17:35:57.599540Z",
+     "iopub.status.busy": "2023-08-06T17:35:57.599448Z",
+     "iopub.status.idle": "2023-08-06T17:35:58.032539Z",
+     "shell.execute_reply": "2023-08-06T17:35:58.032140Z"
     }
    },
    "outputs": [
@@ -2992,7 +3026,7 @@
    "source": [
     "fig, ax = plt.subplots(figsize=(10,10))\n",
     "plot_nci('Complete', ax, cut=140)\n",
-    "ax.axhline(140, c='r', linewidth=4);"
+    "ax.axhline(140, c='r', linewidth=4);\n"
    ]
   },
   {
@@ -3020,10 +3054,10 @@
    "id": "1587e83b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:48.590478Z",
-     "iopub.status.busy": "2023-07-31T02:15:48.590373Z",
-     "iopub.status.idle": "2023-07-31T02:15:48.959939Z",
-     "shell.execute_reply": "2023-07-31T02:15:48.959090Z"
+     "iopub.execute_input": "2023-08-06T17:35:58.034342Z",
+     "iopub.status.busy": "2023-08-06T17:35:58.034213Z",
+     "iopub.status.idle": "2023-08-06T17:35:58.251720Z",
+     "shell.execute_reply": "2023-08-06T17:35:58.251045Z"
     }
    },
    "outputs": [
@@ -3113,7 +3147,7 @@
     "                    random_state=0,\n",
     "                    n_init=20).fit(nci_scaled)\n",
     "pd.crosstab(pd.Series(comp_cut, name='HClust'),\n",
-    "            pd.Series(nci_kmeans.labels_, name='K-means'))"
+    "            pd.Series(nci_kmeans.labels_, name='K-means'))\n"
    ]
   },
   {
@@ -3145,11 +3179,12 @@
    "id": "b09ceeab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:48.964377Z",
-     "iopub.status.busy": "2023-07-31T02:15:48.963942Z",
-     "iopub.status.idle": "2023-07-31T02:15:49.459064Z",
-     "shell.execute_reply": "2023-07-31T02:15:49.458783Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:58.255255Z",
+     "iopub.status.busy": "2023-08-06T17:35:58.254701Z",
+     "iopub.status.idle": "2023-08-06T17:35:58.741393Z",
+     "shell.execute_reply": "2023-08-06T17:35:58.741038Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -3339,15 +3374,26 @@
     "pca_labels = pd.Series(cut_tree(linkage_pca,\n",
     "                                n_clusters=4).reshape(-1),\n",
     "                       name='Complete-PCA')\n",
-    "pd.crosstab(nci_labs['label'], pca_labels)"
+    "pd.crosstab(nci_labs['label'], pca_labels)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e1733b5",
+   "metadata": {},
+   "source": [
+    "\n",
+    " \n",
+    " \n",
+    "\n"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "formats": "ipynb,md:myst",
-   "main_language": "python"
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/Ch13-multiple-lab.ipynb b/Ch13-multiple-lab.ipynb
index 709bfaa..83fad7d 100644
--- a/Ch13-multiple-lab.ipynb
+++ b/Ch13-multiple-lab.ipynb
@@ -5,9 +5,11 @@
    "id": "75b2d75c",
    "metadata": {},
    "source": [
+    "\n",
     "# Chapter 13\n",
     "\n",
-    "# Lab: Multiple Testing"
+    "# Lab: Multiple Testing\n",
+    " "
    ]
   },
   {
@@ -24,10 +26,10 @@
    "id": "1f928b2d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:50.715151Z",
-     "iopub.status.busy": "2023-07-31T02:15:50.714766Z",
-     "iopub.status.idle": "2023-07-31T02:15:51.778071Z",
-     "shell.execute_reply": "2023-07-31T02:15:51.777549Z"
+     "iopub.execute_input": "2023-08-06T17:36:00.152825Z",
+     "iopub.status.busy": "2023-08-06T17:36:00.151975Z",
+     "iopub.status.idle": "2023-08-06T17:36:01.254245Z",
+     "shell.execute_reply": "2023-08-06T17:36:01.253710Z"
     }
    },
    "outputs": [],
@@ -36,7 +38,7 @@
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "import statsmodels.api as sm\n",
-    "from ISLP import load_data"
+    "from ISLP import load_data\n"
    ]
   },
   {
@@ -54,11 +56,12 @@
    "id": "eb4b32aa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:51.780386Z",
-     "iopub.status.busy": "2023-07-31T02:15:51.780091Z",
-     "iopub.status.idle": "2023-07-31T02:15:51.782579Z",
-     "shell.execute_reply": "2023-07-31T02:15:51.782295Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:36:01.256495Z",
+     "iopub.status.busy": "2023-08-06T17:36:01.256216Z",
+     "iopub.status.idle": "2023-08-06T17:36:01.258709Z",
+     "shell.execute_reply": "2023-08-06T17:36:01.258467Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -70,7 +73,7 @@
     "from statsmodels.stats.multicomp import \\\n",
     "     pairwise_tukeyhsd\n",
     "from statsmodels.stats.multitest import \\\n",
-    "     multipletests as mult_test"
+    "     multipletests as mult_test\n"
    ]
   },
   {
@@ -92,10 +95,10 @@
    "id": "e12ac0cd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:51.784074Z",
-     "iopub.status.busy": "2023-07-31T02:15:51.783969Z",
-     "iopub.status.idle": "2023-07-31T02:15:51.786285Z",
-     "shell.execute_reply": "2023-07-31T02:15:51.786035Z"
+     "iopub.execute_input": "2023-08-06T17:36:01.260260Z",
+     "iopub.status.busy": "2023-08-06T17:36:01.260153Z",
+     "iopub.status.idle": "2023-08-06T17:36:01.262414Z",
+     "shell.execute_reply": "2023-08-06T17:36:01.262174Z"
     }
    },
    "outputs": [],
@@ -103,7 +106,7 @@
     "rng = np.random.default_rng(12)\n",
     "X = rng.standard_normal((10, 100))\n",
     "true_mean = np.array([0.5]*50 + [0]*50)\n",
-    "X += true_mean[None,:]"
+    "X += true_mean[None,:]\n"
    ]
   },
   {
@@ -122,10 +125,10 @@
    "id": "04d0f49e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:51.787724Z",
-     "iopub.status.busy": "2023-07-31T02:15:51.787627Z",
-     "iopub.status.idle": "2023-07-31T02:15:51.791755Z",
-     "shell.execute_reply": "2023-07-31T02:15:51.791495Z"
+     "iopub.execute_input": "2023-08-06T17:36:01.263887Z",
+     "iopub.status.busy": "2023-08-06T17:36:01.263792Z",
+     "iopub.status.idle": "2023-08-06T17:36:01.267718Z",
+     "shell.execute_reply": "2023-08-06T17:36:01.267462Z"
     }
    },
    "outputs": [
@@ -142,7 +145,7 @@
    ],
    "source": [
     "result = ttest_1samp(X[:,0], 0)\n",
-    "result.pvalue"
+    "result.pvalue\n"
    ]
   },
   {
@@ -169,11 +172,12 @@
    "id": "d1f0c695",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:51.793305Z",
-     "iopub.status.busy": "2023-07-31T02:15:51.793226Z",
-     "iopub.status.idle": "2023-07-31T02:15:51.815977Z",
-     "shell.execute_reply": "2023-07-31T02:15:51.815697Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:36:01.269204Z",
+     "iopub.status.busy": "2023-08-06T17:36:01.269116Z",
+     "iopub.status.idle": "2023-08-06T17:36:01.292380Z",
+     "shell.execute_reply": "2023-08-06T17:36:01.292124Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -186,7 +190,7 @@
     "                          'Do not reject H0'])\n",
     "truth = pd.Categorical(true_mean == 0,\n",
     "                       categories=[True, False],\n",
-    "                       ordered=True)"
+    "                       ordered=True)\n"
    ]
   },
   {
@@ -204,11 +208,12 @@
    "id": "7a9594a0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:51.817571Z",
-     "iopub.status.busy": "2023-07-31T02:15:51.817485Z",
-     "iopub.status.idle": "2023-07-31T02:15:51.826998Z",
-     "shell.execute_reply": "2023-07-31T02:15:51.826728Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:36:01.293910Z",
+     "iopub.status.busy": "2023-08-06T17:36:01.293823Z",
+     "iopub.status.idle": "2023-08-06T17:36:01.302891Z",
+     "shell.execute_reply": "2023-08-06T17:36:01.302612Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -272,7 +277,7 @@
     "pd.crosstab(decision,\n",
     "            truth,\n",
     "     rownames=['Decision'],\n",
-    "     colnames=['H0'])"
+    "     colnames=['H0'])\n"
    ]
   },
   {
@@ -294,7 +299,7 @@
     "amounts to quite a weak signal, and it resulted in a high number of\n",
     "Type II errors.  Let’s instead simulate data with a stronger signal,\n",
     "so that the ratio of the mean to the standard deviation for the false\n",
-    "null hypotheses equals $1$. We make only 10 Type II errors."
+    "null hypotheses equals $1$. We make only 10 Type II errors.\n"
    ]
   },
   {
@@ -303,11 +308,12 @@
    "id": "25f7fc5d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:51.828557Z",
-     "iopub.status.busy": "2023-07-31T02:15:51.828471Z",
-     "iopub.status.idle": "2023-07-31T02:15:51.857144Z",
-     "shell.execute_reply": "2023-07-31T02:15:51.856863Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:36:01.304398Z",
+     "iopub.status.busy": "2023-08-06T17:36:01.304317Z",
+     "iopub.status.idle": "2023-08-06T17:36:01.331987Z",
+     "shell.execute_reply": "2023-08-06T17:36:01.331720Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -383,7 +389,15 @@
     "pd.crosstab(decision,\n",
     "            truth,\n",
     "            rownames=['Decision'],\n",
-    "            colnames=['H0'])"
+    "            colnames=['H0'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb70c597",
+   "metadata": {},
+   "source": [
+    "    "
    ]
   },
   {
@@ -406,10 +420,10 @@
    "id": "369b5bd3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:51.858641Z",
-     "iopub.status.busy": "2023-07-31T02:15:51.858551Z",
-     "iopub.status.idle": "2023-07-31T02:15:52.158944Z",
-     "shell.execute_reply": "2023-07-31T02:15:52.158640Z"
+     "iopub.execute_input": "2023-08-06T17:36:01.333446Z",
+     "iopub.status.busy": "2023-08-06T17:36:01.333362Z",
+     "iopub.status.idle": "2023-08-06T17:36:01.583878Z",
+     "shell.execute_reply": "2023-08-06T17:36:01.583084Z"
     }
    },
    "outputs": [
@@ -435,7 +449,7 @@
     "ax.set_xlabel('Number of Hypotheses')\n",
     "ax.set_ylabel('Family-Wise Error Rate')\n",
     "ax.legend()\n",
-    "ax.axhline(0.05, c='k', ls='--');"
+    "ax.axhline(0.05, c='k', ls='--');\n"
    ]
   },
   {
@@ -462,10 +476,10 @@
    "id": "9ce7a19f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:52.160608Z",
-     "iopub.status.busy": "2023-07-31T02:15:52.160488Z",
-     "iopub.status.idle": "2023-07-31T02:15:52.198900Z",
-     "shell.execute_reply": "2023-07-31T02:15:52.198590Z"
+     "iopub.execute_input": "2023-08-06T17:36:01.592359Z",
+     "iopub.status.busy": "2023-08-06T17:36:01.591524Z",
+     "iopub.status.idle": "2023-08-06T17:36:01.636450Z",
+     "shell.execute_reply": "2023-08-06T17:36:01.636160Z"
     }
    },
    "outputs": [
@@ -486,7 +500,7 @@
     "fund_mini_pvals = np.empty(5)\n",
     "for i in range(5):\n",
     "    fund_mini_pvals[i] = ttest_1samp(fund_mini.iloc[:,i], 0).pvalue\n",
-    "fund_mini_pvals"
+    "fund_mini_pvals\n"
    ]
   },
   {
@@ -530,11 +544,12 @@
    "id": "de6cffed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:52.200735Z",
-     "iopub.status.busy": "2023-07-31T02:15:52.200617Z",
-     "iopub.status.idle": "2023-07-31T02:15:52.202992Z",
-     "shell.execute_reply": "2023-07-31T02:15:52.202738Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:36:01.638004Z",
+     "iopub.status.busy": "2023-08-06T17:36:01.637923Z",
+     "iopub.status.idle": "2023-08-06T17:36:01.640151Z",
+     "shell.execute_reply": "2023-08-06T17:36:01.639891Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -550,7 +565,7 @@
    ],
    "source": [
     "reject, bonf = mult_test(fund_mini_pvals, method = \"bonferroni\")[:2]\n",
-    "reject"
+    "reject\n"
    ]
   },
   {
@@ -568,10 +583,10 @@
    "id": "0de71500",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:52.204499Z",
-     "iopub.status.busy": "2023-07-31T02:15:52.204407Z",
-     "iopub.status.idle": "2023-07-31T02:15:52.206753Z",
-     "shell.execute_reply": "2023-07-31T02:15:52.206498Z"
+     "iopub.execute_input": "2023-08-06T17:36:01.641646Z",
+     "iopub.status.busy": "2023-08-06T17:36:01.641554Z",
+     "iopub.status.idle": "2023-08-06T17:36:01.643766Z",
+     "shell.execute_reply": "2023-08-06T17:36:01.643529Z"
     }
    },
    "outputs": [
@@ -588,7 +603,7 @@
     }
    ],
    "source": [
-    "bonf, np.minimum(fund_mini_pvals * 5, 1)"
+    "bonf, np.minimum(fund_mini_pvals * 5, 1)\n"
    ]
   },
   {
@@ -610,11 +625,12 @@
    "id": "f7e87bdb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:52.208134Z",
-     "iopub.status.busy": "2023-07-31T02:15:52.208036Z",
-     "iopub.status.idle": "2023-07-31T02:15:52.255948Z",
-     "shell.execute_reply": "2023-07-31T02:15:52.255602Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:36:01.645254Z",
+     "iopub.status.busy": "2023-08-06T17:36:01.645162Z",
+     "iopub.status.idle": "2023-08-06T17:36:01.687110Z",
+     "shell.execute_reply": "2023-08-06T17:36:01.686827Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -630,7 +646,7 @@
     }
    ],
    "source": [
-    "mult_test(fund_mini_pvals, method = \"holm\", alpha=0.05)[:2]"
+    "mult_test(fund_mini_pvals, method = \"holm\", alpha=0.05)[:2]\n"
    ]
   },
   {
@@ -648,11 +664,12 @@
    "id": "e88be376",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:52.257656Z",
-     "iopub.status.busy": "2023-07-31T02:15:52.257540Z",
-     "iopub.status.idle": "2023-07-31T02:15:52.260759Z",
-     "shell.execute_reply": "2023-07-31T02:15:52.260443Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:36:01.688627Z",
+     "iopub.status.busy": "2023-08-06T17:36:01.688527Z",
+     "iopub.status.idle": "2023-08-06T17:36:01.691393Z",
+     "shell.execute_reply": "2023-08-06T17:36:01.691161Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -672,7 +689,7 @@
     }
    ],
    "source": [
-    "fund_mini.mean()"
+    "fund_mini.mean()\n"
    ]
   },
   {
@@ -691,10 +708,10 @@
    "id": "41149af6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:52.262161Z",
-     "iopub.status.busy": "2023-07-31T02:15:52.262063Z",
-     "iopub.status.idle": "2023-07-31T02:15:52.264821Z",
-     "shell.execute_reply": "2023-07-31T02:15:52.264573Z"
+     "iopub.execute_input": "2023-08-06T17:36:01.692844Z",
+     "iopub.status.busy": "2023-08-06T17:36:01.692751Z",
+     "iopub.status.idle": "2023-08-06T17:36:01.695119Z",
+     "shell.execute_reply": "2023-08-06T17:36:01.694881Z"
     }
    },
    "outputs": [
@@ -711,7 +728,7 @@
    ],
    "source": [
     "ttest_rel(fund_mini['Manager1'],\n",
-    "          fund_mini['Manager2']).pvalue"
+    "          fund_mini['Manager2']).pvalue\n"
    ]
   },
   {
@@ -743,11 +760,12 @@
    "id": "61aabda7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:52.266290Z",
-     "iopub.status.busy": "2023-07-31T02:15:52.266197Z",
-     "iopub.status.idle": "2023-07-31T02:15:52.746435Z",
-     "shell.execute_reply": "2023-07-31T02:15:52.746118Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:36:01.696563Z",
+     "iopub.status.busy": "2023-08-06T17:36:01.696465Z",
+     "iopub.status.idle": "2023-08-06T17:36:02.177873Z",
+     "shell.execute_reply": "2023-08-06T17:36:02.177587Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -776,7 +794,7 @@
     "returns = np.hstack([fund_mini.iloc[:,i] for i in range(5)])\n",
     "managers = np.hstack([[i+1]*50 for i in range(5)])\n",
     "tukey = pairwise_tukeyhsd(returns, managers)\n",
-    "print(tukey.summary())"
+    "print(tukey.summary())\n"
    ]
   },
   {
@@ -802,10 +820,10 @@
    "id": "cbcad4de",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:52.748078Z",
-     "iopub.status.busy": "2023-07-31T02:15:52.747975Z",
-     "iopub.status.idle": "2023-07-31T02:15:52.829340Z",
-     "shell.execute_reply": "2023-07-31T02:15:52.829068Z"
+     "iopub.execute_input": "2023-08-06T17:36:02.179444Z",
+     "iopub.status.busy": "2023-08-06T17:36:02.179343Z",
+     "iopub.status.idle": "2023-08-06T17:36:02.260168Z",
+     "shell.execute_reply": "2023-08-06T17:36:02.259865Z"
     }
    },
    "outputs": [
@@ -822,7 +840,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(8,8))\n",
-    "tukey.plot_simultaneous(ax=ax);"
+    "tukey.plot_simultaneous(ax=ax);\n"
    ]
   },
   {
@@ -843,17 +861,17 @@
    "id": "b5842190",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:52.831064Z",
-     "iopub.status.busy": "2023-07-31T02:15:52.830953Z",
-     "iopub.status.idle": "2023-07-31T02:15:53.237615Z",
-     "shell.execute_reply": "2023-07-31T02:15:53.237218Z"
+     "iopub.execute_input": "2023-08-06T17:36:02.261922Z",
+     "iopub.status.busy": "2023-08-06T17:36:02.261817Z",
+     "iopub.status.idle": "2023-08-06T17:36:02.675908Z",
+     "shell.execute_reply": "2023-08-06T17:36:02.675505Z"
     }
    },
    "outputs": [],
    "source": [
     "fund_pvalues = np.empty(2000)\n",
     "for i, manager in enumerate(Fund.columns):\n",
-    "    fund_pvalues[i] = ttest_1samp(Fund[manager], 0).pvalue"
+    "    fund_pvalues[i] = ttest_1samp(Fund[manager], 0).pvalue\n"
    ]
   },
   {
@@ -872,10 +890,10 @@
    "id": "7c9d8bed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:53.239456Z",
-     "iopub.status.busy": "2023-07-31T02:15:53.239345Z",
-     "iopub.status.idle": "2023-07-31T02:15:53.242247Z",
-     "shell.execute_reply": "2023-07-31T02:15:53.241963Z"
+     "iopub.execute_input": "2023-08-06T17:36:02.677787Z",
+     "iopub.status.busy": "2023-08-06T17:36:02.677666Z",
+     "iopub.status.idle": "2023-08-06T17:36:02.680351Z",
+     "shell.execute_reply": "2023-08-06T17:36:02.680097Z"
     }
    },
    "outputs": [
@@ -893,7 +911,7 @@
    ],
    "source": [
     "fund_qvalues = mult_test(fund_pvalues, method = \"fdr_bh\")[1]\n",
-    "fund_qvalues[:10]"
+    "fund_qvalues[:10]\n"
    ]
   },
   {
@@ -917,11 +935,12 @@
    "id": "bfa39f7c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:53.243699Z",
-     "iopub.status.busy": "2023-07-31T02:15:53.243594Z",
-     "iopub.status.idle": "2023-07-31T02:15:53.245817Z",
-     "shell.execute_reply": "2023-07-31T02:15:53.245569Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:36:02.681878Z",
+     "iopub.status.busy": "2023-08-06T17:36:02.681776Z",
+     "iopub.status.idle": "2023-08-06T17:36:02.684078Z",
+     "shell.execute_reply": "2023-08-06T17:36:02.683782Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -936,7 +955,7 @@
     }
    ],
    "source": [
-    "(fund_qvalues <= 0.1).sum()"
+    "(fund_qvalues <= 0.1).sum()\n"
    ]
   },
   {
@@ -960,11 +979,12 @@
    "id": "70b69b47",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:53.247278Z",
-     "iopub.status.busy": "2023-07-31T02:15:53.247171Z",
-     "iopub.status.idle": "2023-07-31T02:15:53.249292Z",
-     "shell.execute_reply": "2023-07-31T02:15:53.249050Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:36:02.685580Z",
+     "iopub.status.busy": "2023-08-06T17:36:02.685487Z",
+     "iopub.status.idle": "2023-08-06T17:36:02.687581Z",
+     "shell.execute_reply": "2023-08-06T17:36:02.687313Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -979,7 +999,7 @@
     }
    ],
    "source": [
-    "(fund_pvalues <= 0.1 / 2000).sum()"
+    "(fund_pvalues <= 0.1 / 2000).sum()\n"
    ]
   },
   {
@@ -1009,10 +1029,10 @@
    "id": "4c0ddea1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:53.250720Z",
-     "iopub.status.busy": "2023-07-31T02:15:53.250626Z",
-     "iopub.status.idle": "2023-07-31T02:15:53.253039Z",
-     "shell.execute_reply": "2023-07-31T02:15:53.252775Z"
+     "iopub.execute_input": "2023-08-06T17:36:02.689041Z",
+     "iopub.status.busy": "2023-08-06T17:36:02.688941Z",
+     "iopub.status.idle": "2023-08-06T17:36:02.691386Z",
+     "shell.execute_reply": "2023-08-06T17:36:02.691129Z"
     }
    },
    "outputs": [],
@@ -1026,7 +1046,7 @@
     "    sorted_set_ = np.arange(sorted_set_.max())\n",
     "else:\n",
     "    selected_ = []\n",
-    "    sorted_set_ = []"
+    "    sorted_set_ = []\n"
    ]
   },
   {
@@ -1043,11 +1063,12 @@
    "id": "0314eac9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:53.254539Z",
-     "iopub.status.busy": "2023-07-31T02:15:53.254447Z",
-     "iopub.status.idle": "2023-07-31T02:15:53.487410Z",
-     "shell.execute_reply": "2023-07-31T02:15:53.487100Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:36:02.692825Z",
+     "iopub.status.busy": "2023-08-06T17:36:02.692729Z",
+     "iopub.status.idle": "2023-08-06T17:36:02.922587Z",
+     "shell.execute_reply": "2023-08-06T17:36:02.922278Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1070,7 +1091,7 @@
     "ax.set_ylabel('P-Value')\n",
     "ax.set_xlabel('Index')\n",
     "ax.scatter(sorted_set_+1, sorted_[sorted_set_], c='r', s=20)\n",
-    "ax.axline((0, 0), (1,q/m), c='k', ls='--', linewidth=3);"
+    "ax.axline((0, 0), (1,q/m), c='k', ls='--', linewidth=3);\n"
    ]
   },
   {
@@ -1092,11 +1113,12 @@
    "id": "b59b8137",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:53.489154Z",
-     "iopub.status.busy": "2023-07-31T02:15:53.489039Z",
-     "iopub.status.idle": "2023-07-31T02:15:53.563916Z",
-     "shell.execute_reply": "2023-07-31T02:15:53.563610Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:36:02.924316Z",
+     "iopub.status.busy": "2023-08-06T17:36:02.924196Z",
+     "iopub.status.idle": "2023-08-06T17:36:02.997644Z",
+     "shell.execute_reply": "2023-08-06T17:36:02.997332Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1118,7 +1140,7 @@
     "Khan = load_data('Khan')      \n",
     "D = pd.concat([Khan['xtrain'], Khan['xtest']])\n",
     "D['Y'] = pd.concat([Khan['ytrain'], Khan['ytest']])\n",
-    "D['Y'].value_counts()"
+    "D['Y'].value_counts()\n"
    ]
   },
   {
@@ -1142,11 +1164,12 @@
    "id": "96fb2f61",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:53.565515Z",
-     "iopub.status.busy": "2023-07-31T02:15:53.565402Z",
-     "iopub.status.idle": "2023-07-31T02:15:53.569018Z",
-     "shell.execute_reply": "2023-07-31T02:15:53.568748Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:36:02.999309Z",
+     "iopub.status.busy": "2023-08-06T17:36:02.999199Z",
+     "iopub.status.idle": "2023-08-06T17:36:03.003203Z",
+     "shell.execute_reply": "2023-08-06T17:36:03.002963Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1167,7 +1190,7 @@
     "observedT, pvalue = ttest_ind(D2[gene_11],\n",
     "                              D4[gene_11],\n",
     "                              equal_var=True)\n",
-    "observedT, pvalue"
+    "observedT, pvalue\n"
    ]
   },
   {
@@ -1194,11 +1217,12 @@
    "id": "fdc229fa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:53.570437Z",
-     "iopub.status.busy": "2023-07-31T02:15:53.570359Z",
-     "iopub.status.idle": "2023-07-31T02:15:55.953846Z",
-     "shell.execute_reply": "2023-07-31T02:15:55.953572Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:36:03.004671Z",
+     "iopub.status.busy": "2023-08-06T17:36:03.004588Z",
+     "iopub.status.idle": "2023-08-06T17:36:05.379699Z",
+     "shell.execute_reply": "2023-08-06T17:36:05.379380Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1224,7 +1248,7 @@
     "                       D_null[n_:],\n",
     "                       equal_var=True)\n",
     "    Tnull[b] = ttest_.statistic\n",
-    "(np.abs(Tnull) > np.abs(observedT)).mean()"
+    "(np.abs(Tnull) > np.abs(observedT)).mean()\n"
    ]
   },
   {
@@ -1244,11 +1268,12 @@
    "id": "e3894695",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:55.955682Z",
-     "iopub.status.busy": "2023-07-31T02:15:55.955570Z",
-     "iopub.status.idle": "2023-07-31T02:15:56.168609Z",
-     "shell.execute_reply": "2023-07-31T02:15:56.168280Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:36:05.381564Z",
+     "iopub.status.busy": "2023-08-06T17:36:05.381435Z",
+     "iopub.status.idle": "2023-08-06T17:36:05.597223Z",
+     "shell.execute_reply": "2023-08-06T17:36:05.596880Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1277,7 +1302,7 @@
     "           c='b',\n",
     "           label='Observed')\n",
     "ax.legend()\n",
-    "ax.set_xlabel(\"Null Distribution of Test Statistic\");"
+    "ax.set_xlabel(\"Null Distribution of Test Statistic\");\n"
    ]
   },
   {
@@ -1303,10 +1328,10 @@
    "id": "3b7392cb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:56.170411Z",
-     "iopub.status.busy": "2023-07-31T02:15:56.170304Z",
-     "iopub.status.idle": "2023-07-31T02:19:52.282774Z",
-     "shell.execute_reply": "2023-07-31T02:19:52.282319Z"
+     "iopub.execute_input": "2023-08-06T17:36:05.599334Z",
+     "iopub.status.busy": "2023-08-06T17:36:05.599205Z",
+     "iopub.status.idle": "2023-08-06T17:40:01.929123Z",
+     "shell.execute_reply": "2023-08-06T17:40:01.928341Z"
     }
    },
    "outputs": [],
@@ -1328,7 +1353,7 @@
     "        ttest_ = ttest_ind(D_null[:n_],\n",
     "                           D_null[n_:],\n",
     "                           equal_var=True)\n",
-    "        Tnull_vals[j,b] = ttest_.statistic"
+    "        Tnull_vals[j,b] = ttest_.statistic\n"
    ]
   },
   {
@@ -1349,10 +1374,10 @@
    "id": "cac15616",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:52.285179Z",
-     "iopub.status.busy": "2023-07-31T02:19:52.285030Z",
-     "iopub.status.idle": "2023-07-31T02:19:52.430820Z",
-     "shell.execute_reply": "2023-07-31T02:19:52.430483Z"
+     "iopub.execute_input": "2023-08-06T17:40:01.931393Z",
+     "iopub.status.busy": "2023-08-06T17:40:01.931250Z",
+     "iopub.status.idle": "2023-08-06T17:40:02.050525Z",
+     "shell.execute_reply": "2023-08-06T17:40:02.050215Z"
     }
    },
    "outputs": [],
@@ -1364,7 +1389,7 @@
     "   V = np.sum(np.abs(Tnull_vals) >= cutoffs[j]) / B\n",
     "   Rs[j] = R\n",
     "   Vs[j] = V\n",
-    "   FDRs[j] = V / R"
+    "   FDRs[j] = V / R\n"
    ]
   },
   {
@@ -1390,10 +1415,10 @@
    "id": "9661eb10",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:52.432747Z",
-     "iopub.status.busy": "2023-07-31T02:19:52.432615Z",
-     "iopub.status.idle": "2023-07-31T02:19:52.435385Z",
-     "shell.execute_reply": "2023-07-31T02:19:52.435090Z"
+     "iopub.execute_input": "2023-08-06T17:40:02.052324Z",
+     "iopub.status.busy": "2023-08-06T17:40:02.052224Z",
+     "iopub.status.idle": "2023-08-06T17:40:02.054968Z",
+     "shell.execute_reply": "2023-08-06T17:40:02.054729Z"
     }
    },
    "outputs": [
@@ -1427,7 +1452,7 @@
     }
    ],
    "source": [
-    "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.1].min()])"
+    "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.1].min()])\n"
    ]
   },
   {
@@ -1445,10 +1470,10 @@
    "id": "18ad4900",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:52.436985Z",
-     "iopub.status.busy": "2023-07-31T02:19:52.436868Z",
-     "iopub.status.idle": "2023-07-31T02:19:52.439478Z",
-     "shell.execute_reply": "2023-07-31T02:19:52.439213Z"
+     "iopub.execute_input": "2023-08-06T17:40:02.056480Z",
+     "iopub.status.busy": "2023-08-06T17:40:02.056382Z",
+     "iopub.status.idle": "2023-08-06T17:40:02.058766Z",
+     "shell.execute_reply": "2023-08-06T17:40:02.058503Z"
     }
    },
    "outputs": [
@@ -1494,7 +1519,7 @@
     }
    ],
    "source": [
-    "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.2].min()])"
+    "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.2].min()])\n"
    ]
   },
   {
@@ -1513,11 +1538,12 @@
    "id": "28c276b6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:52.441008Z",
-     "iopub.status.busy": "2023-07-31T02:19:52.440893Z",
-     "iopub.status.idle": "2023-07-31T02:19:52.515423Z",
-     "shell.execute_reply": "2023-07-31T02:19:52.515126Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:40:02.060234Z",
+     "iopub.status.busy": "2023-08-06T17:40:02.060135Z",
+     "iopub.status.idle": "2023-08-06T17:40:02.135583Z",
+     "shell.execute_reply": "2023-08-06T17:40:02.135228Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1535,15 +1561,23 @@
     "fig, ax = plt.subplots()\n",
     "ax.plot(Rs, FDRs, 'b', linewidth=3)\n",
     "ax.set_xlabel(\"Number of Rejections\")\n",
-    "ax.set_ylabel(\"False Discovery Rate\");"
+    "ax.set_ylabel(\"False Discovery Rate\");\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4b5d621",
+   "metadata": {},
+   "source": [
+    "\n"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "formats": "ipynb,md:myst",
-   "main_language": "python"
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/Ch2-statlearn-lab.ipynb b/Ch2-statlearn-lab.ipynb
index b50a888..d4edc10 100644
--- a/Ch2-statlearn-lab.ipynb
+++ b/Ch2-statlearn-lab.ipynb
@@ -2,17 +2,19 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "46456e09",
+   "id": "4a4000ea",
    "metadata": {},
    "source": [
+    "\n",
     "# Chapter 2\n",
     "\n",
-    "# Lab: Introduction to Python"
+    "# Lab: Introduction to Python\n",
+    "\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e9222139",
+   "id": "ca503bf1",
    "metadata": {},
    "source": [
     "## Getting Started"
@@ -20,26 +22,26 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a84726ab",
+   "id": "4d90d49a",
    "metadata": {},
    "source": [
     "To run the labs in this book, you will need two things:\n",
     "\n",
     "* An installation of `Python3`, which is the specific version of `Python`  used in the labs. \n",
-    "* Access to  `Jupyter`, a very popular `Python` interface that runs code through a file called a *notebook*."
+    "* Access to  `Jupyter`, a very popular `Python` interface that runs code through a file called a *notebook*. "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f922cc9e",
+   "id": "c05e9a74",
    "metadata": {},
    "source": [
-    "You can download and install  `Python3`   by following the instructions available at [anaconda.com](http://anaconda.com)."
+    "You can download and install  `Python3`   by following the instructions available at [anaconda.com](http://anaconda.com). "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "baaeada6",
+   "id": "a4fa6a12",
    "metadata": {},
    "source": [
     " There are a number of ways to get access to `Jupyter`. Here are just a few:\n",
@@ -56,29 +58,32 @@
     "To run this lab, download the file `Ch2-statlearn-lab.ipynb` from the `Python` resources page. \n",
     "Now run the following code at the command line: `jupyter lab Ch2-statlearn-lab.ipynb`.\n",
     "\n",
-    "If you're using Windows, you can use the `start menu` to access `anaconda`, and follow the links. For example, to install `ISLP` and run this lab, you can run the same code above in an `anaconda` shell."
+    "If you're using Windows, you can use the `start menu` to access `anaconda`, and follow the links. For example, to install `ISLP` and run this lab, you can run the same code above in an `anaconda` shell.\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7a44fbd9",
+   "id": "fb245ea1",
    "metadata": {},
    "source": [
-    "## Basic Commands"
+    "## Basic Commands\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3831ff0d",
+   "id": "df72fe8e",
    "metadata": {},
    "source": [
     "In this lab, we will introduce some simple `Python` commands. \n",
-    " For more resources about `Python` in general, readers may want to consult the tutorial at [docs.python.org/3/tutorial/](https://docs.python.org/3/tutorial/)."
+    " For more resources about `Python` in general, readers may want to consult the tutorial at [docs.python.org/3/tutorial/](https://docs.python.org/3/tutorial/). \n",
+    "\n",
+    "\n",
+    " \n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b5460488",
+   "id": "713233e6",
    "metadata": {},
    "source": [
     "Like most programming languages, `Python` uses *functions*\n",
@@ -94,13 +99,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "88d0bfff",
+   "id": "3e6aae64",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:54.930945Z",
-     "iopub.status.busy": "2023-07-31T02:19:54.930255Z",
-     "iopub.status.idle": "2023-07-31T02:19:54.946609Z",
-     "shell.execute_reply": "2023-07-31T02:19:54.945952Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.285475Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.285130Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.294270Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.293851Z"
     }
    },
    "outputs": [
@@ -113,12 +118,12 @@
     }
    ],
    "source": [
-    "print('fit a model with', 11, 'variables')"
+    "print('fit a model with', 11, 'variables')\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9c07629a",
+   "id": "790e89ef",
    "metadata": {},
    "source": [
     " The following command will provide information about the `print()` function."
@@ -127,23 +132,23 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "ebe7aca8",
+   "id": "086066fd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:54.950081Z",
-     "iopub.status.busy": "2023-07-31T02:19:54.949852Z",
-     "iopub.status.idle": "2023-07-31T02:19:54.982824Z",
-     "shell.execute_reply": "2023-07-31T02:19:54.982548Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.296567Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.296382Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.324226Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.323953Z"
     }
    },
    "outputs": [],
    "source": [
-    "print?"
+    "print?\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b0c41d18",
+   "id": "1519b474",
    "metadata": {},
    "source": [
     "Adding two integers in `Python` is pretty intuitive."
@@ -152,13 +157,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "df2f8168",
+   "id": "cb86e827",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:54.984493Z",
-     "iopub.status.busy": "2023-07-31T02:19:54.984388Z",
-     "iopub.status.idle": "2023-07-31T02:19:54.987616Z",
-     "shell.execute_reply": "2023-07-31T02:19:54.987363Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.325751Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.325655Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.328625Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.328385Z"
     }
    },
    "outputs": [
@@ -174,12 +179,12 @@
     }
    ],
    "source": [
-    "3 + 5"
+    "3 + 5\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7e7b4195",
+   "id": "995ac4a8",
    "metadata": {},
    "source": [
     "In `Python`, textual data is handled using\n",
@@ -192,13 +197,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "4cc5c2c1",
+   "id": "f4d45d80",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:54.989196Z",
-     "iopub.status.busy": "2023-07-31T02:19:54.989079Z",
-     "iopub.status.idle": "2023-07-31T02:19:54.991281Z",
-     "shell.execute_reply": "2023-07-31T02:19:54.990967Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.330134Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.330050Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.332103Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.331834Z"
     }
    },
    "outputs": [
@@ -214,22 +219,22 @@
     }
    ],
    "source": [
-    "\"hello\" + \" \" + \"world\""
+    "\"hello\" + \" \" + \"world\"\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cfda314a",
+   "id": "54a86535",
    "metadata": {},
    "source": [
     " A string is actually a type of *sequence*: this is a generic term for an ordered list. \n",
     " The three most important types of sequences are lists, tuples, and strings.  \n",
-    "We introduce lists now."
+    "We introduce lists now. "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "764ca7ea",
+   "id": "f2cd64af",
    "metadata": {},
    "source": [
     "The following command instructs `Python` to join together\n",
@@ -241,13 +246,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "e5d81180",
+   "id": "e386aff9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:54.992960Z",
-     "iopub.status.busy": "2023-07-31T02:19:54.992843Z",
-     "iopub.status.idle": "2023-07-31T02:19:54.994972Z",
-     "shell.execute_reply": "2023-07-31T02:19:54.994710Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.333600Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.333503Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.335528Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.335290Z"
     }
    },
    "outputs": [
@@ -264,12 +269,12 @@
    ],
    "source": [
     "x = [3, 4, 5]\n",
-    "x"
+    "x\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a2e75dce",
+   "id": "185fdcfa",
    "metadata": {},
    "source": [
     "Note that we used the brackets\n",
@@ -282,13 +287,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "425a714c",
+   "id": "f81b1212",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:54.996450Z",
-     "iopub.status.busy": "2023-07-31T02:19:54.996354Z",
-     "iopub.status.idle": "2023-07-31T02:19:54.998412Z",
-     "shell.execute_reply": "2023-07-31T02:19:54.998136Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.336973Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.336873Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.338920Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.338661Z"
     }
    },
    "outputs": [
@@ -305,48 +310,49 @@
    ],
    "source": [
     "y = [4, 9, 7]\n",
-    "x + y"
+    "x + y\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "245f069e",
+   "id": "f421a679",
    "metadata": {},
    "source": [
     "The result may appear slightly counterintuitive: why did `Python` not add the entries of the lists\n",
     "element-by-element? \n",
     " In `Python`, lists hold *arbitrary* objects, and  are added using  *concatenation*. \n",
-    " In fact, concatenation is the behavior that we saw earlier when we entered `\"hello\" + \" \" + \"world\"`."
+    " In fact, concatenation is the behavior that we saw earlier when we entered `\"hello\" + \" \" + \"world\"`. \n",
+    " "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "071caddd",
+   "id": "911db9e2",
    "metadata": {},
    "source": [
     "This example reflects the fact that \n",
     " `Python` is a general-purpose programming language. Much of `Python`'s  data-specific\n",
     "functionality comes from other packages, notably `numpy`\n",
     "and `pandas`. \n",
-    "In the next section, we will introduce the  `numpy` package. More information about `numpy` can be found  at \n",
-    "[docs.scipy.org/doc/numpy/user/quickstart.html](https://docs.scipy.org/doc/numpy/user/quickstart.html)."
+    "In the next section, we will introduce the  `numpy` package. \n",
+    "See [docs.scipy.org/doc/numpy/user/quickstart.html](https://docs.scipy.org/doc/numpy/user/quickstart.html) for more information about `numpy`.\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "25581a8e",
+   "id": "2045f04d",
    "metadata": {},
    "source": [
     "## Introduction to Numerical Python\n",
     "\n",
     "As mentioned earlier, this book makes use of functionality   that is contained in the `numpy` \n",
     " *library*, or *package*. A package is a collection of modules that are not necessarily included in \n",
-    " the base `Python` distribution. The name `numpy` is an abbreviation for *numerical Python*."
+    " the base `Python` distribution. The name `numpy` is an abbreviation for *numerical Python*. "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "851a40d6",
+   "id": "b4ef08ea",
    "metadata": {},
    "source": [
     "  To access `numpy`, we must first `import` it."
@@ -355,14 +361,15 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "2f88669a",
+   "id": "13739a14",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:54.999936Z",
-     "iopub.status.busy": "2023-07-31T02:19:54.999846Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.074905Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.074215Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.340405Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.340311Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.397337Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.396647Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -371,7 +378,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5bdc55d1",
+   "id": "587207d9",
    "metadata": {},
    "source": [
     "In the previous line, we named the `numpy` *module* `np`; an abbreviation for easier referencing."
@@ -379,7 +386,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b2cbe8b",
+   "id": "c485aeb9",
    "metadata": {},
    "source": [
     "In `numpy`, an *array* is  a generic term for a multidimensional\n",
@@ -390,14 +397,15 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "e8ec382a",
+   "id": "f3f62d0a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.080476Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.079040Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.085010Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.084396Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.401823Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.401514Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.406194Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.404998Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -407,35 +415,36 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a5ce193",
+   "id": "d5e7e5e3",
    "metadata": {},
    "source": [
     "Note that if you forgot to run the `import numpy as np` command earlier, then\n",
     "you will encounter an error in calling the `np.array()` function in the previous line. \n",
     " The syntax `np.array()` indicates that the function being called\n",
-    "is part of the `numpy` package, which we have abbreviated as `np`."
+    "is part of the `numpy` package, which we have abbreviated as `np`. "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b21e6ebd",
+   "id": "5ba313c6",
    "metadata": {},
    "source": [
     "Since `x` and `y` have been defined using `np.array()`, we get a sensible result when we add them together. Compare this to our results in the previous section,\n",
-    " when we tried to add two lists without using `numpy`."
+    " when we tried to add two lists without using `numpy`. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "04de39ad",
+   "id": "3cff111f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.093437Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.091813Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.099515Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.098725Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.410267Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.410044Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.416300Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.415133Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -455,24 +464,34 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33df3775",
+   "id": "96db430f",
+   "metadata": {},
+   "source": [
+    "    \n",
+    " \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "801647e7",
    "metadata": {},
    "source": [
     "In `numpy`, matrices are typically represented as two-dimensional arrays, and vectors as one-dimensional arrays. {While it is also possible to create matrices using  `np.matrix()`, we will use `np.array()` throughout the labs in this book.}\n",
-    "We can create a two-dimensional array as follows."
+    "We can create a two-dimensional array as follows. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "fee5d798",
+   "id": "30d6c7f8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.105177Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.104776Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.111056Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.110390Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.420244Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.419921Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.426497Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.425722Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -494,25 +513,34 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86de84a5",
+   "id": "5df2b6c3",
+   "metadata": {},
+   "source": [
+    "    \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7804f5e",
    "metadata": {},
    "source": [
     "The object `x` has several \n",
     "*attributes*, or associated objects. To access an attribute of `x`, we type `x.attribute`, where we replace `attribute`\n",
     "with the name of the attribute. \n",
-    "For instance, we can access the `ndim` attribute of  `x` as follows."
+    "For instance, we can access the `ndim` attribute of  `x` as follows. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "5871ca00",
+   "id": "42d49be4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.114118Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.113892Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.118784Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.118098Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.430547Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.430144Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.436651Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.435793Z"
     }
    },
    "outputs": [
@@ -533,7 +561,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71a44f8e",
+   "id": "765e1496",
    "metadata": {},
    "source": [
     "The output indicates that `x` is a two-dimensional array.  \n",
@@ -544,14 +572,15 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "5a32759c",
+   "id": "de32b275",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.121874Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.121557Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.125886Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.125226Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.440754Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.440389Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.447906Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.447260Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -571,26 +600,27 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e770da51",
+   "id": "044bf011",
    "metadata": {},
    "source": [
     "Why is `x` comprised of integers? This is because we created `x` by passing in exclusively integers to the `np.array()` function.\n",
     "  If\n",
     "we had passed in any decimals, then we would have obtained an array of\n",
-    "*floating point numbers* (i.e. real-valued numbers)."
+    "*floating point numbers* (i.e. real-valued numbers). "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "55f01b16",
+   "id": "528c3fa0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.129292Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.128953Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.133425Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.132824Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.451815Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.451466Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.459974Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.459261Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -605,39 +635,40 @@
     }
    ],
    "source": [
-    "np.array([[1, 2], [3.0, 4]]).dtype"
+    "np.array([[1, 2], [3.0, 4]]).dtype\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4595300f",
+   "id": "053da117",
    "metadata": {},
    "source": [
     "Typing `fun?` will cause `Python` to display \n",
     "documentation associated with the function `fun`, if it exists.\n",
-    "We can try this for `np.array()`."
+    "We can try this for `np.array()`. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "2a9de618",
+   "id": "fbf8763b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.136626Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.136412Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.140205Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.139252Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.463710Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.463173Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.468333Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.467532Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
-    "np.array?"
+    "np.array?\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9640f3fe",
+   "id": "0df45f3d",
    "metadata": {},
    "source": [
     "This documentation indicates that we could create a floating point array by passing a `dtype` argument into `np.array()`."
@@ -646,14 +677,15 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "33e3e6ea",
+   "id": "482d0c05",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.143610Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.143298Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.150031Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.148539Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.472515Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.472028Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.479510Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.478563Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -668,12 +700,12 @@
     }
    ],
    "source": [
-    "np.array([[1, 2], [3, 4]], float).dtype"
+    "np.array([[1, 2], [3, 4]], float).dtype\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fbf59c0e",
+   "id": "757c10a0",
    "metadata": {},
    "source": [
     "The array `x` is two-dimensional. We can find out the number of rows and columns by looking\n",
@@ -683,14 +715,15 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "623f8434",
+   "id": "192cdf26",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.153298Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.153076Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.157147Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.156560Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.483171Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.482882Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.489459Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.488833Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -705,12 +738,12 @@
     }
    ],
    "source": [
-    "x.shape"
+    "x.shape\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "81f0bbae",
+   "id": "b3d091f4",
    "metadata": {},
    "source": [
     "A *method* is a function that is associated with an\n",
@@ -725,14 +758,15 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "235738a7",
+   "id": "4420b967",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.161000Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.160710Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.165032Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.164369Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.493523Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.493057Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.499969Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.499303Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -753,23 +787,24 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72373ccf",
+   "id": "ca0ff297",
    "metadata": {},
    "source": [
-    "We could also sum the elements of `x` by passing in `x` as an argument to the `np.sum()` function."
+    "We could also sum the elements of `x` by passing in `x` as an argument to the `np.sum()` function. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "7f3494e1",
+   "id": "5af5e20f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.168035Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.167814Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.172036Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.171424Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.503658Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.503289Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.510540Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.509832Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -790,7 +825,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69967e23",
+   "id": "355ad58f",
    "metadata": {},
    "source": [
     " As another example, the\n",
@@ -807,13 +842,13 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "0ca8f936",
+   "id": "77a0df31",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.175473Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.175049Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.179255Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.178621Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.514507Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.514084Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.520423Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.519754Z"
     }
    },
    "outputs": [
@@ -833,21 +868,21 @@
     "x = np.array([1, 2, 3, 4, 5, 6])\n",
     "print('beginning x:\\n', x)\n",
     "x_reshape = x.reshape((2, 3))\n",
-    "print('reshaped x:\\n', x_reshape)"
+    "print('reshaped x:\\n', x_reshape)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0c1e36ab",
+   "id": "e68cddfa",
    "metadata": {},
    "source": [
     "The previous output reveals that `numpy` arrays are specified as a sequence\n",
-    "of *rows*. This is  called *row-major ordering*, as opposed to *column-major ordering*."
+    "of *rows*. This is  called *row-major ordering*, as opposed to *column-major ordering*. "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "45263db2",
+   "id": "4b16c07d",
    "metadata": {},
    "source": [
     "`Python` (and hence `numpy`) uses 0-based\n",
@@ -858,14 +893,15 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "23fba624",
+   "id": "c57ce2ca",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.183850Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.183200Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.188356Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.187726Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.524768Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.524519Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.537593Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.536180Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -885,24 +921,25 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26f1717c",
+   "id": "9a4feee5",
    "metadata": {},
    "source": [
     "Similarly, `x_reshape[1,2]` yields the element in the second row and the third column \n",
-    "of `x_reshape`."
+    "of `x_reshape`. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "1a1df926",
+   "id": "ea55b034",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.191472Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.191240Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.195433Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.194720Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.541338Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.541124Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.551129Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.550354Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -922,25 +959,26 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6d6d359",
+   "id": "7a3c27dd",
    "metadata": {},
    "source": [
     "Similarly, `x[2]` yields the\n",
     "third entry of `x`. \n",
     "\n",
-    "Now, let's modify the top left element of `x_reshape`.  To our surprise, we discover that the first element of `x` has been modified as well!"
+    "Now, let's modify the top left element of `x_reshape`.  To our surprise, we discover that the first element of `x` has been modified as well!\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "4bfcc1e1",
+   "id": "b897a965",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.199275Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.198815Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.203321Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.202715Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.555575Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.555273Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.561872Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.560597Z"
     }
    },
    "outputs": [
@@ -966,20 +1004,23 @@
     "print('x_reshape before we modify x_reshape:\\n', x_reshape)\n",
     "x_reshape[0, 0] = 5\n",
     "print('x_reshape after we modify its top left element:\\n', x_reshape)\n",
-    "print('x after we modify top left element of x_reshape:\\n', x)"
+    "print('x after we modify top left element of x_reshape:\\n', x)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "10ff8c21",
+   "id": "77ab8534",
    "metadata": {},
    "source": [
-    "Modifying `x_reshape` also modified `x` because the two objects occupy the same space in memory."
+    "Modifying `x_reshape` also modified `x` because the two objects occupy the same space in memory.\n",
+    " \n",
+    "\n",
+    "    "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ff267ecb",
+   "id": "8d5214bc",
    "metadata": {},
    "source": [
     "We just saw that we can modify an element of an array. Can we also modify a tuple? It turns out that we cannot --- and trying to do so introduces\n",
@@ -989,14 +1030,15 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "973c562a",
+   "id": "1de2f886",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.207215Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.206957Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.336832Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.336545Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.573227Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.572937Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.703228Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.702951Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1013,28 +1055,28 @@
    ],
    "source": [
     "my_tuple = (3, 4, 5)\n",
-    "my_tuple[0] = 2"
+    "my_tuple[0] = 2\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "56393fe3",
+   "id": "5f8a5095",
    "metadata": {},
    "source": [
     "We now briefly mention some attributes of arrays that will come in handy. An array's `shape` attribute contains its dimension; this is always a tuple.\n",
-    "The  `ndim` attribute yields the number of dimensions, and `T` provides its transpose."
+    "The  `ndim` attribute yields the number of dimensions, and `T` provides its transpose. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "56b5c382",
+   "id": "634a18d8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.338558Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.338442Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.340894Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.340636Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.704897Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.704794Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.707079Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.706824Z"
     }
    },
    "outputs": [
@@ -1054,31 +1096,31 @@
     }
    ],
    "source": [
-    "x_reshape.shape, x_reshape.ndim, x_reshape.T"
+    "x_reshape.shape, x_reshape.ndim, x_reshape.T\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "22408350",
+   "id": "1db9f878",
    "metadata": {},
    "source": [
     "Notice that the three individual outputs `(2,3)`, `2`, and `array([[5, 4],[2, 5], [3,6]])` are themselves output as a tuple. \n",
     " \n",
     "We will often want to apply functions to arrays. \n",
     "For instance, we can compute the\n",
-    "square root of the entries using the `np.sqrt()` function:"
+    "square root of the entries using the `np.sqrt()` function: "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "2e624622",
+   "id": "38563574",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.342419Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.342311Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.344447Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.344186Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.708463Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.708366Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.710497Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.710237Z"
     }
    },
    "outputs": [
@@ -1095,12 +1137,12 @@
     }
    ],
    "source": [
-    "np.sqrt(x)"
+    "np.sqrt(x)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6c3e074d",
+   "id": "cdee1904",
    "metadata": {},
    "source": [
     "We can also square the elements:"
@@ -1109,13 +1151,13 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "6f45bd25",
+   "id": "6d7eb044",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.345962Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.345864Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.347930Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.347615Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.711918Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.711824Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.713705Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.713469Z"
     }
    },
    "outputs": [
@@ -1131,12 +1173,12 @@
     }
    ],
    "source": [
-    "x**2"
+    "x**2\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "481ca03c",
+   "id": "7322f04a",
    "metadata": {},
    "source": [
     "We can compute the square roots using the same notation, raising to the power of $1/2$ instead of 2."
@@ -1145,14 +1187,15 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "6980f628",
+   "id": "dcf49c32",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.349275Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.349187Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.351235Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.350993Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.715100Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.715015Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.717032Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.716796Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1168,12 +1211,12 @@
     }
    ],
    "source": [
-    "x**0.5"
+    "x**0.5\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bc357745",
+   "id": "a4ccea4f",
    "metadata": {},
    "source": [
     "Throughout this book, we will often want to generate random data. \n",
@@ -1185,35 +1228,35 @@
     " By default, this function will generate random normal variable(s) with mean (`loc`) $0$ and standard deviation (`scale`) $1$; furthermore, \n",
     " a single random variable will be generated unless the argument to `size` is changed. \n",
     "\n",
-    "We now generate 50 independent random variables from a $N(0,1)$ distribution."
+    "We now generate 50 independent random variables from a $N(0,1)$ distribution. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "89e32100",
+   "id": "dec2f36f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.352623Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.352535Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.354759Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.354511Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.718403Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.718308Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.720577Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.720323Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([-0.32811487,  2.23248403, -1.52447344,  0.87877484,  0.96469789,\n",
-       "        0.30865486,  0.27267724, -0.60057925, -1.05393735, -0.50796536,\n",
-       "       -0.37084711, -2.06355835, -0.33046415, -0.42649073,  0.89919736,\n",
-       "        1.42958323,  1.21817925, -1.09955402,  0.53118572, -1.6542446 ,\n",
-       "        0.01086168,  0.41526654, -1.35753179,  0.45375349,  1.11132718,\n",
-       "        1.19292713,  1.18995843,  0.63104856, -0.78136416, -0.3738745 ,\n",
-       "       -0.10391399, -0.25248889,  1.7725557 , -0.01959626, -0.34732798,\n",
-       "        1.03993987,  0.23681907,  1.07380138, -0.04976414,  2.0716395 ,\n",
-       "       -1.60225312, -1.3209919 ,  0.24733062, -0.01605715,  0.01309674,\n",
-       "        2.15909284, -2.13624645,  0.64257243, -0.33452604,  0.84815965])"
+       "array([-0.98985585,  0.78794313, -1.59659373,  1.11006429, -0.19527724,\n",
+       "       -1.49222447, -1.63557764,  2.0998599 , -1.05798913,  0.22654563,\n",
+       "       -2.48042733,  1.53705085,  1.17455819,  0.19093254, -0.2819633 ,\n",
+       "       -1.11688619, -1.58430027, -0.58351755,  0.52620187, -0.14465477,\n",
+       "        0.84609267, -0.06187834, -0.37385525,  1.27932599,  0.85681759,\n",
+       "       -0.11171155,  0.30777588, -1.08741173,  0.60531981, -0.48213946,\n",
+       "       -0.70477606,  0.34160113, -0.44446051, -0.67950574,  1.02276341,\n",
+       "        2.18072377,  0.46699258,  0.28226576, -0.26951424,  2.06603321,\n",
+       "       -0.0803351 ,  1.04171103, -0.54035202, -1.89733049, -1.86300407,\n",
+       "       -0.06721318,  0.4822852 , -0.29686638, -0.19377943,  2.03320645])"
       ]
      },
      "execution_count": 28,
@@ -1223,12 +1266,12 @@
    ],
    "source": [
     "x = np.random.normal(size=50)\n",
-    "x"
+    "x\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "80939b41",
+   "id": "9c60001d",
    "metadata": {},
    "source": [
     "We create an array `y` by adding an independent $N(50,1)$ random variable to each element of `x`."
@@ -1237,14 +1280,15 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "e3428155",
+   "id": "f535fba4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.356099Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.356006Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.357674Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.357434Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.721992Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.721881Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.723589Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.723344Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -1253,31 +1297,31 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17f93000",
+   "id": "b267be24",
    "metadata": {},
    "source": [
     "The `np.corrcoef()` function computes the correlation matrix between `x` and `y`. The off-diagonal elements give the \n",
-    "correlation between `x` and `y`."
+    "correlation between `x` and `y`. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "df70172e",
+   "id": "faf3be25",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.359035Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.358958Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.361240Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.361002Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.724943Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.724855Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.727100Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.726847Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[1.        , 0.62232452],\n",
-       "       [0.62232452, 1.        ]])"
+       "array([[1.        , 0.78790794],\n",
+       "       [0.78790794, 1.        ]])"
       ]
      },
      "execution_count": 30,
@@ -1291,7 +1335,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8fb44ec4",
+   "id": "c6d0e0ce",
    "metadata": {},
    "source": [
     "If you're following along in your own `Jupyter` notebook, then you probably noticed that you got a different set of results when you ran the past few \n",
@@ -1303,33 +1347,42 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "1d996956",
+   "id": "b6a756e0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.362596Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.362498Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.364433Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.364198Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.728472Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.728385Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.730292Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.730062Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[5.36436837 4.4802098 ]\n",
-      "[5.8496351  4.14855633]\n"
+      "[3.08077237 4.8023279 ]\n",
+      "[-1.55255532  6.09934933]\n"
      ]
     }
    ],
    "source": [
     "print(np.random.normal(scale=5, size=2))\n",
-    "print(np.random.normal(scale=5, size=2)) "
+    "print(np.random.normal(scale=5, size=2)) \n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "988ca34a",
+   "id": "a1071413",
+   "metadata": {},
+   "source": [
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31d9911e",
    "metadata": {},
    "source": [
     "In order to ensure that our code provides exactly the same results\n",
@@ -1345,13 +1398,13 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "37b3a30b",
+   "id": "921b634a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.365832Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.365741Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.367967Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.367727Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.731733Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.731639Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.733796Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.733577Z"
     }
    },
    "outputs": [
@@ -1373,7 +1426,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "039e7f65",
+   "id": "7a4da703",
    "metadata": {},
    "source": [
     "Throughout the labs in this book, we use `np.random.default_rng()`  whenever we\n",
@@ -1391,14 +1444,15 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "b4f99e0e",
+   "id": "fe28973e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.369367Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.369272Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.371528Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.371273Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.735204Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.735110Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.737316Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.737055Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1418,17 +1472,26 @@
     "np.mean(y), y.mean()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b6092d9c",
+   "metadata": {},
+   "source": [
+    "    \n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "dbd70319",
+   "id": "04c26742",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.372848Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.372755Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.374988Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.374743Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.738692Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.738597Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.740858Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.740608Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1448,23 +1511,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba51f245",
+   "id": "b9212f82",
    "metadata": {},
    "source": [
     "Notice that by default `np.var()` divides by the sample size $n$ rather\n",
-    "than $n-1$; see the `ddof` argument in `np.var?`."
+    "than $n-1$; see the `ddof` argument in `np.var?`.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "bf281844",
+   "id": "e35cdcbf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.376355Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.376270Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.378293Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.378046Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.742198Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.742113Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.744175Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.743929Z"
     }
    },
    "outputs": [
@@ -1485,23 +1548,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "877ca8e0",
+   "id": "41eb4c38",
    "metadata": {},
    "source": [
     "The `np.mean()`,  `np.var()`, and `np.std()` functions can also be applied to the rows and columns of a matrix. \n",
-    "To see this, we construct a $10 \\times 3$ matrix of $N(0,1)$ random variables, and consider computing its row sums."
+    "To see this, we construct a $10 \\times 3$ matrix of $N(0,1)$ random variables, and consider computing its row sums. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "f751b1dd",
+   "id": "fbd49766",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.379674Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.379580Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.381747Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.381495Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.745532Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.745443Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.747554Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.747310Z"
     }
    },
    "outputs": [
@@ -1532,22 +1595,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f9028e1a",
+   "id": "5520aeae",
    "metadata": {},
    "source": [
-    "Since arrays are row-major ordered, the first axis, i.e. `axis=0`, refers to its rows. We pass this argument into the `mean()` method for the object `X`."
+    "Since arrays are row-major ordered, the first axis, i.e. `axis=0`, refers to its rows. We pass this argument into the `mean()` method for the object `X`. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "cbf92de5",
+   "id": "f5865859",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.383175Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.383088Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.385113Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.384859Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.748912Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.748822Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.750823Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.750599Z"
     }
    },
    "outputs": [
@@ -1568,7 +1631,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "009195d4",
+   "id": "8aedc41d",
    "metadata": {},
    "source": [
     "The following yields the same result."
@@ -1577,14 +1640,15 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "efacc213",
+   "id": "e27b30bf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.386470Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.386379Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.388376Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.388149Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:43.752212Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.752121Z",
+     "iopub.status.idle": "2023-08-06T17:44:43.754183Z",
+     "shell.execute_reply": "2023-08-06T17:44:43.753947Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1604,7 +1668,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e33fa3fc",
+   "id": "4ee7f516",
+   "metadata": {},
+   "source": [
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80ab3209",
    "metadata": {},
    "source": [
     "## Graphics\n",
@@ -1638,13 +1710,13 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "637fb67d",
+   "id": "fed2376c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.389779Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.389694Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.691223Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.690903Z"
+     "iopub.execute_input": "2023-08-06T17:44:43.755592Z",
+     "iopub.status.busy": "2023-08-06T17:44:43.755504Z",
+     "iopub.status.idle": "2023-08-06T17:44:44.060228Z",
+     "shell.execute_reply": "2023-08-06T17:44:44.059891Z"
     }
    },
    "outputs": [
@@ -1664,12 +1736,12 @@
     "fig, ax = subplots(figsize=(8, 8))\n",
     "x = rng.standard_normal(100)\n",
     "y = rng.standard_normal(100)\n",
-    "ax.plot(x, y);"
+    "ax.plot(x, y);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ca04f15f",
+   "id": "9b697b2a",
    "metadata": {},
    "source": [
     "We pause here to note that we have *unpacked* the tuple of length two returned by `subplots()` into the two distinct\n",
@@ -1680,13 +1752,13 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "109d681d",
+   "id": "228ee04d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.693013Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.692861Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.764802Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.764451Z"
+     "iopub.execute_input": "2023-08-06T17:44:44.062068Z",
+     "iopub.status.busy": "2023-08-06T17:44:44.061933Z",
+     "iopub.status.idle": "2023-08-06T17:44:44.133359Z",
+     "shell.execute_reply": "2023-08-06T17:44:44.133077Z"
     }
    },
    "outputs": [
@@ -1709,7 +1781,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5509508c",
+   "id": "e040ba84",
    "metadata": {},
    "source": [
     "We see that our earlier cell produced a line plot, which is the default. To create a scatterplot, we provide an additional argument to `ax.plot()`, indicating that circles should be displayed."
@@ -1718,14 +1790,15 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "e905982c",
+   "id": "e1dc79d2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.767203Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.767067Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.845613Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.845305Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:44.135074Z",
+     "iopub.status.busy": "2023-08-06T17:44:44.134956Z",
+     "iopub.status.idle": "2023-08-06T17:44:44.210381Z",
+     "shell.execute_reply": "2023-08-06T17:44:44.210084Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1746,17 +1819,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42166016",
+   "id": "960d0ceb",
    "metadata": {},
    "source": [
     "Different values\n",
     "of this additional argument can be used to produce different colored lines\n",
-    "as well as different linestyles."
+    "as well as different linestyles. \n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a99dac78",
+   "id": "833d6751",
    "metadata": {},
    "source": [
     "As an alternative, we could use the  `ax.scatter()` function to create a scatterplot."
@@ -1765,13 +1838,13 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "27133a4e",
+   "id": "d6e6bf34",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.847384Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.847263Z",
-     "iopub.status.idle": "2023-07-31T02:19:55.926583Z",
-     "shell.execute_reply": "2023-07-31T02:19:55.926281Z"
+     "iopub.execute_input": "2023-08-06T17:44:44.212226Z",
+     "iopub.status.busy": "2023-08-06T17:44:44.212111Z",
+     "iopub.status.idle": "2023-08-06T17:44:44.290879Z",
+     "shell.execute_reply": "2023-08-06T17:44:44.290584Z"
     }
    },
    "outputs": [
@@ -1793,32 +1866,33 @@
   },
   {
    "cell_type": "markdown",
-   "id": "000c125e",
+   "id": "ff56b51f",
    "metadata": {},
    "source": [
     "Notice that in the code blocks above, we have ended\n",
     "the last line with a semicolon. This prevents `ax.plot(x, y)` from printing\n",
     "text  to the notebook. However, it does not prevent a plot from being produced. \n",
-    " If we omit the trailing semi-colon, then we obtain the following output:"
+    " If we omit the trailing semi-colon, then we obtain the following output:  "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "ef41f90d",
+   "id": "9c1ea81c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:55.928298Z",
-     "iopub.status.busy": "2023-07-31T02:19:55.928181Z",
-     "iopub.status.idle": "2023-07-31T02:19:56.006857Z",
-     "shell.execute_reply": "2023-07-31T02:19:56.006569Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:44.292652Z",
+     "iopub.status.busy": "2023-08-06T17:44:44.292524Z",
+     "iopub.status.idle": "2023-08-06T17:44:44.371306Z",
+     "shell.execute_reply": "2023-08-06T17:44:44.371033Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x10f0db910>"
+       "<matplotlib.collections.PathCollection at 0x120641e10>"
       ]
      },
      "execution_count": 43,
@@ -1838,38 +1912,42 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "ax.scatter(x, y, marker='o')"
+    "ax.scatter(x, y, marker='o')\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "014f66e5",
+   "id": "abdc9ceb",
    "metadata": {},
    "source": [
     "In what follows, we will use\n",
     " trailing semicolons whenever the text that would be output is not\n",
-    "germane to the discussion at hand."
+    "germane to the discussion at hand.\n",
+    "\n",
+    "\n",
+    "\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "98a1db4a",
+   "id": "4b9491f5",
    "metadata": {},
    "source": [
     "To label our plot, we  make use of the `set_xlabel()`,  `set_ylabel()`, and  `set_title()` methods\n",
-    "of `ax`."
+    "of `ax`.\n",
+    "  "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "16a9cb99",
+   "id": "e6217bc8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:56.008735Z",
-     "iopub.status.busy": "2023-07-31T02:19:56.008603Z",
-     "iopub.status.idle": "2023-07-31T02:19:56.103925Z",
-     "shell.execute_reply": "2023-07-31T02:19:56.103640Z"
+     "iopub.execute_input": "2023-08-06T17:44:44.373100Z",
+     "iopub.status.busy": "2023-08-06T17:44:44.372978Z",
+     "iopub.status.idle": "2023-08-06T17:44:44.469578Z",
+     "shell.execute_reply": "2023-08-06T17:44:44.469292Z"
     }
    },
    "outputs": [
@@ -1894,24 +1972,25 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f0af2d9",
+   "id": "e8228a39",
    "metadata": {},
    "source": [
     " Having access to the figure object `fig` itself means that we can go in and change some aspects and then redisplay it. Here, we change\n",
-    "  the size from `(8, 8)` to `(12, 3)`."
+    "  the size from `(8, 8)` to `(12, 3)`.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "6707420e",
+   "id": "2feef77c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:56.105616Z",
-     "iopub.status.busy": "2023-07-31T02:19:56.105506Z",
-     "iopub.status.idle": "2023-07-31T02:19:56.168565Z",
-     "shell.execute_reply": "2023-07-31T02:19:56.168059Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:44.471293Z",
+     "iopub.status.busy": "2023-08-06T17:44:44.471177Z",
+     "iopub.status.idle": "2023-08-06T17:44:44.533248Z",
+     "shell.execute_reply": "2023-08-06T17:44:44.532956Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1933,7 +2012,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3cf4ed3f",
+   "id": "d22f3886",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9187ea8",
    "metadata": {},
    "source": [
     "Occasionally we will want to create several plots within a figure. This can be\n",
@@ -1943,20 +2030,21 @@
     "situations, there is often a relationship between the axes in the plots. For example,\n",
     "all plots may have a common $x$-axis. The `subplots()` function can automatically handle\n",
     "this situation when passed the keyword argument `sharex=True`.\n",
-    "The `axes` object below is an array pointing to different plots in the figure."
+    "The `axes` object below is an array pointing to different plots in the figure. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "6c78ef8b",
+   "id": "24c9bd3c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:56.172243Z",
-     "iopub.status.busy": "2023-07-31T02:19:56.172045Z",
-     "iopub.status.idle": "2023-07-31T02:19:56.596626Z",
-     "shell.execute_reply": "2023-07-31T02:19:56.596325Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:44.534878Z",
+     "iopub.status.busy": "2023-08-06T17:44:44.534763Z",
+     "iopub.status.idle": "2023-08-06T17:44:44.819697Z",
+     "shell.execute_reply": "2023-08-06T17:44:44.819421Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1978,7 +2066,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40d89b7c",
+   "id": "7c6bdba5",
    "metadata": {},
    "source": [
     "We now produce a scatter plot with `'o'` in the second column of the first row and\n",
@@ -1988,14 +2076,15 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "a18402fb",
+   "id": "dbe7d87c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:56.598317Z",
-     "iopub.status.busy": "2023-07-31T02:19:56.598208Z",
-     "iopub.status.idle": "2023-07-31T02:19:56.792609Z",
-     "shell.execute_reply": "2023-07-31T02:19:56.792223Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:44.821358Z",
+     "iopub.status.busy": "2023-08-06T17:44:44.821244Z",
+     "iopub.status.idle": "2023-08-06T17:44:45.004111Z",
+     "shell.execute_reply": "2023-08-06T17:44:45.003828Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2018,16 +2107,18 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d29648ec",
+   "id": "81d8fa9f",
    "metadata": {},
    "source": [
     "Type  `subplots?` to learn more about \n",
-    "`subplots()`."
+    "`subplots()`. \n",
+    "\n",
+    "\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2a6a0c9e",
+   "id": "d3760a25",
    "metadata": {},
    "source": [
     "To save the output of `fig`, we call its `savefig()`\n",
@@ -2038,39 +2129,40 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "80e18950",
+   "id": "c86bffe6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:56.794381Z",
-     "iopub.status.busy": "2023-07-31T02:19:56.794260Z",
-     "iopub.status.idle": "2023-07-31T02:19:57.918552Z",
-     "shell.execute_reply": "2023-07-31T02:19:57.918233Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:45.005700Z",
+     "iopub.status.busy": "2023-08-06T17:44:45.005580Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.085677Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.085363Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
     "fig.savefig(\"Figure.png\", dpi=400)\n",
-    "fig.savefig(\"Figure.pdf\", dpi=200);"
+    "fig.savefig(\"Figure.pdf\", dpi=200);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b8e17a71",
+   "id": "fd9dbb47",
    "metadata": {},
    "source": [
-    "We can continue to modify `fig` using step-by-step updates; for example, we can modify the range of the $x$-axis, re-save the figure, and even re-display it."
+    "We can continue to modify `fig` using step-by-step updates; for example, we can modify the range of the $x$-axis, re-save the figure, and even re-display it. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "fe8c404d",
+   "id": "07f180e1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:57.920860Z",
-     "iopub.status.busy": "2023-07-31T02:19:57.920410Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.160796Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.160490Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.087595Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.087433Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.317762Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.317456Z"
     }
    },
    "outputs": [
@@ -2094,7 +2186,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55bff84e",
+   "id": "8e013a6f",
    "metadata": {},
    "source": [
     "We now create some more sophisticated plots. The \n",
@@ -2107,21 +2199,22 @@
     "* A matrix whose elements correspond to the `z` value (the third\n",
     "dimension) for each pair of `(x,y)` coordinates.\n",
     "\n",
-    "To create `x` and `y`, we’ll use the function `np.linspace()`. The command  `np.linspace(a, b, n)` \n",
-    "returns a vector of `n` numbers starting at  `a` and  ending at `b`."
+    "To create `x` and `y`, we’ll use the command  `np.linspace(a, b, n)`, \n",
+    "which returns a vector of `n` numbers starting at  `a` and  ending at `b`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "a99034cb",
+   "id": "a465c3aa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.162589Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.162476Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.263958Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.263617Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:46.319492Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.319369Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.420916Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.420589Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2140,12 +2233,12 @@
     "x = np.linspace(-np.pi, np.pi, 50)\n",
     "y = x\n",
     "f = np.multiply.outer(np.cos(y), 1 / (1 + x**2))\n",
-    "ax.contour(x, y, f);"
+    "ax.contour(x, y, f);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fd6d00e6",
+   "id": "df35daed",
    "metadata": {},
    "source": [
     "We can increase the resolution by adding more levels to the image."
@@ -2154,14 +2247,15 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "c613a5df",
+   "id": "469979b0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.265796Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.265669Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.395234Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.394853Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:46.422857Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.422743Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.549561Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.549260Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2182,7 +2276,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1f2c4bd",
+   "id": "e6aa9a72",
    "metadata": {},
    "source": [
     "To fine-tune the output of the\n",
@@ -2199,14 +2293,15 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "bef21527",
+   "id": "31092abb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.397112Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.397015Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.499871Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.499547Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:46.551373Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.551279Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.654104Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.653775Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2222,12 +2317,12 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "ax.imshow(f);"
+    "ax.imshow(f);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "35ed7b29",
+   "id": "aba44db2",
    "metadata": {},
    "source": [
     "## Sequences and Slice Notation"
@@ -2235,7 +2330,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb0cee76",
+   "id": "f0edc818",
    "metadata": {},
    "source": [
     "As seen above, the\n",
@@ -2246,14 +2341,15 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "4435ed50",
+   "id": "4e834c17",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.501674Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.501545Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.504084Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.503742Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:46.655794Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.655683Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.658245Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.657943Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2269,12 +2365,12 @@
    ],
    "source": [
     "seq1 = np.linspace(0, 10, 11)\n",
-    "seq1"
+    "seq1\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f1495e1b",
+   "id": "e95ff002",
    "metadata": {},
    "source": [
     "The function `np.arange()`\n",
@@ -2285,13 +2381,13 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "7a2c664a",
+   "id": "65d67d22",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.505674Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.505572Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.507829Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.507555Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.659770Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.659662Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.661932Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.661619Z"
     }
    },
    "outputs": [
@@ -2308,12 +2404,12 @@
    ],
    "source": [
     "seq2 = np.arange(0, 10)\n",
-    "seq2"
+    "seq2\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7b0e1b2f",
+   "id": "09d51db7",
    "metadata": {},
    "source": [
     "Why isn't $10$ output above? This has to do with *slice* notation in `Python`. \n",
@@ -2326,14 +2422,15 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "810eafdf",
+   "id": "676ab564",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.509309Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.509204Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.511443Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.511173Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:46.663547Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.663431Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.665689Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.665413Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2353,23 +2450,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d73d0b54",
+   "id": "d51b85fc",
    "metadata": {},
    "source": [
     "In the code block above, the notation `3:6` is shorthand for  `slice(3,6)` when used inside\n",
-    "`[]`."
+    "`[]`. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "f7a9f652",
+   "id": "569f45ab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.512897Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.512794Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.514883Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.514637Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.667253Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.667154Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.669257Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.669007Z"
     }
    },
    "outputs": [
@@ -2385,22 +2482,42 @@
     }
    ],
    "source": [
-    "\"hello world\"[slice(3,6)]"
+    "\"hello world\"[slice(3,6)]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "be578cf4",
+   "id": "b82b891c",
    "metadata": {},
    "source": [
     "You might have expected  `slice(3,6)` to output the fourth through seventh characters in the text string (recalling that  `Python` begins its indexing at zero),  but instead it output  the fourth through sixth. \n",
     " This also explains why the earlier `np.arange(0, 10)` command output only the integers from $0$ to $9$. \n",
-    "See the documentation `slice?` for useful options in creating slices."
+    "See the documentation `slice?` for useful options in creating slices. \n",
+    "\n",
+    "    \n",
+    "\n",
+    "\n",
+    "\n",
+    "    \n",
+    "\n",
+    "\n",
+    "    \n",
+    "\n",
+    " \n",
+    "\n",
+    "    \n",
+    "\n",
+    " \n",
+    "\n",
+    "    \n",
+    "\n",
+    "\n",
+    "    \n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7a9c183a",
+   "id": "46aa45f7",
    "metadata": {},
    "source": [
     "## Indexing Data\n",
@@ -2410,13 +2527,13 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "24427538",
+   "id": "7cf343bd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.516445Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.516346Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.518539Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.518299Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.670804Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.670697Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.673052Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.672801Z"
     }
    },
    "outputs": [
@@ -2436,12 +2553,12 @@
    ],
    "source": [
     "A = np.array(np.arange(16)).reshape((4, 4))\n",
-    "A"
+    "A\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d2c583a0",
+   "id": "a396d5a6",
    "metadata": {},
    "source": [
     "Typing `A[1,2]` retrieves the element corresponding to the second row and third\n",
@@ -2451,13 +2568,13 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "id": "18684ed5",
+   "id": "2f4c1b37",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.520019Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.519923Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.521980Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.521677Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.674647Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.674533Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.676931Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.676679Z"
     }
    },
    "outputs": [
@@ -2473,12 +2590,12 @@
     }
    ],
    "source": [
-    "A[1,2]"
+    "A[1,2]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b25734ec",
+   "id": "839feee0",
    "metadata": {},
    "source": [
     "The first number after the open-bracket symbol `[`\n",
@@ -2492,13 +2609,13 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "34d27b73",
+   "id": "a8ef1be5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.523496Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.523397Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.525560Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.525249Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.678456Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.678345Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.680588Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.680306Z"
     }
    },
    "outputs": [
@@ -2515,12 +2632,12 @@
     }
    ],
    "source": [
-    "A[[1,3]]"
+    "A[[1,3]]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "97149703",
+   "id": "abe68415",
    "metadata": {},
    "source": [
     "To select the first and third columns, we pass in  `[0,2]` as the second argument in the square brackets.\n",
@@ -2531,13 +2648,13 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "1bb799c5",
+   "id": "830897a9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.527101Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.526990Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.529215Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.528954Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.682117Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.682023Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.684256Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.683990Z"
     }
    },
    "outputs": [
@@ -2556,12 +2673,12 @@
     }
    ],
    "source": [
-    "A[:,[0,2]]"
+    "A[:,[0,2]]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2b9ce4a3",
+   "id": "004f3600",
    "metadata": {},
    "source": [
     "Now, suppose that we want to select the submatrix made up of the second and fourth \n",
@@ -2572,13 +2689,13 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "de853e7c",
+   "id": "12e88036",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.530658Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.530558Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.532880Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.532627Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.685848Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.685735Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.687944Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.687676Z"
     }
    },
    "outputs": [
@@ -2594,27 +2711,27 @@
     }
    ],
    "source": [
-    "A[[1,3],[0,2]]"
+    "A[[1,3],[0,2]]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dd57a0bf",
+   "id": "9b978348",
    "metadata": {},
    "source": [
-    " Oops --- what happened? We got a one-dimensional array of length 2 identical to"
+    " Oops --- what happened? We got a one-dimensional array of length two identical to"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "e71bf672",
+   "id": "c4098144",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.534505Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.534389Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.536830Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.536568Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.689456Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.689356Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.691673Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.691401Z"
     }
    },
    "outputs": [
@@ -2630,12 +2747,12 @@
     }
    ],
    "source": [
-    "np.array([A[1,0],A[3,2]])"
+    "np.array([A[1,0],A[3,2]])\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9cd50d44",
+   "id": "2dcd98eb",
    "metadata": {},
    "source": [
     " Similarly,  the following code fails to extract the submatrix comprised of the second and fourth rows and the first, third, and fourth columns:"
@@ -2644,13 +2761,13 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "458fe5c9",
+   "id": "b4d6bd49",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.538377Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.538273Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.561070Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.560804Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.693166Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.693066Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.715905Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.715629Z"
     }
    },
    "outputs": [
@@ -2667,30 +2784,31 @@
     }
    ],
    "source": [
-    "A[[1,3],[0,2,3]]"
+    "A[[1,3],[0,2,3]]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "67657309",
+   "id": "dcac422f",
    "metadata": {},
    "source": [
     "We can see what has gone wrong here. When supplied with two indexing lists, the `numpy` interpretation is that these provide pairs of $i,j$ indices for a series of entries. That is why the pair of lists must have the same length. However, that was not our intent, since we are looking for a submatrix.\n",
     "\n",
-    "One easy way to do this is as follows. We first create a submatrix by subsetting the rows of `A`, and then on the fly we make a further submatrix by subsetting its columns."
+    "One easy way to do this is as follows. We first create a submatrix by subsetting the rows of `A`, and then on the fly we make a further submatrix by subsetting its columns.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "703832ac",
+   "id": "e46c3267",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.562524Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.562447Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.564964Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.564682Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:46.717564Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.717458Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.719793Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.719525Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2706,12 +2824,20 @@
     }
    ],
    "source": [
-    "A[[1,3]][:,[0,2]]"
+    "A[[1,3]][:,[0,2]]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "25c3a988",
+   "id": "d14bb320",
+   "metadata": {},
+   "source": [
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3d7e667",
    "metadata": {},
    "source": [
     "There are more efficient ways of achieving the same result.\n",
@@ -2723,14 +2849,15 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "dbf328b7",
+   "id": "96d2355e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.566513Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.566403Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.568835Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.568568Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:46.721325Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.721214Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.723623Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.723345Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2747,12 +2874,12 @@
    ],
    "source": [
     "idx = np.ix_([1,3],[0,2,3])\n",
-    "A[idx]"
+    "A[idx]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "13f73d32",
+   "id": "d462675f",
    "metadata": {},
    "source": [
     "Alternatively, we can subset matrices efficiently using slices.\n",
@@ -2765,14 +2892,15 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "65784eb0",
+   "id": "326ef740",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.570355Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.570245Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.572567Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.572289Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:46.725067Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.724962Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.727352Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.727075Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2788,41 +2916,61 @@
     }
    ],
    "source": [
-    "A[1:4:2,0:3:2]"
+    "A[1:4:2,0:3:2]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "481bc57a",
+   "id": "e2eeed28",
+   "metadata": {},
+   "source": [
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5139b99f",
    "metadata": {},
    "source": [
     "Why are we able to retrieve a submatrix directly using slices but not using lists?\n",
     "Its because they are different `Python` types, and\n",
     "are treated differently by `numpy`.\n",
-    "Slices can be used to extract objects from arbitrary sequences, such as strings, lists, and tuples, while the use of lists for indexing is more limited."
+    "Slices can be used to extract objects from arbitrary sequences, such as strings, lists, and tuples, while the use of lists for indexing is more limited.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "    \n",
+    "\n",
+    " \n",
+    "\n",
+    "    \n",
+    "\n",
+    " "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f1c55b8a",
+   "id": "417c640e",
    "metadata": {},
    "source": [
     "### Boolean Indexing\n",
     "In `numpy`, a *Boolean* is a type  that equals either   `True` or  `False` (also represented as $1$ and $0$, respectively).\n",
-    "The next line creates a vector of $0$'s, represented as Booleans, of length equal to the first dimension of `A`."
+    "The next line creates a vector of $0$'s, represented as Booleans, of length equal to the first dimension of `A`. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 67,
-   "id": "3ddb0d83",
+   "id": "62ad65f2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.574085Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.573986Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.576126Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.575865Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:46.728977Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.728881Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.731155Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.730869Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2843,22 +2991,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3007c8d",
+   "id": "117d1718",
    "metadata": {},
    "source": [
-    "We now set two of the elements to `True`."
+    "We now set two of the elements to `True`. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "90e72009",
+   "id": "683f49a3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.577766Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.577642Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.579940Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.579655Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.732749Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.732642Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.734812Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.734546Z"
     }
    },
    "outputs": [
@@ -2875,12 +3023,12 @@
    ],
    "source": [
     "keep_rows[[1,3]] = True\n",
-    "keep_rows"
+    "keep_rows\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "932dac22",
+   "id": "5036e0f2",
    "metadata": {},
    "source": [
     "Note that the elements of `keep_rows`, when viewed as integers, are the same as the\n",
@@ -2891,13 +3039,13 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "id": "fb3b6534",
+   "id": "ede73656",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.581598Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.581485Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.583765Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.583490Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.736256Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.736141Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.738461Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.738206Z"
     }
    },
    "outputs": [
@@ -2913,12 +3061,12 @@
     }
    ],
    "source": [
-    "np.all(keep_rows == np.array([0,1,0,1]))"
+    "np.all(keep_rows == np.array([0,1,0,1]))\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a8cb48c0",
+   "id": "45587df2",
    "metadata": {},
    "source": [
     "(Here, the function `np.all()` has checked whether\n",
@@ -2927,23 +3075,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ac824821",
+   "id": "cd3e2b53",
    "metadata": {},
    "source": [
     "   However, even though `np.array([0,1,0,1])`  and `keep_rows` are equal according to `==`, they index different sets of rows!\n",
-    "The former retrieves the first, second, first, and second rows of `A`."
+    "The former retrieves the first, second, first, and second rows of `A`. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "b146db22",
+   "id": "f65c75d6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.585330Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.585230Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.587540Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.587269Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.740063Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.739965Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.742303Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.742014Z"
     }
    },
    "outputs": [
@@ -2962,27 +3110,27 @@
     }
    ],
    "source": [
-    "A[np.array([0,1,0,1])]"
+    "A[np.array([0,1,0,1])]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c0b18644",
+   "id": "12479298",
    "metadata": {},
    "source": [
-    " By contrast, `keep_rows` retrieves only the second and fourth rows  of `A` --- i.e. the rows for which the Boolean equals `TRUE`."
+    " By contrast, `keep_rows` retrieves only the second and fourth rows  of `A` --- i.e. the rows for which the Boolean equals `TRUE`. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 71,
-   "id": "6bf7b4a3",
+   "id": "31a8ff76",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.588980Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.588882Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.591016Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.590750Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.743800Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.743693Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.745942Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.745692Z"
     }
    },
    "outputs": [
@@ -2999,12 +3147,12 @@
     }
    ],
    "source": [
-    "A[keep_rows]"
+    "A[keep_rows]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "80686b74",
+   "id": "40932333",
    "metadata": {},
    "source": [
     "This example shows that Booleans and integers are treated differently by `numpy`."
@@ -3012,7 +3160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7905166",
+   "id": "8751cf99",
    "metadata": {},
    "source": [
     "We again make use of the `np.ix_()` function\n",
@@ -3023,13 +3171,13 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "id": "288011d9",
+   "id": "c91f330e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.592530Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.592425Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.595045Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.594768Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.747553Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.747444Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.750009Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.749713Z"
     }
    },
    "outputs": [
@@ -3049,12 +3197,12 @@
     "keep_cols = np.zeros(A.shape[1], bool)\n",
     "keep_cols[[0, 2, 3]] = True\n",
     "idx_bool = np.ix_(keep_rows, keep_cols)\n",
-    "A[idx_bool]"
+    "A[idx_bool]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "37900313",
+   "id": "e8b9619b",
    "metadata": {},
    "source": [
     "We can also mix a list with an array of Booleans in the arguments to `np.ix_()`:"
@@ -3063,14 +3211,15 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "id": "3e3b1954",
+   "id": "fb53de70",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.596526Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.596423Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.598772Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.598514Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:46.751453Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.751351Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.753544Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.753283Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -3087,21 +3236,29 @@
    ],
    "source": [
     "idx_mixed = np.ix_([1,3], keep_cols)\n",
-    "A[idx_mixed]"
+    "A[idx_mixed]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2f4d70f3",
+   "id": "18f16a0a",
+   "metadata": {},
+   "source": [
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6e932f4",
    "metadata": {},
    "source": [
     "For more details on indexing in `numpy`, readers are referred\n",
-    "to the `numpy` tutorial mentioned earlier."
+    "to the `numpy` tutorial mentioned earlier.\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3c59c8fb",
+   "id": "8c59352f",
    "metadata": {},
    "source": [
     "## Loading Data\n",
@@ -3118,7 +3275,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95297c29",
+   "id": "4417d4d4",
    "metadata": {},
    "source": [
     "### Reading in a Data Set\n",
@@ -3132,27 +3289,27 @@
     "as this notebook file, then we are all set. \n",
     "Otherwise, \n",
     "the command\n",
-    "`os.chdir()`  can be used to *change directory*. (You will need to call `import os` before calling `os.chdir()`.)"
+    "`os.chdir()`  can be used to *change directory*. (You will need to call `import os` before calling `os.chdir()`.) "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "42760180",
+   "id": "f9fd0896",
    "metadata": {},
    "source": [
-    "We will begin by reading in `Auto.csv`, available on the book website. This is a comma-separated file, and can be read in using `pd.read_csv()`:"
+    "We will begin by reading in `Auto.csv`, available on the book website. This is a comma-separated file, and can be read in using `pd.read_csv()`: "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 74,
-   "id": "d02bfb28",
+   "id": "cc87ed8c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.600410Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.600309Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.820725Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.820427Z"
+     "iopub.execute_input": "2023-08-06T17:44:46.755171Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.755065Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.984772Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.984442Z"
     }
    },
    "outputs": [
@@ -3364,12 +3521,12 @@
    "source": [
     "import pandas as pd\n",
     "Auto = pd.read_csv('Auto.csv')\n",
-    "Auto"
+    "Auto\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5acc3596",
+   "id": "5c4cf682",
    "metadata": {},
    "source": [
     "The book website also has a whitespace-delimited version of this data, called `Auto.data`. This can be read in as follows:"
@@ -3378,49 +3535,52 @@
   {
    "cell_type": "code",
    "execution_count": 75,
-   "id": "ca6090af",
+   "id": "69b34e00",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.822300Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.822200Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.825666Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.825417Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:46.986269Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.986168Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.989605Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.989331Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
-    "Auto = pd.read_csv('Auto.data', delim_whitespace=True)"
+    "Auto = pd.read_csv('Auto.data', delim_whitespace=True)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "350f1f42",
+   "id": "61b12987",
    "metadata": {},
    "source": [
     " Both `Auto.csv` and `Auto.data` are simply text\n",
     "files. Before loading data into `Python`, it is a good idea to view it using\n",
-    "a text editor or other software, such as Microsoft Excel."
+    "a text editor or other software, such as Microsoft Excel.\n",
+    "\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1cae3ad2",
+   "id": "8de34877",
    "metadata": {},
    "source": [
-    "We now take a look at the column of `Auto` corresponding to the variable `horsepower`:"
+    "We now take a look at the column of `Auto` corresponding to the variable `horsepower`: "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 76,
-   "id": "7220aa87",
+   "id": "c45b3e32",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.827227Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.827134Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.829740Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.829490Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:46.991142Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.991052Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.993697Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.993441Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -3446,12 +3606,12 @@
     }
    ],
    "source": [
-    "Auto['horsepower']"
+    "Auto['horsepower']\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c0adabf1",
+   "id": "7b5df8f9",
    "metadata": {},
    "source": [
     "We see that the `dtype` of this column is `object`. \n",
@@ -3463,14 +3623,15 @@
   {
    "cell_type": "code",
    "execution_count": 77,
-   "id": "ca1939f0",
+   "id": "fdac0ada",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.831176Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.831087Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.833413Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.833176Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:46.995126Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.995036Z",
+     "iopub.status.idle": "2023-08-06T17:44:46.997375Z",
+     "shell.execute_reply": "2023-08-06T17:44:46.997124Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -3498,20 +3659,21 @@
     }
    ],
    "source": [
-    "np.unique(Auto['horsepower'])"
+    "np.unique(Auto['horsepower'])\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c35e48dc",
+   "id": "dc6e92aa",
    "metadata": {},
    "source": [
-    "We see the culprit is the value `?`, which is being used to encode missing values."
+    "We see the culprit is the value `?`, which is being used to encode missing values.\n",
+    "\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ccf8e5de",
+   "id": "f15457ad",
    "metadata": {},
    "source": [
     "To fix the problem, we must provide `pd.read_csv()` with an argument called `na_values`.\n",
@@ -3522,14 +3684,15 @@
   {
    "cell_type": "code",
    "execution_count": 78,
-   "id": "cfb084e7",
+   "id": "883e0b73",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.834807Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.834718Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.838363Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.838120Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:46.998839Z",
+     "iopub.status.busy": "2023-08-06T17:44:46.998751Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.002360Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.002105Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -3547,12 +3710,12 @@
     "Auto = pd.read_csv('Auto.data',\n",
     "                   na_values=['?'],\n",
     "                   delim_whitespace=True)\n",
-    "Auto['horsepower'].sum()"
+    "Auto['horsepower'].sum()\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2e8e504f",
+   "id": "cbc53f2a",
    "metadata": {},
    "source": [
     "The `Auto.shape`  attribute tells us that the data has 397\n",
@@ -3562,13 +3725,13 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "id": "2e02b6a5",
+   "id": "c733c1e8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.839782Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.839709Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.841717Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.841484Z"
+     "iopub.execute_input": "2023-08-06T17:44:47.003760Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.003683Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.005701Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.005460Z"
     }
    },
    "outputs": [
@@ -3584,12 +3747,12 @@
     }
    ],
    "source": [
-    "Auto.shape"
+    "Auto.shape\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "df109192",
+   "id": "94c32cfb",
    "metadata": {},
    "source": [
     "There are\n",
@@ -3601,14 +3764,15 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "id": "d391f8d0",
+   "id": "0e0e9956",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.843111Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.843031Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.845585Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.845354Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.007197Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.007116Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.009545Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.009321Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -3624,12 +3788,12 @@
    ],
    "source": [
     "Auto_new = Auto.dropna()\n",
-    "Auto_new.shape"
+    "Auto_new.shape\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e1ddfb53",
+   "id": "7250498b",
    "metadata": {},
    "source": [
     "### Basics of Selecting Rows and Columns\n",
@@ -3640,14 +3804,15 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "id": "a222ca60",
+   "id": "0477b4ff",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.846908Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.846837Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.848998Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.848769Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.010883Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.010793Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.012894Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.012633Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -3665,12 +3830,12 @@
    ],
    "source": [
     "Auto = Auto_new # overwrite the previous value\n",
-    "Auto.columns"
+    "Auto.columns\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "be6f2daf",
+   "id": "c2084300",
    "metadata": {},
    "source": [
     "Accessing the rows and columns of a data frame is similar, but not identical, to accessing the rows and columns of an array. \n",
@@ -3683,14 +3848,15 @@
   {
    "cell_type": "code",
    "execution_count": 82,
-   "id": "a75921ac",
+   "id": "a989e541",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.850348Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.850276Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.854632Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.854378Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.014269Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.014179Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.018504Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.018245Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -3784,12 +3950,12 @@
     }
    ],
    "source": [
-    "Auto[:3]"
+    "Auto[:3]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "17c02ad7",
+   "id": "b5af114e",
    "metadata": {},
    "source": [
     "Similarly, an array of Booleans can be used to subset the rows:"
@@ -3798,14 +3964,15 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "id": "7469ff2b",
+   "id": "d336e7ce",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.856018Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.855938Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.868410Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.868163Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.019899Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.019821Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.032395Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.032144Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -4670,28 +4837,29 @@
    ],
    "source": [
     "idx_80 = Auto['year'] > 80\n",
-    "Auto[idx_80]"
+    "Auto[idx_80]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "27eb1780",
+   "id": "2f09c4df",
    "metadata": {},
    "source": [
-    "However, if we pass  in a list of strings to the `[]` method, then we obtain a data frame containing the corresponding set of *columns*."
+    "However, if we pass  in a list of strings to the `[]` method, then we obtain a data frame containing the corresponding set of *columns*. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "ac455404",
+   "id": "88e9a63b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.869894Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.869804Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.874286Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.874040Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.033786Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.033705Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.038029Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.037800Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -4803,12 +4971,12 @@
     }
    ],
    "source": [
-    "Auto[['mpg', 'horsepower']]"
+    "Auto[['mpg', 'horsepower']]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1629d302",
+   "id": "b372687d",
    "metadata": {},
    "source": [
     "Since we did not specify an *index* column when we loaded our data frame, the rows are labeled using integers\n",
@@ -4818,14 +4986,15 @@
   {
    "cell_type": "code",
    "execution_count": 85,
-   "id": "f41235d4",
+   "id": "aed049d8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.875720Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.875641Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.877794Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.877564Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.039393Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.039316Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.041519Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.041279Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -4843,28 +5012,28 @@
     }
    ],
    "source": [
-    "Auto.index"
+    "Auto.index\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "79d3cebb",
+   "id": "f18f3695",
    "metadata": {},
    "source": [
     "We can use the\n",
-    "`set_index()` method to re-name the rows using the contents of `Auto['name']`."
+    "`set_index()` method to re-name the rows using the contents of `Auto['name']`. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 86,
-   "id": "31caba24",
+   "id": "c93cefd3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.879194Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.879116Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.885170Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.884897Z"
+     "iopub.execute_input": "2023-08-06T17:44:47.043059Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.042954Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.048986Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.048726Z"
     }
    },
    "outputs": [
@@ -5076,20 +5245,21 @@
    ],
    "source": [
     "Auto_re = Auto.set_index('name')\n",
-    "Auto_re"
+    "Auto_re\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 87,
-   "id": "594bedfa",
+   "id": "84947a78",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.886600Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.886516Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.888663Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.888411Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.050266Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.050189Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.052314Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.052075Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -5106,12 +5276,12 @@
     }
    ],
    "source": [
-    "Auto_re.columns"
+    "Auto_re.columns\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "184977f2",
+   "id": "fc160806",
    "metadata": {},
    "source": [
     "We see that the column `'name'` is no longer there.\n",
@@ -5124,14 +5294,15 @@
   {
    "cell_type": "code",
    "execution_count": 88,
-   "id": "9a59b083",
+   "id": "7ee59fae",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.890089Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.890009Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.894256Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.894030Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.053681Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.053604Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.057791Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.057552Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -5222,12 +5393,12 @@
    ],
    "source": [
     "rows = ['amc rebel sst', 'ford torino']\n",
-    "Auto_re.loc[rows]"
+    "Auto_re.loc[rows]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "066af73b",
+   "id": "232fe34c",
    "metadata": {},
    "source": [
     "As an alternative to using the index name, we could retrieve the 4th and 5th rows of `Auto` using the `{iloc[]`} method:"
@@ -5236,14 +5407,15 @@
   {
    "cell_type": "code",
    "execution_count": 89,
-   "id": "54457a11",
+   "id": "4b5a0e01",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.895674Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.895595Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.899819Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.899555Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.059126Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.059053Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.063134Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.062896Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -5333,12 +5505,12 @@
     }
    ],
    "source": [
-    "Auto_re.iloc[[3,4]]"
+    "Auto_re.iloc[[3,4]]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "069ca71e",
+   "id": "6c409a01",
    "metadata": {},
    "source": [
     "We can also use it to retrieve the 1st, 3rd and and 4th columns of `Auto_re`:"
@@ -5347,14 +5519,15 @@
   {
    "cell_type": "code",
    "execution_count": 90,
-   "id": "8a4f3faf",
+   "id": "8814803e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.901242Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.901166Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.905836Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.905588Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.064565Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.064486Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.069061Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.068830Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -5485,12 +5658,12 @@
     }
    ],
    "source": [
-    "Auto_re.iloc[:,[0,2,3]]"
+    "Auto_re.iloc[:,[0,2,3]]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c6f4f9a2",
+   "id": "f02f0d24",
    "metadata": {},
    "source": [
     "We can extract the 4th and 5th rows, as well as the 1st, 3rd and 4th columns, using\n",
@@ -5500,14 +5673,15 @@
   {
    "cell_type": "code",
    "execution_count": 91,
-   "id": "8f1e6e56",
+   "id": "c7567dd0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.907325Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.907236Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.910862Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.910565Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.070446Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.070375Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.073852Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.073601Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -5572,12 +5746,12 @@
     }
    ],
    "source": [
-    "Auto_re.iloc[[3,4],[0,2,3]]"
+    "Auto_re.iloc[[3,4],[0,2,3]]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6e4b778c",
+   "id": "3073ab09",
    "metadata": {},
    "source": [
     "Index entries need not be unique: there are several cars  in the data frame named `ford galaxie 500`."
@@ -5586,14 +5760,15 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "id": "f1d0e795",
+   "id": "63638341",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.912283Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.912205Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.915809Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.915515Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.075218Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.075139Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.078529Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.078280Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -5660,12 +5835,12 @@
     }
    ],
    "source": [
-    "Auto_re.loc['ford galaxie 500', ['mpg', 'origin']]"
+    "Auto_re.loc['ford galaxie 500', ['mpg', 'origin']]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b10eabb7",
+   "id": "2fbe4a9b",
    "metadata": {},
    "source": [
     "### More on Selecting Rows and Columns\n",
@@ -5678,14 +5853,15 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "id": "392f10a1",
+   "id": "57d6de11",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.917357Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.917238Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.923725Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.923444Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.079924Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.079849Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.085281Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.085034Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -6083,28 +6259,29 @@
    ],
    "source": [
     "idx_80 = Auto_re['year'] > 80\n",
-    "Auto_re.loc[idx_80, ['weight', 'origin']]"
+    "Auto_re.loc[idx_80, ['weight', 'origin']]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d3f85975",
+   "id": "a58067ce",
    "metadata": {},
    "source": [
-    "To do this more concisely, we can use an anonymous function called a `lambda`:"
+    "To do this more concisely, we can use an anonymous function called a `lambda`: "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 94,
-   "id": "5f5b7906",
+   "id": "ed95866c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.925315Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.925200Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.931279Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.931014Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.086682Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.086607Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.092129Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.091888Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -6501,12 +6678,12 @@
     }
    ],
    "source": [
-    "Auto_re.loc[lambda df: df['year'] > 80, ['weight', 'origin']]"
+    "Auto_re.loc[lambda df: df['year'] > 80, ['weight', 'origin']]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cc67849e",
+   "id": "13bb6d1f",
    "metadata": {},
    "source": [
     "The `lambda` call creates a function that takes a single\n",
@@ -6520,14 +6697,15 @@
   {
    "cell_type": "code",
    "execution_count": 95,
-   "id": "b13c5728",
+   "id": "e3725c98",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.932750Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.932655Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.937659Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.937405Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.093438Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.093366Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.098000Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.097769Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -6782,12 +6960,12 @@
    "source": [
     "Auto_re.loc[lambda df: (df['year'] > 80) & (df['mpg'] > 30),\n",
     "            ['weight', 'origin']\n",
-    "           ]"
+    "           ]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7885bba9",
+   "id": "6da7a099",
    "metadata": {},
    "source": [
     "The symbol `&` computes an element-wise *and* operation.\n",
@@ -6799,14 +6977,15 @@
   {
    "cell_type": "code",
    "execution_count": 96,
-   "id": "d5a2c3be",
+   "id": "f617c333",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.939058Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.938981Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.944784Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.944527Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.099319Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.099241Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.105067Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.104849Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -7165,12 +7344,12 @@
     "                       & (df.index.str.contains('ford')\n",
     "                       | df.index.str.contains('datsun')),\n",
     "            ['weight', 'origin']\n",
-    "           ]"
+    "           ]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8a7a6c62",
+   "id": "924428a0",
    "metadata": {},
    "source": [
     "Here, the symbol `|` computes an element-wise *or* operation.\n",
@@ -7189,14 +7368,15 @@
   {
    "cell_type": "code",
    "execution_count": 97,
-   "id": "a6278441",
+   "id": "17f13a72",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.946180Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.946101Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.948018Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.947792Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.106536Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.106458Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.108316Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.108072Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -7211,12 +7391,12 @@
     "total = 0\n",
     "for value in [3,2,19]:\n",
     "    total += value\n",
-    "print('Total is: {0}'.format(total))"
+    "print('Total is: {0}'.format(total))\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "92948288",
+   "id": "15f0c1b1",
    "metadata": {},
    "source": [
     "The indented code beneath the line with the `for` statement is run\n",
@@ -7232,14 +7412,15 @@
   {
    "cell_type": "code",
    "execution_count": 98,
-   "id": "2aa065a8",
+   "id": "07e6a011",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.949452Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.949334Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.951372Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.951143Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.109780Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.109691Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.111636Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.111408Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -7260,7 +7441,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aaeb60c5",
+   "id": "fd8124cb",
    "metadata": {},
    "source": [
     "Above, we summed over each combination of `value` and `weight`.\n",
@@ -7283,13 +7464,13 @@
   {
    "cell_type": "code",
    "execution_count": 99,
-   "id": "832c1b7d",
+   "id": "377ab16f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.952776Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.952701Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.954676Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.954428Z"
+     "iopub.execute_input": "2023-08-06T17:44:47.113038Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.112949Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.114837Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.114609Z"
     }
    },
    "outputs": [
@@ -7306,12 +7487,12 @@
     "for value, weight in zip([2,3,19],\n",
     "                         [0.2,0.3,0.5]):\n",
     "    total += weight * value\n",
-    "print('Weighted average is: {0}'.format(total))"
+    "print('Weighted average is: {0}'.format(total))\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ac163d59",
+   "id": "345a595b",
    "metadata": {},
    "source": [
     "### String Formatting\n",
@@ -7338,14 +7519,15 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "id": "2ca2e7cd",
+   "id": "91276344",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.956035Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.955955Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.960636Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.960393Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.116243Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.116152Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.120630Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.120383Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -7427,20 +7609,21 @@
     "                             'pickle',\n",
     "                             'snack',\n",
     "                             'popcorn'])\n",
-    "D[:3]"
+    "D[:3]\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 101,
-   "id": "d8ae1dcc",
+   "id": "c655fcd1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.962038Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.961965Z",
-     "iopub.status.idle": "2023-07-31T02:19:58.964439Z",
-     "shell.execute_reply": "2023-07-31T02:19:58.964187Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.122014Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.121932Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.124432Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.124188Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -7459,12 +7642,12 @@
     "for col in D.columns:\n",
     "    template = 'Column \"{0}\" has {1:.2%} missing values'\n",
     "    print(template.format(col,\n",
-    "          np.isnan(D[col]).mean()))"
+    "          np.isnan(D[col]).mean()))\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e4687a4a",
+   "id": "7267a350",
    "metadata": {},
    "source": [
     "We see that the `template.format()` method expects two arguments `{0}`\n",
@@ -7478,7 +7661,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "22226024",
+   "id": "763fe84a",
    "metadata": {},
    "source": [
     "## Additional Graphical and Numerical Summaries\n",
@@ -7489,14 +7672,15 @@
   {
    "cell_type": "code",
    "execution_count": 102,
-   "id": "30a8a663",
+   "id": "cd9e0046",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:58.965855Z",
-     "iopub.status.busy": "2023-07-31T02:19:58.965769Z",
-     "iopub.status.idle": "2023-07-31T02:19:59.059137Z",
-     "shell.execute_reply": "2023-07-31T02:19:59.058798Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.125901Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.125812Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.219348Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.219031Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -7528,7 +7712,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f09f4e10",
+   "id": "0f0f16b8",
    "metadata": {},
    "source": [
     "We can address this by accessing the columns directly:"
@@ -7537,14 +7721,15 @@
   {
    "cell_type": "code",
    "execution_count": 103,
-   "id": "81fdedb1",
+   "id": "49d239e5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:59.060801Z",
-     "iopub.status.busy": "2023-07-31T02:19:59.060700Z",
-     "iopub.status.idle": "2023-07-31T02:19:59.151129Z",
-     "shell.execute_reply": "2023-07-31T02:19:59.150780Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.221132Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.221021Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.309097Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.308817Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -7560,32 +7745,33 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "ax.plot(Auto['horsepower'], Auto['mpg'], 'o');"
+    "ax.plot(Auto['horsepower'], Auto['mpg'], 'o');\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a64455fa",
+   "id": "154e405c",
    "metadata": {},
    "source": [
     "Alternatively, we can use the `plot()` method with the call `Auto.plot()`.\n",
     "Using this method,\n",
     "the variables  can be accessed by name.\n",
     "The plot methods of a data frame return a familiar object:\n",
-    "an axes. We can use it to update the plot as we did previously:"
+    "an axes. We can use it to update the plot as we did previously: "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 104,
-   "id": "61850515",
+   "id": "5c892a6a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:59.152898Z",
-     "iopub.status.busy": "2023-07-31T02:19:59.152782Z",
-     "iopub.status.idle": "2023-07-31T02:19:59.249760Z",
-     "shell.execute_reply": "2023-07-31T02:19:59.249444Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.310772Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.310600Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.408029Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.407733Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -7616,7 +7802,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c73790f",
+   "id": "74ed42da",
    "metadata": {},
    "source": [
     "If we want to save\n",
@@ -7627,13 +7813,13 @@
   {
    "cell_type": "code",
    "execution_count": 105,
-   "id": "f2e0c165",
+   "id": "92a626b0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:59.251454Z",
-     "iopub.status.busy": "2023-07-31T02:19:59.251353Z",
-     "iopub.status.idle": "2023-07-31T02:19:59.291857Z",
-     "shell.execute_reply": "2023-07-31T02:19:59.291567Z"
+     "iopub.execute_input": "2023-08-06T17:44:47.409678Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.409555Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.449669Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.449382Z"
     }
    },
    "outputs": [],
@@ -7644,7 +7830,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2378a5bb",
+   "id": "7c47c9a8",
    "metadata": {},
    "source": [
     "We can further instruct the data frame to plot to a particular axes object. In this\n",
@@ -7658,13 +7844,13 @@
   {
    "cell_type": "code",
    "execution_count": 106,
-   "id": "35c72e77",
+   "id": "bf9771b9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:59.293503Z",
-     "iopub.status.busy": "2023-07-31T02:19:59.293415Z",
-     "iopub.status.idle": "2023-07-31T02:19:59.462765Z",
-     "shell.execute_reply": "2023-07-31T02:19:59.462430Z"
+     "iopub.execute_input": "2023-08-06T17:44:47.451420Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.451314Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.622582Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.622279Z"
     }
    },
    "outputs": [
@@ -7681,20 +7867,20 @@
    ],
    "source": [
     "fig, axes = subplots(ncols=3, figsize=(15, 5))\n",
-    "Auto.plot.scatter('horsepower', 'mpg', ax=axes[1]);"
+    "Auto.plot.scatter('horsepower', 'mpg', ax=axes[1]);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "95b0f3de",
+   "id": "a27e61c6",
    "metadata": {},
    "source": [
-    "Note also that the columns of a data frame can be accessed as attributes: try typing in `Auto.horsepower`."
+    "Note also that the columns of a data frame can be accessed as attributes: try typing in `Auto.horsepower`. "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5c6c4fa2",
+   "id": "7d4e028a",
    "metadata": {},
    "source": [
     "We now consider the `cylinders` variable. Typing in `Auto.cylinders.dtype` reveals that it is being treated as a quantitative variable. \n",
@@ -7706,14 +7892,15 @@
   {
    "cell_type": "code",
    "execution_count": 107,
-   "id": "2b6fb1ad",
+   "id": "745d2c5f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:59.464559Z",
-     "iopub.status.busy": "2023-07-31T02:19:59.464441Z",
-     "iopub.status.idle": "2023-07-31T02:19:59.467500Z",
-     "shell.execute_reply": "2023-07-31T02:19:59.467197Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.624352Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.624220Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.627666Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.627359Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -7729,12 +7916,12 @@
    ],
    "source": [
     "Auto.cylinders = pd.Series(Auto.cylinders, dtype='category')\n",
-    "Auto.cylinders.dtype"
+    "Auto.cylinders.dtype\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2038c16d",
+   "id": "d9c6dc9e",
    "metadata": {},
    "source": [
     " Now that `cylinders` is qualitative, we can display it using\n",
@@ -7744,13 +7931,13 @@
   {
    "cell_type": "code",
    "execution_count": 108,
-   "id": "1f19f48c",
+   "id": "8b1069bc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:59.469015Z",
-     "iopub.status.busy": "2023-07-31T02:19:59.468931Z",
-     "iopub.status.idle": "2023-07-31T02:19:59.576174Z",
-     "shell.execute_reply": "2023-07-31T02:19:59.575830Z"
+     "iopub.execute_input": "2023-08-06T17:44:47.629273Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.629176Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.736110Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.735790Z"
     }
    },
    "outputs": [
@@ -7767,12 +7954,12 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "Auto.boxplot('mpg', by='cylinders', ax=ax);"
+    "Auto.boxplot('mpg', by='cylinders', ax=ax);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4ae72e1b",
+   "id": "f96288b4",
    "metadata": {},
    "source": [
     "The `hist()`  method can be used to plot a *histogram*."
@@ -7781,14 +7968,15 @@
   {
    "cell_type": "code",
    "execution_count": 109,
-   "id": "929b6901",
+   "id": "d9faa85a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:59.577889Z",
-     "iopub.status.busy": "2023-07-31T02:19:59.577777Z",
-     "iopub.status.idle": "2023-07-31T02:19:59.680396Z",
-     "shell.execute_reply": "2023-07-31T02:19:59.680097Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.737744Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.737633Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.840290Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.839977Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -7804,12 +7992,12 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "Auto.hist('mpg', ax=ax);"
+    "Auto.hist('mpg', ax=ax);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "db7f6e7c",
+   "id": "8f743bb2",
    "metadata": {},
    "source": [
     "The color of the bars and the number of bins can be changed:"
@@ -7818,14 +8006,15 @@
   {
    "cell_type": "code",
    "execution_count": 110,
-   "id": "8d48f1e9",
+   "id": "59d4a0b6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:59.682075Z",
-     "iopub.status.busy": "2023-07-31T02:19:59.681968Z",
-     "iopub.status.idle": "2023-07-31T02:19:59.779240Z",
-     "shell.execute_reply": "2023-07-31T02:19:59.778959Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.841851Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.841742Z",
+     "iopub.status.idle": "2023-08-06T17:44:47.938350Z",
+     "shell.execute_reply": "2023-08-06T17:44:47.938066Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -7841,12 +8030,12 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "Auto.hist('mpg', color='red', bins=12, ax=ax);"
+    "Auto.hist('mpg', color='red', bins=12, ax=ax);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0e53b8b6",
+   "id": "59d4f72f",
    "metadata": {},
    "source": [
     " See `Auto.hist?` for more plotting\n",
@@ -7859,14 +8048,15 @@
   {
    "cell_type": "code",
    "execution_count": 111,
-   "id": "400690e6",
+   "id": "fb5ed57d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:19:59.781028Z",
-     "iopub.status.busy": "2023-07-31T02:19:59.780913Z",
-     "iopub.status.idle": "2023-07-31T02:20:00.729186Z",
-     "shell.execute_reply": "2023-07-31T02:20:00.728923Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:47.940123Z",
+     "iopub.status.busy": "2023-08-06T17:44:47.939990Z",
+     "iopub.status.idle": "2023-08-06T17:44:48.895176Z",
+     "shell.execute_reply": "2023-08-06T17:44:48.894902Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -7881,12 +8071,12 @@
     }
    ],
    "source": [
-    "pd.plotting.scatter_matrix(Auto);"
+    "pd.plotting.scatter_matrix(Auto);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "48a9a4f5",
+   "id": "7f671845",
    "metadata": {},
    "source": [
     " We can also produce scatterplots\n",
@@ -7896,14 +8086,15 @@
   {
    "cell_type": "code",
    "execution_count": 112,
-   "id": "6bbeca8b",
+   "id": "8e117e38",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:20:00.730857Z",
-     "iopub.status.busy": "2023-07-31T02:20:00.730752Z",
-     "iopub.status.idle": "2023-07-31T02:20:00.952615Z",
-     "shell.execute_reply": "2023-07-31T02:20:00.952298Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:48.896866Z",
+     "iopub.status.busy": "2023-08-06T17:44:48.896756Z",
+     "iopub.status.idle": "2023-08-06T17:44:49.119272Z",
+     "shell.execute_reply": "2023-08-06T17:44:49.118911Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -7920,12 +8111,12 @@
    "source": [
     "pd.plotting.scatter_matrix(Auto[['mpg',\n",
     "                                 'displacement',\n",
-    "                                 'weight']]);"
+    "                                 'weight']]);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d3bb4736",
+   "id": "97921078",
    "metadata": {},
    "source": [
     "The `describe()`  method produces a numerical summary of each column in a data frame."
@@ -7934,14 +8125,15 @@
   {
    "cell_type": "code",
    "execution_count": 113,
-   "id": "1727f604",
+   "id": "9e0b12f2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:20:00.954233Z",
-     "iopub.status.busy": "2023-07-31T02:20:00.954113Z",
-     "iopub.status.idle": "2023-07-31T02:20:00.960067Z",
-     "shell.execute_reply": "2023-07-31T02:20:00.959797Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:49.121032Z",
+     "iopub.status.busy": "2023-08-06T17:44:49.120921Z",
+     "iopub.status.idle": "2023-08-06T17:44:49.126814Z",
+     "shell.execute_reply": "2023-08-06T17:44:49.126535Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -8032,12 +8224,12 @@
     }
    ],
    "source": [
-    "Auto[['mpg', 'weight']].describe()"
+    "Auto[['mpg', 'weight']].describe()\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1b508635",
+   "id": "93fc12de",
    "metadata": {},
    "source": [
     "We can also produce a summary of just a single column."
@@ -8046,14 +8238,15 @@
   {
    "cell_type": "code",
    "execution_count": 114,
-   "id": "39bf4091",
+   "id": "ba4aa0f6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:20:00.961663Z",
-     "iopub.status.busy": "2023-07-31T02:20:00.961547Z",
-     "iopub.status.idle": "2023-07-31T02:20:00.965751Z",
-     "shell.execute_reply": "2023-07-31T02:20:00.965471Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:44:49.128408Z",
+     "iopub.status.busy": "2023-08-06T17:44:49.128319Z",
+     "iopub.status.idle": "2023-08-06T17:44:49.132582Z",
+     "shell.execute_reply": "2023-08-06T17:44:49.132299Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -8077,23 +8270,26 @@
    ],
    "source": [
     "Auto['cylinders'].describe()\n",
-    "Auto['mpg'].describe()"
+    "Auto['mpg'].describe()\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "22ecf5c3",
+   "id": "c029ae61",
    "metadata": {},
    "source": [
-    "To exit `Jupyter`,  select `File / Close and Halt`."
+    "To exit `Jupyter`,  select `File / Close and Halt`.\n",
+    "\n",
+    " \n",
+    "\n"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "formats": "ipynb,md:myst",
-   "main_language": "python"
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/Ch3-linreg-lab.ipynb b/Ch3-linreg-lab.ipynb
index a70b9db..910b824 100644
--- a/Ch3-linreg-lab.ipynb
+++ b/Ch3-linreg-lab.ipynb
@@ -5,7 +5,9 @@
    "id": "82bce88a",
    "metadata": {},
    "source": [
-    "# Chapter 3"
+    "\n",
+    "# Chapter 3\n",
+    "\n"
    ]
   },
   {
@@ -26,17 +28,18 @@
    "id": "ca5277a6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:37.098059Z",
-     "iopub.status.busy": "2023-07-31T02:14:37.097629Z",
-     "iopub.status.idle": "2023-07-31T02:14:37.599285Z",
-     "shell.execute_reply": "2023-07-31T02:14:37.598959Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:45.279319Z",
+     "iopub.status.busy": "2023-08-06T17:34:45.279082Z",
+     "iopub.status.idle": "2023-08-06T17:34:45.953848Z",
+     "shell.execute_reply": "2023-08-06T17:34:45.953518Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from matplotlib.pyplot import subplots"
+    "from matplotlib.pyplot import subplots\n"
    ]
   },
   {
@@ -58,15 +61,16 @@
    "id": "675f24e6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:37.601365Z",
-     "iopub.status.busy": "2023-07-31T02:14:37.601115Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.189274Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.188923Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:45.955884Z",
+     "iopub.status.busy": "2023-08-06T17:34:45.955666Z",
+     "iopub.status.idle": "2023-08-06T17:34:46.765820Z",
+     "shell.execute_reply": "2023-08-06T17:34:46.765525Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
-    "import statsmodels.api as sm"
+    "import statsmodels.api as sm\n"
    ]
   },
   {
@@ -90,17 +94,17 @@
    "id": "a0ee23c2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.191201Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.191048Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.194262Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.194012Z"
+     "iopub.execute_input": "2023-08-06T17:34:46.767689Z",
+     "iopub.status.busy": "2023-08-06T17:34:46.767547Z",
+     "iopub.status.idle": "2023-08-06T17:34:46.770326Z",
+     "shell.execute_reply": "2023-08-06T17:34:46.770048Z"
     }
    },
    "outputs": [],
    "source": [
     "from statsmodels.stats.outliers_influence \\\n",
     "     import variance_inflation_factor as VIF\n",
-    "from statsmodels.stats.anova import anova_lm"
+    "from statsmodels.stats.anova import anova_lm\n"
    ]
   },
   {
@@ -121,10 +125,10 @@
    "id": "b35eb887",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.195737Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.195636Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.348861Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.348443Z"
+     "iopub.execute_input": "2023-08-06T17:34:46.771834Z",
+     "iopub.status.busy": "2023-08-06T17:34:46.771733Z",
+     "iopub.status.idle": "2023-08-06T17:34:46.958904Z",
+     "shell.execute_reply": "2023-08-06T17:34:46.958467Z"
     }
    },
    "outputs": [],
@@ -132,7 +136,7 @@
     "from ISLP import load_data\n",
     "from ISLP.models import (ModelSpec as MS,\n",
     "                         summarize,\n",
-    "                         poly)"
+    "                         poly)\n"
    ]
   },
   {
@@ -153,11 +157,12 @@
    "id": "961908f7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.351011Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.350696Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.354455Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.354198Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:46.960948Z",
+     "iopub.status.busy": "2023-08-06T17:34:46.960687Z",
+     "iopub.status.idle": "2023-08-06T17:34:46.964347Z",
+     "shell.execute_reply": "2023-08-06T17:34:46.964073Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -208,7 +213,7 @@
     }
    ],
    "source": [
-    "dir()"
+    "dir()\n"
    ]
   },
   {
@@ -233,11 +238,12 @@
    "id": "662caa15",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.355929Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.355825Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.358768Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.358500Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:46.966063Z",
+     "iopub.status.busy": "2023-08-06T17:34:46.965960Z",
+     "iopub.status.idle": "2023-08-06T17:34:46.968939Z",
+     "shell.execute_reply": "2023-08-06T17:34:46.968662Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -418,7 +424,7 @@
    ],
    "source": [
     "A = np.array([3,5,11])\n",
-    "dir(A)"
+    "dir(A)\n"
    ]
   },
   {
@@ -436,11 +442,12 @@
    "id": "ebb7d126",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.360173Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.360095Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.362221Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.361978Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:46.970359Z",
+     "iopub.status.busy": "2023-08-06T17:34:46.970263Z",
+     "iopub.status.idle": "2023-08-06T17:34:46.972364Z",
+     "shell.execute_reply": "2023-08-06T17:34:46.972124Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -455,7 +462,15 @@
     }
    ],
    "source": [
-    "A.sum()"
+    "A.sum()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b9db985",
+   "metadata": {},
+   "source": [
+    "    "
    ]
   },
   {
@@ -485,10 +500,10 @@
    "id": "1ea46cee",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.363717Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.363629Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.368806Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.368543Z"
+     "iopub.execute_input": "2023-08-06T17:34:46.973818Z",
+     "iopub.status.busy": "2023-08-06T17:34:46.973741Z",
+     "iopub.status.idle": "2023-08-06T17:34:46.982452Z",
+     "shell.execute_reply": "2023-08-06T17:34:46.982201Z"
     }
    },
    "outputs": [
@@ -507,7 +522,7 @@
    ],
    "source": [
     "Boston = load_data(\"Boston\")\n",
-    "Boston.columns"
+    "Boston.columns\n"
    ]
   },
   {
@@ -520,7 +535,7 @@
     "We start by using the `sm.OLS()`  function to fit a\n",
     "simple linear regression model.  Our response will be\n",
     " `medv`  and  `lstat`  will be the single predictor.\n",
-    "For this model, we can create the model matrix by hand."
+    "For this model, we can create the model matrix by hand.\n"
    ]
   },
   {
@@ -529,10 +544,10 @@
    "id": "26c0ba88",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.370249Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.370153Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.375404Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.375134Z"
+     "iopub.execute_input": "2023-08-06T17:34:46.983850Z",
+     "iopub.status.busy": "2023-08-06T17:34:46.983745Z",
+     "iopub.status.idle": "2023-08-06T17:34:46.990247Z",
+     "shell.execute_reply": "2023-08-06T17:34:46.990002Z"
     }
    },
    "outputs": [
@@ -602,7 +617,7 @@
    "source": [
     "X = pd.DataFrame({'intercept': np.ones(Boston.shape[0]),\n",
     "                  'lstat': Boston['lstat']})\n",
-    "X[:4]"
+    "X[:4]\n"
    ]
   },
   {
@@ -619,17 +634,18 @@
    "id": "d4dd511b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.376828Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.376741Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.379010Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.378764Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:46.991664Z",
+     "iopub.status.busy": "2023-08-06T17:34:46.991561Z",
+     "iopub.status.idle": "2023-08-06T17:34:46.994290Z",
+     "shell.execute_reply": "2023-08-06T17:34:46.994049Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
     "y = Boston['medv']\n",
     "model = sm.OLS(y, X)\n",
-    "results = model.fit()"
+    "results = model.fit()\n"
    ]
   },
   {
@@ -653,11 +669,12 @@
    "id": "eef9f8e3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.380359Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.380275Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.437623Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.437333Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:46.995620Z",
+     "iopub.status.busy": "2023-08-06T17:34:46.995530Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.057569Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.057305Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -718,7 +735,7 @@
     }
    ],
    "source": [
-    "summarize(results)"
+    "summarize(results)\n"
    ]
   },
   {
@@ -754,7 +771,7 @@
     "initial computations on it, as specified in the transform object.\n",
     "For example, it may compute means and standard deviations for centering and scaling.\n",
     "The `transform()` \n",
-    "method applies the fitted transformation to the array of data, and produces the model matrix."
+    "method applies the fitted transformation to the array of data, and produces the model matrix.\n"
    ]
   },
   {
@@ -763,11 +780,12 @@
    "id": "557170d4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.439217Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.439073Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.443761Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.443507Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.059196Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.059050Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.063793Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.063548Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -857,11 +875,12 @@
    "id": "b83ec097",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.445159Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.445084Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.449274Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.449028Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.065239Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.065152Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.069286Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.068998Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -960,10 +979,10 @@
    "id": "d4dce5f6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.450699Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.450620Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.459848Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.459594Z"
+     "iopub.execute_input": "2023-08-06T17:34:47.070742Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.070666Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.079919Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.079672Z"
     }
    },
    "outputs": [
@@ -982,10 +1001,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   601.6</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Sun, 30 Jul 2023</td> <th>  Prob (F-statistic):</th> <td>5.08e-88</td>\n",
+       "  <th>Date:</th>             <td>Sun, 06 Aug 2023</td> <th>  Prob (F-statistic):</th> <td>5.08e-88</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>22:14:38</td>     <th>  Log-Likelihood:    </th> <td> -1641.5</td>\n",
+       "  <th>Time:</th>                 <td>10:34:47</td>     <th>  Log-Likelihood:    </th> <td> -1641.5</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3287.</td>\n",
@@ -1033,8 +1052,8 @@
        "\\textbf{Dep. Variable:}    &       medv       & \\textbf{  R-squared:         } &     0.544   \\\\\n",
        "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.543   \\\\\n",
        "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     601.6   \\\\\n",
-       "\\textbf{Date:}             & Sun, 30 Jul 2023 & \\textbf{  Prob (F-statistic):} &  5.08e-88   \\\\\n",
-       "\\textbf{Time:}             &     22:14:38     & \\textbf{  Log-Likelihood:    } &   -1641.5   \\\\\n",
+       "\\textbf{Date:}             & Sun, 06 Aug 2023 & \\textbf{  Prob (F-statistic):} &  5.08e-88   \\\\\n",
+       "\\textbf{Time:}             &     10:34:47     & \\textbf{  Log-Likelihood:    } &   -1641.5   \\\\\n",
        "\\textbf{No. Observations:} &         506      & \\textbf{  AIC:               } &     3287.   \\\\\n",
        "\\textbf{Df Residuals:}     &         504      & \\textbf{  BIC:               } &     3295.   \\\\\n",
        "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
@@ -1069,8 +1088,8 @@
        "Dep. Variable:                   medv   R-squared:                       0.544\n",
        "Model:                            OLS   Adj. R-squared:                  0.543\n",
        "Method:                 Least Squares   F-statistic:                     601.6\n",
-       "Date:                Sun, 30 Jul 2023   Prob (F-statistic):           5.08e-88\n",
-       "Time:                        22:14:38   Log-Likelihood:                -1641.5\n",
+       "Date:                Sun, 06 Aug 2023   Prob (F-statistic):           5.08e-88\n",
+       "Time:                        10:34:47   Log-Likelihood:                -1641.5\n",
        "No. Observations:                 506   AIC:                             3287.\n",
        "Df Residuals:                     504   BIC:                             3295.\n",
        "Df Model:                           1                                         \n",
@@ -1098,7 +1117,7 @@
     }
    ],
    "source": [
-    "results.summary()"
+    "results.summary()\n"
    ]
   },
   {
@@ -1116,11 +1135,12 @@
    "id": "a0edf555",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.461298Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.461215Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.463809Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.463563Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.081336Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.081257Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.083680Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.083425Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1137,7 +1157,7 @@
     }
    ],
    "source": [
-    "results.params"
+    "results.params\n"
    ]
   },
   {
@@ -1158,10 +1178,10 @@
    "id": "fdc5a3f3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.465231Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.465150Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.468821Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.468569Z"
+     "iopub.execute_input": "2023-08-06T17:34:47.085093Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.085015Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.088625Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.088392Z"
     }
    },
    "outputs": [
@@ -1225,7 +1245,7 @@
    "source": [
     "new_df = pd.DataFrame({'lstat':[5, 10, 15]})\n",
     "newX = design.transform(new_df)\n",
-    "newX"
+    "newX\n"
    ]
   },
   {
@@ -1242,11 +1262,12 @@
    "id": "2c6acbf0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.470242Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.470162Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.472450Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.472199Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.090091Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.090008Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.092428Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.092183Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1262,7 +1283,7 @@
    ],
    "source": [
     "new_predictions = results.get_prediction(newX);\n",
-    "new_predictions.predicted_mean"
+    "new_predictions.predicted_mean\n"
    ]
   },
   {
@@ -1279,11 +1300,12 @@
    "id": "c472ef33",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.473835Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.473762Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.476010Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.475756Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.093785Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.093688Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.096010Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.095781Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1300,7 +1322,7 @@
     }
    ],
    "source": [
-    "new_predictions.conf_int(alpha=0.05)"
+    "new_predictions.conf_int(alpha=0.05)\n"
    ]
   },
   {
@@ -1317,11 +1339,12 @@
    "id": "3e2ffc7a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.477420Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.477344Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.479639Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.479371Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.097324Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.097234Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.099513Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.099275Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1338,7 +1361,7 @@
     }
    ],
    "source": [
-    "new_predictions.conf_int(obs=True, alpha=0.05)"
+    "new_predictions.conf_int(obs=True, alpha=0.05)\n"
    ]
   },
   {
@@ -1376,11 +1399,12 @@
    "id": "4e56a1d3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.481027Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.480949Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.482803Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.482553Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.100972Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.100892Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.102793Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.102549Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -1388,7 +1412,7 @@
     "    \"Add a line with slope m and intercept b to ax\"\n",
     "    xlim = ax.get_xlim()\n",
     "    ylim = [m * xlim[0] + b, m * xlim[1] + b]\n",
-    "    ax.plot(xlim, ylim)"
+    "    ax.plot(xlim, ylim)\n"
    ]
   },
   {
@@ -1409,11 +1433,12 @@
    "id": "7f43ffe7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.484281Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.484202Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.486090Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.485818Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.104148Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.104079Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.106062Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.105836Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -1421,7 +1446,7 @@
     "    \"Add a line with slope m and intercept b to ax\"\n",
     "    xlim = ax.get_xlim()\n",
     "    ylim = [m * xlim[0] + b, m * xlim[1] + b]\n",
-    "    ax.plot(xlim, ylim, *args, **kwargs)"
+    "    ax.plot(xlim, ylim, *args, **kwargs)\n"
    ]
   },
   {
@@ -1448,11 +1473,12 @@
    "id": "3f7b67c9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.487491Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.487422Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.594015Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.593271Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.107460Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.107386Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.254700Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.253915Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1472,7 +1498,7 @@
     "       results.params[0],\n",
     "       results.params[1],\n",
     "       'r--',\n",
-    "       linewidth=3)"
+    "       linewidth=3)\n"
    ]
   },
   {
@@ -1485,7 +1511,8 @@
     "an argument to make it of width 3.\n",
     "There is some evidence for non-linearity in the relationship between  `lstat`  and  `medv`. We will explore this issue later in this lab.\n",
     "\n",
-    "As mentioned above, there is an existing function to add a line to a plot --- `ax.axline()` --- but knowing how to write such functions empowers us to create more expressive displays."
+    "As mentioned above, there is an existing function to add a line to a plot --- `ax.axline()` --- but knowing how to write such functions empowers us to create more expressive displays.\n",
+    "\n"
    ]
   },
   {
@@ -1510,11 +1537,12 @@
    "id": "b35a2fd3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.597846Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.597587Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.716373Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.714325Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.259557Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.259229Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.402403Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.402135Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1533,7 +1561,7 @@
     "ax.scatter(results.fittedvalues, results.resid)\n",
     "ax.set_xlabel('Fitted value')\n",
     "ax.set_ylabel('Residual')\n",
-    "ax.axhline(0, c='k', ls='--');"
+    "ax.axhline(0, c='k', ls='--');\n"
    ]
   },
   {
@@ -1557,11 +1585,12 @@
    "id": "82673b80",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.720789Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.720353Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.829000Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.828696Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.404190Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.404052Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.494230Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.493905Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1591,7 +1620,7 @@
     "ax.scatter(np.arange(X.shape[0]), infl.hat_matrix_diag)\n",
     "ax.set_xlabel('Index')\n",
     "ax.set_ylabel('Leverage')\n",
-    "np.argmax(infl.hat_matrix_diag)"
+    "np.argmax(infl.hat_matrix_diag)\n"
    ]
   },
   {
@@ -1624,11 +1653,12 @@
    "id": "54596dc4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.830833Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.830723Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.841593Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.841314Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.495999Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.495872Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.506251Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.505979Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1721,10 +1751,10 @@
    "id": "75c78238",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.843301Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.843182Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.845817Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.845544Z"
+     "iopub.execute_input": "2023-08-06T17:34:47.508238Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.508087Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.510826Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.510491Z"
     }
    },
    "outputs": [
@@ -1743,7 +1773,7 @@
    ],
    "source": [
     "terms = Boston.columns.drop('medv')\n",
-    "terms"
+    "terms\n"
    ]
   },
   {
@@ -1761,10 +1791,10 @@
    "id": "f14b9e1a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.847324Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.847229Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.861947Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.861682Z"
+     "iopub.execute_input": "2023-08-06T17:34:47.512561Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.512448Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.526980Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.526612Z"
     }
    },
    "outputs": [
@@ -1917,7 +1947,7 @@
     "X = MS(terms).fit_transform(Boston)\n",
     "model = sm.OLS(y, X)\n",
     "results = model.fit()\n",
-    "summarize(results)"
+    "summarize(results)\n"
    ]
   },
   {
@@ -1937,10 +1967,10 @@
    "id": "0a2714b1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.863470Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.863382Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.877283Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.877001Z"
+     "iopub.execute_input": "2023-08-06T17:34:47.528637Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.528526Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.542270Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.541976Z"
     }
    },
    "outputs": [
@@ -2085,7 +2115,7 @@
     "minus_age = Boston.columns.drop(['medv', 'age']) \n",
     "Xma = MS(minus_age).fit_transform(Boston)\n",
     "model1 = sm.OLS(y, Xma)\n",
-    "summarize(model1.fit())"
+    "summarize(model1.fit())\n"
    ]
   },
   {
@@ -2116,7 +2146,7 @@
     "lists of `Python` objects. The language also supports\n",
     "dictionary and *generator* comprehension, though these are\n",
     "beyond our scope here. Let's look at an example. We compute the VIF for each of the variables\n",
-    "in the model matrix `X`, using the function `variance_inflation_factor()`."
+    "in the model matrix `X`, using the function `variance_inflation_factor()`.\n"
    ]
   },
   {
@@ -2125,11 +2155,12 @@
    "id": "961c9128",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.878849Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.878736Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.885983Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.885709Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.543839Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.543752Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.551040Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.550770Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2235,7 +2266,7 @@
     "        for i in range(1, X.shape[1])]\n",
     "vif = pd.DataFrame({'vif':vals},\n",
     "                   index=X.columns[1:])\n",
-    "vif"
+    "vif\n"
    ]
   },
   {
@@ -2256,17 +2287,18 @@
    "id": "4886f9e9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.887620Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.887509Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.892708Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.892460Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.552596Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.552485Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.557775Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.557510Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
     "vals = []\n",
     "for i in range(1, X.values.shape[1]):\n",
-    "    vals.append(VIF(X.values, i))"
+    "    vals.append(VIF(X.values, i))\n"
    ]
   },
   {
@@ -2288,11 +2320,12 @@
    "id": "b54d2da1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.894293Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.894204Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.904859Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.904591Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.559401Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.559315Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.570264Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.570004Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2373,7 +2406,7 @@
     "        'age',\n",
     "        ('lstat', 'age')]).fit_transform(Boston)\n",
     "model2 = sm.OLS(y, X)\n",
-    "summarize(model2.fit())"
+    "summarize(model2.fit())\n"
    ]
   },
   {
@@ -2395,11 +2428,12 @@
    "id": "1b71633a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.906973Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.906845Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.918896Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.918604Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.571718Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.571635Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.583621Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.583342Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2479,7 +2513,7 @@
     "X = MS([poly('lstat', degree=2), 'age']).fit_transform(Boston)\n",
     "model3 = sm.OLS(y, X)\n",
     "results3 = model3.fit()\n",
-    "summarize(results3)"
+    "summarize(results3)\n"
    ]
   },
   {
@@ -2512,11 +2546,12 @@
    "id": "6d30a306",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.920531Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.920425Z",
-     "iopub.status.idle": "2023-07-31T02:14:38.926296Z",
-     "shell.execute_reply": "2023-07-31T02:14:38.926036Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.585282Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.585169Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.591106Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.590768Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2583,7 +2618,7 @@
     }
    ],
    "source": [
-    "anova_lm(results1, results3)"
+    "anova_lm(results1, results3)\n"
    ]
   },
   {
@@ -2612,7 +2647,7 @@
     "The function `anova_lm()` can take more than two nested models\n",
     "as input, in which case it compares every successive pair of models.\n",
     "That also explains why their are `NaN`s in the first row above, since\n",
-    "there is no previous model with which to compare the first."
+    "there is no previous model with which to compare the first.\n"
    ]
   },
   {
@@ -2621,17 +2656,18 @@
    "id": "9a5ec13f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:38.927819Z",
-     "iopub.status.busy": "2023-07-31T02:14:38.927721Z",
-     "iopub.status.idle": "2023-07-31T02:14:39.027635Z",
-     "shell.execute_reply": "2023-07-31T02:14:39.027301Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.592639Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.592536Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.692002Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.691684Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.lines.Line2D at 0x154a82f50>"
+       "<matplotlib.lines.Line2D at 0x15f00b090>"
       ]
      },
      "execution_count": 34,
@@ -2654,7 +2690,7 @@
     "ax.scatter(results3.fittedvalues, results3.resid)\n",
     "ax.set_xlabel('Fitted value')\n",
     "ax.set_ylabel('Residual')\n",
-    "ax.axhline(0, c='k', ls='--')"
+    "ax.axhline(0, c='k', ls='--')\n"
    ]
   },
   {
@@ -2665,7 +2701,7 @@
     "We see that when the quadratic term is included in the model,\n",
     "there is little discernible pattern in the residuals.\n",
     "In order to create a cubic or higher-degree polynomial fit, we can simply change the degree argument\n",
-    "to `poly()`."
+    "to `poly()`.\n"
    ]
   },
   {
@@ -2686,11 +2722,12 @@
    "id": "09bbc0c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:39.029410Z",
-     "iopub.status.busy": "2023-07-31T02:14:39.029292Z",
-     "iopub.status.idle": "2023-07-31T02:14:39.034985Z",
-     "shell.execute_reply": "2023-07-31T02:14:39.034677Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.693776Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.693649Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.700319Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.700055Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2708,7 +2745,7 @@
    ],
    "source": [
     "Carseats = load_data('Carseats')\n",
-    "Carseats.columns"
+    "Carseats.columns\n"
    ]
   },
   {
@@ -2736,11 +2773,12 @@
    "id": "2e1da1fa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:39.036665Z",
-     "iopub.status.busy": "2023-07-31T02:14:39.036539Z",
-     "iopub.status.idle": "2023-07-31T02:14:39.057391Z",
-     "shell.execute_reply": "2023-07-31T02:14:39.057099Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:47.701981Z",
+     "iopub.status.busy": "2023-08-06T17:34:47.701852Z",
+     "iopub.status.idle": "2023-08-06T17:34:47.722346Z",
+     "shell.execute_reply": "2023-08-06T17:34:47.722062Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2903,7 +2941,7 @@
     "                   ('Price', 'Age')]\n",
     "X = MS(final).fit_transform(Carseats)\n",
     "model = sm.OLS(y, X)\n",
-    "summarize(model.fit())"
+    "summarize(model.fit())\n"
    ]
   },
   {
@@ -2922,15 +2960,16 @@
     "positive indicates that a good shelving location is associated with high sales (relative to a bad location).\n",
     "And `ShelveLoc[Medium]` has a smaller positive coefficient,\n",
     "indicating that a medium shelving location leads to higher sales than a bad\n",
-    "shelving location, but lower sales than a good shelving location."
+    "shelving location, but lower sales than a good shelving location.\n",
+    "\n"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "formats": "ipynb,md:myst",
-   "main_language": "python"
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/Ch4-classification-lab.ipynb b/Ch4-classification-lab.ipynb
index daa47b2..d9a9c90 100644
--- a/Ch4-classification-lab.ipynb
+++ b/Ch4-classification-lab.ipynb
@@ -2,23 +2,26 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e6cc127e",
+   "id": "cb3c07d1",
    "metadata": {},
    "source": [
-    "# Chapter 4"
+    "\n",
+    "# Chapter 4\n",
+    "\n",
+    "\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "af3093e5",
+   "id": "daef3e2e",
    "metadata": {},
    "source": [
-    "# Lab: Logistic Regression, LDA, QDA, and KNN"
+    "# Lab: Logistic Regression, LDA, QDA, and KNN\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a4e58fe1",
+   "id": "3cb93478",
    "metadata": {},
    "source": [
     "## The Stock Market Data\n",
@@ -40,13 +43,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "71523a7f",
+   "id": "7b8d78b9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:40.325132Z",
-     "iopub.status.busy": "2023-07-31T02:14:40.324680Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.370472Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.370081Z"
+     "iopub.execute_input": "2023-08-06T17:34:48.984438Z",
+     "iopub.status.busy": "2023-08-06T17:34:48.984147Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.014450Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.013816Z"
     }
    },
    "outputs": [],
@@ -62,7 +65,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4759a767",
+   "id": "f420141c",
    "metadata": {},
    "source": [
     "We also collect together the new imports needed for this lab."
@@ -71,14 +74,15 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "901ed7c5",
+   "id": "b5938862",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.372738Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.372471Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.389931Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.389646Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.016761Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.016466Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.037738Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.037473Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -91,12 +95,12 @@
     "from sklearn.neighbors import KNeighborsClassifier\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.linear_model import LogisticRegression"
+    "from sklearn.linear_model import LogisticRegression\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dd5a4d61",
+   "id": "34413b1c",
    "metadata": {},
    "source": [
     "Now we are ready to load the `Smarket` data."
@@ -105,14 +109,15 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "8b56ee0f",
+   "id": "e9fdf040",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.391561Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.391451Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.402806Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.402527Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.039423Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.039307Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.052006Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.051756Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -314,7 +319,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "400eb0f9",
+   "id": "d3533960",
    "metadata": {},
    "source": [
     "This gives a truncated listing of the data.\n",
@@ -324,14 +329,15 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "62d49b47",
+   "id": "69d5cfae",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.404304Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.404209Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.406450Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.406198Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.053494Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.053400Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.055808Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.055549Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -353,7 +359,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "82e38285",
+   "id": "c1e0cc8c",
    "metadata": {},
    "source": [
     "We compute the correlation matrix using the `corr()` method\n",
@@ -361,20 +367,21 @@
     "the pairwise correlations among the variables.\n",
     " \n",
     "By instructing `pandas` to use only numeric variables, the `corr()` method does not report a correlation for the `Direction`  variable because it is\n",
-    "qualitative."
+    " qualitative."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "bc34e7b7",
+   "id": "a4c0648e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.407806Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.407714Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.412417Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.412175Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.057221Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.057127Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.061952Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.061721Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -529,32 +536,33 @@
     }
    ],
    "source": [
-    "Smarket.corr(numeric_only=True)"
+    "Smarket.corr(numeric_only=True)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "53a52be5",
+   "id": "c68bb37a",
    "metadata": {},
    "source": [
     "As one would expect, the correlations between the lagged return  variables and\n",
     "today’s return are close to zero.  The only substantial correlation is between  `Year`  and\n",
     " `Volume`. By plotting the data we see that  `Volume`\n",
     "is increasing over time. In other words, the average number of shares traded\n",
-    "daily increased from 2001 to 2005."
+    "daily increased from 2001 to 2005.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "9f075144",
+   "id": "cd40274b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.413837Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.413752Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.533365Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.532005Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.063425Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.063336Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.183910Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.181946Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -569,12 +577,12 @@
     }
    ],
    "source": [
-    "Smarket.plot(y='Volume');"
+    "Smarket.plot(y='Volume');\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b8428c9e",
+   "id": "685b96e8",
    "metadata": {},
    "source": [
     "## Logistic Regression\n",
@@ -593,14 +601,15 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "2ab15aa3",
+   "id": "35b8bffe",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.537766Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.537504Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.642638Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.641325Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.187799Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.187442Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.293260Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.292298Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -709,12 +718,12 @@
     "             X,\n",
     "             family=sm.families.Binomial())\n",
     "results = glm.fit()\n",
-    "summarize(results)"
+    "summarize(results)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ce1f682c",
+   "id": "68cca865",
    "metadata": {},
    "source": [
     "The smallest *p*-value here is associated with  `Lag1`. The\n",
@@ -732,14 +741,15 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "0faa6a6c",
+   "id": "4f9c5324",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.647058Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.646077Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.656939Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.655796Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.297671Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.297156Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.305507Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.303828Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -761,12 +771,12 @@
     }
    ],
    "source": [
-    "results.params"
+    "results.params\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b989df1d",
+   "id": "681ca0cb",
    "metadata": {},
    "source": [
     "Likewise we can use the\n",
@@ -776,13 +786,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "108aff98",
+   "id": "75e44d01",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.665137Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.664124Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.675522Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.674386Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.309998Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.309526Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.317199Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.316460Z"
     }
    },
    "outputs": [
@@ -805,12 +815,12 @@
     }
    ],
    "source": [
-    "results.pvalues"
+    "results.pvalues\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5c5c1f8e",
+   "id": "d4fd87bf",
    "metadata": {},
    "source": [
     "The `predict()`  method of `results` can be used to predict the\n",
@@ -827,14 +837,15 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "54b4b96c",
+   "id": "40164af5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.679830Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.679368Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.685281Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.684603Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.321143Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.320696Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.328214Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.327465Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -851,12 +862,12 @@
    ],
    "source": [
     "probs = results.predict()\n",
-    "probs[:10]"
+    "probs[:10]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3b8f3c42",
+   "id": "94607ba9",
    "metadata": {},
    "source": [
     "In order to make a prediction as to whether the market will go up or\n",
@@ -870,24 +881,25 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "46c2887c",
+   "id": "daf20de2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.688585Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.688321Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.693491Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.691326Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.332514Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.332173Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.337377Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.336735Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
     "labels = np.array(['Down']*1250)\n",
-    "labels[probs>0.5] = \"Up\""
+    "labels[probs>0.5] = \"Up\"\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "47ac8c81",
+   "id": "085aead4",
    "metadata": {},
    "source": [
     "The `confusion_table()`\n",
@@ -902,13 +914,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "0f816c4d",
+   "id": "71f4a40c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.699484Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.698905Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.706435Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.706160Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.342435Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.342079Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.354901Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.354601Z"
     }
    },
    "outputs": [
@@ -970,12 +982,12 @@
     }
    ],
    "source": [
-    "confusion_table(labels, Smarket.Direction)"
+    "confusion_table(labels, Smarket.Direction)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "327a9525",
+   "id": "3a338f26",
    "metadata": {},
    "source": [
     "The diagonal elements of the confusion matrix indicate correct\n",
@@ -985,20 +997,21 @@
     "total of 507 + 145 = 652 correct predictions. The `np.mean()`\n",
     "function can be used to compute the fraction of days for which the\n",
     "prediction was correct. In this case, logistic regression correctly\n",
-    "predicted the movement of the market 52.2% of the time."
+    "predicted the movement of the market 52.2% of the time.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "23668517",
+   "id": "78b2946b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.708001Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.707915Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.710519Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.710254Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.356729Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.356597Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.359259Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.359007Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1013,12 +1026,20 @@
     }
    ],
    "source": [
-    "(507+145)/1250, np.mean(labels == Smarket.Direction)"
+    "(507+145)/1250, np.mean(labels == Smarket.Direction)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1143b9c3",
+   "id": "54962226",
+   "metadata": {},
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa9be981",
    "metadata": {},
    "source": [
     "At first glance, it appears that the logistic regression model is\n",
@@ -1045,14 +1066,15 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "86a02d7c",
+   "id": "e8a4d12e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.711888Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.711795Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.714657Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.714338Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.360879Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.360770Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.363453Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.363204Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1070,12 +1092,12 @@
     "train = (Smarket.Year < 2005)\n",
     "Smarket_train = Smarket.loc[train]\n",
     "Smarket_test = Smarket.loc[~train]\n",
-    "Smarket_test.shape"
+    "Smarket_test.shape\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "26b3a6d2",
+   "id": "946fac68",
    "metadata": {},
    "source": [
     "The object `train` is a vector of 1,250 elements, corresponding\n",
@@ -1110,13 +1132,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "f23b1c7b",
+   "id": "fd16bc2c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.716084Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.715984Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.720637Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.720390Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.364912Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.364808Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.369741Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.369422Z"
     }
    },
    "outputs": [],
@@ -1127,12 +1149,12 @@
     "                   X_train,\n",
     "                   family=sm.families.Binomial())\n",
     "results = glm_train.fit()\n",
-    "probs = results.predict(exog=X_test)"
+    "probs = results.predict(exog=X_test)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6ebe1900",
+   "id": "12382c18",
    "metadata": {},
    "source": [
     "Notice that we have trained and tested our model on two completely\n",
@@ -1147,24 +1169,25 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "82f849b9",
+   "id": "aaa9d07c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.722093Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.722017Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.724072Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.723827Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.371195Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.371114Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.373018Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.372763Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
     "D = Smarket.Direction\n",
-    "L_train, L_test = D.loc[train], D.loc[~train]"
+    "L_train, L_test = D.loc[train], D.loc[~train]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ac89c253",
+   "id": "cc0d0c3a",
    "metadata": {},
    "source": [
     "Now we threshold the\n",
@@ -1175,14 +1198,15 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "e216583a",
+   "id": "5f0b3282",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.725496Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.725421Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.729883Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.729620Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.374386Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.374307Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.378599Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.378366Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1245,12 +1269,12 @@
    "source": [
     "labels = np.array(['Down']*252)\n",
     "labels[probs>0.5] = 'Up'\n",
-    "confusion_table(labels, L_test)"
+    "confusion_table(labels, L_test)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f43e1451",
+   "id": "71ca598e",
    "metadata": {},
    "source": [
     "The test accuracy is about 48% while the error rate is about 52%"
@@ -1259,14 +1283,15 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "5538dc17",
+   "id": "7f4e49c0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.731486Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.731397Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.733982Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.733721Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.380021Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.379944Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.382451Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.382198Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1281,12 +1306,12 @@
     }
    ],
    "source": [
-    "np.mean(labels == L_test), np.mean(labels != L_test)"
+    "np.mean(labels == L_test), np.mean(labels != L_test)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3ddf7670",
+   "id": "96d75735",
    "metadata": {},
    "source": [
     "The `!=` notation means *not equal to*, and so the last command\n",
@@ -1316,14 +1341,15 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "016110f9",
+   "id": "9f94de4b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.735443Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.735343Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.743484Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.743214Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.383801Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.383724Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.391808Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.391549Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1394,12 +1420,12 @@
     "probs = results.predict(exog=X_test)\n",
     "labels = np.array(['Down']*252)\n",
     "labels[probs>0.5] = 'Up'\n",
-    "confusion_table(labels, L_test)"
+    "confusion_table(labels, L_test)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a7468a74",
+   "id": "1dd9ce3d",
    "metadata": {},
    "source": [
     "Let’s evaluate the overall accuracy as well as the accuracy within the days when\n",
@@ -1409,13 +1435,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "70984cd6",
+   "id": "69a4dd6b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.744937Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.744855Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.747115Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.746876Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.393244Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.393158Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.395426Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.395177Z"
     }
    },
    "outputs": [
@@ -1431,12 +1457,12 @@
     }
    ],
    "source": [
-    "(35+106)/252,106/(106+76)"
+    "(35+106)/252,106/(106+76)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "393c0a26",
+   "id": "14cf6b55",
    "metadata": {},
    "source": [
     "Now the results appear to be a little better: 56% of the daily\n",
@@ -1463,14 +1489,15 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "4b745fd8",
+   "id": "975fa3ca",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.748502Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.748427Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.751963Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.751699Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.396775Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.396693Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.400210Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.399964Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1490,12 +1517,12 @@
     "newdata = pd.DataFrame({'Lag1':[1.2, 1.5],\n",
     "                        'Lag2':[1.1, -0.8]});\n",
     "newX = model.transform(newdata)\n",
-    "results.predict(newX)"
+    "results.predict(newX)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ae928a3e",
+   "id": "bdc2c0bb",
    "metadata": {},
    "source": [
     "## Linear Discriminant Analysis"
@@ -1503,7 +1530,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aab265c4",
+   "id": "41deab79",
    "metadata": {},
    "source": [
     "We begin by performing LDA on the  `Smarket`  data, using the function\n",
@@ -1514,23 +1541,23 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "cd533313",
+   "id": "7237ffa7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.753386Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.753314Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.754789Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.754554Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.401581Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.401504Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.403256Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.403010Z"
     }
    },
    "outputs": [],
    "source": [
-    "lda = LDA(store_covariance=True)"
+    "lda = LDA(store_covariance=True)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c84aa056",
+   "id": "7289db40",
    "metadata": {},
    "source": [
     "Since the `LDA` estimator automatically \n",
@@ -1542,14 +1569,15 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "953dc6ac",
+   "id": "43a850eb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.756225Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.756141Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.762198Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.761933Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.404657Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.404587Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.414595Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.414353Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1569,12 +1597,12 @@
    "source": [
     "X_train, X_test = [M.drop(columns=['intercept'])\n",
     "                   for M in [X_train, X_test]]\n",
-    "lda.fit(X_train, L_train)"
+    "lda.fit(X_train, L_train)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0a51b5cd",
+   "id": "d3e0901f",
    "metadata": {},
    "source": [
     "Here we have used the list comprehensions introduced\n",
@@ -1597,14 +1625,15 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "8917085e",
+   "id": "86b6cece",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.763655Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.763574Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.765854Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.765620Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.416057Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.415959Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.418098Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.417857Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1620,12 +1649,12 @@
     }
    ],
    "source": [
-    "lda.means_"
+    "lda.means_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "14bc4692",
+   "id": "6d75a932",
    "metadata": {},
    "source": [
     "The estimated prior probabilities are stored in the `priors_` attribute.\n",
@@ -1637,14 +1666,15 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "efb3e9bd",
+   "id": "9362c7c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.767245Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.767167Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.769225Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.768981Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.419417Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.419327Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.421334Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.421085Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1659,29 +1689,30 @@
     }
    ],
    "source": [
-    "lda.classes_"
+    "lda.classes_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "50697f11",
+   "id": "61f77e19",
    "metadata": {},
    "source": [
     "The LDA output indicates that $\\hat\\pi_{Down}=0.492$ and\n",
-    "$\\hat\\pi_{Up}=0.508$."
+    "$\\hat\\pi_{Up}=0.508$.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "efce91fb",
+   "id": "e59858dc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.770556Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.770470Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.772525Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.772289Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.422583Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.422510Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.424567Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.424332Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1696,12 +1727,12 @@
     }
    ],
    "source": [
-    "lda.priors_"
+    "lda.priors_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a27bc841",
+   "id": "43a8fde1",
    "metadata": {},
    "source": [
     "The linear discriminant vectors can be found in the `scalings_` attribute:"
@@ -1710,13 +1741,13 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "e33cd6c5",
+   "id": "9b5f74e8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.773919Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.773832Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.775930Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.775700Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.425939Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.425863Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.427943Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.427700Z"
     }
    },
    "outputs": [
@@ -1733,12 +1764,12 @@
     }
    ],
    "source": [
-    "lda.scalings_"
+    "lda.scalings_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b011d741",
+   "id": "4655809e",
    "metadata": {},
    "source": [
     "These values provide the linear combination of `Lag1`  and `Lag2`  that are used to form the LDA decision rule. In other words, these are the multipliers of the elements of $X=x$ in (4.24).\n",
@@ -1748,23 +1779,23 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "dc8a5178",
+   "id": "fd8e6794",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.777341Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.777243Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.779374Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.779140Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.429283Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.429207Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.431271Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.431030Z"
     }
    },
    "outputs": [],
    "source": [
-    "lda_pred = lda.predict(X_test)"
+    "lda_pred = lda.predict(X_test)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3c8a8a75",
+   "id": "2d211f0a",
    "metadata": {},
    "source": [
     "As we observed in our comparison of classification methods\n",
@@ -1775,14 +1806,15 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "31a6782b",
+   "id": "3bb63fb1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.780768Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.780673Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.784574Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.784302Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.432644Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.432568Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.436354Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.436113Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1843,32 +1875,33 @@
     }
    ],
    "source": [
-    "confusion_table(lda_pred, L_test)"
+    "confusion_table(lda_pred, L_test)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c3534a8f",
+   "id": "8e31152b",
    "metadata": {},
    "source": [
     "We can also estimate the\n",
     "probability of each class for\n",
     "each point in a training set. Applying a 50% threshold to the posterior probabilities of\n",
-    "being in class 1 allows us to\n",
+    "being in class one allows us to\n",
     "recreate the predictions contained in `lda_pred`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "da1bffa3",
+   "id": "2b91f491",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.785993Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.785917Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.788726Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.788490Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.437709Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.437628Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.440441Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.440196Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1886,12 +1919,12 @@
     "lda_prob = lda.predict_proba(X_test)\n",
     "np.all(\n",
     "       np.where(lda_prob[:,1] >= 0.5, 'Up','Down') == lda_pred\n",
-    "       )"
+    "       )\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6ab71d7c",
+   "id": "d3a76822",
    "metadata": {},
    "source": [
     "Above, we used the `np.where()`  function that\n",
@@ -1904,14 +1937,15 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "3d7eae3c",
+   "id": "3a085714",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.790079Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.790009Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.792346Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.792106Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.441897Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.441821Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.444090Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.443865Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1928,12 +1962,12 @@
    "source": [
     "np.all(\n",
     "       [lda.classes_[i] for i in np.argmax(lda_prob, 1)] == lda_pred\n",
-    "       )"
+    "       )\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6a67baf1",
+   "id": "0aa9cbb3",
    "metadata": {},
    "source": [
     "If we wanted to use a posterior probability threshold other than\n",
@@ -1949,13 +1983,13 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "18fa175c",
+   "id": "345fb8c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.793730Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.793654Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.795835Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.795598Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.445520Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.445423Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.447522Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.447281Z"
     }
    },
    "outputs": [
@@ -1971,12 +2005,12 @@
     }
    ],
    "source": [
-    "np.sum(lda_prob[:,0] > 0.9)"
+    "np.sum(lda_prob[:,0] > 0.9)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "527be6fe",
+   "id": "6798146c",
    "metadata": {},
    "source": [
     "No days in 2005 meet that threshold! In fact, the greatest posterior\n",
@@ -1995,12 +2029,12 @@
     "then producing predictions is an explicit design choice of `sklearn`. This uniformity\n",
     "makes it possible to cleanly copy the classifier so that it can be fit\n",
     "on different data; e.g. different training sets arising in cross-validation.\n",
-    "This standard pattern also allows for a predictable formation of workflows."
+    "This standard pattern also allows for a predictable formation of workflows.\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "66ecd10f",
+   "id": "58f2984f",
    "metadata": {},
    "source": [
     "## Quadratic Discriminant Analysis\n",
@@ -2014,13 +2048,13 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "62499aad",
+   "id": "22c6c2a3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.797259Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.797186Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.800789Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.800548Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.448942Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.448849Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.452411Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.452173Z"
     }
    },
    "outputs": [
@@ -2040,12 +2074,12 @@
    ],
    "source": [
     "qda = QDA(store_covariance=True)\n",
-    "qda.fit(X_train, L_train)"
+    "qda.fit(X_train, L_train)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "17b38989",
+   "id": "fba0bb3a",
    "metadata": {},
    "source": [
     "The `QDA()` function will again compute `means_` and `priors_`."
@@ -2054,13 +2088,13 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "296fdd89",
+   "id": "992a790a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.802222Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.802144Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.804333Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.804091Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.453767Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.453689Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.455832Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.455573Z"
     }
    },
    "outputs": [
@@ -2078,12 +2112,12 @@
     }
    ],
    "source": [
-    "qda.means_, qda.priors_"
+    "qda.means_, qda.priors_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f439efa9",
+   "id": "8bc030e2",
    "metadata": {},
    "source": [
     "The `QDA()` classifier will estimate one covariance per class. Here is the\n",
@@ -2093,14 +2127,15 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "7fbbefb0",
+   "id": "074c9d46",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.805729Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.805659Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.807775Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.807542Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.457173Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.457102Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.459117Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.458865Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2116,12 +2151,12 @@
     }
    ],
    "source": [
-    "qda.covariance_[0]"
+    "qda.covariance_[0]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4b33a370",
+   "id": "fa5963f6",
    "metadata": {},
    "source": [
     "The output contains the group means. But it does not contain the\n",
@@ -2134,14 +2169,15 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "03094dcd",
+   "id": "3c95aae1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.809152Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.809080Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.813317Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.813067Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.460495Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.460408Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.464611Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.464381Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2203,12 +2239,12 @@
    ],
    "source": [
     "qda_pred = qda.predict(X_test)\n",
-    "confusion_table(qda_pred, L_test)"
+    "confusion_table(qda_pred, L_test)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2037df4b",
+   "id": "0a72c1b8",
    "metadata": {},
    "source": [
     "Interestingly, the QDA predictions are accurate almost 60% of the\n",
@@ -2218,14 +2254,15 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "25022d1a",
+   "id": "935b2079",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.814632Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.814560Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.816802Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.816572Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.466057Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.465982Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.468265Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.468008Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2240,12 +2277,12 @@
     }
    ],
    "source": [
-    "np.mean(qda_pred == L_test)"
+    "np.mean(qda_pred == L_test)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cf85b45e",
+   "id": "cbf4d7d1",
    "metadata": {},
    "source": [
     "This level of accuracy is quite impressive for stock market data, which is\n",
@@ -2259,7 +2296,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "258f98b8",
+   "id": "b89dfb88",
    "metadata": {},
    "source": [
     "## Naive Bayes\n",
@@ -2273,14 +2310,15 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "f73eb202",
+   "id": "628b0355",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.818249Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.818174Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.821763Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.821514Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.469713Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.469632Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.473211Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.472970Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2299,12 +2337,12 @@
    ],
    "source": [
     "NB = GaussianNB()\n",
-    "NB.fit(X_train, L_train)"
+    "NB.fit(X_train, L_train)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9911dcdc",
+   "id": "66916c68",
    "metadata": {},
    "source": [
     "The classes are stored as `classes_`."
@@ -2313,14 +2351,15 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "8e860cc3",
+   "id": "8a5fc302",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.823149Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.823078Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.825164Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.824932Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.474613Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.474532Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.476603Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.476363Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2335,12 +2374,12 @@
     }
    ],
    "source": [
-    "NB.classes_"
+    "NB.classes_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8d6b9b33",
+   "id": "753c7d37",
    "metadata": {},
    "source": [
     "The class prior probabilities are stored in the `class_prior_` attribute."
@@ -2349,14 +2388,15 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "c28bf3b5",
+   "id": "6b1b5cc6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.826634Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.826530Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.828758Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.828517Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.477928Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.477856Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.479875Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.479628Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2371,12 +2411,12 @@
     }
    ],
    "source": [
-    "NB.class_prior_"
+    "NB.class_prior_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fed2c4b4",
+   "id": "848e5d7c",
    "metadata": {},
    "source": [
     "The parameters of the features can be found in the `theta_` and `var_` attributes. The number of rows\n",
@@ -2387,13 +2427,13 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "84a9f086",
+   "id": "1a4b7cb0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.830123Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.830032Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.832018Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.831763Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.481268Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.481197Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.483169Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.482928Z"
     }
    },
    "outputs": [
@@ -2410,12 +2450,12 @@
     }
    ],
    "source": [
-    "NB.theta_"
+    "NB.theta_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "66f0a08d",
+   "id": "60efd24c",
    "metadata": {},
    "source": [
     "Its variance is 1.503."
@@ -2424,14 +2464,15 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "885f5165",
+   "id": "f59d7cb4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.833338Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.833247Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.835227Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.834993Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.484569Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.484500Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.486506Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.486244Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2447,15 +2488,15 @@
     }
    ],
    "source": [
-    "NB.var_"
+    "NB.var_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "db0a1ce6",
+   "id": "4518d5db",
    "metadata": {},
    "source": [
-    "How do we know the names of these attributes? We use `NB?` (or \\textR?NB}).\n",
+    "How do we know the names of these attributes? We use `NB?` (or `?NB`).\n",
     "\n",
     "We can easily verify the mean computation:"
    ]
@@ -2463,14 +2504,15 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "61473094",
+   "id": "aa50d0b8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.836565Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.836472Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.839241Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.838987Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.487873Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.487800Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.490700Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.490454Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2487,12 +2529,12 @@
     }
    ],
    "source": [
-    "X_train[L_train == 'Down'].mean()"
+    "X_train[L_train == 'Down'].mean()\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d4ffc86b",
+   "id": "3ee8e214",
    "metadata": {},
    "source": [
     "Similarly for the variance:"
@@ -2501,14 +2543,15 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "db203a23",
+   "id": "3a2b0653",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.840635Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.840547Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.843269Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.843049Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.492036Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.491958Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.494698Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.494456Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2525,12 +2568,12 @@
     }
    ],
    "source": [
-    "X_train[L_train == 'Down'].var(ddof=0)"
+    "X_train[L_train == 'Down'].var(ddof=0)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7c9078c1",
+   "id": "5cb3b59d",
    "metadata": {},
    "source": [
     "Since `NB()` is a classifier in the `sklearn` library, making predictions\n",
@@ -2540,13 +2583,13 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "b387227e",
+   "id": "938f09ea",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.844649Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.844557Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.848832Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.848577Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.496066Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.495993Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.500129Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.499891Z"
     }
    },
    "outputs": [
@@ -2609,12 +2652,12 @@
    ],
    "source": [
     "nb_labels = NB.predict(X_test)\n",
-    "confusion_table(nb_labels, L_test)"
+    "confusion_table(nb_labels, L_test)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ab979471",
+   "id": "a0ce38d6",
    "metadata": {},
    "source": [
     "Naive Bayes performs well on these data, with accurate predictions over 59% of the time. This is slightly worse than QDA, but much better than LDA.\n",
@@ -2625,14 +2668,15 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "f14ebe61",
+   "id": "42f0e4a2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.850206Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.850128Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.852871Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.852624Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.501537Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.501460Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.505359Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.505091Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2651,12 +2695,12 @@
     }
    ],
    "source": [
-    "NB.predict_proba(X_test)[:5]"
+    "NB.predict_proba(X_test)[:5]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f14c520d",
+   "id": "ede194da",
    "metadata": {},
    "source": [
     "## K-Nearest Neighbors\n",
@@ -2674,13 +2718,13 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "41a12f4a",
+   "id": "8b833ee4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.854241Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.854168Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.863633Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.863384Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.506696Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.506614Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.517822Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.517573Z"
     }
    },
    "outputs": [
@@ -2746,12 +2790,12 @@
     "X_train, X_test = [np.asarray(X) for X in [X_train, X_test]]\n",
     "knn1.fit(X_train, L_train)\n",
     "knn1_pred = knn1.predict(X_test)\n",
-    "confusion_table(knn1_pred, L_test)"
+    "confusion_table(knn1_pred, L_test)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "22ee6dff",
+   "id": "5fb57a8d",
    "metadata": {},
    "source": [
     "The results using $K=1$ are not very good, since only $50%$ of the\n",
@@ -2762,13 +2806,13 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "1dd2f3e4",
+   "id": "45410b7f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.865116Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.865042Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.867424Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.867177Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.519187Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.519097Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.521354Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.521132Z"
     }
    },
    "outputs": [
@@ -2784,29 +2828,30 @@
     }
    ],
    "source": [
-    "(83+43)/252, np.mean(knn1_pred == L_test)"
+    "(83+43)/252, np.mean(knn1_pred == L_test)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cda2465d",
+   "id": "500e00a1",
    "metadata": {},
    "source": [
-    "Below, we repeat the\n",
-    "analysis using $K=3$."
+    "We repeat the\n",
+    "analysis below using $K=3$."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "8ec72efc",
+   "id": "ae6e0156",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.868773Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.868701Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.876424Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.876191Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.522772Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.522694Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.530247Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.530016Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2828,7 +2873,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff4533bd",
+   "id": "11a369af",
    "metadata": {},
    "source": [
     "The results have improved slightly. But increasing *K* further\n",
@@ -2839,7 +2884,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5810022d",
+   "id": "ab3b1e64",
    "metadata": {},
    "source": [
     "KNN does not perform well on the `Smarket`  data, but it often does provide impressive results. As an example we will apply the KNN approach to the `Caravan`  data set, which is part of the `ISLP` library.  This data set includes 85\n",
@@ -2853,14 +2898,15 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "98fef4ed",
+   "id": "9338564d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.878113Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.878035Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.897194Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.896915Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.531745Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.531662Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.550259Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.549964Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2879,12 +2925,12 @@
    "source": [
     "Caravan = load_data('Caravan')\n",
     "Purchase = Caravan.Purchase\n",
-    "Purchase.value_counts()"
+    "Purchase.value_counts()\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4fc1a1df",
+   "id": "ba5e14a9",
    "metadata": {},
    "source": [
     "The method `value_counts()` takes a `pd.Series` or `pd.DataFrame` and returns\n",
@@ -2896,14 +2942,15 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "1af72c0b",
+   "id": "a94f2ea7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.898649Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.898566Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.900656Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.900416Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.551792Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.551695Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.553719Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.553499Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2918,12 +2965,12 @@
     }
    ],
    "source": [
-    "348 / 5822"
+    "348 / 5822\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "78c25fda",
+   "id": "a753d948",
    "metadata": {},
    "source": [
     "Our features will include all columns except `Purchase`."
@@ -2932,23 +2979,23 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "9ea841e4",
+   "id": "6b6438b0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.902065Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.901988Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.904327Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.904080Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.555038Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.554947Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.557311Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.557082Z"
     }
    },
    "outputs": [],
    "source": [
-    "feature_df = Caravan.drop(columns=['Purchase'])"
+    "feature_df = Caravan.drop(columns=['Purchase'])\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "55efb306",
+   "id": "52a62ab9",
    "metadata": {},
    "source": [
     "Because the KNN classifier predicts the class of a given test\n",
@@ -2981,25 +3028,26 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "6e26b14f",
+   "id": "158bfb3f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.905739Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.905634Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.907473Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.907212Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.558624Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.558538Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.560136Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.559910Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
     "scaler = StandardScaler(with_mean=True,\n",
     "                        with_std=True,\n",
-    "                        copy=True)"
+    "                        copy=True)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0c0d1faf",
+   "id": "45baf26c",
    "metadata": {},
    "source": [
     "The argument `with_mean` indicates whether or not\n",
@@ -3019,24 +3067,25 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "bd178f38",
+   "id": "d11b04c4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.908837Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.908766Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.914689Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.914421Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.561466Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.561387Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.567529Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.567268Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
     "scaler.fit(feature_df)\n",
-    "X_std = scaler.transform(feature_df)"
+    "X_std = scaler.transform(feature_df)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "30bfaa05",
+   "id": "f388fe5b",
    "metadata": {},
    "source": [
     "Now every column of `feature_std` below has a standard deviation of\n",
@@ -3046,13 +3095,13 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "18929b37",
+   "id": "f0c65b34",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.916221Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.916142Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.921286Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.921033Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.569138Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.569036Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.577122Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.576863Z"
     }
    },
    "outputs": [
@@ -3082,15 +3131,15 @@
     "feature_std = pd.DataFrame(\n",
     "                 X_std,\n",
     "                 columns=feature_df.columns);\n",
-    "feature_std.std()"
+    "feature_std.std()\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4f536808",
+   "id": "9668a83b",
    "metadata": {},
    "source": [
-    "Notice that the standard deviations are not quite $1$ here; this is again due to some procedures using the $1/n$ convention for variances (in this case `scaler()`), while others use $1/(n-1)$ (the `std()` method). See the footnote on page 198.\n",
+    "Notice that the standard deviations are not quite $1$ here; this is again due to some procedures using the $1/n$ convention for variances (in this case `scaler()`), while others use $1/(n-1)$ (the `std()` method). See the footnote on page 103.\n",
     "In this case it does not matter, as long as the variables are all on the same scale.\n",
     "\n",
     "Using the function `train_test_split()`  we now split the observations into a test set,\n",
@@ -3102,14 +3151,15 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "9d439d0b",
+   "id": "3861cc4f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.922729Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.922648Z",
-     "iopub.status.idle": "2023-07-31T02:14:41.925647Z",
-     "shell.execute_reply": "2023-07-31T02:14:41.925388Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.578604Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.578502Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.582699Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.582426Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -3119,16 +3169,16 @@
     " y_test) = train_test_split(np.asarray(feature_std),\n",
     "                            Purchase,\n",
     "                            test_size=1000,\n",
-    "                            random_state=0)"
+    "                            random_state=0)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "87fcf773",
+   "id": "3cd24d0a",
    "metadata": {},
    "source": [
-    "`?train_test_split` reveals that the non-keyword arguments can be `lists`, `arrays`, `pandas dataframes` etc that all have the same length (`shape[0]`) and hence are *indexable*. In this case they are the dataframe `feature_std` and the response variable `Purchase`. We have converted `feature_std` to an `ndarray`\n",
-    "so that `X_train` and `X_test` are `ndarray`.\n",
+    "`?train_test_split` reveals that the non-keyword arguments can be `lists`, `arrays`, `pandas dataframes` etc that all have the same length (`shape[0]`) and hence are *indexable*. In this case they are the dataframe `feature_std` and the response variable `Purchase`.\n",
+    " {Note that we have converted `feature_std` to an `ndarray` to address a bug in `sklearn`.}\n",
     "We fit a KNN model on the training data using $K=1$,\n",
     "and evaluate its performance on the test data."
    ]
@@ -3136,14 +3186,15 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "9b8c0171",
+   "id": "1b9d7b32",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:41.927120Z",
-     "iopub.status.busy": "2023-07-31T02:14:41.927042Z",
-     "iopub.status.idle": "2023-07-31T02:14:42.245324Z",
-     "shell.execute_reply": "2023-07-31T02:14:42.244929Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.584364Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.584256Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.908198Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.907710Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -3160,12 +3211,12 @@
    "source": [
     "knn1 = KNeighborsClassifier(n_neighbors=1)\n",
     "knn1_pred = knn1.fit(X_train, y_train).predict(X_test)\n",
-    "np.mean(y_test != knn1_pred), np.mean(y_test != \"No\")"
+    "np.mean(y_test != knn1_pred), np.mean(y_test != \"No\")\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3aa030b6",
+   "id": "0464c302",
    "metadata": {},
    "source": [
     "The KNN error rate on the 1,000 test observations is about $11%$.\n",
@@ -3188,13 +3239,13 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "id": "f31b6e26",
+   "id": "d683b2f9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:42.247712Z",
-     "iopub.status.busy": "2023-07-31T02:14:42.247583Z",
-     "iopub.status.idle": "2023-07-31T02:14:42.253630Z",
-     "shell.execute_reply": "2023-07-31T02:14:42.253319Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.910374Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.910235Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.916751Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.916357Z"
     }
    },
    "outputs": [
@@ -3256,12 +3307,12 @@
     }
    ],
    "source": [
-    "confusion_table(knn1_pred, y_test)"
+    "confusion_table(knn1_pred, y_test)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "de5fb529",
+   "id": "19ed36b1",
    "metadata": {},
    "source": [
     "It turns out that KNN with $K=1$ does far better than random guessing\n",
@@ -3273,14 +3324,15 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "4a8c37aa",
+   "id": "3498856a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:42.255392Z",
-     "iopub.status.busy": "2023-07-31T02:14:42.255280Z",
-     "iopub.status.idle": "2023-07-31T02:14:42.257446Z",
-     "shell.execute_reply": "2023-07-31T02:14:42.257109Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.918891Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.918771Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.921176Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.920892Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -3300,7 +3352,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a898b7d0",
+   "id": "3ec215b9",
    "metadata": {},
    "source": [
     "### Tuning Parameters\n",
@@ -3316,14 +3368,15 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "153662f8",
+   "id": "9ed36c50",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:42.259217Z",
-     "iopub.status.busy": "2023-07-31T02:14:42.259122Z",
-     "iopub.status.idle": "2023-07-31T02:14:42.328441Z",
-     "shell.execute_reply": "2023-07-31T02:14:42.328089Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:50.923079Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.922965Z",
+     "iopub.status.idle": "2023-08-06T17:34:50.996569Z",
+     "shell.execute_reply": "2023-08-06T17:34:50.996126Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -3351,12 +3404,12 @@
     "          K,\n",
     "          pred,\n",
     "          did_rent,\n",
-    "          did_rent / pred))"
+    "          did_rent / pred))\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "39ba18b3",
+   "id": "0718d8f0",
    "metadata": {},
    "source": [
     "We see some variability ---  the numbers for `K=4` are very different from the rest.\n",
@@ -3382,13 +3435,13 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "55b99a3f",
+   "id": "8605a327",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:42.330417Z",
-     "iopub.status.busy": "2023-07-31T02:14:42.330299Z",
-     "iopub.status.idle": "2023-07-31T02:14:42.957528Z",
-     "shell.execute_reply": "2023-07-31T02:14:42.956809Z"
+     "iopub.execute_input": "2023-08-06T17:34:50.998620Z",
+     "iopub.status.busy": "2023-08-06T17:34:50.998492Z",
+     "iopub.status.idle": "2023-08-06T17:34:51.629504Z",
+     "shell.execute_reply": "2023-08-06T17:34:51.628869Z"
     }
    },
    "outputs": [
@@ -3454,12 +3507,12 @@
     "logit.fit(X_train, y_train)\n",
     "logit_pred = logit.predict_proba(X_test)\n",
     "logit_labels = np.where(logit_pred[:,1] > .5, 'Yes', 'No')\n",
-    "confusion_table(logit_labels, y_test)"
+    "confusion_table(logit_labels, y_test)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4e0ebc83",
+   "id": "daf1655f",
    "metadata": {},
    "source": [
     "We used the argument `solver='liblinear'` above to\n",
@@ -3468,8 +3521,8 @@
     "\n",
     "If we use $0.5$ as the predicted probability cut-off for the\n",
     "classifier, then we have a problem: only two of the test observations\n",
-    "are predicted to purchase insurance.  However, we are not required to\n",
-    "use a cut-off of $0.5$. If we instead predict a purchase any time the\n",
+    "are predicted to purchase insurance.  However, we are not required to use a\n",
+    "cut-off of $0.5$. If we instead predict a purchase any time the\n",
     "predicted probability of purchase exceeds $0.25$, we get much better\n",
     "results: we predict that 29 people will purchase insurance, and we are\n",
     "correct for about 31% of these people. This is almost five times\n",
@@ -3479,13 +3532,13 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "0c2ea9f5",
+   "id": "e686f675",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:42.961684Z",
-     "iopub.status.busy": "2023-07-31T02:14:42.961435Z",
-     "iopub.status.idle": "2023-07-31T02:14:42.975639Z",
-     "shell.execute_reply": "2023-07-31T02:14:42.973951Z"
+     "iopub.execute_input": "2023-08-06T17:34:51.634589Z",
+     "iopub.status.busy": "2023-08-06T17:34:51.634253Z",
+     "iopub.status.idle": "2023-08-06T17:34:51.649626Z",
+     "shell.execute_reply": "2023-08-06T17:34:51.648919Z"
     }
    },
    "outputs": [
@@ -3554,14 +3607,15 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "3a6bf0a4",
+   "id": "d92cfcd4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:42.982471Z",
-     "iopub.status.busy": "2023-07-31T02:14:42.982210Z",
-     "iopub.status.idle": "2023-07-31T02:14:42.988215Z",
-     "shell.execute_reply": "2023-07-31T02:14:42.987527Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:51.653353Z",
+     "iopub.status.busy": "2023-08-06T17:34:51.653069Z",
+     "iopub.status.idle": "2023-08-06T17:34:51.659794Z",
+     "shell.execute_reply": "2023-08-06T17:34:51.658989Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -3576,12 +3630,12 @@
     }
    ],
    "source": [
-    "9/(20+9)"
+    "9/(20+9)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4d04bbc7",
+   "id": "8a0c1140",
    "metadata": {},
    "source": [
     "## Linear and Poisson Regression on the Bikeshare Data\n",
@@ -3593,23 +3647,24 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "769300df",
+   "id": "6f93060c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:42.992570Z",
-     "iopub.status.busy": "2023-07-31T02:14:42.992186Z",
-     "iopub.status.idle": "2023-07-31T02:14:43.006512Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.005812Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:51.664042Z",
+     "iopub.status.busy": "2023-08-06T17:34:51.663443Z",
+     "iopub.status.idle": "2023-08-06T17:34:51.685390Z",
+     "shell.execute_reply": "2023-08-06T17:34:51.684689Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
-    "Bike = load_data('Bikeshare')"
+    "Bike = load_data('Bikeshare')\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8cc82fa9",
+   "id": "af82d61e",
    "metadata": {},
    "source": [
     "Let's have a peek at the dimensions and names of the variables in this dataframe."
@@ -3618,13 +3673,13 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "02b351f5",
+   "id": "725ce173",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:43.010755Z",
-     "iopub.status.busy": "2023-07-31T02:14:43.010421Z",
-     "iopub.status.idle": "2023-07-31T02:14:43.017317Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.016675Z"
+     "iopub.execute_input": "2023-08-06T17:34:51.689828Z",
+     "iopub.status.busy": "2023-08-06T17:34:51.689209Z",
+     "iopub.status.idle": "2023-08-06T17:34:51.696435Z",
+     "shell.execute_reply": "2023-08-06T17:34:51.695780Z"
     }
    },
    "outputs": [
@@ -3644,12 +3699,12 @@
     }
    ],
    "source": [
-    "Bike.shape, Bike.columns"
+    "Bike.shape, Bike.columns\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7784e3a1",
+   "id": "9b133dc3",
    "metadata": {},
    "source": [
     "### Linear Regression\n",
@@ -3660,13 +3715,13 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "926c908d",
+   "id": "ee3e5e3b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:43.021588Z",
-     "iopub.status.busy": "2023-07-31T02:14:43.021262Z",
-     "iopub.status.idle": "2023-07-31T02:14:43.106392Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.105190Z"
+     "iopub.execute_input": "2023-08-06T17:34:51.700999Z",
+     "iopub.status.busy": "2023-08-06T17:34:51.700652Z",
+     "iopub.status.idle": "2023-08-06T17:34:51.771460Z",
+     "shell.execute_reply": "2023-08-06T17:34:51.770793Z"
     }
    },
    "outputs": [
@@ -4039,12 +4094,12 @@
     "        'weathersit']).fit_transform(Bike)\n",
     "Y = Bike['bikers']\n",
     "M_lm = sm.OLS(Y, X).fit()\n",
-    "summarize(M_lm)"
+    "summarize(M_lm)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b29a0708",
+   "id": "36f3063f",
    "metadata": {},
    "source": [
     "There are 24 levels in `hr` and 40 rows in all.\n",
@@ -4064,24 +4119,25 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "id": "064b24d1",
+   "id": "d6a047f5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:43.110343Z",
-     "iopub.status.busy": "2023-07-31T02:14:43.109711Z",
-     "iopub.status.idle": "2023-07-31T02:14:43.117475Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.113978Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:51.777342Z",
+     "iopub.status.busy": "2023-08-06T17:34:51.777095Z",
+     "iopub.status.idle": "2023-08-06T17:34:51.781327Z",
+     "shell.execute_reply": "2023-08-06T17:34:51.780152Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
     "hr_encode = contrast('hr', 'sum')\n",
-    "mnth_encode = contrast('mnth', 'sum')"
+    "mnth_encode = contrast('mnth', 'sum')\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fba11e8e",
+   "id": "6ed435ca",
    "metadata": {},
    "source": [
     "Refitting again:"
@@ -4090,13 +4146,13 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "27c1aa54",
+   "id": "d7f6edaf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:43.121462Z",
-     "iopub.status.busy": "2023-07-31T02:14:43.121162Z",
-     "iopub.status.idle": "2023-07-31T02:14:43.188156Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.187391Z"
+     "iopub.execute_input": "2023-08-06T17:34:51.785799Z",
+     "iopub.status.busy": "2023-08-06T17:34:51.785477Z",
+     "iopub.status.idle": "2023-08-06T17:34:51.868170Z",
+     "shell.execute_reply": "2023-08-06T17:34:51.867180Z"
     }
    },
    "outputs": [
@@ -4474,7 +4530,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d057b162",
+   "id": "edea9530",
    "metadata": {},
    "source": [
     "What is the difference between the two codings?  In `M2_lm`, a\n",
@@ -4500,13 +4556,13 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "id": "dd0114c8",
+   "id": "412ce964",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:43.197210Z",
-     "iopub.status.busy": "2023-07-31T02:14:43.191504Z",
-     "iopub.status.idle": "2023-07-31T02:14:43.205987Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.203561Z"
+     "iopub.execute_input": "2023-08-06T17:34:51.872605Z",
+     "iopub.status.busy": "2023-08-06T17:34:51.872217Z",
+     "iopub.status.idle": "2023-08-06T17:34:51.880273Z",
+     "shell.execute_reply": "2023-08-06T17:34:51.879520Z"
     }
    },
    "outputs": [
@@ -4522,12 +4578,12 @@
     }
    ],
    "source": [
-    "np.sum((M_lm.fittedvalues - M2_lm.fittedvalues)**2)"
+    "np.sum((M_lm.fittedvalues - M2_lm.fittedvalues)**2)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bcb6ae87",
+   "id": "69539d1c",
    "metadata": {},
    "source": [
     "The sum of squared differences is zero. We can also see this using the\n",
@@ -4537,14 +4593,15 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "292585a1",
+   "id": "5afd0d5d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:43.210658Z",
-     "iopub.status.busy": "2023-07-31T02:14:43.210136Z",
-     "iopub.status.idle": "2023-07-31T02:14:43.216572Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.216006Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:51.885577Z",
+     "iopub.status.busy": "2023-08-06T17:34:51.885206Z",
+     "iopub.status.idle": "2023-08-06T17:34:51.893099Z",
+     "shell.execute_reply": "2023-08-06T17:34:51.892367Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -4559,12 +4616,12 @@
     }
    ],
    "source": [
-    "np.allclose(M_lm.fittedvalues, M2_lm.fittedvalues)"
+    "np.allclose(M_lm.fittedvalues, M2_lm.fittedvalues)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5bffbdd1",
+   "id": "fdc83f2e",
    "metadata": {},
    "source": [
     "To reproduce the left-hand side of Figure 4.13\n",
@@ -4579,14 +4636,15 @@
   {
    "cell_type": "code",
    "execution_count": 71,
-   "id": "e05e0575",
+   "id": "4629f2c9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:43.221686Z",
-     "iopub.status.busy": "2023-07-31T02:14:43.219575Z",
-     "iopub.status.idle": "2023-07-31T02:14:43.228105Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.227361Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:51.896788Z",
+     "iopub.status.busy": "2023-08-06T17:34:51.896443Z",
+     "iopub.status.idle": "2023-08-06T17:34:51.903051Z",
+     "shell.execute_reply": "2023-08-06T17:34:51.902328Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -4613,12 +4671,12 @@
    ],
    "source": [
     "coef_month = S2[S2.index.str.contains('mnth')]['coef']\n",
-    "coef_month"
+    "coef_month\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d542caac",
+   "id": "6fdd5f23",
    "metadata": {},
    "source": [
     "Next, we append `Dec` as the negative of the sum of all other months."
@@ -4627,14 +4685,15 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "id": "9f04a25e",
+   "id": "b3e55833",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:43.231864Z",
-     "iopub.status.busy": "2023-07-31T02:14:43.231239Z",
-     "iopub.status.idle": "2023-07-31T02:14:43.240501Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.239364Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:51.906797Z",
+     "iopub.status.busy": "2023-08-06T17:34:51.906546Z",
+     "iopub.status.idle": "2023-08-06T17:34:51.914013Z",
+     "shell.execute_reply": "2023-08-06T17:34:51.913335Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -4668,12 +4727,12 @@
     "                                  index=['mnth[Dec]'\n",
     "                                 ])\n",
     "                       ])\n",
-    "coef_month"
+    "coef_month\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6e3ef375",
+   "id": "4d1f1d30",
    "metadata": {},
    "source": [
     "Finally, to make the plot neater, we’ll just use the first letter of each month, which is the $6$th entry of each of\n",
@@ -4683,13 +4742,13 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "id": "20c7c054",
+   "id": "cdbebc03",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:43.243792Z",
-     "iopub.status.busy": "2023-07-31T02:14:43.243520Z",
-     "iopub.status.idle": "2023-07-31T02:14:43.367727Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.366856Z"
+     "iopub.execute_input": "2023-08-06T17:34:51.917669Z",
+     "iopub.status.busy": "2023-08-06T17:34:51.917450Z",
+     "iopub.status.idle": "2023-08-06T17:34:52.060250Z",
+     "shell.execute_reply": "2023-08-06T17:34:52.059542Z"
     }
    },
    "outputs": [
@@ -4711,12 +4770,12 @@
     "ax_month.set_xticks(x_month)\n",
     "ax_month.set_xticklabels([l[5] for l in coef_month.index], fontsize=20)\n",
     "ax_month.set_xlabel('Month', fontsize=20)\n",
-    "ax_month.set_ylabel('Coefficient', fontsize=20);"
+    "ax_month.set_ylabel('Coefficient', fontsize=20);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6032f6a9",
+   "id": "6f845be8",
    "metadata": {},
    "source": [
     "Reproducing the  right-hand plot in Figure 4.13  follows a similar process."
@@ -4725,13 +4784,13 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "id": "3f83f4bf",
+   "id": "170fd6fd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:43.372852Z",
-     "iopub.status.busy": "2023-07-31T02:14:43.372564Z",
-     "iopub.status.idle": "2023-07-31T02:14:43.379643Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.378490Z"
+     "iopub.execute_input": "2023-08-06T17:34:52.064472Z",
+     "iopub.status.busy": "2023-08-06T17:34:52.064205Z",
+     "iopub.status.idle": "2023-08-06T17:34:52.071346Z",
+     "shell.execute_reply": "2023-08-06T17:34:52.070685Z"
     }
    },
    "outputs": [],
@@ -4740,12 +4799,12 @@
     "coef_hr = coef_hr.reindex(['hr[{0}]'.format(h) for h in range(23)])\n",
     "coef_hr = pd.concat([coef_hr,\n",
     "                     pd.Series([-coef_hr.sum()], index=['hr[23]'])\n",
-    "                    ])"
+    "                    ])\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "58200be9",
+   "id": "100190e3",
    "metadata": {},
    "source": [
     "We now make the hour plot."
@@ -4754,13 +4813,13 @@
   {
    "cell_type": "code",
    "execution_count": 75,
-   "id": "86e9a741",
+   "id": "1e09e126",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:43.384029Z",
-     "iopub.status.busy": "2023-07-31T02:14:43.383803Z",
-     "iopub.status.idle": "2023-07-31T02:14:43.523993Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.523687Z"
+     "iopub.execute_input": "2023-08-06T17:34:52.075772Z",
+     "iopub.status.busy": "2023-08-06T17:34:52.075406Z",
+     "iopub.status.idle": "2023-08-06T17:34:52.204803Z",
+     "shell.execute_reply": "2023-08-06T17:34:52.204415Z"
     }
    },
    "outputs": [
@@ -4782,12 +4841,12 @@
     "ax_hr.set_xticks(x_hr[::2])\n",
     "ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n",
     "ax_hr.set_xlabel('Hour', fontsize=20)\n",
-    "ax_hr.set_ylabel('Coefficient', fontsize=20);"
+    "ax_hr.set_ylabel('Coefficient', fontsize=20);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5db06a3e",
+   "id": "bf730e5d",
    "metadata": {},
    "source": [
     "### Poisson Regression\n",
@@ -4800,23 +4859,23 @@
   {
    "cell_type": "code",
    "execution_count": 76,
-   "id": "a9448278",
+   "id": "a6667dcf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:43.525638Z",
-     "iopub.status.busy": "2023-07-31T02:14:43.525522Z",
-     "iopub.status.idle": "2023-07-31T02:14:43.607623Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.606685Z"
+     "iopub.execute_input": "2023-08-06T17:34:52.206616Z",
+     "iopub.status.busy": "2023-08-06T17:34:52.206494Z",
+     "iopub.status.idle": "2023-08-06T17:34:52.270836Z",
+     "shell.execute_reply": "2023-08-06T17:34:52.270019Z"
     }
    },
    "outputs": [],
    "source": [
-    "M_pois = sm.GLM(Y, X2, family=sm.families.Poisson()).fit()"
+    "M_pois = sm.GLM(Y, X2, family=sm.families.Poisson()).fit()\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e7b3bc38",
+   "id": "8309c1fc",
    "metadata": {},
    "source": [
     "We can plot the coefficients associated with `mnth` and `hr`, in order to reproduce  Figure 4.15. We first complete these coefficients as before."
@@ -4825,14 +4884,15 @@
   {
    "cell_type": "code",
    "execution_count": 77,
-   "id": "ec8505be",
+   "id": "c20de5d8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:43.611740Z",
-     "iopub.status.busy": "2023-07-31T02:14:43.611416Z",
-     "iopub.status.idle": "2023-07-31T02:14:43.632412Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.631755Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:52.275471Z",
+     "iopub.status.busy": "2023-08-06T17:34:52.275064Z",
+     "iopub.status.idle": "2023-08-06T17:34:52.302707Z",
+     "shell.execute_reply": "2023-08-06T17:34:52.302018Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -4849,7 +4909,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6fb37ef",
+   "id": "41e58943",
    "metadata": {},
    "source": [
     "The plotting is as before."
@@ -4858,21 +4918,22 @@
   {
    "cell_type": "code",
    "execution_count": 78,
-   "id": "0b8a5edf",
+   "id": "4a1a8488",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:43.636286Z",
-     "iopub.status.busy": "2023-07-31T02:14:43.635967Z",
-     "iopub.status.idle": "2023-07-31T02:14:43.889991Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.889682Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:52.306937Z",
+     "iopub.status.busy": "2023-08-06T17:34:52.306687Z",
+     "iopub.status.idle": "2023-08-06T17:34:52.568583Z",
+     "shell.execute_reply": "2023-08-06T17:34:52.568258Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_87709/3779510511.py:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_84993/3779905754.py:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n"
      ]
     },
@@ -4897,12 +4958,12 @@
     "ax_hr.plot(x_hr, coef_hr, marker='o', ms=10)\n",
     "ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n",
     "ax_hr.set_xlabel('Hour', fontsize=20)\n",
-    "ax_hr.set_ylabel('Coefficient', fontsize=20);"
+    "ax_hr.set_ylabel('Coefficient', fontsize=20);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3c256bf2",
+   "id": "5e74d13e",
    "metadata": {},
    "source": [
     "We compare the fitted values of the two models.\n",
@@ -4914,13 +4975,13 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "id": "d487c7ed",
+   "id": "a3158334",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:43.891538Z",
-     "iopub.status.busy": "2023-07-31T02:14:43.891447Z",
-     "iopub.status.idle": "2023-07-31T02:14:44.000225Z",
-     "shell.execute_reply": "2023-07-31T02:14:43.999853Z"
+     "iopub.execute_input": "2023-08-06T17:34:52.570243Z",
+     "iopub.status.busy": "2023-08-06T17:34:52.570130Z",
+     "iopub.status.idle": "2023-08-06T17:34:52.678176Z",
+     "shell.execute_reply": "2023-08-06T17:34:52.677845Z"
     }
    },
    "outputs": [
@@ -4943,12 +5004,12 @@
     "ax.set_xlabel('Linear Regression Fit', fontsize=20)\n",
     "ax.set_ylabel('Poisson Regression Fit', fontsize=20)\n",
     "ax.axline([0,0], c='black', linewidth=3,\n",
-    "          linestyle='--', slope=1);"
+    "          linestyle='--', slope=1);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "17ccc1bf",
+   "id": "2b03ac4f",
    "metadata": {},
    "source": [
     "The predictions from the Poisson regression model are correlated with\n",
@@ -4962,15 +5023,16 @@
     "with `family=sm.families.Binomial()` to perform logistic regression. Other\n",
     "choices for the `family` argument can be used to fit other types\n",
     "of GLMs. For instance, `family=sm.families.Gamma()` fits a Gamma regression\n",
-    "model."
+    "model.\n",
+    "\n"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "formats": "ipynb,md:myst",
-   "main_language": "python"
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
   },
   "kernelspec": {
    "display_name": "Python3 (islp_freeze_311)",
diff --git a/Ch5-resample-lab.ipynb b/Ch5-resample-lab.ipynb
index 04938f0..950726d 100644
--- a/Ch5-resample-lab.ipynb
+++ b/Ch5-resample-lab.ipynb
@@ -5,7 +5,9 @@
    "id": "3a3f2f85",
    "metadata": {},
    "source": [
-    "# Chapter 5"
+    "\n",
+    "# Chapter 5\n",
+    "\n"
    ]
   },
   {
@@ -27,11 +29,12 @@
    "id": "60fad148",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:45.345335Z",
-     "iopub.status.busy": "2023-07-31T02:14:45.345019Z",
-     "iopub.status.idle": "2023-07-31T02:14:46.175442Z",
-     "shell.execute_reply": "2023-07-31T02:14:46.174945Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:54.032413Z",
+     "iopub.status.busy": "2023-08-06T17:34:54.032111Z",
+     "iopub.status.idle": "2023-08-06T17:34:54.873921Z",
+     "shell.execute_reply": "2023-08-06T17:34:54.873325Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -41,7 +44,7 @@
     "from ISLP.models import (ModelSpec as MS,\n",
     "                         summarize,\n",
     "                         poly)\n",
-    "from sklearn.model_selection import train_test_split"
+    "from sklearn.model_selection import train_test_split\n"
    ]
   },
   {
@@ -58,11 +61,12 @@
    "id": "2478aeb4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:46.177622Z",
-     "iopub.status.busy": "2023-07-31T02:14:46.177374Z",
-     "iopub.status.idle": "2023-07-31T02:14:46.179577Z",
-     "shell.execute_reply": "2023-07-31T02:14:46.179312Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:54.876060Z",
+     "iopub.status.busy": "2023-08-06T17:34:54.875842Z",
+     "iopub.status.idle": "2023-08-06T17:34:54.878002Z",
+     "shell.execute_reply": "2023-08-06T17:34:54.877731Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -72,7 +76,7 @@
     "      KFold,\n",
     "      ShuffleSplit)\n",
     "from sklearn.base import clone\n",
-    "from ISLP.models import sklearn_sm"
+    "from ISLP.models import sklearn_sm\n"
    ]
   },
   {
@@ -92,7 +96,7 @@
     "when performing operations like this that contain an\n",
     "element of randomness, so that the results obtained can be reproduced\n",
     "precisely at a later time. We set the random seed of the splitter\n",
-    "with the argument `random_state=0`."
+    "with the argument `random_state=0`. "
    ]
   },
   {
@@ -101,10 +105,10 @@
    "id": "99c95faf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:46.181100Z",
-     "iopub.status.busy": "2023-07-31T02:14:46.180998Z",
-     "iopub.status.idle": "2023-07-31T02:14:46.187844Z",
-     "shell.execute_reply": "2023-07-31T02:14:46.187574Z"
+     "iopub.execute_input": "2023-08-06T17:34:54.879485Z",
+     "iopub.status.busy": "2023-08-06T17:34:54.879378Z",
+     "iopub.status.idle": "2023-08-06T17:34:54.886401Z",
+     "shell.execute_reply": "2023-08-06T17:34:54.886117Z"
     }
    },
    "outputs": [],
@@ -112,7 +116,7 @@
     "Auto = load_data('Auto')\n",
     "Auto_train, Auto_valid = train_test_split(Auto,\n",
     "                                         test_size=196,\n",
-    "                                         random_state=0)"
+    "                                         random_state=0)\n"
    ]
   },
   {
@@ -129,10 +133,10 @@
    "id": "41b0717d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:46.189426Z",
-     "iopub.status.busy": "2023-07-31T02:14:46.189344Z",
-     "iopub.status.idle": "2023-07-31T02:14:46.193204Z",
-     "shell.execute_reply": "2023-07-31T02:14:46.192935Z"
+     "iopub.execute_input": "2023-08-06T17:34:54.887942Z",
+     "iopub.status.busy": "2023-08-06T17:34:54.887854Z",
+     "iopub.status.idle": "2023-08-06T17:34:54.891560Z",
+     "shell.execute_reply": "2023-08-06T17:34:54.891297Z"
     }
    },
    "outputs": [],
@@ -141,7 +145,7 @@
     "X_train = hp_mm.fit_transform(Auto_train)\n",
     "y_train = Auto_train['mpg']\n",
     "model = sm.OLS(y_train, X_train)\n",
-    "results = model.fit()"
+    "results = model.fit()\n"
    ]
   },
   {
@@ -159,10 +163,10 @@
    "id": "d7ea3c0d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:46.194683Z",
-     "iopub.status.busy": "2023-07-31T02:14:46.194591Z",
-     "iopub.status.idle": "2023-07-31T02:14:46.198962Z",
-     "shell.execute_reply": "2023-07-31T02:14:46.198705Z"
+     "iopub.execute_input": "2023-08-06T17:34:54.893051Z",
+     "iopub.status.busy": "2023-08-06T17:34:54.892965Z",
+     "iopub.status.idle": "2023-08-06T17:34:54.897036Z",
+     "shell.execute_reply": "2023-08-06T17:34:54.896785Z"
     }
    },
    "outputs": [
@@ -181,7 +185,7 @@
     "X_valid = hp_mm.transform(Auto_valid)\n",
     "y_valid = Auto_valid['mpg']\n",
     "valid_pred = results.predict(X_valid)\n",
-    "np.mean((y_valid - valid_pred)**2)"
+    "np.mean((y_valid - valid_pred)**2)\n"
    ]
   },
   {
@@ -203,10 +207,10 @@
    "id": "a02a2d05",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:46.200468Z",
-     "iopub.status.busy": "2023-07-31T02:14:46.200388Z",
-     "iopub.status.idle": "2023-07-31T02:14:46.202553Z",
-     "shell.execute_reply": "2023-07-31T02:14:46.202309Z"
+     "iopub.execute_input": "2023-08-06T17:34:54.898500Z",
+     "iopub.status.busy": "2023-08-06T17:34:54.898412Z",
+     "iopub.status.idle": "2023-08-06T17:34:54.900767Z",
+     "shell.execute_reply": "2023-08-06T17:34:54.900515Z"
     }
    },
    "outputs": [],
@@ -226,7 +230,7 @@
     "   results = sm.OLS(y_train, X_train).fit()\n",
     "   test_pred = results.predict(X_test)\n",
     "\n",
-    "   return np.mean((y_test - test_pred)**2)"
+    "   return np.mean((y_test - test_pred)**2)\n"
    ]
   },
   {
@@ -246,10 +250,10 @@
    "id": "51d93dea",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:46.203992Z",
-     "iopub.status.busy": "2023-07-31T02:14:46.203891Z",
-     "iopub.status.idle": "2023-07-31T02:14:46.213999Z",
-     "shell.execute_reply": "2023-07-31T02:14:46.213730Z"
+     "iopub.execute_input": "2023-08-06T17:34:54.902229Z",
+     "iopub.status.busy": "2023-08-06T17:34:54.902150Z",
+     "iopub.status.idle": "2023-08-06T17:34:54.912255Z",
+     "shell.execute_reply": "2023-08-06T17:34:54.912027Z"
     }
    },
    "outputs": [
@@ -271,7 +275,7 @@
     "                       'mpg',\n",
     "                       Auto_train,\n",
     "                       Auto_valid)\n",
-    "MSE"
+    "MSE\n"
    ]
   },
   {
@@ -290,10 +294,10 @@
    "id": "83432f06",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:46.215488Z",
-     "iopub.status.busy": "2023-07-31T02:14:46.215401Z",
-     "iopub.status.idle": "2023-07-31T02:14:46.225856Z",
-     "shell.execute_reply": "2023-07-31T02:14:46.225598Z"
+     "iopub.execute_input": "2023-08-06T17:34:54.913788Z",
+     "iopub.status.busy": "2023-08-06T17:34:54.913696Z",
+     "iopub.status.idle": "2023-08-06T17:34:54.924230Z",
+     "shell.execute_reply": "2023-08-06T17:34:54.923983Z"
     }
    },
    "outputs": [
@@ -373,11 +377,12 @@
    "id": "bcfc433f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:46.227362Z",
-     "iopub.status.busy": "2023-07-31T02:14:46.227281Z",
-     "iopub.status.idle": "2023-07-31T02:14:46.785216Z",
-     "shell.execute_reply": "2023-07-31T02:14:46.784936Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:54.925794Z",
+     "iopub.status.busy": "2023-08-06T17:34:54.925711Z",
+     "iopub.status.idle": "2023-08-06T17:34:55.485718Z",
+     "shell.execute_reply": "2023-08-06T17:34:55.485445Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -400,7 +405,7 @@
     "                            Y,\n",
     "                            cv=Auto.shape[0])\n",
     "cv_err = np.mean(cv_results['test_score'])\n",
-    "cv_err"
+    "cv_err\n"
    ]
   },
   {
@@ -440,11 +445,12 @@
    "id": "f951ffc8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:46.786874Z",
-     "iopub.status.busy": "2023-07-31T02:14:46.786773Z",
-     "iopub.status.idle": "2023-07-31T02:14:47.387263Z",
-     "shell.execute_reply": "2023-07-31T02:14:47.386958Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:55.487370Z",
+     "iopub.status.busy": "2023-08-06T17:34:55.487270Z",
+     "iopub.status.idle": "2023-08-06T17:34:56.086269Z",
+     "shell.execute_reply": "2023-08-06T17:34:56.085986Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -469,7 +475,7 @@
     "                          Y,\n",
     "                          cv=Auto.shape[0])\n",
     "    cv_error[i] = np.mean(M_CV['test_score'])\n",
-    "cv_error"
+    "cv_error\n"
    ]
   },
   {
@@ -487,7 +493,7 @@
     "It has two arrays as\n",
     "arguments, and then forms a larger\n",
     "array where the operation is applied to each pair of elements of the\n",
-    "two arrays."
+    "two arrays. "
    ]
   },
   {
@@ -496,10 +502,10 @@
    "id": "e3610b5a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:47.388905Z",
-     "iopub.status.busy": "2023-07-31T02:14:47.388796Z",
-     "iopub.status.idle": "2023-07-31T02:14:47.391290Z",
-     "shell.execute_reply": "2023-07-31T02:14:47.391034Z"
+     "iopub.execute_input": "2023-08-06T17:34:56.087846Z",
+     "iopub.status.busy": "2023-08-06T17:34:56.087739Z",
+     "iopub.status.idle": "2023-08-06T17:34:56.090120Z",
+     "shell.execute_reply": "2023-08-06T17:34:56.089850Z"
     }
    },
    "outputs": [
@@ -519,7 +525,7 @@
    "source": [
     "A = np.array([3, 5, 9])\n",
     "B = np.array([2, 4])\n",
-    "np.add.outer(A, B)"
+    "np.add.outer(A, B)\n"
    ]
   },
   {
@@ -538,11 +544,12 @@
    "id": "1627460d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:47.392816Z",
-     "iopub.status.busy": "2023-07-31T02:14:47.392715Z",
-     "iopub.status.idle": "2023-07-31T02:14:47.414278Z",
-     "shell.execute_reply": "2023-07-31T02:14:47.414015Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:56.091649Z",
+     "iopub.status.busy": "2023-08-06T17:34:56.091540Z",
+     "iopub.status.idle": "2023-08-06T17:34:56.113415Z",
+     "shell.execute_reply": "2023-08-06T17:34:56.113164Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -568,7 +575,7 @@
     "                          Y,\n",
     "                          cv=cv)\n",
     "    cv_error[i] = np.mean(M_CV['test_score'])\n",
-    "cv_error"
+    "cv_error\n"
    ]
   },
   {
@@ -602,11 +609,12 @@
    "id": "8a636468",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:47.415780Z",
-     "iopub.status.busy": "2023-07-31T02:14:47.415699Z",
-     "iopub.status.idle": "2023-07-31T02:14:47.421148Z",
-     "shell.execute_reply": "2023-07-31T02:14:47.420911Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:56.114990Z",
+     "iopub.status.busy": "2023-08-06T17:34:56.114909Z",
+     "iopub.status.idle": "2023-08-06T17:34:56.120375Z",
+     "shell.execute_reply": "2023-08-06T17:34:56.120121Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -628,7 +636,7 @@
     "                         Auto.drop(['mpg'], axis=1),\n",
     "                         Auto['mpg'],\n",
     "                         cv=validation);\n",
-    "results['test_score']"
+    "results['test_score']\n"
    ]
   },
   {
@@ -645,10 +653,10 @@
    "id": "746aeccd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:47.422643Z",
-     "iopub.status.busy": "2023-07-31T02:14:47.422562Z",
-     "iopub.status.idle": "2023-07-31T02:14:47.442227Z",
-     "shell.execute_reply": "2023-07-31T02:14:47.441935Z"
+     "iopub.execute_input": "2023-08-06T17:34:56.121875Z",
+     "iopub.status.busy": "2023-08-06T17:34:56.121788Z",
+     "iopub.status.idle": "2023-08-06T17:34:56.141044Z",
+     "shell.execute_reply": "2023-08-06T17:34:56.140787Z"
     }
    },
    "outputs": [
@@ -671,7 +679,7 @@
     "                         Auto.drop(['mpg'], axis=1),\n",
     "                         Auto['mpg'],\n",
     "                         cv=validation)\n",
-    "results['test_score'].mean(), results['test_score'].std()"
+    "results['test_score'].mean(), results['test_score'].std()\n"
    ]
   },
   {
@@ -719,11 +727,12 @@
    "id": "daa53d0c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:47.443783Z",
-     "iopub.status.busy": "2023-07-31T02:14:47.443671Z",
-     "iopub.status.idle": "2023-07-31T02:14:47.447006Z",
-     "shell.execute_reply": "2023-07-31T02:14:47.446693Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:56.142563Z",
+     "iopub.status.busy": "2023-08-06T17:34:56.142482Z",
+     "iopub.status.idle": "2023-08-06T17:34:56.146459Z",
+     "shell.execute_reply": "2023-08-06T17:34:56.146215Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -731,7 +740,7 @@
     "def alpha_func(D, idx):\n",
     "   cov_ = np.cov(D[['X','Y']].loc[idx], rowvar=False)\n",
     "   return ((cov_[1,1] - cov_[0,1]) /\n",
-    "           (cov_[0,0]+cov_[1,1]-2*cov_[0,1]))"
+    "           (cov_[0,0]+cov_[1,1]-2*cov_[0,1]))\n"
    ]
   },
   {
@@ -752,10 +761,10 @@
    "id": "578c9564",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:47.448908Z",
-     "iopub.status.busy": "2023-07-31T02:14:47.448792Z",
-     "iopub.status.idle": "2023-07-31T02:14:47.451963Z",
-     "shell.execute_reply": "2023-07-31T02:14:47.451646Z"
+     "iopub.execute_input": "2023-08-06T17:34:56.147902Z",
+     "iopub.status.busy": "2023-08-06T17:34:56.147820Z",
+     "iopub.status.idle": "2023-08-06T17:34:56.150542Z",
+     "shell.execute_reply": "2023-08-06T17:34:56.150288Z"
     }
    },
    "outputs": [
@@ -791,11 +800,12 @@
    "id": "5754d6d5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:47.453581Z",
-     "iopub.status.busy": "2023-07-31T02:14:47.453484Z",
-     "iopub.status.idle": "2023-07-31T02:14:47.457771Z",
-     "shell.execute_reply": "2023-07-31T02:14:47.457529Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:56.151951Z",
+     "iopub.status.busy": "2023-08-06T17:34:56.151874Z",
+     "iopub.status.idle": "2023-08-06T17:34:56.155780Z",
+     "shell.execute_reply": "2023-08-06T17:34:56.155537Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -833,11 +843,12 @@
    "id": "8320a49c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:47.459153Z",
-     "iopub.status.busy": "2023-07-31T02:14:47.459066Z",
-     "iopub.status.idle": "2023-07-31T02:14:47.461370Z",
-     "shell.execute_reply": "2023-07-31T02:14:47.461113Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:56.157150Z",
+     "iopub.status.busy": "2023-08-06T17:34:56.157060Z",
+     "iopub.status.idle": "2023-08-06T17:34:56.159342Z",
+     "shell.execute_reply": "2023-08-06T17:34:56.159133Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -868,7 +879,7 @@
     "unimportant and simply makes sure  the loop is executed `B` times.\n",
     "\n",
     "Let’s use our function to evaluate the accuracy of our\n",
-    "estimate of $\\alpha$ using $B=1{,}000$ bootstrap replications."
+    "estimate of $\\alpha$ using $B=1{,}000$ bootstrap replications. "
    ]
   },
   {
@@ -877,10 +888,10 @@
    "id": "e656aa1f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:47.462792Z",
-     "iopub.status.busy": "2023-07-31T02:14:47.462698Z",
-     "iopub.status.idle": "2023-07-31T02:14:47.745378Z",
-     "shell.execute_reply": "2023-07-31T02:14:47.745111Z"
+     "iopub.execute_input": "2023-08-06T17:34:56.160707Z",
+     "iopub.status.busy": "2023-08-06T17:34:56.160617Z",
+     "iopub.status.idle": "2023-08-06T17:34:56.455515Z",
+     "shell.execute_reply": "2023-08-06T17:34:56.455259Z"
     }
    },
    "outputs": [
@@ -900,7 +911,7 @@
     "                   Portfolio,\n",
     "                   B=1000,\n",
     "                   seed=0)\n",
-    "alpha_SE"
+    "alpha_SE\n"
    ]
   },
   {
@@ -943,11 +954,12 @@
    "id": "c5d14195",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:47.746827Z",
-     "iopub.status.busy": "2023-07-31T02:14:47.746750Z",
-     "iopub.status.idle": "2023-07-31T02:14:47.748785Z",
-     "shell.execute_reply": "2023-07-31T02:14:47.748522Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:56.457024Z",
+     "iopub.status.busy": "2023-08-06T17:34:56.456940Z",
+     "iopub.status.idle": "2023-08-06T17:34:56.459011Z",
+     "shell.execute_reply": "2023-08-06T17:34:56.458766Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -977,15 +989,16 @@
    "id": "7e0523f0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:47.750186Z",
-     "iopub.status.busy": "2023-07-31T02:14:47.750111Z",
-     "iopub.status.idle": "2023-07-31T02:14:47.751794Z",
-     "shell.execute_reply": "2023-07-31T02:14:47.751573Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:56.460430Z",
+     "iopub.status.busy": "2023-08-06T17:34:56.460350Z",
+     "iopub.status.idle": "2023-08-06T17:34:56.462034Z",
+     "shell.execute_reply": "2023-08-06T17:34:56.461808Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
-    "hp_func = partial(boot_OLS, MS(['horsepower']), 'mpg')"
+    "hp_func = partial(boot_OLS, MS(['horsepower']), 'mpg')\n"
    ]
   },
   {
@@ -1009,11 +1022,12 @@
    "id": "32836e93",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:47.753215Z",
-     "iopub.status.busy": "2023-07-31T02:14:47.753126Z",
-     "iopub.status.idle": "2023-07-31T02:14:47.768431Z",
-     "shell.execute_reply": "2023-07-31T02:14:47.768162Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:56.463386Z",
+     "iopub.status.busy": "2023-08-06T17:34:56.463311Z",
+     "iopub.status.idle": "2023-08-06T17:34:56.477900Z",
+     "shell.execute_reply": "2023-08-06T17:34:56.477641Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1041,7 +1055,7 @@
     "np.array([hp_func(Auto,\n",
     "          rng.choice(392,\n",
     "                     392,\n",
-    "                     replace=True)) for _ in range(10)])"
+    "                     replace=True)) for _ in range(10)])\n"
    ]
   },
   {
@@ -1059,11 +1073,12 @@
    "id": "14ce3afa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:47.769912Z",
-     "iopub.status.busy": "2023-07-31T02:14:47.769830Z",
-     "iopub.status.idle": "2023-07-31T02:14:48.926601Z",
-     "shell.execute_reply": "2023-07-31T02:14:48.926292Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:56.479382Z",
+     "iopub.status.busy": "2023-08-06T17:34:56.479299Z",
+     "iopub.status.idle": "2023-08-06T17:34:57.627980Z",
+     "shell.execute_reply": "2023-08-06T17:34:57.627706Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1084,7 +1099,7 @@
     "                Auto,\n",
     "                B=1000,\n",
     "                seed=10)\n",
-    "hp_se"
+    "hp_se\n"
    ]
   },
   {
@@ -1108,11 +1123,12 @@
    "id": "6b1213ac",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:48.928216Z",
-     "iopub.status.busy": "2023-07-31T02:14:48.928105Z",
-     "iopub.status.idle": "2023-07-31T02:14:48.985288Z",
-     "shell.execute_reply": "2023-07-31T02:14:48.985012Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:57.629628Z",
+     "iopub.status.busy": "2023-08-06T17:34:57.629520Z",
+     "iopub.status.idle": "2023-08-06T17:34:57.687018Z",
+     "shell.execute_reply": "2023-08-06T17:34:57.686748Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1131,7 +1147,7 @@
    "source": [
     "hp_model.fit(Auto, Auto['mpg'])\n",
     "model_se = summarize(hp_model.results_)['std err']\n",
-    "model_se"
+    "model_se\n"
    ]
   },
   {
@@ -1179,10 +1195,10 @@
    "id": "af99b778",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:48.986895Z",
-     "iopub.status.busy": "2023-07-31T02:14:48.986777Z",
-     "iopub.status.idle": "2023-07-31T02:14:50.767999Z",
-     "shell.execute_reply": "2023-07-31T02:14:50.767687Z"
+     "iopub.execute_input": "2023-08-06T17:34:57.688662Z",
+     "iopub.status.busy": "2023-08-06T17:34:57.688521Z",
+     "iopub.status.idle": "2023-08-06T17:34:59.481117Z",
+     "shell.execute_reply": "2023-08-06T17:34:59.480813Z"
     }
    },
    "outputs": [
@@ -1205,7 +1221,7 @@
     "quad_func = partial(boot_OLS,\n",
     "                    quad_model,\n",
     "                    'mpg')\n",
-    "boot_SE(quad_func, Auto, B=1000)"
+    "boot_SE(quad_func, Auto, B=1000)\n"
    ]
   },
   {
@@ -1222,11 +1238,12 @@
    "id": "0206281e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:50.769670Z",
-     "iopub.status.busy": "2023-07-31T02:14:50.769556Z",
-     "iopub.status.idle": "2023-07-31T02:14:50.778195Z",
-     "shell.execute_reply": "2023-07-31T02:14:50.777948Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:34:59.482760Z",
+     "iopub.status.busy": "2023-08-06T17:34:59.482640Z",
+     "iopub.status.idle": "2023-08-06T17:34:59.491295Z",
+     "shell.execute_reply": "2023-08-06T17:34:59.491041Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1246,15 +1263,24 @@
    "source": [
     "M = sm.OLS(Auto['mpg'],\n",
     "           quad_model.fit_transform(Auto))\n",
-    "summarize(M.fit())['std err']"
+    "summarize(M.fit())['std err']\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c11a71f",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "formats": "ipynb,md:myst",
-   "main_language": "python"
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/Ch6-varselect-lab.ipynb b/Ch6-varselect-lab.ipynb
index 9b0292d..9850367 100644
--- a/Ch6-varselect-lab.ipynb
+++ b/Ch6-varselect-lab.ipynb
@@ -2,33 +2,34 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "1e9ee9d8",
+   "id": "73428d07",
    "metadata": {},
    "source": [
+    "\n",
     "# Chapter 6"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "98c1f26d",
+   "id": "3479aab9",
    "metadata": {},
    "source": [
     "# Lab: Linear Models and Regularization Methods\n",
     "In this lab we implement many of the techniques discussed in this chapter.\n",
     "We import some of our libraries at this top\n",
-    "level."
+    "level. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "564883b2",
+   "id": "a121caba",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:51.941671Z",
-     "iopub.status.busy": "2023-07-31T02:14:51.941379Z",
-     "iopub.status.idle": "2023-07-31T02:14:52.976659Z",
-     "shell.execute_reply": "2023-07-31T02:14:52.976043Z"
+     "iopub.execute_input": "2023-08-06T17:35:00.786819Z",
+     "iopub.status.busy": "2023-08-06T17:35:00.786401Z",
+     "iopub.status.idle": "2023-08-06T17:35:01.840861Z",
+     "shell.execute_reply": "2023-08-06T17:35:01.840276Z"
     }
    },
    "outputs": [],
@@ -42,12 +43,12 @@
     "from sklearn.preprocessing import StandardScaler\n",
     "from ISLP import load_data\n",
     "from ISLP.models import ModelSpec as MS\n",
-    "from functools import partial"
+    "from functools import partial\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0594e13e",
+   "id": "b846a10e",
    "metadata": {},
    "source": [
     "We again collect the new imports\n",
@@ -57,14 +58,15 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "9aa8d3ee",
+   "id": "91b5adfb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:52.978919Z",
-     "iopub.status.busy": "2023-07-31T02:14:52.978625Z",
-     "iopub.status.idle": "2023-07-31T02:14:54.800247Z",
-     "shell.execute_reply": "2023-07-31T02:14:54.799907Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:01.843052Z",
+     "iopub.status.busy": "2023-08-06T17:35:01.842795Z",
+     "iopub.status.idle": "2023-08-06T17:35:03.677217Z",
+     "shell.execute_reply": "2023-08-06T17:35:03.676896Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -88,12 +90,12 @@
     "      sklearn_selected,\n",
     "      sklearn_selection_path)\n",
     "!pip install l0bnb\n",
-    "from l0bnb import fit_path"
+    "from l0bnb import fit_path\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "06a58c0b",
+   "id": "d363de72",
    "metadata": {},
    "source": [
     "We have installed the package `l0bnb` on the fly. Note the escaped `!pip install` --- this is run as a separate system command. \n",
@@ -120,13 +122,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "05371e33",
+   "id": "01b6e940",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:54.802107Z",
-     "iopub.status.busy": "2023-07-31T02:14:54.801990Z",
-     "iopub.status.idle": "2023-07-31T02:14:54.809259Z",
-     "shell.execute_reply": "2023-07-31T02:14:54.809012Z"
+     "iopub.execute_input": "2023-08-06T17:35:03.679036Z",
+     "iopub.status.busy": "2023-08-06T17:35:03.678916Z",
+     "iopub.status.idle": "2023-08-06T17:35:03.686163Z",
+     "shell.execute_reply": "2023-08-06T17:35:03.685904Z"
     }
    },
    "outputs": [
@@ -143,12 +145,12 @@
    ],
    "source": [
     "Hitters = load_data('Hitters')\n",
-    "np.isnan(Hitters['Salary']).sum()"
+    "np.isnan(Hitters['Salary']).sum()\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8071b084",
+   "id": "409d79c0",
    "metadata": {},
    "source": [
     " We see that `Salary` is missing for 59 players. The\n",
@@ -159,14 +161,15 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "aa5bc5b8",
+   "id": "3472ea4b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:54.810704Z",
-     "iopub.status.busy": "2023-07-31T02:14:54.810602Z",
-     "iopub.status.idle": "2023-07-31T02:14:54.813996Z",
-     "shell.execute_reply": "2023-07-31T02:14:54.813735Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:03.687686Z",
+     "iopub.status.busy": "2023-08-06T17:35:03.687591Z",
+     "iopub.status.idle": "2023-08-06T17:35:03.691017Z",
+     "shell.execute_reply": "2023-08-06T17:35:03.690736Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -182,12 +185,12 @@
    ],
    "source": [
     "Hitters = Hitters.dropna();\n",
-    "Hitters.shape"
+    "Hitters.shape\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d5017407",
+   "id": "b3a1b082",
    "metadata": {},
    "source": [
     "We first choose the best model using forward selection based on $C_p$ (6.2). This score\n",
@@ -199,14 +202,15 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "10494837",
+   "id": "4090cab5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:54.815512Z",
-     "iopub.status.busy": "2023-07-31T02:14:54.815419Z",
-     "iopub.status.idle": "2023-07-31T02:14:54.817462Z",
-     "shell.execute_reply": "2023-07-31T02:14:54.817200Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:03.692519Z",
+     "iopub.status.busy": "2023-08-06T17:35:03.692417Z",
+     "iopub.status.idle": "2023-08-06T17:35:03.694378Z",
+     "shell.execute_reply": "2023-08-06T17:35:03.694116Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -215,12 +219,12 @@
     "    n, p = X.shape\n",
     "    Yhat = estimator.predict(X)\n",
     "    RSS = np.sum((Y - Yhat)**2)\n",
-    "    return -(RSS + 2 * p * sigma2) / n "
+    "    return -(RSS + 2 * p * sigma2) / n \n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "126e9322",
+   "id": "c37b9aed",
    "metadata": {},
    "source": [
     "We need to estimate the residual variance $\\sigma^2$, which is the first argument in our scoring function above.\n",
@@ -230,13 +234,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "b8173cef",
+   "id": "2b85c1d6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:54.818903Z",
-     "iopub.status.busy": "2023-07-31T02:14:54.818801Z",
-     "iopub.status.idle": "2023-07-31T02:14:54.831727Z",
-     "shell.execute_reply": "2023-07-31T02:14:54.831458Z"
+     "iopub.execute_input": "2023-08-06T17:35:03.695743Z",
+     "iopub.status.busy": "2023-08-06T17:35:03.695653Z",
+     "iopub.status.idle": "2023-08-06T17:35:03.708301Z",
+     "shell.execute_reply": "2023-08-06T17:35:03.708031Z"
     }
    },
    "outputs": [],
@@ -244,12 +248,12 @@
     "design = MS(Hitters.columns.drop('Salary')).fit(Hitters)\n",
     "Y = np.array(Hitters['Salary'])\n",
     "X = design.transform(Hitters)\n",
-    "sigma2 = OLS(Y,X).fit().scale"
+    "sigma2 = OLS(Y,X).fit().scale\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f873c04f",
+   "id": "e8b97efd",
    "metadata": {},
    "source": [
     "The function `sklearn_selected()` expects a scorer with just three arguments --- the last three in the definition of `nCp()` above. We use the function `partial()` first seen in Section 5.3.3 to freeze the first argument with our estimate of $\\sigma^2$."
@@ -258,31 +262,32 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "0cc8d7bd",
+   "id": "7c6f93c0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:54.833260Z",
-     "iopub.status.busy": "2023-07-31T02:14:54.833174Z",
-     "iopub.status.idle": "2023-07-31T02:14:54.835084Z",
-     "shell.execute_reply": "2023-07-31T02:14:54.834749Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:03.709763Z",
+     "iopub.status.busy": "2023-08-06T17:35:03.709684Z",
+     "iopub.status.idle": "2023-08-06T17:35:03.711458Z",
+     "shell.execute_reply": "2023-08-06T17:35:03.711205Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
-    "neg_Cp = partial(nCp, sigma2)"
+    "neg_Cp = partial(nCp, sigma2)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3ff5a859",
+   "id": "392e5247",
    "metadata": {},
    "source": [
-    "We can now use `neg_Cp()` as a scorer for model selection."
+    "We can now use `neg_Cp()` as a scorer for model selection.\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2b30b7bd",
+   "id": "47461c19",
    "metadata": {},
    "source": [
     "Along with a score we need to specify the search strategy. This is done through the object\n",
@@ -295,25 +300,25 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "440b4c50",
+   "id": "4b0d8d44",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:54.836548Z",
-     "iopub.status.busy": "2023-07-31T02:14:54.836467Z",
-     "iopub.status.idle": "2023-07-31T02:14:54.838223Z",
-     "shell.execute_reply": "2023-07-31T02:14:54.837976Z"
+     "iopub.execute_input": "2023-08-06T17:35:03.712955Z",
+     "iopub.status.busy": "2023-08-06T17:35:03.712871Z",
+     "iopub.status.idle": "2023-08-06T17:35:03.714691Z",
+     "shell.execute_reply": "2023-08-06T17:35:03.714446Z"
     }
    },
    "outputs": [],
    "source": [
     "strategy = Stepwise.first_peak(design,\n",
     "                               direction='forward',\n",
-    "                               max_terms=len(design.terms))"
+    "                               max_terms=len(design.terms))\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "77079484",
+   "id": "0f8a2102",
    "metadata": {},
    "source": [
     "We now fit a linear regression model with `Salary` as outcome using forward\n",
@@ -326,13 +331,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "b33a8617",
+   "id": "6d3c1d1f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:54.839611Z",
-     "iopub.status.busy": "2023-07-31T02:14:54.839537Z",
-     "iopub.status.idle": "2023-07-31T02:14:55.493223Z",
-     "shell.execute_reply": "2023-07-31T02:14:55.492936Z"
+     "iopub.execute_input": "2023-08-06T17:35:03.716059Z",
+     "iopub.status.busy": "2023-08-06T17:35:03.715979Z",
+     "iopub.status.idle": "2023-08-06T17:35:04.363068Z",
+     "shell.execute_reply": "2023-08-06T17:35:04.362795Z"
     }
    },
    "outputs": [
@@ -369,12 +374,12 @@
     "hitters_MSE = sklearn_selected(OLS,\n",
     "                               strategy)\n",
     "hitters_MSE.fit(Hitters, Y)\n",
-    "hitters_MSE.selected_state_"
+    "hitters_MSE.selected_state_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "233e45a1",
+   "id": "cf1ad8eb",
    "metadata": {},
    "source": [
     "Using `neg_Cp` results in a smaller model, as expected, with just 10 variables selected."
@@ -383,13 +388,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "e7f554cf",
+   "id": "5644971d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:55.494899Z",
-     "iopub.status.busy": "2023-07-31T02:14:55.494782Z",
-     "iopub.status.idle": "2023-07-31T02:14:55.903007Z",
-     "shell.execute_reply": "2023-07-31T02:14:55.902723Z"
+     "iopub.execute_input": "2023-08-06T17:35:04.364696Z",
+     "iopub.status.busy": "2023-08-06T17:35:04.364585Z",
+     "iopub.status.idle": "2023-08-06T17:35:04.769869Z",
+     "shell.execute_reply": "2023-08-06T17:35:04.769597Z"
     }
    },
    "outputs": [
@@ -418,12 +423,12 @@
     "                               strategy,\n",
     "                               scoring=neg_Cp)\n",
     "hitters_Cp.fit(Hitters, Y)\n",
-    "hitters_Cp.selected_state_"
+    "hitters_Cp.selected_state_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "75e8008b",
+   "id": "1db8634e",
    "metadata": {},
    "source": [
     "### Choosing Among Models Using the Validation Set Approach and Cross-Validation\n",
@@ -438,7 +443,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc753395",
+   "id": "08bdcec5",
    "metadata": {},
    "source": [
     "Here we define a strategy that fits the full forward selection path.\n",
@@ -449,13 +454,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "fb7091b8",
+   "id": "23f47f58",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:55.904614Z",
-     "iopub.status.busy": "2023-07-31T02:14:55.904500Z",
-     "iopub.status.idle": "2023-07-31T02:14:55.906419Z",
-     "shell.execute_reply": "2023-07-31T02:14:55.906168Z"
+     "iopub.execute_input": "2023-08-06T17:35:04.771518Z",
+     "iopub.status.busy": "2023-08-06T17:35:04.771411Z",
+     "iopub.status.idle": "2023-08-06T17:35:04.773352Z",
+     "shell.execute_reply": "2023-08-06T17:35:04.773084Z"
     }
    },
    "outputs": [],
@@ -463,12 +468,12 @@
     "strategy = Stepwise.fixed_steps(design,\n",
     "                                len(design.terms),\n",
     "                                direction='forward')\n",
-    "full_path = sklearn_selection_path(OLS, strategy)"
+    "full_path = sklearn_selection_path(OLS, strategy)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7f5f599f",
+   "id": "63e94493",
    "metadata": {},
    "source": [
     "We now fit the full forward-selection path on the `Hitters` data and compute the fitted values."
@@ -477,14 +482,15 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "56adc728",
+   "id": "e8e0a90d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:55.908048Z",
-     "iopub.status.busy": "2023-07-31T02:14:55.907949Z",
-     "iopub.status.idle": "2023-07-31T02:14:56.300215Z",
-     "shell.execute_reply": "2023-07-31T02:14:56.299939Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:04.774784Z",
+     "iopub.status.busy": "2023-08-06T17:35:04.774690Z",
+     "iopub.status.idle": "2023-08-06T17:35:05.168838Z",
+     "shell.execute_reply": "2023-08-06T17:35:05.168549Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -501,12 +507,12 @@
    "source": [
     "full_path.fit(Hitters, Y)\n",
     "Yhat_in = full_path.predict(Hitters)\n",
-    "Yhat_in.shape"
+    "Yhat_in.shape\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0b771d23",
+   "id": "36008ae0",
    "metadata": {},
    "source": [
     "This gives us an array of fitted values --- 20 steps in all, including the fitted mean for the null model --- which we can use to evaluate\n",
@@ -521,14 +527,15 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "387267d9",
+   "id": "4e44d7ad",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:56.301852Z",
-     "iopub.status.busy": "2023-07-31T02:14:56.301734Z",
-     "iopub.status.idle": "2023-07-31T02:14:56.487394Z",
-     "shell.execute_reply": "2023-07-31T02:14:56.486667Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:05.170387Z",
+     "iopub.status.busy": "2023-08-06T17:35:05.170300Z",
+     "iopub.status.idle": "2023-08-06T17:35:05.352387Z",
+     "shell.execute_reply": "2023-08-06T17:35:05.350850Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -556,12 +563,12 @@
     "              fontsize=20)\n",
     "ax.set_xticks(np.arange(n_steps)[::2])\n",
     "ax.legend()\n",
-    "ax.set_ylim([50000,250000]);"
+    "ax.set_ylim([50000,250000]);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fc09acdc",
+   "id": "f0113aa5",
    "metadata": {},
    "source": [
     "Notice the expression `None` in `Y[:,None]` above.\n",
@@ -571,7 +578,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2a1511cd",
+   "id": "e3daf6ac",
    "metadata": {},
    "source": [
     "We are now ready to use cross-validation to estimate test error along\n",
@@ -589,14 +596,15 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "0047ba88",
+   "id": "c8b63bdf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:56.491760Z",
-     "iopub.status.busy": "2023-07-31T02:14:56.491487Z",
-     "iopub.status.idle": "2023-07-31T02:14:58.464393Z",
-     "shell.execute_reply": "2023-07-31T02:14:58.464098Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:05.356634Z",
+     "iopub.status.busy": "2023-08-06T17:35:05.356263Z",
+     "iopub.status.idle": "2023-08-06T17:35:07.379649Z",
+     "shell.execute_reply": "2023-08-06T17:35:07.379349Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -619,14 +627,15 @@
     "                                Hitters,\n",
     "                                Y,\n",
     "                                cv=kfold)\n",
-    "Yhat_cv.shape"
+    "Yhat_cv.shape\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c6df5d31",
+   "id": "734ebc1a",
    "metadata": {},
    "source": [
+    "`skm.cross_val_predict()`\n",
     "The prediction matrix `Yhat_cv` is the same shape as `Yhat_in`; the difference is that the predictions in each row, corresponding to a particular sample index, were made from models fit on a training fold that did not include that row.\n",
     "\n",
     "At each model along the path, we compute the MSE in each of the cross-validation folds.\n",
@@ -641,13 +650,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "27b0eb63",
+   "id": "c2a5b743",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:58.466035Z",
-     "iopub.status.busy": "2023-07-31T02:14:58.465931Z",
-     "iopub.status.idle": "2023-07-31T02:14:58.469179Z",
-     "shell.execute_reply": "2023-07-31T02:14:58.468827Z"
+     "iopub.execute_input": "2023-08-06T17:35:07.381451Z",
+     "iopub.status.busy": "2023-08-06T17:35:07.381333Z",
+     "iopub.status.idle": "2023-08-06T17:35:07.384547Z",
+     "shell.execute_reply": "2023-08-06T17:35:07.384245Z"
     }
    },
    "outputs": [
@@ -668,28 +677,28 @@
     "    errors = (Yhat_cv[test_idx] - Y[test_idx,None])**2\n",
     "    cv_mse.append(errors.mean(0)) # column means\n",
     "cv_mse = np.array(cv_mse).T\n",
-    "cv_mse.shape"
+    "cv_mse.shape\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "223556f8",
+   "id": "eeace224",
    "metadata": {},
    "source": [
     "We now add the cross-validation error estimates to our MSE plot.\n",
-    "We include the mean error across the five folds, and the estimate of the standard error of the mean."
+    "We include the mean error across the five folds, and the estimate of the standard error of the mean. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "a45fe587",
+   "id": "678b6284",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:58.470717Z",
-     "iopub.status.busy": "2023-07-31T02:14:58.470635Z",
-     "iopub.status.idle": "2023-07-31T02:14:58.557218Z",
-     "shell.execute_reply": "2023-07-31T02:14:58.556947Z"
+     "iopub.execute_input": "2023-08-06T17:35:07.385961Z",
+     "iopub.status.busy": "2023-08-06T17:35:07.385880Z",
+     "iopub.status.idle": "2023-08-06T17:35:07.473192Z",
+     "shell.execute_reply": "2023-08-06T17:35:07.472899Z"
     }
    },
    "outputs": [
@@ -713,30 +722,31 @@
     "            c='r') # color red\n",
     "ax.set_ylim([50000,250000])\n",
     "ax.legend()\n",
-    "mse_fig"
+    "mse_fig\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fb4fa850",
+   "id": "5e27d9a1",
    "metadata": {},
    "source": [
     "To repeat the above using the validation set approach, we simply change our\n",
     "`cv` argument to a validation set: one random split of the data into a test and training. We choose a test size\n",
-    "of 20%, similar to the size of each test set in 5-fold cross-validation."
+    "of 20%, similar to the size of each test set in 5-fold cross-validation.`skm.ShuffleSplit()`"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "c71c2079",
+   "id": "f55f5449",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:58.558792Z",
-     "iopub.status.busy": "2023-07-31T02:14:58.558689Z",
-     "iopub.status.idle": "2023-07-31T02:14:58.952107Z",
-     "shell.execute_reply": "2023-07-31T02:14:58.951723Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:07.474839Z",
+     "iopub.status.busy": "2023-08-06T17:35:07.474729Z",
+     "iopub.status.idle": "2023-08-06T17:35:07.866358Z",
+     "shell.execute_reply": "2023-08-06T17:35:07.866074Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -748,12 +758,12 @@
     "                  Y[train_idx])\n",
     "    Yhat_val = full_path.predict(Hitters.iloc[test_idx])\n",
     "    errors = (Yhat_val - Y[test_idx,None])**2\n",
-    "    validation_mse = errors.mean(0)"
+    "    validation_mse = errors.mean(0)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d34bb7ab",
+   "id": "b778cde1",
    "metadata": {},
    "source": [
     " As for the in-sample MSE case, the validation set approach does not provide standard errors."
@@ -762,14 +772,15 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "2ff34edf",
+   "id": "791c91cf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:58.953820Z",
-     "iopub.status.busy": "2023-07-31T02:14:58.953737Z",
-     "iopub.status.idle": "2023-07-31T02:14:59.043030Z",
-     "shell.execute_reply": "2023-07-31T02:14:59.042743Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:07.868260Z",
+     "iopub.status.busy": "2023-08-06T17:35:07.868151Z",
+     "iopub.status.idle": "2023-08-06T17:35:07.959616Z",
+     "shell.execute_reply": "2023-08-06T17:35:07.959298Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -792,12 +803,12 @@
     "ax.set_xticks(np.arange(n_steps)[::2])\n",
     "ax.set_ylim([50000,250000])\n",
     "ax.legend()\n",
-    "mse_fig"
+    "mse_fig\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e5517334",
+   "id": "13144579",
    "metadata": {},
    "source": [
     "### Best Subset Selection\n",
@@ -808,31 +819,32 @@
     "best subset selection.\n",
     "Instead of constraining the subset to be a given size,\n",
     "this package produces a path of solutions using the subset size as a\n",
-    "penalty rather than a constraint. Although the distinction is subtle, the difference comes when we cross-validate."
+    "penalty rather than a constraint. Although the distinction is subtle, the difference comes when we cross-validate. \n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "845f3fa0",
+   "id": "2c796192",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:59.044595Z",
-     "iopub.status.busy": "2023-07-31T02:14:59.044486Z",
-     "iopub.status.idle": "2023-07-31T02:14:59.055901Z",
-     "shell.execute_reply": "2023-07-31T02:14:59.055632Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:07.961298Z",
+     "iopub.status.busy": "2023-08-06T17:35:07.961209Z",
+     "iopub.status.idle": "2023-08-06T17:35:07.972689Z",
+     "shell.execute_reply": "2023-08-06T17:35:07.972416Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
     "D = design.fit_transform(Hitters)\n",
     "D = D.drop('intercept', axis=1)\n",
-    "X = np.asarray(D)"
+    "X = np.asarray(D)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4c0d0e19",
+   "id": "95a3f639",
    "metadata": {},
    "source": [
     "Here we excluded the first column corresponding to the intercept, as\n",
@@ -842,13 +854,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "e9f943e7",
+   "id": "abb9e7ac",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:14:59.057369Z",
-     "iopub.status.busy": "2023-07-31T02:14:59.057289Z",
-     "iopub.status.idle": "2023-07-31T02:15:01.228717Z",
-     "shell.execute_reply": "2023-07-31T02:15:01.228431Z"
+     "iopub.execute_input": "2023-08-06T17:35:07.974253Z",
+     "iopub.status.busy": "2023-08-06T17:35:07.974167Z",
+     "iopub.status.idle": "2023-08-06T17:35:10.125189Z",
+     "shell.execute_reply": "2023-08-06T17:35:10.124893Z"
     }
    },
    "outputs": [
@@ -883,12 +895,12 @@
    "source": [
     "path = fit_path(X, \n",
     "                Y,\n",
-    "                max_nonzeros=X.shape[1])"
+    "                max_nonzeros=X.shape[1])\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "67ef90b5",
+   "id": "6c577e2a",
    "metadata": {},
    "source": [
     "The function `fit_path()` returns a list whose values include the fitted coefficients as `B`, an intercept as `B0`, as well as a few other attributes related to the particular path algorithm used. Such details are beyond the scope of this book."
@@ -897,14 +909,15 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "657ac171",
+   "id": "70f1c6c2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:01.230368Z",
-     "iopub.status.busy": "2023-07-31T02:15:01.230274Z",
-     "iopub.status.idle": "2023-07-31T02:15:01.232838Z",
-     "shell.execute_reply": "2023-07-31T02:15:01.232586Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:10.126871Z",
+     "iopub.status.busy": "2023-08-06T17:35:10.126769Z",
+     "iopub.status.idle": "2023-08-06T17:35:10.129192Z",
+     "shell.execute_reply": "2023-08-06T17:35:10.128949Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -926,12 +939,12 @@
     }
    ],
    "source": [
-    "path[3]"
+    "path[3]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2c9e040a",
+   "id": "1882702b",
    "metadata": {},
    "source": [
     "In the example above, we see that at the fourth step in the path, we have two nonzero coefficients in `'B'`, corresponding to the value $0.114$ for the penalty parameter `lambda_0`.\n",
@@ -961,14 +974,15 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "e576ad54",
+   "id": "9f1fc533",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:01.234342Z",
-     "iopub.status.busy": "2023-07-31T02:15:01.234246Z",
-     "iopub.status.idle": "2023-07-31T02:15:01.302141Z",
-     "shell.execute_reply": "2023-07-31T02:15:01.301874Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:10.130683Z",
+     "iopub.status.busy": "2023-08-06T17:35:10.130585Z",
+     "iopub.status.idle": "2023-08-06T17:35:10.201951Z",
+     "shell.execute_reply": "2023-08-06T17:35:10.201689Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1397,12 +1411,12 @@
     "                                 Y,\n",
     "                                 l1_ratio=0.,\n",
     "                                 alphas=lambdas)[1]\n",
-    "soln_array.shape"
+    "soln_array.shape\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5ba706cd",
+   "id": "cefe307b",
    "metadata": {},
    "source": [
     "Here we extract the array of coefficients corresponding to the solutions along the regularization path.\n",
@@ -1427,13 +1441,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "3ef15192",
+   "id": "642287d1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:01.303943Z",
-     "iopub.status.busy": "2023-07-31T02:15:01.303842Z",
-     "iopub.status.idle": "2023-07-31T02:15:01.313074Z",
-     "shell.execute_reply": "2023-07-31T02:15:01.312802Z"
+     "iopub.execute_input": "2023-08-06T17:35:10.203715Z",
+     "iopub.status.busy": "2023-08-06T17:35:10.203613Z",
+     "iopub.status.idle": "2023-08-06T17:35:10.214312Z",
+     "shell.execute_reply": "2023-08-06T17:35:10.214061Z"
     }
    },
    "outputs": [
@@ -1833,12 +1847,12 @@
     "                         columns=D.columns,\n",
     "                         index=-np.log(lambdas))\n",
     "soln_path.index.name = 'negative log(lambda)'\n",
-    "soln_path"
+    "soln_path\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f73f5191",
+   "id": "8e2a5cd7",
    "metadata": {},
    "source": [
     "We plot the paths to get a sense of how the coefficients vary with $\\lambda$.\n",
@@ -1849,14 +1863,15 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "40b67c85",
+   "id": "734c1a05",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:01.314467Z",
-     "iopub.status.busy": "2023-07-31T02:15:01.314375Z",
-     "iopub.status.idle": "2023-07-31T02:15:01.569812Z",
-     "shell.execute_reply": "2023-07-31T02:15:01.569478Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:10.215773Z",
+     "iopub.status.busy": "2023-08-06T17:35:10.215682Z",
+     "iopub.status.idle": "2023-08-06T17:35:10.473871Z",
+     "shell.execute_reply": "2023-08-06T17:35:10.473500Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1875,12 +1890,12 @@
     "soln_path.plot(ax=ax, legend=False)\n",
     "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
     "ax.set_ylabel('Standardized coefficients', fontsize=20)\n",
-    "ax.legend(loc='upper left');"
+    "ax.legend(loc='upper left');\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "84e52006",
+   "id": "75cfe676",
    "metadata": {},
    "source": [
     "(We have used `latex` formatting in the horizontal label, in order to format the Greek $\\lambda$ appropriately.) \n",
@@ -1893,13 +1908,13 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "d14bd86c",
+   "id": "5a55b76b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:01.571580Z",
-     "iopub.status.busy": "2023-07-31T02:15:01.571459Z",
-     "iopub.status.idle": "2023-07-31T02:15:01.576721Z",
-     "shell.execute_reply": "2023-07-31T02:15:01.576433Z"
+     "iopub.execute_input": "2023-08-06T17:35:10.475713Z",
+     "iopub.status.busy": "2023-08-06T17:35:10.475586Z",
+     "iopub.status.idle": "2023-08-06T17:35:10.479270Z",
+     "shell.execute_reply": "2023-08-06T17:35:10.479006Z"
     }
    },
    "outputs": [
@@ -1936,12 +1951,12 @@
    ],
    "source": [
     "beta_hat = soln_path.loc[soln_path.index[39]]\n",
-    "lambdas[39], beta_hat"
+    "lambdas[39], beta_hat\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "98ba8e99",
+   "id": "a8f4071c",
    "metadata": {},
    "source": [
     "Let’s compute the $\\ell_2$ norm of the standardized coefficients."
@@ -1950,13 +1965,13 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "759e215a",
+   "id": "cdbee899",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:01.578217Z",
-     "iopub.status.busy": "2023-07-31T02:15:01.578101Z",
-     "iopub.status.idle": "2023-07-31T02:15:01.580441Z",
-     "shell.execute_reply": "2023-07-31T02:15:01.580160Z"
+     "iopub.execute_input": "2023-08-06T17:35:10.480798Z",
+     "iopub.status.busy": "2023-08-06T17:35:10.480689Z",
+     "iopub.status.idle": "2023-08-06T17:35:10.483091Z",
+     "shell.execute_reply": "2023-08-06T17:35:10.482738Z"
     }
    },
    "outputs": [
@@ -1972,12 +1987,12 @@
     }
    ],
    "source": [
-    "np.linalg.norm(beta_hat)"
+    "np.linalg.norm(beta_hat)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ce79e7cb",
+   "id": "2511270d",
    "metadata": {},
    "source": [
     "In contrast, here is the $\\ell_2$ norm when $\\lambda$ is 2.44e-01.\n",
@@ -1988,14 +2003,15 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "b58d5ccb",
+   "id": "23fe1cb5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:01.581984Z",
-     "iopub.status.busy": "2023-07-31T02:15:01.581885Z",
-     "iopub.status.idle": "2023-07-31T02:15:01.584293Z",
-     "shell.execute_reply": "2023-07-31T02:15:01.584014Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:10.484686Z",
+     "iopub.status.busy": "2023-08-06T17:35:10.484597Z",
+     "iopub.status.idle": "2023-08-06T17:35:10.487152Z",
+     "shell.execute_reply": "2023-08-06T17:35:10.486881Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2011,12 +2027,12 @@
    ],
    "source": [
     "beta_hat = soln_path.loc[soln_path.index[59]]\n",
-    "lambdas[59], np.linalg.norm(beta_hat)"
+    "lambdas[59], np.linalg.norm(beta_hat)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cc9a1f70",
+   "id": "c610fcf7",
    "metadata": {},
    "source": [
     "Above we normalized `X` upfront, and fit the ridge model using `Xs`.\n",
@@ -2028,13 +2044,13 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "74b5cae2",
+   "id": "fa46c4d0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:01.585818Z",
-     "iopub.status.busy": "2023-07-31T02:15:01.585716Z",
-     "iopub.status.idle": "2023-07-31T02:15:01.598062Z",
-     "shell.execute_reply": "2023-07-31T02:15:01.597752Z"
+     "iopub.execute_input": "2023-08-06T17:35:10.488575Z",
+     "iopub.status.busy": "2023-08-06T17:35:10.488492Z",
+     "iopub.status.idle": "2023-08-06T17:35:10.500853Z",
+     "shell.execute_reply": "2023-08-06T17:35:10.500570Z"
     }
    },
    "outputs": [
@@ -2067,12 +2083,12 @@
     "ridge = skl.ElasticNet(alpha=lambdas[59], l1_ratio=0)\n",
     "scaler = StandardScaler(with_mean=True,  with_std=True)\n",
     "pipe = Pipeline(steps=[('scaler', scaler), ('ridge', ridge)])\n",
-    "pipe.fit(X, Y)"
+    "pipe.fit(X, Y)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7a69991b",
+   "id": "a5607efa",
    "metadata": {},
    "source": [
     "We show that it gives the same $\\ell_2$ norm as in our previous fit on the standardized data."
@@ -2081,14 +2097,15 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "a89989c3",
+   "id": "8e775d4c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:01.599593Z",
-     "iopub.status.busy": "2023-07-31T02:15:01.599513Z",
-     "iopub.status.idle": "2023-07-31T02:15:01.601548Z",
-     "shell.execute_reply": "2023-07-31T02:15:01.601293Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:10.502445Z",
+     "iopub.status.busy": "2023-08-06T17:35:10.502342Z",
+     "iopub.status.idle": "2023-08-06T17:35:10.504676Z",
+     "shell.execute_reply": "2023-08-06T17:35:10.504390Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2103,12 +2120,12 @@
     }
    ],
    "source": [
-    "np.linalg.norm(ridge.coef_)"
+    "np.linalg.norm(ridge.coef_)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c86254e4",
+   "id": "06771f4e",
    "metadata": {},
    "source": [
     " Notice that the operation `pipe.fit(X, Y)` above has changed the `ridge` object, and in particular has added attributes such as `coef_` that were not there before. \n",
@@ -2127,13 +2144,13 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "3eb41945",
+   "id": "531f7c0e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:01.603062Z",
-     "iopub.status.busy": "2023-07-31T02:15:01.602986Z",
-     "iopub.status.idle": "2023-07-31T02:15:01.610209Z",
-     "shell.execute_reply": "2023-07-31T02:15:01.609888Z"
+     "iopub.execute_input": "2023-08-06T17:35:10.506171Z",
+     "iopub.status.busy": "2023-08-06T17:35:10.506087Z",
+     "iopub.status.idle": "2023-08-06T17:35:10.513511Z",
+     "shell.execute_reply": "2023-08-06T17:35:10.513210Z"
     }
    },
    "outputs": [
@@ -2166,12 +2183,12 @@
     "                             Y,\n",
     "                             scoring='neg_mean_squared_error',\n",
     "                             cv=validation)\n",
-    "-results['test_score']"
+    "-results['test_score']\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "660f7c63",
+   "id": "951f3189",
    "metadata": {},
    "source": [
     "The test MSE is 1.342e+05.  Note\n",
@@ -2185,14 +2202,15 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "635bc363",
+   "id": "9d6abcd6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:01.611782Z",
-     "iopub.status.busy": "2023-07-31T02:15:01.611662Z",
-     "iopub.status.idle": "2023-07-31T02:15:01.619073Z",
-     "shell.execute_reply": "2023-07-31T02:15:01.618775Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:10.515133Z",
+     "iopub.status.busy": "2023-08-06T17:35:10.515021Z",
+     "iopub.status.idle": "2023-08-06T17:35:10.522324Z",
+     "shell.execute_reply": "2023-08-06T17:35:10.522079Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2221,12 +2239,12 @@
     "                             Y,\n",
     "                             scoring='neg_mean_squared_error',\n",
     "                             cv=validation)\n",
-    "-results['test_score']"
+    "-results['test_score']\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c14ad9a1",
+   "id": "60702c2d",
    "metadata": {},
    "source": [
     "Obviously choosing $\\lambda=0.01$ is arbitrary,  so we will  use cross-validation or the validation-set\n",
@@ -2241,13 +2259,13 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "e117267f",
+   "id": "4009c7c9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:01.620657Z",
-     "iopub.status.busy": "2023-07-31T02:15:01.620538Z",
-     "iopub.status.idle": "2023-07-31T02:15:02.079833Z",
-     "shell.execute_reply": "2023-07-31T02:15:02.079544Z"
+     "iopub.execute_input": "2023-08-06T17:35:10.523797Z",
+     "iopub.status.busy": "2023-08-06T17:35:10.523701Z",
+     "iopub.status.idle": "2023-08-06T17:35:10.984369Z",
+     "shell.execute_reply": "2023-08-06T17:35:10.984118Z"
     }
    },
    "outputs": [
@@ -2484,12 +2502,12 @@
     "                        scoring='neg_mean_squared_error')\n",
     "grid.fit(X, Y)\n",
     "grid.best_params_['ridge__alpha']\n",
-    "grid.best_estimator_"
+    "grid.best_estimator_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bc09d074",
+   "id": "8e22f625",
    "metadata": {},
    "source": [
     "Alternatively, we can use 5-fold cross-validation."
@@ -2498,14 +2516,15 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "0037e9e4",
+   "id": "1fd51cd6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:02.081467Z",
-     "iopub.status.busy": "2023-07-31T02:15:02.081358Z",
-     "iopub.status.idle": "2023-07-31T02:15:05.191496Z",
-     "shell.execute_reply": "2023-07-31T02:15:05.191210Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:10.985928Z",
+     "iopub.status.busy": "2023-08-06T17:35:10.985818Z",
+     "iopub.status.idle": "2023-08-06T17:35:14.088216Z",
+     "shell.execute_reply": "2023-08-06T17:35:14.087921Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -3540,12 +3559,12 @@
     "                        scoring='neg_mean_squared_error')\n",
     "grid.fit(X, Y)\n",
     "grid.best_params_['ridge__alpha']\n",
-    "grid.best_estimator_"
+    "grid.best_estimator_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9690407d",
+   "id": "245526c5",
    "metadata": {},
    "source": [
     "Recall we set up the `kfold` object for 5-fold cross-validation on page 296. We now plot the cross-validated MSE as a function of $-\\log(\\lambda)$, which has shrinkage decreasing from left\n",
@@ -3555,13 +3574,13 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "71f0f5c5",
+   "id": "1679fd6a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:05.193084Z",
-     "iopub.status.busy": "2023-07-31T02:15:05.192970Z",
-     "iopub.status.idle": "2023-07-31T02:15:05.291368Z",
-     "shell.execute_reply": "2023-07-31T02:15:05.291066Z"
+     "iopub.execute_input": "2023-08-06T17:35:14.089895Z",
+     "iopub.status.busy": "2023-08-06T17:35:14.089782Z",
+     "iopub.status.idle": "2023-08-06T17:35:14.187870Z",
+     "shell.execute_reply": "2023-08-06T17:35:14.187444Z"
     }
    },
    "outputs": [
@@ -3583,12 +3602,12 @@
     "            yerr=grid.cv_results_['std_test_score'] / np.sqrt(K))\n",
     "ax.set_ylim([50000,250000])\n",
     "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Cross-validated MSE', fontsize=20);"
+    "ax.set_ylabel('Cross-validated MSE', fontsize=20);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "11087e26",
+   "id": "36cc5ca2",
    "metadata": {},
    "source": [
     "One can cross-validate different metrics to choose a parameter. The default\n",
@@ -3599,13 +3618,13 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "fb265379",
+   "id": "f59557ed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:05.293023Z",
-     "iopub.status.busy": "2023-07-31T02:15:05.292897Z",
-     "iopub.status.idle": "2023-07-31T02:15:08.398570Z",
-     "shell.execute_reply": "2023-07-31T02:15:08.398296Z"
+     "iopub.execute_input": "2023-08-06T17:35:14.189531Z",
+     "iopub.status.busy": "2023-08-06T17:35:14.189417Z",
+     "iopub.status.idle": "2023-08-06T17:35:17.306928Z",
+     "shell.execute_reply": "2023-08-06T17:35:17.306627Z"
     }
    },
    "outputs": [
@@ -4675,12 +4694,12 @@
     "grid_r2 = skm.GridSearchCV(pipe, \n",
     "                           param_grid,\n",
     "                           cv=kfold)\n",
-    "grid_r2.fit(X, Y)"
+    "grid_r2.fit(X, Y)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9881fe49",
+   "id": "5ac19760",
    "metadata": {},
    "source": [
     "Finally, let’s plot the results for cross-validated $R^2$."
@@ -4689,14 +4708,15 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "79616f48",
+   "id": "12d94ae6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:08.400177Z",
-     "iopub.status.busy": "2023-07-31T02:15:08.400068Z",
-     "iopub.status.idle": "2023-07-31T02:15:08.495262Z",
-     "shell.execute_reply": "2023-07-31T02:15:08.494931Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:17.308495Z",
+     "iopub.status.busy": "2023-08-06T17:35:17.308379Z",
+     "iopub.status.idle": "2023-08-06T17:35:17.402970Z",
+     "shell.execute_reply": "2023-08-06T17:35:17.402653Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -4716,12 +4736,12 @@
     "            grid_r2.cv_results_['mean_test_score'],\n",
     "            yerr=grid_r2.cv_results_['std_test_score'] / np.sqrt(K))\n",
     "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Cross-validated $R^2$', fontsize=20);"
+    "ax.set_ylabel('Cross-validated $R^2$', fontsize=20);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5e700bf5",
+   "id": "f9eda9aa",
    "metadata": {},
    "source": [
     "### Fast Cross-Validation for Solution Paths\n",
@@ -4737,13 +4757,13 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "11e55883",
+   "id": "b1fd1281",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:08.496994Z",
-     "iopub.status.busy": "2023-07-31T02:15:08.496872Z",
-     "iopub.status.idle": "2023-07-31T02:15:08.840479Z",
-     "shell.execute_reply": "2023-07-31T02:15:08.840173Z"
+     "iopub.execute_input": "2023-08-06T17:35:17.404648Z",
+     "iopub.status.busy": "2023-08-06T17:35:17.404531Z",
+     "iopub.status.idle": "2023-08-06T17:35:17.759816Z",
+     "shell.execute_reply": "2023-08-06T17:35:17.759499Z"
     }
    },
    "outputs": [
@@ -6825,12 +6845,12 @@
     "                           cv=kfold)\n",
     "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
     "                         ('ridge', ridgeCV)])\n",
-    "pipeCV.fit(X, Y)"
+    "pipeCV.fit(X, Y)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "75eb9d33",
+   "id": "da8395bb",
    "metadata": {},
    "source": [
     "Let’s produce a plot again of the cross-validation error to see that\n",
@@ -6840,13 +6860,13 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "d107f961",
+   "id": "5cf09512",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:08.842157Z",
-     "iopub.status.busy": "2023-07-31T02:15:08.842034Z",
-     "iopub.status.idle": "2023-07-31T02:15:08.943297Z",
-     "shell.execute_reply": "2023-07-31T02:15:08.943005Z"
+     "iopub.execute_input": "2023-08-06T17:35:17.761454Z",
+     "iopub.status.busy": "2023-08-06T17:35:17.761346Z",
+     "iopub.status.idle": "2023-08-06T17:35:17.865044Z",
+     "shell.execute_reply": "2023-08-06T17:35:17.864747Z"
     }
    },
    "outputs": [
@@ -6870,12 +6890,12 @@
     "ax.axvline(-np.log(tuned_ridge.alpha_), c='k', ls='--')\n",
     "ax.set_ylim([50000,250000])\n",
     "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Cross-validated MSE', fontsize=20);"
+    "ax.set_ylabel('Cross-validated MSE', fontsize=20);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "23a89187",
+   "id": "6f5eb495",
    "metadata": {},
    "source": [
     "We see that the value of $\\lambda$ that results in the\n",
@@ -6887,13 +6907,13 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "21d65d1f",
+   "id": "a8d6770f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:08.945015Z",
-     "iopub.status.busy": "2023-07-31T02:15:08.944889Z",
-     "iopub.status.idle": "2023-07-31T02:15:08.947334Z",
-     "shell.execute_reply": "2023-07-31T02:15:08.947057Z"
+     "iopub.execute_input": "2023-08-06T17:35:17.866746Z",
+     "iopub.status.busy": "2023-08-06T17:35:17.866628Z",
+     "iopub.status.idle": "2023-08-06T17:35:17.869096Z",
+     "shell.execute_reply": "2023-08-06T17:35:17.868823Z"
     }
    },
    "outputs": [
@@ -6909,12 +6929,12 @@
     }
    ],
    "source": [
-    "np.min(tuned_ridge.mse_path_.mean(1))"
+    "np.min(tuned_ridge.mse_path_.mean(1))\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e8bb2108",
+   "id": "4da37400",
    "metadata": {},
    "source": [
     "This represents a further improvement over the test MSE that we got\n",
@@ -6926,14 +6946,15 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "11373de9",
+   "id": "852b1e39",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:08.948924Z",
-     "iopub.status.busy": "2023-07-31T02:15:08.948820Z",
-     "iopub.status.idle": "2023-07-31T02:15:08.951102Z",
-     "shell.execute_reply": "2023-07-31T02:15:08.950833Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:17.870890Z",
+     "iopub.status.busy": "2023-08-06T17:35:17.870776Z",
+     "iopub.status.idle": "2023-08-06T17:35:17.873149Z",
+     "shell.execute_reply": "2023-08-06T17:35:17.872893Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -6952,12 +6973,12 @@
     }
    ],
    "source": [
-    "tuned_ridge.coef_"
+    "tuned_ridge.coef_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8e7ab31e",
+   "id": "93ea5623",
    "metadata": {},
    "source": [
     "As expected, none of the coefficients are zero—ridge regression does\n",
@@ -6966,7 +6987,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e2cb7c4",
+   "id": "3e01383a",
    "metadata": {},
    "source": [
     "### Evaluating Test Error of Cross-Validated Ridge\n",
@@ -6990,13 +7011,13 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "72181964",
+   "id": "81f6259f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:08.952771Z",
-     "iopub.status.busy": "2023-07-31T02:15:08.952657Z",
-     "iopub.status.idle": "2023-07-31T02:15:08.954928Z",
-     "shell.execute_reply": "2023-07-31T02:15:08.954666Z"
+     "iopub.execute_input": "2023-08-06T17:35:17.874806Z",
+     "iopub.status.busy": "2023-08-06T17:35:17.874678Z",
+     "iopub.status.idle": "2023-08-06T17:35:17.876932Z",
+     "shell.execute_reply": "2023-08-06T17:35:17.876690Z"
     }
    },
    "outputs": [],
@@ -7011,20 +7032,21 @@
     "                           l1_ratio=0,\n",
     "                           cv=inner_cv)\n",
     "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
-    "                         ('ridge', ridgeCV)]);"
+    "                         ('ridge', ridgeCV)]);\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "4c3054b5",
+   "id": "e44495e5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:08.956479Z",
-     "iopub.status.busy": "2023-07-31T02:15:08.956377Z",
-     "iopub.status.idle": "2023-07-31T02:15:09.272036Z",
-     "shell.execute_reply": "2023-07-31T02:15:09.271767Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:17.878420Z",
+     "iopub.status.busy": "2023-08-06T17:35:17.878316Z",
+     "iopub.status.idle": "2023-08-06T17:35:18.202556Z",
+     "shell.execute_reply": "2023-08-06T17:35:18.202283Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -9052,12 +9074,20 @@
     "                             Y,\n",
     "                             cv=outer_valid,\n",
     "                             scoring='neg_mean_squared_error')\n",
-    "-results['test_score']"
+    "-results['test_score']\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f2f899f2",
+   "id": "9fed598e",
+   "metadata": {},
+   "source": [
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ecaca1a",
    "metadata": {},
    "source": [
     "### The Lasso\n",
@@ -9074,14 +9104,15 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "72bb0c1d",
+   "id": "aae18029",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:09.274161Z",
-     "iopub.status.busy": "2023-07-31T02:15:09.274043Z",
-     "iopub.status.idle": "2023-07-31T02:15:09.329832Z",
-     "shell.execute_reply": "2023-07-31T02:15:09.329579Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:18.204830Z",
+     "iopub.status.busy": "2023-08-06T17:35:18.204720Z",
+     "iopub.status.idle": "2023-08-06T17:35:18.262253Z",
+     "shell.execute_reply": "2023-08-06T17:35:18.261987Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -9103,20 +9134,21 @@
     "                         ('lasso', lassoCV)])\n",
     "pipeCV.fit(X, Y)\n",
     "tuned_lasso = pipeCV.named_steps['lasso']\n",
-    "tuned_lasso.alpha_"
+    "tuned_lasso.alpha_\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "69adc7ac",
+   "id": "b6d9dd06",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:09.331247Z",
-     "iopub.status.busy": "2023-07-31T02:15:09.331165Z",
-     "iopub.status.idle": "2023-07-31T02:15:09.341815Z",
-     "shell.execute_reply": "2023-07-31T02:15:09.341560Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:18.263674Z",
+     "iopub.status.busy": "2023-08-06T17:35:18.263592Z",
+     "iopub.status.idle": "2023-08-06T17:35:18.274556Z",
+     "shell.execute_reply": "2023-08-06T17:35:18.274311Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -9126,12 +9158,12 @@
     "                                    n_alphas=100)[:2]\n",
     "soln_path = pd.DataFrame(soln_array.T,\n",
     "                         columns=D.columns,\n",
-    "                         index=-np.log(lambdas))"
+    "                         index=-np.log(lambdas))\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cc6d84d3",
+   "id": "52817006",
    "metadata": {},
    "source": [
     "We can see from the coefficient plot of the standardized coefficients that depending on the choice of\n",
@@ -9142,14 +9174,15 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "2aa75789",
+   "id": "6e5f9a74",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:09.343254Z",
-     "iopub.status.busy": "2023-07-31T02:15:09.343175Z",
-     "iopub.status.idle": "2023-07-31T02:15:09.510403Z",
-     "shell.execute_reply": "2023-07-31T02:15:09.510040Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:18.276056Z",
+     "iopub.status.busy": "2023-08-06T17:35:18.275973Z",
+     "iopub.status.idle": "2023-08-06T17:35:18.444193Z",
+     "shell.execute_reply": "2023-08-06T17:35:18.443901Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -9168,12 +9201,12 @@
     "soln_path.plot(ax=ax, legend=False)\n",
     "ax.legend(loc='upper left')\n",
     "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Standardized coefficiients', fontsize=20);"
+    "ax.set_ylabel('Standardized coefficiients', fontsize=20);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "87d43d52",
+   "id": "2e1e6626",
    "metadata": {},
    "source": [
     "The smallest cross-validated error is lower than the test set MSE of the null model\n",
@@ -9184,13 +9217,13 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "e38c9bed",
+   "id": "9b57296a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:09.512076Z",
-     "iopub.status.busy": "2023-07-31T02:15:09.511953Z",
-     "iopub.status.idle": "2023-07-31T02:15:09.514391Z",
-     "shell.execute_reply": "2023-07-31T02:15:09.514111Z"
+     "iopub.execute_input": "2023-08-06T17:35:18.445914Z",
+     "iopub.status.busy": "2023-08-06T17:35:18.445792Z",
+     "iopub.status.idle": "2023-08-06T17:35:18.448316Z",
+     "shell.execute_reply": "2023-08-06T17:35:18.448072Z"
     }
    },
    "outputs": [
@@ -9206,27 +9239,27 @@
     }
    ],
    "source": [
-    "np.min(tuned_lasso.mse_path_.mean(1))"
+    "np.min(tuned_lasso.mse_path_.mean(1))\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d091d74c",
+   "id": "5f35282c",
    "metadata": {},
    "source": [
-    "Let’s again produce a plot of the cross-validation error."
+    "Let’s again produce a plot of the cross-validation error.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "53c42724",
+   "id": "b1b1a84a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:09.515991Z",
-     "iopub.status.busy": "2023-07-31T02:15:09.515876Z",
-     "iopub.status.idle": "2023-07-31T02:15:09.620734Z",
-     "shell.execute_reply": "2023-07-31T02:15:09.620427Z"
+     "iopub.execute_input": "2023-08-06T17:35:18.449905Z",
+     "iopub.status.busy": "2023-08-06T17:35:18.449752Z",
+     "iopub.status.idle": "2023-08-06T17:35:18.555457Z",
+     "shell.execute_reply": "2023-08-06T17:35:18.555156Z"
     }
    },
    "outputs": [
@@ -9254,7 +9287,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fad5c750",
+   "id": "7250e5c0",
    "metadata": {},
    "source": [
     "However, the lasso has a substantial advantage over ridge regression\n",
@@ -9267,13 +9300,13 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "5f4942ba",
+   "id": "44246aee",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:09.622460Z",
-     "iopub.status.busy": "2023-07-31T02:15:09.622343Z",
-     "iopub.status.idle": "2023-07-31T02:15:09.624897Z",
-     "shell.execute_reply": "2023-07-31T02:15:09.624611Z"
+     "iopub.execute_input": "2023-08-06T17:35:18.557237Z",
+     "iopub.status.busy": "2023-08-06T17:35:18.557121Z",
+     "iopub.status.idle": "2023-08-06T17:35:18.559615Z",
+     "shell.execute_reply": "2023-08-06T17:35:18.559331Z"
     }
    },
    "outputs": [
@@ -9293,12 +9326,12 @@
     }
    ],
    "source": [
-    "tuned_lasso.coef_"
+    "tuned_lasso.coef_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "af600bbc",
+   "id": "bc08d038",
    "metadata": {},
    "source": [
     "As in ridge regression, we could evaluate the test error\n",
@@ -9310,7 +9343,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d9aeb7b",
+   "id": "a55364da",
    "metadata": {},
    "source": [
     "## PCR and PLS Regression\n",
@@ -9320,7 +9353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ef4790c",
+   "id": "2806f021",
    "metadata": {},
    "source": [
     "Principal components regression (PCR) can be performed using\n",
@@ -9337,13 +9370,13 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "72a876bb",
+   "id": "33a88f6a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:09.626555Z",
-     "iopub.status.busy": "2023-07-31T02:15:09.626439Z",
-     "iopub.status.idle": "2023-07-31T02:15:09.631082Z",
-     "shell.execute_reply": "2023-07-31T02:15:09.630769Z"
+     "iopub.execute_input": "2023-08-06T17:35:18.561277Z",
+     "iopub.status.busy": "2023-08-06T17:35:18.561175Z",
+     "iopub.status.idle": "2023-08-06T17:35:18.565614Z",
+     "shell.execute_reply": "2023-08-06T17:35:18.565342Z"
     }
    },
    "outputs": [
@@ -9364,12 +9397,12 @@
     "pipe = Pipeline([('pca', pca),\n",
     "                 ('linreg', linreg)])\n",
     "pipe.fit(X, Y)\n",
-    "pipe.named_steps['linreg'].coef_"
+    "pipe.named_steps['linreg'].coef_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "77d5a225",
+   "id": "4f94c0cc",
    "metadata": {},
    "source": [
     "When performing PCA, the results vary depending\n",
@@ -9381,13 +9414,13 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "e0e821c6",
+   "id": "86fa37a7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:09.632552Z",
-     "iopub.status.busy": "2023-07-31T02:15:09.632453Z",
-     "iopub.status.idle": "2023-07-31T02:15:09.636255Z",
-     "shell.execute_reply": "2023-07-31T02:15:09.636009Z"
+     "iopub.execute_input": "2023-08-06T17:35:18.567133Z",
+     "iopub.status.busy": "2023-08-06T17:35:18.567034Z",
+     "iopub.status.idle": "2023-08-06T17:35:18.570714Z",
+     "shell.execute_reply": "2023-08-06T17:35:18.570443Z"
     }
    },
    "outputs": [
@@ -9407,12 +9440,12 @@
     "                 ('pca', pca),\n",
     "                 ('linreg', linreg)])\n",
     "pipe.fit(X, Y)\n",
-    "pipe.named_steps['linreg'].coef_"
+    "pipe.named_steps['linreg'].coef_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0c5ebec9",
+   "id": "05e0acfe",
    "metadata": {},
    "source": [
     "We can of course use CV to choose the number of components, by\n",
@@ -9424,13 +9457,13 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "1ac6886c",
+   "id": "1e84065b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:09.637683Z",
-     "iopub.status.busy": "2023-07-31T02:15:09.637599Z",
-     "iopub.status.idle": "2023-07-31T02:15:09.756903Z",
-     "shell.execute_reply": "2023-07-31T02:15:09.756621Z"
+     "iopub.execute_input": "2023-08-06T17:35:18.572236Z",
+     "iopub.status.busy": "2023-08-06T17:35:18.572149Z",
+     "iopub.status.idle": "2023-08-06T17:35:18.692323Z",
+     "shell.execute_reply": "2023-08-06T17:35:18.692024Z"
     }
    },
    "outputs": [
@@ -9470,12 +9503,12 @@
     "                        param_grid,\n",
     "                        cv=kfold,\n",
     "                        scoring='neg_mean_squared_error')\n",
-    "grid.fit(X, Y)"
+    "grid.fit(X, Y)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dc21d28d",
+   "id": "965677ed",
    "metadata": {},
    "source": [
     "Let’s plot the results as we have for other methods."
@@ -9484,13 +9517,13 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "5e0c5a96",
+   "id": "ff432faf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:09.758614Z",
-     "iopub.status.busy": "2023-07-31T02:15:09.758500Z",
-     "iopub.status.idle": "2023-07-31T02:15:09.854740Z",
-     "shell.execute_reply": "2023-07-31T02:15:09.854449Z"
+     "iopub.execute_input": "2023-08-06T17:35:18.693955Z",
+     "iopub.status.busy": "2023-08-06T17:35:18.693854Z",
+     "iopub.status.idle": "2023-08-06T17:35:18.790857Z",
+     "shell.execute_reply": "2023-08-06T17:35:18.790544Z"
     }
    },
    "outputs": [
@@ -9514,12 +9547,12 @@
     "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n",
     "ax.set_xlabel('# principal components', fontsize=20)\n",
     "ax.set_xticks(n_comp[::2])\n",
-    "ax.set_ylim([50000,250000]);"
+    "ax.set_ylim([50000,250000]);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0c0aadc3",
+   "id": "8d3ca451",
    "metadata": {},
    "source": [
     "We see that the smallest cross-validation error occurs when\n",
@@ -9539,14 +9572,15 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "9df0fb7f",
+   "id": "57d21a61",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:09.856396Z",
-     "iopub.status.busy": "2023-07-31T02:15:09.856286Z",
-     "iopub.status.idle": "2023-07-31T02:15:09.862198Z",
-     "shell.execute_reply": "2023-07-31T02:15:09.861920Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:18.792385Z",
+     "iopub.status.busy": "2023-08-06T17:35:18.792283Z",
+     "iopub.status.idle": "2023-08-06T17:35:18.797861Z",
+     "shell.execute_reply": "2023-08-06T17:35:18.797615Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -9567,12 +9601,12 @@
     "                             Y,\n",
     "                             cv=kfold,\n",
     "                             scoring='neg_mean_squared_error')\n",
-    "-cv_null['test_score'].mean()"
+    "-cv_null['test_score'].mean()\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "89f0e0bb",
+   "id": "333d11bd",
    "metadata": {},
    "source": [
     "The `explained_variance_ratio_`\n",
@@ -9584,13 +9618,13 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "458c21a4",
+   "id": "8d170a51",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:09.863785Z",
-     "iopub.status.busy": "2023-07-31T02:15:09.863668Z",
-     "iopub.status.idle": "2023-07-31T02:15:09.866170Z",
-     "shell.execute_reply": "2023-07-31T02:15:09.865877Z"
+     "iopub.execute_input": "2023-08-06T17:35:18.799375Z",
+     "iopub.status.busy": "2023-08-06T17:35:18.799269Z",
+     "iopub.status.idle": "2023-08-06T17:35:18.801474Z",
+     "shell.execute_reply": "2023-08-06T17:35:18.801155Z"
     }
    },
    "outputs": [
@@ -9606,12 +9640,12 @@
     }
    ],
    "source": [
-    "pipe.named_steps['pca'].explained_variance_ratio_"
+    "pipe.named_steps['pca'].explained_variance_ratio_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bdd994d2",
+   "id": "e0413213",
    "metadata": {},
    "source": [
     "Briefly, we can think of\n",
@@ -9619,29 +9653,33 @@
     "that is captured using $M$ principal components. For example, setting\n",
     "$M=1$ only captures 38.31% of the variance, while $M=2$ captures an additional 21.84%, for a total of 60.15% of the variance.\n",
     "By  $M=6$ it increases to\n",
-    "88.63%. Beyond this the increments continue to diminish, until we use all $M=p=19$ components, which captures all  100% of the variance."
+    "88.63%. Beyond this the increments continue to diminish, until we use all $M=p=19$ components, which captures all  100% of the variance.\n",
+    "\n",
+    " "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "af5147dc",
+   "id": "e04f26d1",
    "metadata": {},
    "source": [
     "### Partial Least Squares\n",
     "Partial least squares (PLS) is implemented in the\n",
-    "`PLSRegression()`  function."
+    "`PLSRegression()`  function.\n",
+    "\n",
+    " "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "f49d464e",
+   "id": "9d35e0d6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:09.867758Z",
-     "iopub.status.busy": "2023-07-31T02:15:09.867639Z",
-     "iopub.status.idle": "2023-07-31T02:15:09.871140Z",
-     "shell.execute_reply": "2023-07-31T02:15:09.870852Z"
+     "iopub.execute_input": "2023-08-06T17:35:18.802981Z",
+     "iopub.status.busy": "2023-08-06T17:35:18.802864Z",
+     "iopub.status.idle": "2023-08-06T17:35:18.806324Z",
+     "shell.execute_reply": "2023-08-06T17:35:18.806053Z"
     }
    },
    "outputs": [
@@ -9662,12 +9700,12 @@
    "source": [
     "pls = PLSRegression(n_components=2, \n",
     "                    scale=True)\n",
-    "pls.fit(X, Y)"
+    "pls.fit(X, Y)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7a07b988",
+   "id": "5945c8cf",
    "metadata": {},
    "source": [
     "As was the case in PCR, we will want to\n",
@@ -9677,13 +9715,13 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "8e118b5b",
+   "id": "35867372",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:09.872678Z",
-     "iopub.status.busy": "2023-07-31T02:15:09.872592Z",
-     "iopub.status.idle": "2023-07-31T02:15:09.978064Z",
-     "shell.execute_reply": "2023-07-31T02:15:09.977780Z"
+     "iopub.execute_input": "2023-08-06T17:35:18.807917Z",
+     "iopub.status.busy": "2023-08-06T17:35:18.807830Z",
+     "iopub.status.idle": "2023-08-06T17:35:18.913647Z",
+     "shell.execute_reply": "2023-08-06T17:35:18.913377Z"
     }
    },
    "outputs": [
@@ -9716,12 +9754,12 @@
     "                        param_grid,\n",
     "                        cv=kfold,\n",
     "                        scoring='neg_mean_squared_error')\n",
-    "grid.fit(X, Y)"
+    "grid.fit(X, Y)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "99dec910",
+   "id": "a0d1ecb6",
    "metadata": {},
    "source": [
     "As for our other methods, we plot the MSE."
@@ -9730,13 +9768,13 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "0f98407c",
+   "id": "6712c8b3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:09.980000Z",
-     "iopub.status.busy": "2023-07-31T02:15:09.979873Z",
-     "iopub.status.idle": "2023-07-31T02:15:10.077523Z",
-     "shell.execute_reply": "2023-07-31T02:15:10.077240Z"
+     "iopub.execute_input": "2023-08-06T17:35:18.915382Z",
+     "iopub.status.busy": "2023-08-06T17:35:18.915270Z",
+     "iopub.status.idle": "2023-08-06T17:35:19.012142Z",
+     "shell.execute_reply": "2023-08-06T17:35:19.011857Z"
     }
    },
    "outputs": [
@@ -9760,24 +9798,25 @@
     "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n",
     "ax.set_xlabel('# principal components', fontsize=20)\n",
     "ax.set_xticks(n_comp[::2])\n",
-    "ax.set_ylim([50000,250000]);"
+    "ax.set_ylim([50000,250000]);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "59ef9b7f",
+   "id": "8fc7d826",
    "metadata": {},
    "source": [
     "CV error is minimized at 12,\n",
-    "though there is little noticable difference between this point and a much lower number like 2 or 3 components."
+    "though there is little noticable difference between this point and a much lower number like 2 or 3 components.\n",
+    "\n"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "formats": "ipynb,md:myst",
-   "main_language": "python"
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/Ch7-nonlin-lab.ipynb b/Ch7-nonlin-lab.ipynb
index a8764ab..bba8744 100644
--- a/Ch7-nonlin-lab.ipynb
+++ b/Ch7-nonlin-lab.ipynb
@@ -5,6 +5,7 @@
    "id": "8d006b12",
    "metadata": {},
    "source": [
+    "\n",
     "# Chapter 7\n",
     "\n",
     "# Lab: Non-Linear Modeling\n",
@@ -20,10 +21,10 @@
    "id": "77af2dc4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:11.685667Z",
-     "iopub.status.busy": "2023-07-31T02:15:11.685420Z",
-     "iopub.status.idle": "2023-07-31T02:15:12.757566Z",
-     "shell.execute_reply": "2023-07-31T02:15:12.757160Z"
+     "iopub.execute_input": "2023-08-06T17:35:20.335450Z",
+     "iopub.status.busy": "2023-08-06T17:35:20.335193Z",
+     "iopub.status.idle": "2023-08-06T17:35:21.360168Z",
+     "shell.execute_reply": "2023-08-06T17:35:21.359570Z"
     }
    },
    "outputs": [],
@@ -54,10 +55,10 @@
    "id": "b57d1d51",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:12.759746Z",
-     "iopub.status.busy": "2023-07-31T02:15:12.759462Z",
-     "iopub.status.idle": "2023-07-31T02:15:12.776000Z",
-     "shell.execute_reply": "2023-07-31T02:15:12.775719Z"
+     "iopub.execute_input": "2023-08-06T17:35:21.362340Z",
+     "iopub.status.busy": "2023-08-06T17:35:21.362076Z",
+     "iopub.status.idle": "2023-08-06T17:35:21.377981Z",
+     "shell.execute_reply": "2023-08-06T17:35:21.377704Z"
     }
    },
    "outputs": [],
@@ -74,7 +75,7 @@
     "from ISLP.pygam import (approx_lam,\n",
     "                        degrees_of_freedom,\n",
     "                        plot as plot_gam,\n",
-    "                        anova as anova_gam)"
+    "                        anova as anova_gam)\n"
    ]
   },
   {
@@ -93,17 +94,17 @@
    "id": "65f0e562",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:12.777609Z",
-     "iopub.status.busy": "2023-07-31T02:15:12.777504Z",
-     "iopub.status.idle": "2023-07-31T02:15:12.785974Z",
-     "shell.execute_reply": "2023-07-31T02:15:12.785723Z"
+     "iopub.execute_input": "2023-08-06T17:35:21.379577Z",
+     "iopub.status.busy": "2023-08-06T17:35:21.379467Z",
+     "iopub.status.idle": "2023-08-06T17:35:21.388428Z",
+     "shell.execute_reply": "2023-08-06T17:35:21.388153Z"
     }
    },
    "outputs": [],
    "source": [
     "Wage = load_data('Wage')\n",
     "y = Wage['wage']\n",
-    "age = Wage['age']"
+    "age = Wage['age']\n"
    ]
   },
   {
@@ -123,11 +124,12 @@
    "id": "a391fae6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:12.787544Z",
-     "iopub.status.busy": "2023-07-31T02:15:12.787443Z",
-     "iopub.status.idle": "2023-07-31T02:15:12.860682Z",
-     "shell.execute_reply": "2023-07-31T02:15:12.859957Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:21.390038Z",
+     "iopub.status.busy": "2023-08-06T17:35:21.389943Z",
+     "iopub.status.idle": "2023-08-06T17:35:21.463801Z",
+     "shell.execute_reply": "2023-08-06T17:35:21.463112Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -214,7 +216,7 @@
    "source": [
     "poly_age = MS([poly('age', degree=4)]).fit(Wage)\n",
     "M = sm.OLS(y, poly_age.transform(Wage)).fit()\n",
-    "summarize(M)"
+    "summarize(M)\n"
    ]
   },
   {
@@ -236,7 +238,10 @@
     "`Wage`. This recomputes and stores as attributes any parameters needed by `Poly()`\n",
     "on the training data, and these will be used on all subsequent\n",
     "evaluations of the `transform()` method. For example, it is used\n",
-    "on the second line, as well as in the plotting function developed below."
+    "on the second line, as well as in the plotting function developed below.\n",
+    "\n",
+    "\n",
+    "    \n"
    ]
   },
   {
@@ -254,10 +259,10 @@
    "id": "d672f40e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:12.865379Z",
-     "iopub.status.busy": "2023-07-31T02:15:12.865014Z",
-     "iopub.status.idle": "2023-07-31T02:15:12.871120Z",
-     "shell.execute_reply": "2023-07-31T02:15:12.870397Z"
+     "iopub.execute_input": "2023-08-06T17:35:21.469029Z",
+     "iopub.status.busy": "2023-08-06T17:35:21.468417Z",
+     "iopub.status.idle": "2023-08-06T17:35:21.474585Z",
+     "shell.execute_reply": "2023-08-06T17:35:21.473921Z"
     }
    },
    "outputs": [],
@@ -265,7 +270,7 @@
     "age_grid = np.linspace(age.min(),\n",
     "                       age.max(),\n",
     "                       100)\n",
-    "age_df = pd.DataFrame({'age': age_grid})"
+    "age_df = pd.DataFrame({'age': age_grid})\n"
    ]
   },
   {
@@ -280,7 +285,7 @@
     "transform), as well as a grid of `age` values. The function\n",
     "produces a fitted curve as well as 95% confidence bands. By using\n",
     "an argument for `basis` we can produce and plot the results with several different\n",
-    "transforms, such as the splines we will see shortly."
+    "transforms, such as the splines we will see shortly. "
    ]
   },
   {
@@ -289,11 +294,12 @@
    "id": "56174265",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:12.876622Z",
-     "iopub.status.busy": "2023-07-31T02:15:12.876187Z",
-     "iopub.status.idle": "2023-07-31T02:15:12.902509Z",
-     "shell.execute_reply": "2023-07-31T02:15:12.900607Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:21.478948Z",
+     "iopub.status.busy": "2023-08-06T17:35:21.478666Z",
+     "iopub.status.idle": "2023-08-06T17:35:21.485079Z",
+     "shell.execute_reply": "2023-08-06T17:35:21.483529Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -319,7 +325,7 @@
     "    ax.set_title(title, fontsize=20)\n",
     "    ax.set_xlabel('Age', fontsize=20)\n",
     "    ax.set_ylabel('Wage', fontsize=20);\n",
-    "    return ax"
+    "    return ax\n"
    ]
   },
   {
@@ -345,11 +351,12 @@
    "id": "1a10422f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:12.906678Z",
-     "iopub.status.busy": "2023-07-31T02:15:12.906136Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.107710Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.087852Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:21.488959Z",
+     "iopub.status.busy": "2023-08-06T17:35:21.488554Z",
+     "iopub.status.idle": "2023-08-06T17:35:21.692366Z",
+     "shell.execute_reply": "2023-08-06T17:35:21.691588Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -366,7 +373,17 @@
    "source": [
     "plot_wage_fit(age_df, \n",
     "              poly_age,\n",
-    "              'Degree-4 Polynomial');"
+    "              'Degree-4 Polynomial');\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c5aa81b",
+   "metadata": {},
+   "source": [
+    " \n",
+    "\n",
+    "    "
    ]
   },
   {
@@ -403,10 +420,10 @@
    "id": "8105e672",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.114340Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.114020Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.141638Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.140560Z"
+     "iopub.execute_input": "2023-08-06T17:35:21.697560Z",
+     "iopub.status.busy": "2023-08-06T17:35:21.696560Z",
+     "iopub.status.idle": "2023-08-06T17:35:21.728524Z",
+     "shell.execute_reply": "2023-08-06T17:35:21.727179Z"
     }
    },
    "outputs": [
@@ -508,7 +525,7 @@
     "          for d in range(1, 6)]\n",
     "Xs = [model.fit_transform(Wage) for model in models]\n",
     "anova_lm(*[sm.OLS(y, X_).fit()\n",
-    "           for X_ in Xs])"
+    "           for X_ in Xs])\n"
    ]
   },
   {
@@ -543,11 +560,12 @@
    "id": "095fca7f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.145828Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.145445Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.169693Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.167380Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:21.733139Z",
+     "iopub.status.busy": "2023-08-06T17:35:21.732766Z",
+     "iopub.status.idle": "2023-08-06T17:35:21.750879Z",
+     "shell.execute_reply": "2023-08-06T17:35:21.749464Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -632,7 +650,7 @@
     }
    ],
    "source": [
-    "summarize(M)"
+    "summarize(M)\n"
    ]
   },
   {
@@ -642,7 +660,7 @@
    "source": [
     "Notice that the p-values are the same, and in fact the square of\n",
     "the  t-statistics are equal to the F-statistics from the\n",
-    "`anova_lm()`  function;  for example:"
+    "`anova_lm()`  function;  for example: "
    ]
   },
   {
@@ -651,11 +669,12 @@
    "id": "0b576607",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.175385Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.174868Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.182332Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.180605Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:21.755203Z",
+     "iopub.status.busy": "2023-08-06T17:35:21.754884Z",
+     "iopub.status.idle": "2023-08-06T17:35:21.761326Z",
+     "shell.execute_reply": "2023-08-06T17:35:21.760717Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -670,7 +689,7 @@
     }
    ],
    "source": [
-    "(-11.983)**2"
+    "(-11.983)**2\n"
    ]
   },
   {
@@ -691,11 +710,12 @@
    "id": "7242df77",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.186100Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.185344Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.208027Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.207412Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:21.765904Z",
+     "iopub.status.busy": "2023-08-06T17:35:21.764924Z",
+     "iopub.status.idle": "2023-08-06T17:35:21.791670Z",
+     "shell.execute_reply": "2023-08-06T17:35:21.790956Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -776,7 +796,7 @@
     "          for d in range(1, 4)]\n",
     "XEs = [model.fit_transform(Wage)\n",
     "       for model in models]\n",
-    "anova_lm(*[sm.OLS(y, X_).fit() for X_ in XEs])"
+    "anova_lm(*[sm.OLS(y, X_).fit() for X_ in XEs])\n"
    ]
   },
   {
@@ -800,11 +820,12 @@
    "id": "c7972aea",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.211801Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.211211Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.242153Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.241395Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:21.795901Z",
+     "iopub.status.busy": "2023-08-06T17:35:21.795658Z",
+     "iopub.status.idle": "2023-08-06T17:35:21.825289Z",
+     "shell.execute_reply": "2023-08-06T17:35:21.824414Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -895,7 +916,7 @@
     "             X,\n",
     "             family=sm.families.Binomial())\n",
     "B = glm.fit()\n",
-    "summarize(B)"
+    "summarize(B)\n"
    ]
   },
   {
@@ -912,17 +933,17 @@
    "id": "84f172f2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.247244Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.246976Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.255536Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.254338Z"
+     "iopub.execute_input": "2023-08-06T17:35:21.829774Z",
+     "iopub.status.busy": "2023-08-06T17:35:21.829523Z",
+     "iopub.status.idle": "2023-08-06T17:35:21.836744Z",
+     "shell.execute_reply": "2023-08-06T17:35:21.836082Z"
     }
    },
    "outputs": [],
    "source": [
     "newX = poly_age.transform(age_df)\n",
     "preds = B.get_prediction(newX)\n",
-    "bands = preds.conf_int(alpha=0.05)"
+    "bands = preds.conf_int(alpha=0.05)\n"
    ]
   },
   {
@@ -939,11 +960,12 @@
    "id": "fcf376bd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.259381Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.258869Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.400610Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.399907Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:21.840987Z",
+     "iopub.status.busy": "2023-08-06T17:35:21.840629Z",
+     "iopub.status.idle": "2023-08-06T17:35:21.990343Z",
+     "shell.execute_reply": "2023-08-06T17:35:21.989640Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -973,7 +995,7 @@
     "ax.set_title('Degree-4 Polynomial', fontsize=20)\n",
     "ax.set_xlabel('Age', fontsize=20)\n",
     "ax.set_ylim([0,0.2])\n",
-    "ax.set_ylabel('P(Wage > 250)', fontsize=20);"
+    "ax.set_ylabel('P(Wage > 250)', fontsize=20);\n"
    ]
   },
   {
@@ -1003,11 +1025,12 @@
    "id": "c4d00900",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.404776Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.404541Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.431905Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.430918Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:21.994681Z",
+     "iopub.status.busy": "2023-08-06T17:35:21.994430Z",
+     "iopub.status.idle": "2023-08-06T17:35:22.019141Z",
+     "shell.execute_reply": "2023-08-06T17:35:22.018456Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1085,7 +1108,7 @@
    ],
    "source": [
     "cut_age = pd.qcut(age, 4)\n",
-    "summarize(sm.OLS(y, pd.get_dummies(cut_age)).fit())"
+    "summarize(sm.OLS(y, pd.get_dummies(cut_age)).fit())\n"
    ]
   },
   {
@@ -1131,11 +1154,12 @@
    "id": "d3817102",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.436340Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.435972Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.446536Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.445841Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:22.023709Z",
+     "iopub.status.busy": "2023-08-06T17:35:22.023437Z",
+     "iopub.status.idle": "2023-08-06T17:35:22.035865Z",
+     "shell.execute_reply": "2023-08-06T17:35:22.035182Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1152,7 +1176,7 @@
    "source": [
     "bs_ = BSpline(internal_knots=[25,40,60], intercept=True).fit(age)\n",
     "bs_age = bs_.transform(age)\n",
-    "bs_age.shape"
+    "bs_age.shape\n"
    ]
   },
   {
@@ -1164,7 +1188,7 @@
     "We can form this same matrix using the `bs()` object,\n",
     "which facilitates adding this to a model-matrix builder (as in `poly()` versus its workhorse `Poly()`) described in Section 7.8.1.\n",
     "\n",
-    "We now fit a cubic spline model to the `Wage`  data."
+    "We now fit a cubic spline model to the `Wage`  data. "
    ]
   },
   {
@@ -1173,11 +1197,12 @@
    "id": "c06c0f27",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.450289Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.450050Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.484718Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.484068Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:22.039936Z",
+     "iopub.status.busy": "2023-08-06T17:35:22.039676Z",
+     "iopub.status.idle": "2023-08-06T17:35:22.075107Z",
+     "shell.execute_reply": "2023-08-06T17:35:22.073405Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1281,7 +1306,7 @@
     "bs_age = MS([bs('age', internal_knots=[25,40,60])])\n",
     "Xbs = bs_age.fit_transform(Wage)\n",
     "M = sm.OLS(y, Xbs).fit()\n",
-    "summarize(M)"
+    "summarize(M)\n"
    ]
   },
   {
@@ -1298,10 +1323,10 @@
    "id": "8cb32db0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.491702Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.491344Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.515616Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.514950Z"
+     "iopub.execute_input": "2023-08-06T17:35:22.079217Z",
+     "iopub.status.busy": "2023-08-06T17:35:22.078971Z",
+     "iopub.status.idle": "2023-08-06T17:35:22.109222Z",
+     "shell.execute_reply": "2023-08-06T17:35:22.108320Z"
     }
    },
    "outputs": [
@@ -1408,7 +1433,7 @@
     "                name='bs(age)')])\n",
     "Xbs = bs_age.fit_transform(Wage)\n",
     "M = sm.OLS(y, Xbs).fit()\n",
-    "summarize(M)"
+    "summarize(M)\n"
    ]
   },
   {
@@ -1434,11 +1459,12 @@
    "id": "f26b715f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.519557Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.519136Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.527502Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.526873Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:22.113083Z",
+     "iopub.status.busy": "2023-08-06T17:35:22.112406Z",
+     "iopub.status.idle": "2023-08-06T17:35:22.118984Z",
+     "shell.execute_reply": "2023-08-06T17:35:22.118255Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1453,7 +1479,7 @@
     }
    ],
    "source": [
-    "BSpline(df=6).fit(age).internal_knots_"
+    "BSpline(df=6).fit(age).internal_knots_\n"
    ]
   },
   {
@@ -1469,7 +1495,7 @@
     "When using B-splines we need not limit ourselves to cubic polynomials\n",
     "(i.e. `degree=3`). For instance, using `degree=0` results\n",
     "in piecewise constant functions, as in our example with\n",
-    "`pd.qcut()` above."
+    "`pd.qcut()` above.\n"
    ]
   },
   {
@@ -1478,11 +1504,12 @@
    "id": "d6109816",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.531197Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.530889Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.556693Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.554858Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:22.121895Z",
+     "iopub.status.busy": "2023-08-06T17:35:22.121657Z",
+     "iopub.status.idle": "2023-08-06T17:35:22.143870Z",
+     "shell.execute_reply": "2023-08-06T17:35:22.143234Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1563,7 +1590,7 @@
     "                 df=3, \n",
     "                 degree=0)]).fit(Wage)\n",
     "Xbs0 = bs_age0.transform(Wage)\n",
-    "summarize(sm.OLS(y, Xbs0).fit())"
+    "summarize(sm.OLS(y, Xbs0).fit())\n"
    ]
   },
   {
@@ -1583,7 +1610,16 @@
     "of ones, so the second, third and fourth coefficients are increments\n",
     "for those bins. Why is the sum not exactly the same? It turns out that\n",
     "the `qcut()` uses $\\leq$, while `bs()` uses $<$ when\n",
-    "deciding bin membership."
+    "deciding bin membership.\n",
+    "\n",
+    "\n",
+    "    \n",
+    " \n",
+    "\n",
+    " \n",
+    "\n",
+    "    \n",
+    " "
    ]
   },
   {
@@ -1602,11 +1638,12 @@
    "id": "7c73a3da",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.561268Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.560708Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.586124Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.584767Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:22.147520Z",
+     "iopub.status.busy": "2023-08-06T17:35:22.146792Z",
+     "iopub.status.idle": "2023-08-06T17:35:22.169905Z",
+     "shell.execute_reply": "2023-08-06T17:35:22.169307Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1718,10 +1755,10 @@
    "id": "d73789b4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.590643Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.590004Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.736172Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.735465Z"
+     "iopub.execute_input": "2023-08-06T17:35:22.173921Z",
+     "iopub.status.busy": "2023-08-06T17:35:22.173285Z",
+     "iopub.status.idle": "2023-08-06T17:35:22.327012Z",
+     "shell.execute_reply": "2023-08-06T17:35:22.326243Z"
     }
    },
    "outputs": [
@@ -1739,7 +1776,7 @@
    "source": [
     "plot_wage_fit(age_df,\n",
     "              ns_age,\n",
-    "              'Natural spline, df=5');"
+    "              'Natural spline, df=5');\n"
    ]
   },
   {
@@ -1766,10 +1803,10 @@
    "id": "3e70b87d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.740381Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.739967Z",
-     "iopub.status.idle": "2023-07-31T02:15:13.777379Z",
-     "shell.execute_reply": "2023-07-31T02:15:13.776553Z"
+     "iopub.execute_input": "2023-08-06T17:35:22.331968Z",
+     "iopub.status.busy": "2023-08-06T17:35:22.331518Z",
+     "iopub.status.idle": "2023-08-06T17:35:22.372362Z",
+     "shell.execute_reply": "2023-08-06T17:35:22.370490Z"
     }
    },
    "outputs": [
@@ -1789,7 +1826,7 @@
    "source": [
     "X_age = np.asarray(age).reshape((-1,1))\n",
     "gam = LinearGAM(s_gam(0, lam=0.6))\n",
-    "gam.fit(X_age, y)"
+    "gam.fit(X_age, y)\n"
    ]
   },
   {
@@ -1812,10 +1849,10 @@
    "id": "efe03b12",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:13.781500Z",
-     "iopub.status.busy": "2023-07-31T02:15:13.781187Z",
-     "iopub.status.idle": "2023-07-31T02:15:14.064112Z",
-     "shell.execute_reply": "2023-07-31T02:15:14.063430Z"
+     "iopub.execute_input": "2023-08-06T17:35:22.376074Z",
+     "iopub.status.busy": "2023-08-06T17:35:22.375540Z",
+     "iopub.status.idle": "2023-08-06T17:35:22.722554Z",
+     "shell.execute_reply": "2023-08-06T17:35:22.721832Z"
     }
    },
    "outputs": [
@@ -1841,7 +1878,7 @@
     "            linewidth=3)\n",
     "ax.set_xlabel('Age', fontsize=20)\n",
     "ax.set_ylabel('Wage', fontsize=20);\n",
-    "ax.legend(title='$\\lambda$');"
+    "ax.legend(title='$\\lambda$');\n"
    ]
   },
   {
@@ -1858,10 +1895,10 @@
    "id": "acff2af2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:14.068893Z",
-     "iopub.status.busy": "2023-07-31T02:15:14.068635Z",
-     "iopub.status.idle": "2023-07-31T02:15:14.408960Z",
-     "shell.execute_reply": "2023-07-31T02:15:14.408263Z"
+     "iopub.execute_input": "2023-08-06T17:35:22.724493Z",
+     "iopub.status.busy": "2023-08-06T17:35:22.724387Z",
+     "iopub.status.idle": "2023-08-06T17:35:23.386569Z",
+     "shell.execute_reply": "2023-08-06T17:35:23.385869Z"
     }
    },
    "outputs": [
@@ -1891,7 +1928,7 @@
     "        label='Grid search',\n",
     "        linewidth=4)\n",
     "ax.legend()\n",
-    "fig"
+    "fig\n"
    ]
   },
   {
@@ -1913,11 +1950,12 @@
    "id": "a2d25550",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:14.413448Z",
-     "iopub.status.busy": "2023-07-31T02:15:14.413122Z",
-     "iopub.status.idle": "2023-07-31T02:15:14.436392Z",
-     "shell.execute_reply": "2023-07-31T02:15:14.435474Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:23.390727Z",
+     "iopub.status.busy": "2023-08-06T17:35:23.390039Z",
+     "iopub.status.idle": "2023-08-06T17:35:23.409507Z",
+     "shell.execute_reply": "2023-08-06T17:35:23.408815Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1935,7 +1973,7 @@
     "age_term = gam.terms[0]\n",
     "lam_4 = approx_lam(X_age, age_term, 4)\n",
     "age_term.lam = lam_4\n",
-    "degrees_of_freedom(X_age, age_term)"
+    "degrees_of_freedom(X_age, age_term)\n"
    ]
   },
   {
@@ -1954,10 +1992,10 @@
    "id": "bc6322de",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:14.440489Z",
-     "iopub.status.busy": "2023-07-31T02:15:14.440065Z",
-     "iopub.status.idle": "2023-07-31T02:15:14.764590Z",
-     "shell.execute_reply": "2023-07-31T02:15:14.763753Z"
+     "iopub.execute_input": "2023-08-06T17:35:23.412957Z",
+     "iopub.status.busy": "2023-08-06T17:35:23.412505Z",
+     "iopub.status.idle": "2023-08-06T17:35:23.730459Z",
+     "shell.execute_reply": "2023-08-06T17:35:23.729594Z"
     }
    },
    "outputs": [
@@ -1988,7 +2026,7 @@
     "            linewidth=4)\n",
     "ax.set_xlabel('Age', fontsize=20)\n",
     "ax.set_ylabel('Wage', fontsize=20);\n",
-    "ax.legend(title='Degrees of freedom');"
+    "ax.legend(title='Degrees of freedom');\n"
    ]
   },
   {
@@ -2016,11 +2054,12 @@
    "id": "703d2570",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:14.769077Z",
-     "iopub.status.busy": "2023-07-31T02:15:14.768693Z",
-     "iopub.status.idle": "2023-07-31T02:15:14.787733Z",
-     "shell.execute_reply": "2023-07-31T02:15:14.787073Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:23.734256Z",
+     "iopub.status.busy": "2023-08-06T17:35:23.733981Z",
+     "iopub.status.idle": "2023-08-06T17:35:23.754589Z",
+     "shell.execute_reply": "2023-08-06T17:35:23.753337Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -2030,7 +2069,7 @@
     "      ns_year.transform(Wage['year']),\n",
     "      pd.get_dummies(Wage['education']).values]\n",
     "X_bh = np.hstack(Xs)\n",
-    "gam_bh = sm.OLS(y, X_bh).fit()"
+    "gam_bh = sm.OLS(y, X_bh).fit()\n"
    ]
   },
   {
@@ -2054,11 +2093,12 @@
    "id": "766a7b1f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:14.791620Z",
-     "iopub.status.busy": "2023-07-31T02:15:14.791297Z",
-     "iopub.status.idle": "2023-07-31T02:15:14.975843Z",
-     "shell.execute_reply": "2023-07-31T02:15:14.975148Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:23.759585Z",
+     "iopub.status.busy": "2023-08-06T17:35:23.759049Z",
+     "iopub.status.idle": "2023-08-06T17:35:23.884772Z",
+     "shell.execute_reply": "2023-08-06T17:35:23.884035Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2091,7 +2131,7 @@
     "ax.plot(age_grid, bounds_age[:,1], 'r--', linewidth=3)\n",
     "ax.set_xlabel('Age')\n",
     "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of age on wage', fontsize=20);"
+    "ax.set_title('Partial dependence of age on wage', fontsize=20);\n"
    ]
   },
   {
@@ -2116,10 +2156,10 @@
    "id": "2a9ed841",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:14.980341Z",
-     "iopub.status.busy": "2023-07-31T02:15:14.980093Z",
-     "iopub.status.idle": "2023-07-31T02:15:15.126673Z",
-     "shell.execute_reply": "2023-07-31T02:15:15.126362Z"
+     "iopub.execute_input": "2023-08-06T17:35:23.888947Z",
+     "iopub.status.busy": "2023-08-06T17:35:23.888274Z",
+     "iopub.status.idle": "2023-08-06T17:35:24.065464Z",
+     "shell.execute_reply": "2023-08-06T17:35:24.065124Z"
     }
    },
    "outputs": [
@@ -2154,7 +2194,7 @@
     "ax.plot(year_grid, bounds_year[:,1], 'r--', linewidth=3)\n",
     "ax.set_xlabel('Year')\n",
     "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of year on wage', fontsize=20);"
+    "ax.set_title('Partial dependence of year on wage', fontsize=20);\n"
    ]
   },
   {
@@ -2177,11 +2217,12 @@
    "id": "ccf068b1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:15.128328Z",
-     "iopub.status.busy": "2023-07-31T02:15:15.128214Z",
-     "iopub.status.idle": "2023-07-31T02:15:15.153262Z",
-     "shell.execute_reply": "2023-07-31T02:15:15.150282Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:24.067233Z",
+     "iopub.status.busy": "2023-08-06T17:35:24.067111Z",
+     "iopub.status.idle": "2023-08-06T17:35:24.089243Z",
+     "shell.execute_reply": "2023-08-06T17:35:24.088305Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -2191,7 +2232,7 @@
     "Xgam = np.column_stack([age,\n",
     "                        Wage['year'],\n",
     "                        Wage['education'].cat.codes])\n",
-    "gam_full = gam_full.fit(Xgam, y)"
+    "gam_full = gam_full.fit(Xgam, y)\n"
    ]
   },
   {
@@ -2213,10 +2254,10 @@
    "id": "38b719f1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:15.158595Z",
-     "iopub.status.busy": "2023-07-31T02:15:15.158140Z",
-     "iopub.status.idle": "2023-07-31T02:15:15.303226Z",
-     "shell.execute_reply": "2023-07-31T02:15:15.301663Z"
+     "iopub.execute_input": "2023-08-06T17:35:24.093193Z",
+     "iopub.status.busy": "2023-08-06T17:35:24.092751Z",
+     "iopub.status.idle": "2023-08-06T17:35:24.282656Z",
+     "shell.execute_reply": "2023-08-06T17:35:24.281548Z"
     }
    },
    "outputs": [
@@ -2236,7 +2277,7 @@
     "plot_gam(gam_full, 0, ax=ax)\n",
     "ax.set_xlabel('Age')\n",
     "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of age on wage - default lam=0.6', fontsize=20);"
+    "ax.set_title('Partial dependence of age on wage - default lam=0.6', fontsize=20);\n"
    ]
   },
   {
@@ -2256,11 +2297,12 @@
    "id": "02142f6e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:15.308692Z",
-     "iopub.status.busy": "2023-07-31T02:15:15.308306Z",
-     "iopub.status.idle": "2023-07-31T02:15:15.345188Z",
-     "shell.execute_reply": "2023-07-31T02:15:15.344373Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:24.286568Z",
+     "iopub.status.busy": "2023-08-06T17:35:24.286086Z",
+     "iopub.status.idle": "2023-08-06T17:35:24.343400Z",
+     "shell.execute_reply": "2023-08-06T17:35:24.342678Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -2268,7 +2310,7 @@
     "age_term.lam = approx_lam(Xgam, age_term, df=4+1)\n",
     "year_term = gam_full.terms[1]\n",
     "year_term.lam = approx_lam(Xgam, year_term, df=4+1)\n",
-    "gam_full = gam_full.fit(Xgam, y)"
+    "gam_full = gam_full.fit(Xgam, y)\n"
    ]
   },
   {
@@ -2288,11 +2330,12 @@
    "id": "94587b05",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:15.349565Z",
-     "iopub.status.busy": "2023-07-31T02:15:15.348808Z",
-     "iopub.status.idle": "2023-07-31T02:15:15.493969Z",
-     "shell.execute_reply": "2023-07-31T02:15:15.493186Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:24.347280Z",
+     "iopub.status.busy": "2023-08-06T17:35:24.346878Z",
+     "iopub.status.idle": "2023-08-06T17:35:24.511252Z",
+     "shell.execute_reply": "2023-08-06T17:35:24.510569Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2323,7 +2366,7 @@
     "         ax=ax)\n",
     "ax.set_xlabel('Year')\n",
     "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of year on wage', fontsize=20)"
+    "ax.set_title('Partial dependence of year on wage', fontsize=20)\n"
    ]
   },
   {
@@ -2331,7 +2374,7 @@
    "id": "5b373d5c",
    "metadata": {},
    "source": [
-    "Finally we plot `education`, which is categorical. The partial dependence plot is different, and more suitable for the set of fitted constants for each level of this variable."
+    "Finally we plot `education`, which is categorical. The partial dependence plot is different, and more suitable for the set of fitted constants for each level of this variable. "
    ]
   },
   {
@@ -2340,10 +2383,10 @@
    "id": "bba4c757",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:15.497724Z",
-     "iopub.status.busy": "2023-07-31T02:15:15.497435Z",
-     "iopub.status.idle": "2023-07-31T02:15:15.654244Z",
-     "shell.execute_reply": "2023-07-31T02:15:15.653870Z"
+     "iopub.execute_input": "2023-08-06T17:35:24.515732Z",
+     "iopub.status.busy": "2023-08-06T17:35:24.515470Z",
+     "iopub.status.idle": "2023-08-06T17:35:24.666853Z",
+     "shell.execute_reply": "2023-08-06T17:35:24.666565Z"
     }
    },
    "outputs": [
@@ -2365,7 +2408,7 @@
     "ax.set_ylabel('Effect on wage')\n",
     "ax.set_title('Partial dependence of wage on education',\n",
     "             fontsize=20);\n",
-    "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);"
+    "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);\n"
    ]
   },
   {
@@ -2387,10 +2430,10 @@
    "id": "32368085",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:15.655935Z",
-     "iopub.status.busy": "2023-07-31T02:15:15.655824Z",
-     "iopub.status.idle": "2023-07-31T02:15:15.686432Z",
-     "shell.execute_reply": "2023-07-31T02:15:15.685688Z"
+     "iopub.execute_input": "2023-08-06T17:35:24.668508Z",
+     "iopub.status.busy": "2023-08-06T17:35:24.668401Z",
+     "iopub.status.idle": "2023-08-06T17:35:24.701806Z",
+     "shell.execute_reply": "2023-08-06T17:35:24.700424Z"
     }
    },
    "outputs": [
@@ -2413,7 +2456,7 @@
     "gam_linear = LinearGAM(age_term +\n",
     "                       l_gam(1, lam=0) +\n",
     "                       f_gam(2, lam=0))\n",
-    "gam_linear.fit(Xgam, y)"
+    "gam_linear.fit(Xgam, y)\n"
    ]
   },
   {
@@ -2434,11 +2477,12 @@
    "id": "e7ba9957",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:15.691690Z",
-     "iopub.status.busy": "2023-07-31T02:15:15.691016Z",
-     "iopub.status.idle": "2023-07-31T02:15:15.703660Z",
-     "shell.execute_reply": "2023-07-31T02:15:15.702988Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:24.705887Z",
+     "iopub.status.busy": "2023-08-06T17:35:24.705492Z",
+     "iopub.status.idle": "2023-08-06T17:35:24.719138Z",
+     "shell.execute_reply": "2023-08-06T17:35:24.717774Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2515,7 +2559,7 @@
     }
    ],
    "source": [
-    "anova_gam(gam_0, gam_linear, gam_full)"
+    "anova_gam(gam_0, gam_linear, gam_full)\n"
    ]
   },
   {
@@ -2541,10 +2585,10 @@
    "id": "ffc0099a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:15.707900Z",
-     "iopub.status.busy": "2023-07-31T02:15:15.707547Z",
-     "iopub.status.idle": "2023-07-31T02:15:15.750369Z",
-     "shell.execute_reply": "2023-07-31T02:15:15.749682Z"
+     "iopub.execute_input": "2023-08-06T17:35:24.725028Z",
+     "iopub.status.busy": "2023-08-06T17:35:24.724616Z",
+     "iopub.status.idle": "2023-08-06T17:35:24.770728Z",
+     "shell.execute_reply": "2023-08-06T17:35:24.769960Z"
     }
    },
    "outputs": [
@@ -2651,10 +2695,10 @@
    "id": "08026a6b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:15.754363Z",
-     "iopub.status.busy": "2023-07-31T02:15:15.753938Z",
-     "iopub.status.idle": "2023-07-31T02:15:15.761799Z",
-     "shell.execute_reply": "2023-07-31T02:15:15.760383Z"
+     "iopub.execute_input": "2023-08-06T17:35:24.776059Z",
+     "iopub.status.busy": "2023-08-06T17:35:24.775063Z",
+     "iopub.status.idle": "2023-08-06T17:35:24.783501Z",
+     "shell.execute_reply": "2023-08-06T17:35:24.781979Z"
     }
    },
    "outputs": [
@@ -2693,7 +2737,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_87737/3870570873.py:1: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
+      "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_85019/2135516388.py:1: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
       " \n",
       "Please do not make inferences based on these values! \n",
       "\n",
@@ -2705,7 +2749,7 @@
     }
    ],
    "source": [
-    "gam_full.summary()"
+    "gam_full.summary()\n"
    ]
   },
   {
@@ -2724,15 +2768,15 @@
    "id": "9191d615",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:15.766013Z",
-     "iopub.status.busy": "2023-07-31T02:15:15.765724Z",
-     "iopub.status.idle": "2023-07-31T02:15:15.779820Z",
-     "shell.execute_reply": "2023-07-31T02:15:15.779119Z"
+     "iopub.execute_input": "2023-08-06T17:35:24.787789Z",
+     "iopub.status.busy": "2023-08-06T17:35:24.787540Z",
+     "iopub.status.idle": "2023-08-06T17:35:24.802733Z",
+     "shell.execute_reply": "2023-08-06T17:35:24.801551Z"
     }
    },
    "outputs": [],
    "source": [
-    "Yhat = gam_full.predict(Xgam)"
+    "Yhat = gam_full.predict(Xgam)\n"
    ]
   },
   {
@@ -2750,11 +2794,12 @@
    "id": "92007a5f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:15.783636Z",
-     "iopub.status.busy": "2023-07-31T02:15:15.783296Z",
-     "iopub.status.idle": "2023-07-31T02:15:15.907664Z",
-     "shell.execute_reply": "2023-07-31T02:15:15.906806Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:24.807058Z",
+     "iopub.status.busy": "2023-08-06T17:35:24.806737Z",
+     "iopub.status.idle": "2023-08-06T17:35:24.915626Z",
+     "shell.execute_reply": "2023-08-06T17:35:24.914906Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2774,7 +2819,7 @@
     "gam_logit = LogisticGAM(age_term + \n",
     "                        l_gam(1, lam=0) +\n",
     "                        f_gam(2, lam=0))\n",
-    "gam_logit.fit(Xgam, high_earn)"
+    "gam_logit.fit(Xgam, high_earn)\n"
    ]
   },
   {
@@ -2783,11 +2828,12 @@
    "id": "4dd6aa2f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:15.911434Z",
-     "iopub.status.busy": "2023-07-31T02:15:15.910708Z",
-     "iopub.status.idle": "2023-07-31T02:15:16.048530Z",
-     "shell.execute_reply": "2023-07-31T02:15:16.047846Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:24.919443Z",
+     "iopub.status.busy": "2023-08-06T17:35:24.919192Z",
+     "iopub.status.idle": "2023-08-06T17:35:25.080317Z",
+     "shell.execute_reply": "2023-08-06T17:35:25.078256Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2808,7 +2854,7 @@
     "ax.set_ylabel('Effect on wage')\n",
     "ax.set_title('Partial dependence of wage on education',\n",
     "             fontsize=20);\n",
-    "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);"
+    "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);\n"
    ]
   },
   {
@@ -2826,11 +2872,12 @@
    "id": "5a6e8754",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:16.052843Z",
-     "iopub.status.busy": "2023-07-31T02:15:16.052407Z",
-     "iopub.status.idle": "2023-07-31T02:15:16.079850Z",
-     "shell.execute_reply": "2023-07-31T02:15:16.078984Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:25.084731Z",
+     "iopub.status.busy": "2023-08-06T17:35:25.084443Z",
+     "iopub.status.idle": "2023-08-06T17:35:25.109581Z",
+     "shell.execute_reply": "2023-08-06T17:35:25.108827Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -2908,7 +2955,7 @@
     }
    ],
    "source": [
-    "pd.crosstab(Wage['high_earn'], Wage['education'])"
+    "pd.crosstab(Wage['high_earn'], Wage['education'])\n"
    ]
   },
   {
@@ -2925,7 +2972,7 @@
     "we could subset the model matrix, though this will not remove the\n",
     "column from `Xgam`. While we can deduce which column corresponds\n",
     "to this feature, for reproducibility’s sake we reform the model matrix\n",
-    "on this smaller subset."
+    "on this smaller subset.\n"
    ]
   },
   {
@@ -2934,11 +2981,12 @@
    "id": "c92b60be",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:16.083887Z",
-     "iopub.status.busy": "2023-07-31T02:15:16.083527Z",
-     "iopub.status.idle": "2023-07-31T02:15:16.091266Z",
-     "shell.execute_reply": "2023-07-31T02:15:16.090319Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:25.114462Z",
+     "iopub.status.busy": "2023-08-06T17:35:25.114086Z",
+     "iopub.status.idle": "2023-08-06T17:35:25.120741Z",
+     "shell.execute_reply": "2023-08-06T17:35:25.120108Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -2947,7 +2995,7 @@
     "Xgam_ = np.column_stack([Wage_['age'],\n",
     "                         Wage_['year'],\n",
     "                         Wage_['education'].cat.codes-1])\n",
-    "high_earn_ = Wage_['high_earn']"
+    "high_earn_ = Wage_['high_earn']\n"
    ]
   },
   {
@@ -2967,11 +3015,12 @@
    "id": "e525e8d0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:16.095914Z",
-     "iopub.status.busy": "2023-07-31T02:15:16.095270Z",
-     "iopub.status.idle": "2023-07-31T02:15:16.153654Z",
-     "shell.execute_reply": "2023-07-31T02:15:16.152986Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:25.124664Z",
+     "iopub.status.busy": "2023-08-06T17:35:25.124412Z",
+     "iopub.status.idle": "2023-08-06T17:35:25.187015Z",
+     "shell.execute_reply": "2023-08-06T17:35:25.186350Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -2991,7 +3040,7 @@
     "gam_logit_ = LogisticGAM(age_term +\n",
     "                         year_term +\n",
     "                         f_gam(2, lam=0))\n",
-    "gam_logit_.fit(Xgam_, high_earn_)"
+    "gam_logit_.fit(Xgam_, high_earn_)\n"
    ]
   },
   {
@@ -3009,10 +3058,10 @@
    "id": "c3e66cf9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:16.157725Z",
-     "iopub.status.busy": "2023-07-31T02:15:16.157380Z",
-     "iopub.status.idle": "2023-07-31T02:15:16.283378Z",
-     "shell.execute_reply": "2023-07-31T02:15:16.282779Z"
+     "iopub.execute_input": "2023-08-06T17:35:25.191341Z",
+     "iopub.status.busy": "2023-08-06T17:35:25.191048Z",
+     "iopub.status.idle": "2023-08-06T17:35:25.340618Z",
+     "shell.execute_reply": "2023-08-06T17:35:25.339704Z"
     }
    },
    "outputs": [
@@ -3034,7 +3083,7 @@
     "ax.set_ylabel('Effect on wage')\n",
     "ax.set_title('Partial dependence of high earner status on education', fontsize=20);\n",
     "ax.set_xticklabels(Wage['education'].cat.categories[1:],\n",
-    "                   fontsize=8);"
+    "                   fontsize=8);\n"
    ]
   },
   {
@@ -3043,10 +3092,10 @@
    "id": "fd924348",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:16.286495Z",
-     "iopub.status.busy": "2023-07-31T02:15:16.286234Z",
-     "iopub.status.idle": "2023-07-31T02:15:16.460530Z",
-     "shell.execute_reply": "2023-07-31T02:15:16.460174Z"
+     "iopub.execute_input": "2023-08-06T17:35:25.345459Z",
+     "iopub.status.busy": "2023-08-06T17:35:25.345074Z",
+     "iopub.status.idle": "2023-08-06T17:35:25.463152Z",
+     "shell.execute_reply": "2023-08-06T17:35:25.462836Z"
     }
    },
    "outputs": [
@@ -3067,7 +3116,7 @@
     "ax.set_xlabel('Year')\n",
     "ax.set_ylabel('Effect on wage')\n",
     "ax.set_title('Partial dependence of high earner status on year',\n",
-    "             fontsize=20);"
+    "             fontsize=20);\n"
    ]
   },
   {
@@ -3076,11 +3125,12 @@
    "id": "2d3ec90a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:16.462091Z",
-     "iopub.status.busy": "2023-07-31T02:15:16.461971Z",
-     "iopub.status.idle": "2023-07-31T02:15:16.573978Z",
-     "shell.execute_reply": "2023-07-31T02:15:16.573664Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:25.464839Z",
+     "iopub.status.busy": "2023-08-06T17:35:25.464711Z",
+     "iopub.status.idle": "2023-08-06T17:35:25.574888Z",
+     "shell.execute_reply": "2023-08-06T17:35:25.574562Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -3099,7 +3149,7 @@
     "ax = plot_gam(gam_logit_, 0)\n",
     "ax.set_xlabel('Age')\n",
     "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of high earner status on age', fontsize=20);"
+    "ax.set_title('Partial dependence of high earner status on age', fontsize=20);\n"
    ]
   },
   {
@@ -3123,11 +3173,12 @@
    "id": "4f2bc0eb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:16.575762Z",
-     "iopub.status.busy": "2023-07-31T02:15:16.575621Z",
-     "iopub.status.idle": "2023-07-31T02:15:16.719422Z",
-     "shell.execute_reply": "2023-07-31T02:15:16.719075Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:25.576662Z",
+     "iopub.status.busy": "2023-08-06T17:35:25.576532Z",
+     "iopub.status.idle": "2023-08-06T17:35:25.718350Z",
+     "shell.execute_reply": "2023-08-06T17:35:25.718022Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -3156,15 +3207,24 @@
     "            linewidth=4)\n",
     "ax.set_xlabel('Age', fontsize=20)\n",
     "ax.set_ylabel('Wage', fontsize=20);\n",
-    "ax.legend(title='span', fontsize=15);"
+    "ax.legend(title='span', fontsize=15);\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ce87f4b",
+   "metadata": {},
+   "source": [
+    "  \n",
+    " \n"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "formats": "ipynb,md:myst",
-   "main_language": "python"
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/Ch8-baggboost-lab.ipynb b/Ch8-baggboost-lab.ipynb
index 826f96f..98394d6 100644
--- a/Ch8-baggboost-lab.ipynb
+++ b/Ch8-baggboost-lab.ipynb
@@ -2,15 +2,17 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7b0242c6",
+   "id": "15894965",
    "metadata": {},
    "source": [
-    "# Chapter 8"
+    "\n",
+    "# Chapter 8\n",
+    "\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "db138cdf",
+   "id": "997ad844",
    "metadata": {},
    "source": [
     "# Lab: Tree-Based Methods\n",
@@ -21,14 +23,15 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "4cc7120f",
+   "id": "03f4774e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:40.300816Z",
-     "iopub.status.busy": "2023-07-31T04:30:40.300524Z",
-     "iopub.status.idle": "2023-07-31T04:30:41.427594Z",
-     "shell.execute_reply": "2023-07-31T04:30:41.427210Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:26.973875Z",
+     "iopub.status.busy": "2023-08-06T17:35:26.973371Z",
+     "iopub.status.idle": "2023-08-06T17:35:28.034474Z",
+     "shell.execute_reply": "2023-08-06T17:35:28.034036Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -37,12 +40,12 @@
     "from matplotlib.pyplot import subplots\n",
     "import sklearn.model_selection as skm\n",
     "from ISLP import load_data, confusion_table\n",
-    "from ISLP.models import ModelSpec as MS"
+    "from ISLP.models import ModelSpec as MS\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "75645461",
+   "id": "87f2f0d6",
    "metadata": {},
    "source": [
     "We also  collect the new imports\n",
@@ -52,14 +55,15 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "864fa15e",
+   "id": "45944bc2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:41.429527Z",
-     "iopub.status.busy": "2023-07-31T04:30:41.429365Z",
-     "iopub.status.idle": "2023-07-31T04:30:41.482154Z",
-     "shell.execute_reply": "2023-07-31T04:30:41.481876Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:28.036518Z",
+     "iopub.status.busy": "2023-08-06T17:35:28.036337Z",
+     "iopub.status.idle": "2023-08-06T17:35:28.099133Z",
+     "shell.execute_reply": "2023-08-06T17:35:28.098845Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
@@ -72,12 +76,12 @@
     "from sklearn.ensemble import \\\n",
     "     (RandomForestRegressor as RF,\n",
     "      GradientBoostingRegressor as GBR)\n",
-    "from ISLP.bart import BART"
+    "from ISLP.bart import BART\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7b68e1e9",
+   "id": "72228f44",
    "metadata": {},
    "source": [
     "## Fitting Classification Trees"
@@ -85,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85b1d9af",
+   "id": "de444669",
    "metadata": {},
    "source": [
     "We first use classification trees to analyze the  `Carseats`  data set.\n",
@@ -99,13 +103,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "bfb4c83b",
+   "id": "f5b68c46",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:41.483971Z",
-     "iopub.status.busy": "2023-07-31T04:30:41.483851Z",
-     "iopub.status.idle": "2023-07-31T04:30:41.488856Z",
-     "shell.execute_reply": "2023-07-31T04:30:41.488596Z"
+     "iopub.execute_input": "2023-08-06T17:35:28.100949Z",
+     "iopub.status.busy": "2023-08-06T17:35:28.100843Z",
+     "iopub.status.idle": "2023-08-06T17:35:28.106734Z",
+     "shell.execute_reply": "2023-08-06T17:35:28.106477Z"
     }
    },
    "outputs": [],
@@ -113,43 +117,44 @@
     "Carseats = load_data('Carseats')\n",
     "High = np.where(Carseats.Sales > 8,\n",
     "                \"Yes\",\n",
-    "                \"No\")"
+    "                \"No\")\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8e50828c",
+   "id": "0b64c42c",
    "metadata": {},
    "source": [
     "We now use `DecisionTreeClassifier()`  to fit a classification tree in\n",
     "order to predict `High` using all variables but `Sales`.\n",
     "To do so, we must form a model matrix as we did when fitting regression\n",
-    "models."
+    "models.  "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "5d9e7a14",
+   "id": "a04ebae3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:41.490354Z",
-     "iopub.status.busy": "2023-07-31T04:30:41.490251Z",
-     "iopub.status.idle": "2023-07-31T04:30:41.500107Z",
-     "shell.execute_reply": "2023-07-31T04:30:41.499827Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:28.108295Z",
+     "iopub.status.busy": "2023-08-06T17:35:28.108194Z",
+     "iopub.status.idle": "2023-08-06T17:35:28.117988Z",
+     "shell.execute_reply": "2023-08-06T17:35:28.117702Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
     "model = MS(Carseats.columns.drop('Sales'), intercept=False)\n",
     "D = model.fit_transform(Carseats)\n",
     "feature_names = list(D.columns)\n",
-    "X = np.asarray(D)"
+    "X = np.asarray(D)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b42d6270",
+   "id": "48a7240b",
    "metadata": {},
    "source": [
     "We have converted `D` from a data frame to an array `X`, which is needed in some of the analysis below. We also need the `feature_names` for annotating our plots later.\n",
@@ -164,14 +169,15 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "970c4515",
+   "id": "c859e1bc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:41.501612Z",
-     "iopub.status.busy": "2023-07-31T04:30:41.501506Z",
-     "iopub.status.idle": "2023-07-31T04:30:41.506829Z",
-     "shell.execute_reply": "2023-07-31T04:30:41.506566Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:28.119451Z",
+     "iopub.status.busy": "2023-08-06T17:35:28.119371Z",
+     "iopub.status.idle": "2023-08-06T17:35:28.125117Z",
+     "shell.execute_reply": "2023-08-06T17:35:28.124870Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -192,12 +198,12 @@
     "clf = DTC(criterion='entropy',\n",
     "          max_depth=3,\n",
     "          random_state=0)        \n",
-    "clf.fit(X, High)"
+    "clf.fit(X, High)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5ccdc9e7",
+   "id": "1f96b0d3",
    "metadata": {},
    "source": [
     "In our discussion of qualitative features in Section 3.3,\n",
@@ -215,14 +221,15 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "2d9a51dc",
+   "id": "25f3d7d9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:41.508361Z",
-     "iopub.status.busy": "2023-07-31T04:30:41.508261Z",
-     "iopub.status.idle": "2023-07-31T04:30:41.511129Z",
-     "shell.execute_reply": "2023-07-31T04:30:41.510866Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:28.126685Z",
+     "iopub.status.busy": "2023-08-06T17:35:28.126586Z",
+     "iopub.status.idle": "2023-08-06T17:35:28.129337Z",
+     "shell.execute_reply": "2023-08-06T17:35:28.129084Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -237,12 +244,12 @@
     }
    ],
    "source": [
-    "accuracy_score(High, clf.predict(X))"
+    "accuracy_score(High, clf.predict(X))\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7fd4219f",
+   "id": "0be04bee",
    "metadata": {},
    "source": [
     "With only the default arguments, the training error rate is\n",
@@ -261,13 +268,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "cbc04b67",
+   "id": "1aee216b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:41.512528Z",
-     "iopub.status.busy": "2023-07-31T04:30:41.512435Z",
-     "iopub.status.idle": "2023-07-31T04:30:41.515596Z",
-     "shell.execute_reply": "2023-07-31T04:30:41.515356Z"
+     "iopub.execute_input": "2023-08-06T17:35:28.130735Z",
+     "iopub.status.busy": "2023-08-06T17:35:28.130643Z",
+     "iopub.status.idle": "2023-08-06T17:35:28.133827Z",
+     "shell.execute_reply": "2023-08-06T17:35:28.133540Z"
     }
    },
    "outputs": [
@@ -284,12 +291,12 @@
    ],
    "source": [
     "resid_dev = np.sum(log_loss(High, clf.predict_proba(X)))\n",
-    "resid_dev"
+    "resid_dev\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bca81c07",
+   "id": "e226e2e0",
    "metadata": {},
    "source": [
     "This is closely related to the *entropy*, defined in (8.7).\n",
@@ -304,14 +311,15 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "809830c7",
+   "id": "7404fab4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:41.516955Z",
-     "iopub.status.busy": "2023-07-31T04:30:41.516861Z",
-     "iopub.status.idle": "2023-07-31T04:30:41.819909Z",
-     "shell.execute_reply": "2023-07-31T04:30:41.819123Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:28.135195Z",
+     "iopub.status.busy": "2023-08-06T17:35:28.135115Z",
+     "iopub.status.idle": "2023-08-06T17:35:28.448517Z",
+     "shell.execute_reply": "2023-08-06T17:35:28.447797Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -329,12 +337,12 @@
     "ax = subplots(figsize=(12,12))[1]\n",
     "plot_tree(clf,\n",
     "          feature_names=feature_names,\n",
-    "          ax=ax);"
+    "          ax=ax);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c7cf2e12",
+   "id": "95052722",
    "metadata": {},
    "source": [
     "The most important indicator of `Sales` appears to be `ShelveLoc`.\n",
@@ -351,13 +359,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "abe8c7fc",
+   "id": "f9774fa0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:41.825044Z",
-     "iopub.status.busy": "2023-07-31T04:30:41.824582Z",
-     "iopub.status.idle": "2023-07-31T04:30:41.833443Z",
-     "shell.execute_reply": "2023-07-31T04:30:41.831954Z"
+     "iopub.execute_input": "2023-08-06T17:35:28.452296Z",
+     "iopub.status.busy": "2023-08-06T17:35:28.451809Z",
+     "iopub.status.idle": "2023-08-06T17:35:28.458584Z",
+     "shell.execute_reply": "2023-08-06T17:35:28.456974Z"
     }
    },
    "outputs": [
@@ -394,12 +402,12 @@
    "source": [
     "print(export_text(clf,\n",
     "                  feature_names=feature_names,\n",
-    "                  show_weights=True))"
+    "                  show_weights=True))\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f06c2f4c",
+   "id": "b4bdd532",
    "metadata": {},
    "source": [
     "In order to properly evaluate the performance of a classification tree\n",
@@ -416,14 +424,15 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "f7a1f736",
+   "id": "17d3bb25",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:41.839397Z",
-     "iopub.status.busy": "2023-07-31T04:30:41.839140Z",
-     "iopub.status.idle": "2023-07-31T04:30:41.855525Z",
-     "shell.execute_reply": "2023-07-31T04:30:41.854837Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:28.464919Z",
+     "iopub.status.busy": "2023-08-06T17:35:28.464498Z",
+     "iopub.status.idle": "2023-08-06T17:35:28.477773Z",
+     "shell.execute_reply": "2023-08-06T17:35:28.477055Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -445,12 +454,20 @@
     "                             D,\n",
     "                             High,\n",
     "                             cv=validation)\n",
-    "results['test_score']"
+    "results['test_score']\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "533af069",
+   "id": "890d1cd6",
+   "metadata": {},
+   "source": [
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "273ee930",
    "metadata": {},
    "source": [
     "Next, we consider whether pruning the tree might lead to improved\n",
@@ -463,14 +480,15 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "c663a623",
+   "id": "0534db0a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:41.859695Z",
-     "iopub.status.busy": "2023-07-31T04:30:41.859181Z",
-     "iopub.status.idle": "2023-07-31T04:30:41.871569Z",
-     "shell.execute_reply": "2023-07-31T04:30:41.869736Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:28.481429Z",
+     "iopub.status.busy": "2023-08-06T17:35:28.480921Z",
+     "iopub.status.idle": "2023-08-06T17:35:28.488563Z",
+     "shell.execute_reply": "2023-08-06T17:35:28.485938Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -486,23 +504,24 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e618049",
+   "id": "666f4683",
    "metadata": {},
    "source": [
-    "We first refit the full tree on the training set; here we do not set a `max_depth` parameter, since we will learn that through cross-validation."
+    "We first refit the full tree on the training set; here we do not set a `max_depth` parameter, since we will learn that through cross-validation.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "c2f2a403",
+   "id": "32518104",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:41.875825Z",
-     "iopub.status.busy": "2023-07-31T04:30:41.875521Z",
-     "iopub.status.idle": "2023-07-31T04:30:41.887622Z",
-     "shell.execute_reply": "2023-07-31T04:30:41.886986Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:28.492422Z",
+     "iopub.status.busy": "2023-08-06T17:35:28.491746Z",
+     "iopub.status.idle": "2023-08-06T17:35:28.500287Z",
+     "shell.execute_reply": "2023-08-06T17:35:28.499728Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -519,41 +538,42 @@
    "source": [
     "clf = DTC(criterion='entropy', random_state=0)\n",
     "clf.fit(X_train, High_train)\n",
-    "accuracy_score(High_test, clf.predict(X_test))"
+    "accuracy_score(High_test, clf.predict(X_test))\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bb59eecd",
+   "id": "fa0ef12b",
    "metadata": {},
    "source": [
     "Next we use the `cost_complexity_pruning_path()` method of\n",
-    "`clf` to extract cost-complexity values."
+    "`clf` to extract cost-complexity values. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "b2505658",
+   "id": "72399ed8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:41.891688Z",
-     "iopub.status.busy": "2023-07-31T04:30:41.891302Z",
-     "iopub.status.idle": "2023-07-31T04:30:41.900931Z",
-     "shell.execute_reply": "2023-07-31T04:30:41.900021Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:28.505447Z",
+     "iopub.status.busy": "2023-08-06T17:35:28.504547Z",
+     "iopub.status.idle": "2023-08-06T17:35:28.512277Z",
+     "shell.execute_reply": "2023-08-06T17:35:28.511610Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
     "ccp_path = clf.cost_complexity_pruning_path(X_train, High_train)\n",
     "kfold = skm.KFold(10,\n",
     "                  random_state=1,\n",
-    "                  shuffle=True)"
+    "                  shuffle=True)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0fdf1106",
+   "id": "0ebebce3",
    "metadata": {},
    "source": [
     "This yields a set of impurities and $\\alpha$ values\n",
@@ -563,14 +583,15 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "09a7bd33",
+   "id": "65729550",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:41.906506Z",
-     "iopub.status.busy": "2023-07-31T04:30:41.906147Z",
-     "iopub.status.idle": "2023-07-31T04:30:42.181528Z",
-     "shell.execute_reply": "2023-07-31T04:30:42.181248Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:28.517138Z",
+     "iopub.status.busy": "2023-08-06T17:35:28.516471Z",
+     "iopub.status.idle": "2023-08-06T17:35:28.789396Z",
+     "shell.execute_reply": "2023-08-06T17:35:28.789088Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -591,12 +612,12 @@
     "                        cv=kfold,\n",
     "                        scoring='accuracy')\n",
     "grid.fit(X_train, High_train)\n",
-    "grid.best_score_"
+    "grid.best_score_\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ff585d2c",
+   "id": "9df5690f",
    "metadata": {},
    "source": [
     "Let’s take a look at the pruned true."
@@ -605,14 +626,15 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "a75dea32",
+   "id": "f08fac89",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:42.183092Z",
-     "iopub.status.busy": "2023-07-31T04:30:42.183007Z",
-     "iopub.status.idle": "2023-07-31T04:30:42.935282Z",
-     "shell.execute_reply": "2023-07-31T04:30:42.934918Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:28.791085Z",
+     "iopub.status.busy": "2023-08-06T17:35:28.790968Z",
+     "iopub.status.idle": "2023-08-06T17:35:29.552519Z",
+     "shell.execute_reply": "2023-08-06T17:35:29.552196Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -631,12 +653,12 @@
     "best_ = grid.best_estimator_\n",
     "plot_tree(best_,\n",
     "          feature_names=feature_names,\n",
-    "          ax=ax);"
+    "          ax=ax);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c81a06b3",
+   "id": "ad338c5d",
    "metadata": {},
    "source": [
     "This is quite a bushy tree. We could count the leaves, or query\n",
@@ -646,14 +668,15 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "3f1e3d19",
+   "id": "9d9f3282",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:42.937030Z",
-     "iopub.status.busy": "2023-07-31T04:30:42.936913Z",
-     "iopub.status.idle": "2023-07-31T04:30:42.939665Z",
-     "shell.execute_reply": "2023-07-31T04:30:42.939410Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:29.554224Z",
+     "iopub.status.busy": "2023-08-06T17:35:29.554116Z",
+     "iopub.status.idle": "2023-08-06T17:35:29.556614Z",
+     "shell.execute_reply": "2023-08-06T17:35:29.556269Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -668,12 +691,12 @@
     }
    ],
    "source": [
-    "best_.tree_.n_leaves"
+    "best_.tree_.n_leaves\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7a32f63a",
+   "id": "c7026950",
    "metadata": {},
    "source": [
     "The tree with 30 terminal\n",
@@ -685,14 +708,15 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "30f5c2f9",
+   "id": "acf1ff61",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:42.941120Z",
-     "iopub.status.busy": "2023-07-31T04:30:42.941021Z",
-     "iopub.status.idle": "2023-07-31T04:30:42.947592Z",
-     "shell.execute_reply": "2023-07-31T04:30:42.947326Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:29.558209Z",
+     "iopub.status.busy": "2023-08-06T17:35:29.558093Z",
+     "iopub.status.idle": "2023-08-06T17:35:29.564243Z",
+     "shell.execute_reply": "2023-08-06T17:35:29.563972Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -764,20 +788,22 @@
     "                     best_.predict(X_test)))\n",
     "confusion = confusion_table(best_.predict(X_test),\n",
     "                            High_test)\n",
-    "confusion"
+    "confusion\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "adbe5709",
+   "id": "18a49a50",
    "metadata": {},
    "source": [
-    "Now 72.0% of the test observations are correctly classified, which is slightly worse than the error for the full tree (with 35 leaves). So cross-validation has not helped us much here; it only pruned off 5 leaves, at a cost of a slightly worse error. These results would change if we were to change the random number seeds above; even though cross-validation gives an unbiased approach to model selection, it does have variance."
+    "Now 72.0% of the test observations are correctly classified, which is slightly worse than the error for the full tree (with 35 leaves). So cross-validation has not helped us much here; it only pruned off 5 leaves, at a cost of a slightly worse error. These results would change if we were to change the random number seeds above; even though cross-validation gives an unbiased approach to model selection, it does have variance.\n",
+    "\n",
+    "  "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ad50114a",
+   "id": "29a0f063",
    "metadata": {},
    "source": [
     "## Fitting Regression Trees\n",
@@ -788,13 +814,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "1ca6bc7a",
+   "id": "852e2e4e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:42.949053Z",
-     "iopub.status.busy": "2023-07-31T04:30:42.948951Z",
-     "iopub.status.idle": "2023-07-31T04:30:42.956250Z",
-     "shell.execute_reply": "2023-07-31T04:30:42.955993Z"
+     "iopub.execute_input": "2023-08-06T17:35:29.565881Z",
+     "iopub.status.busy": "2023-08-06T17:35:29.565776Z",
+     "iopub.status.idle": "2023-08-06T17:35:29.573325Z",
+     "shell.execute_reply": "2023-08-06T17:35:29.573078Z"
     }
    },
    "outputs": [],
@@ -803,28 +829,28 @@
     "model = MS(Boston.columns.drop('medv'), intercept=False)\n",
     "D = model.fit_transform(Boston)\n",
     "feature_names = list(D.columns)\n",
-    "X = np.asarray(D)"
+    "X = np.asarray(D)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ebebb4a7",
+   "id": "1c386d58",
    "metadata": {},
    "source": [
     "First, we split the data into training and test sets, and fit the tree\n",
-    "to the training data. Here we use 30% of the data for the test set."
+    "to the training data. Here we use 30% of the data for the test set.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "15cfb553",
+   "id": "2e09e93b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:42.957800Z",
-     "iopub.status.busy": "2023-07-31T04:30:42.957699Z",
-     "iopub.status.idle": "2023-07-31T04:30:42.960385Z",
-     "shell.execute_reply": "2023-07-31T04:30:42.960128Z"
+     "iopub.execute_input": "2023-08-06T17:35:29.574985Z",
+     "iopub.status.busy": "2023-08-06T17:35:29.574896Z",
+     "iopub.status.idle": "2023-08-06T17:35:29.577600Z",
+     "shell.execute_reply": "2023-08-06T17:35:29.577322Z"
     }
    },
    "outputs": [],
@@ -835,12 +861,12 @@
     " y_test) = skm.train_test_split(X,\n",
     "                                Boston['medv'],\n",
     "                                test_size=0.3,\n",
-    "                                random_state=0)"
+    "                                random_state=0)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ae21c490",
+   "id": "f60141a3",
    "metadata": {},
    "source": [
     "Having formed  our training  and test data sets, we fit the regression tree."
@@ -849,19 +875,19 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "d18820a3",
+   "id": "74aa3bf0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:42.961789Z",
-     "iopub.status.busy": "2023-07-31T04:30:42.961700Z",
-     "iopub.status.idle": "2023-07-31T04:30:43.237847Z",
-     "shell.execute_reply": "2023-07-31T04:30:43.237495Z"
+     "iopub.execute_input": "2023-08-06T17:35:29.579141Z",
+     "iopub.status.busy": "2023-08-06T17:35:29.579050Z",
+     "iopub.status.idle": "2023-08-06T17:35:29.856587Z",
+     "shell.execute_reply": "2023-08-06T17:35:29.856261Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 1200x1200 with 1 Axes>"
       ]
@@ -876,12 +902,12 @@
     "ax = subplots(figsize=(12,12))[1]\n",
     "plot_tree(reg,\n",
     "          feature_names=feature_names,\n",
-    "          ax=ax);"
+    "          ax=ax);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5a4deec7",
+   "id": "baeedcf4",
    "metadata": {},
    "source": [
     "The variable `lstat` measures the percentage of individuals with\n",
@@ -893,7 +919,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5cad860",
+   "id": "bf4867c8",
    "metadata": {},
    "source": [
     "Now we use the cross-validation function to see whether pruning\n",
@@ -903,13 +929,13 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "619f8d94",
+   "id": "9e563bf2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:43.239633Z",
-     "iopub.status.busy": "2023-07-31T04:30:43.239507Z",
-     "iopub.status.idle": "2023-07-31T04:30:43.285310Z",
-     "shell.execute_reply": "2023-07-31T04:30:43.285044Z"
+     "iopub.execute_input": "2023-08-06T17:35:29.858346Z",
+     "iopub.status.busy": "2023-08-06T17:35:29.858233Z",
+     "iopub.status.idle": "2023-08-06T17:35:29.904185Z",
+     "shell.execute_reply": "2023-08-06T17:35:29.903909Z"
     }
    },
    "outputs": [],
@@ -923,12 +949,12 @@
     "                        refit=True,\n",
     "                        cv=kfold,\n",
     "                        scoring='neg_mean_squared_error')\n",
-    "G = grid.fit(X_train, y_train)"
+    "G = grid.fit(X_train, y_train)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b971e775",
+   "id": "de0ed389",
    "metadata": {},
    "source": [
     "In keeping with the cross-validation results, we use the pruned tree\n",
@@ -938,14 +964,15 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "779a14b6",
+   "id": "3a635c52",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:43.287027Z",
-     "iopub.status.busy": "2023-07-31T04:30:43.286940Z",
-     "iopub.status.idle": "2023-07-31T04:30:43.290148Z",
-     "shell.execute_reply": "2023-07-31T04:30:43.289861Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:29.905726Z",
+     "iopub.status.busy": "2023-08-06T17:35:29.905630Z",
+     "iopub.status.idle": "2023-08-06T17:35:29.908294Z",
+     "shell.execute_reply": "2023-08-06T17:35:29.908021Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -961,12 +988,12 @@
    ],
    "source": [
     "best_ = grid.best_estimator_\n",
-    "np.mean((y_test - best_.predict(X_test))**2)"
+    "np.mean((y_test - best_.predict(X_test))**2)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "507d174f",
+   "id": "41a34a36",
    "metadata": {},
    "source": [
     "In other words, the test set MSE associated with the regression tree\n",
@@ -983,14 +1010,15 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "2b04030b",
+   "id": "5542ece6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:43.291793Z",
-     "iopub.status.busy": "2023-07-31T04:30:43.291674Z",
-     "iopub.status.idle": "2023-07-31T04:30:43.564079Z",
-     "shell.execute_reply": "2023-07-31T04:30:43.563765Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:29.909676Z",
+     "iopub.status.busy": "2023-08-06T17:35:29.909598Z",
+     "iopub.status.idle": "2023-08-06T17:35:30.183559Z",
+     "shell.execute_reply": "2023-08-06T17:35:30.183181Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1008,12 +1036,21 @@
     "ax = subplots(figsize=(12,12))[1]\n",
     "plot_tree(G.best_estimator_,\n",
     "          feature_names=feature_names,\n",
-    "          ax=ax);"
+    "          ax=ax);\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8e9aba94",
+   "id": "817eff2c",
+   "metadata": {},
+   "source": [
+    " \n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e4b98e4",
    "metadata": {},
    "source": [
     "## Bagging and Random Forests"
@@ -1021,7 +1058,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81afb557",
+   "id": "878b4a89",
    "metadata": {},
    "source": [
     "Here we apply bagging and random forests to the `Boston` data, using\n",
@@ -1034,14 +1071,15 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "7dedce31",
+   "id": "c82eb98b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:43.565753Z",
-     "iopub.status.busy": "2023-07-31T04:30:43.565635Z",
-     "iopub.status.idle": "2023-07-31T04:30:43.724357Z",
-     "shell.execute_reply": "2023-07-31T04:30:43.724077Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:30.185335Z",
+     "iopub.status.busy": "2023-08-06T17:35:30.185213Z",
+     "iopub.status.idle": "2023-08-06T17:35:30.352413Z",
+     "shell.execute_reply": "2023-08-06T17:35:30.352112Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1060,12 +1098,12 @@
    ],
    "source": [
     "bag_boston = RF(max_features=X_train.shape[1], random_state=0)\n",
-    "bag_boston.fit(X_train, y_train)"
+    "bag_boston.fit(X_train, y_train)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b020aef3",
+   "id": "06cf548a",
    "metadata": {},
    "source": [
     "The argument `max_features` indicates that all 12 predictors should\n",
@@ -1077,13 +1115,13 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "1f2c7336",
+   "id": "7d592004",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:43.725963Z",
-     "iopub.status.busy": "2023-07-31T04:30:43.725843Z",
-     "iopub.status.idle": "2023-07-31T04:30:43.814871Z",
-     "shell.execute_reply": "2023-07-31T04:30:43.814581Z"
+     "iopub.execute_input": "2023-08-06T17:35:30.353934Z",
+     "iopub.status.busy": "2023-08-06T17:35:30.353844Z",
+     "iopub.status.idle": "2023-08-06T17:35:30.443422Z",
+     "shell.execute_reply": "2023-08-06T17:35:30.443112Z"
     }
    },
    "outputs": [
@@ -1112,12 +1150,12 @@
     "ax = subplots(figsize=(8,8))[1]\n",
     "y_hat_bag = bag_boston.predict(X_test)\n",
     "ax.scatter(y_hat_bag, y_test)\n",
-    "np.mean((y_test - y_hat_bag)**2)"
+    "np.mean((y_test - y_hat_bag)**2)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6e6db784",
+   "id": "f38f7522",
    "metadata": {},
    "source": [
     "The test set MSE associated with the bagged regression tree is\n",
@@ -1130,14 +1168,15 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "0bba2439",
+   "id": "892aa556",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:43.816519Z",
-     "iopub.status.busy": "2023-07-31T04:30:43.816405Z",
-     "iopub.status.idle": "2023-07-31T04:30:44.608840Z",
-     "shell.execute_reply": "2023-07-31T04:30:44.608555Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:30.445151Z",
+     "iopub.status.busy": "2023-08-06T17:35:30.445025Z",
+     "iopub.status.idle": "2023-08-06T17:35:31.282001Z",
+     "shell.execute_reply": "2023-08-06T17:35:31.281728Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1161,7 +1200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fa447de",
+   "id": "6c2a8868",
    "metadata": {},
    "source": [
     "There is not much change. Bagging and random forests cannot overfit by\n",
@@ -1178,14 +1217,15 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "90457fbe",
+   "id": "35636d87",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:44.610512Z",
-     "iopub.status.busy": "2023-07-31T04:30:44.610403Z",
-     "iopub.status.idle": "2023-07-31T04:30:44.716253Z",
-     "shell.execute_reply": "2023-07-31T04:30:44.715979Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:31.283642Z",
+     "iopub.status.busy": "2023-08-06T17:35:31.283529Z",
+     "iopub.status.idle": "2023-08-06T17:35:31.394819Z",
+     "shell.execute_reply": "2023-08-06T17:35:31.394556Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1203,12 +1243,12 @@
     "RF_boston = RF(max_features=6,\n",
     "               random_state=0).fit(X_train, y_train)\n",
     "y_hat_RF = RF_boston.predict(X_test)\n",
-    "np.mean((y_test - y_hat_RF)**2)"
+    "np.mean((y_test - y_hat_RF)**2)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f9ddb8fa",
+   "id": "a6edace1",
    "metadata": {},
    "source": [
     "The test set MSE is 20.04;\n",
@@ -1220,14 +1260,15 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "8aae1857",
+   "id": "b5a0869f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:44.717793Z",
-     "iopub.status.busy": "2023-07-31T04:30:44.717706Z",
-     "iopub.status.idle": "2023-07-31T04:30:44.723886Z",
-     "shell.execute_reply": "2023-07-31T04:30:44.723635Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:31.396329Z",
+     "iopub.status.busy": "2023-08-06T17:35:31.396243Z",
+     "iopub.status.idle": "2023-08-06T17:35:31.402359Z",
+     "shell.execute_reply": "2023-08-06T17:35:31.402086Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1337,7 +1378,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74b9ffb6",
+   "id": "4acf5bdc",
    "metadata": {},
    "source": [
     " This\n",
@@ -1346,12 +1387,14 @@
     "\n",
     "The results indicate that across all of the trees considered in the\n",
     "random forest, the wealth level of the community (`lstat`) and the\n",
-    "house size (`rm`) are by far the two most important variables."
+    "house size (`rm`) are by far the two most important variables.\n",
+    "\n",
+    "  "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "82ed6937",
+   "id": "9a07b01d",
    "metadata": {},
    "source": [
     "## Boosting"
@@ -1359,7 +1402,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d420075c",
+   "id": "f2b3dde2",
    "metadata": {},
    "source": [
     "Here we use `GradientBoostingRegressor()` from `sklearn.ensemble`\n",
@@ -1375,13 +1418,13 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "887b4fa9",
+   "id": "844fce26",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:44.725445Z",
-     "iopub.status.busy": "2023-07-31T04:30:44.725345Z",
-     "iopub.status.idle": "2023-07-31T04:30:48.357276Z",
-     "shell.execute_reply": "2023-07-31T04:30:48.357015Z"
+     "iopub.execute_input": "2023-08-06T17:35:31.403876Z",
+     "iopub.status.busy": "2023-08-06T17:35:31.403792Z",
+     "iopub.status.idle": "2023-08-06T17:35:35.087934Z",
+     "shell.execute_reply": "2023-08-06T17:35:35.087643Z"
     }
    },
    "outputs": [
@@ -1407,12 +1450,12 @@
     "                   learning_rate=0.001,\n",
     "                   max_depth=3,\n",
     "                   random_state=0)\n",
-    "boost_boston.fit(X_train, y_train)"
+    "boost_boston.fit(X_train, y_train)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "97019e71",
+   "id": "15f35667",
    "metadata": {},
    "source": [
     "We can see how the training error decreases with the `train_score_` attribute.\n",
@@ -1423,13 +1466,13 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "6c56696c",
+   "id": "eae8687c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:48.358860Z",
-     "iopub.status.busy": "2023-07-31T04:30:48.358753Z",
-     "iopub.status.idle": "2023-07-31T04:30:48.808502Z",
-     "shell.execute_reply": "2023-07-31T04:30:48.808208Z"
+     "iopub.execute_input": "2023-08-06T17:35:35.089607Z",
+     "iopub.status.busy": "2023-08-06T17:35:35.089492Z",
+     "iopub.status.idle": "2023-08-06T17:35:35.561123Z",
+     "shell.execute_reply": "2023-08-06T17:35:35.560847Z"
     }
    },
    "outputs": [
@@ -1459,12 +1502,12 @@
     "        test_error,\n",
     "        'r',\n",
     "        label='Test')\n",
-    "ax.legend();"
+    "ax.legend();\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f1a851f2",
+   "id": "e0499e04",
    "metadata": {},
    "source": [
     "We now use the boosted model to predict `medv` on the test set:"
@@ -1473,13 +1516,13 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "8a636f63",
+   "id": "46947fc6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:48.810244Z",
-     "iopub.status.busy": "2023-07-31T04:30:48.810139Z",
-     "iopub.status.idle": "2023-07-31T04:30:48.821958Z",
-     "shell.execute_reply": "2023-07-31T04:30:48.821612Z"
+     "iopub.execute_input": "2023-08-06T17:35:35.562826Z",
+     "iopub.status.busy": "2023-08-06T17:35:35.562711Z",
+     "iopub.status.idle": "2023-08-06T17:35:35.574783Z",
+     "shell.execute_reply": "2023-08-06T17:35:35.574501Z"
     }
    },
    "outputs": [
@@ -1496,12 +1539,12 @@
    ],
    "source": [
     "y_hat_boost = boost_boston.predict(X_test);\n",
-    "np.mean((y_test - y_hat_boost)**2)"
+    "np.mean((y_test - y_hat_boost)**2)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fb7f53af",
+   "id": "07d3ccd4",
    "metadata": {},
    "source": [
     " The test MSE obtained is 14.48,\n",
@@ -1514,14 +1557,15 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "4e2ab3c7",
+   "id": "f67fdfec",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:48.823909Z",
-     "iopub.status.busy": "2023-07-31T04:30:48.823807Z",
-     "iopub.status.idle": "2023-07-31T04:30:51.196994Z",
-     "shell.execute_reply": "2023-07-31T04:30:51.196708Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:35.576453Z",
+     "iopub.status.busy": "2023-08-06T17:35:35.576344Z",
+     "iopub.status.idle": "2023-08-06T17:35:37.994899Z",
+     "shell.execute_reply": "2023-08-06T17:35:37.994597Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1543,21 +1587,23 @@
     "boost_boston.fit(X_train,\n",
     "                 y_train)\n",
     "y_hat_boost = boost_boston.predict(X_test);\n",
-    "np.mean((y_test - y_hat_boost)**2)"
+    "np.mean((y_test - y_hat_boost)**2)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9360b5b0",
+   "id": "f767c57a",
    "metadata": {},
    "source": [
     "In this case, using $\\lambda=0.2$ leads to a almost the same test MSE\n",
-    "as $\\lambda=0.001$."
+    "as when using $\\lambda=0.001$.\n",
+    "\n",
+    " "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1693f5da",
+   "id": "37cf0481",
    "metadata": {},
    "source": [
     "## Bayesian Additive Regression Trees"
@@ -1565,7 +1611,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47c83ab9",
+   "id": "c3a3419a",
    "metadata": {},
    "source": [
     "In this section we demonstrate a  `Python` implementation of BART found in the\n",
@@ -1578,14 +1624,15 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "b0b41c04",
+   "id": "06b92877",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:51.198695Z",
-     "iopub.status.busy": "2023-07-31T04:30:51.198593Z",
-     "iopub.status.idle": "2023-07-31T04:30:52.615191Z",
-     "shell.execute_reply": "2023-07-31T04:30:52.614890Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:37.996608Z",
+     "iopub.status.busy": "2023-08-06T17:35:37.996500Z",
+     "iopub.status.idle": "2023-08-06T17:35:39.436950Z",
+     "shell.execute_reply": "2023-08-06T17:35:39.436644Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1604,12 +1651,12 @@
    ],
    "source": [
     "bart_boston = BART(random_state=0, burnin=5, ndraw=15)\n",
-    "bart_boston.fit(X_train, y_train)"
+    "bart_boston.fit(X_train, y_train)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "af48867c",
+   "id": "d6d7ad70",
    "metadata": {},
    "source": [
     "On this data set, with this split into test and training, we see that the test error of BART is similar to that of  random forest."
@@ -1618,14 +1665,15 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "fda8fffc",
+   "id": "c09cb099",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:52.616789Z",
-     "iopub.status.busy": "2023-07-31T04:30:52.616675Z",
-     "iopub.status.idle": "2023-07-31T04:30:52.911570Z",
-     "shell.execute_reply": "2023-07-31T04:30:52.911292Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:39.438580Z",
+     "iopub.status.busy": "2023-08-06T17:35:39.438474Z",
+     "iopub.status.idle": "2023-08-06T17:35:39.742100Z",
+     "shell.execute_reply": "2023-08-06T17:35:39.741831Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -1641,12 +1689,12 @@
    ],
    "source": [
     "yhat_test = bart_boston.predict(X_test.astype(np.float32))\n",
-    "np.mean((y_test - yhat_test)**2)"
+    "np.mean((y_test - yhat_test)**2)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bad852b7",
+   "id": "bb48211a",
    "metadata": {},
    "source": [
     "We can check how many times each variable appeared in the collection of trees.\n",
@@ -1656,14 +1704,15 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "be77f0e4",
+   "id": "f9d6b394",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T04:30:52.913135Z",
-     "iopub.status.busy": "2023-07-31T04:30:52.913052Z",
-     "iopub.status.idle": "2023-07-31T04:30:52.915894Z",
-     "shell.execute_reply": "2023-07-31T04:30:52.915657Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:39.743981Z",
+     "iopub.status.busy": "2023-08-06T17:35:39.743858Z",
+     "iopub.status.idle": "2023-08-06T17:35:39.746694Z",
+     "shell.execute_reply": "2023-08-06T17:35:39.746443Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1692,20 +1741,26 @@
    "source": [
     "var_inclusion = pd.Series(bart_boston.variable_inclusion_.mean(0),\n",
     "                               index=D.columns)\n",
-    "var_inclusion"
+    "var_inclusion\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0aac2d50",
+   "metadata": {},
+   "source": [
+    "    \n",
+    "  \n",
+    " \n",
+    "\n"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "formats": "ipynb,md:myst",
-   "main_language": "python"
-  },
-  "kernelspec": {
-   "display_name": "Python3 (islp_freeze_311)",
-   "language": "python",
-   "name": "islp_freeze_311"
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/Ch9-svm-lab.ipynb b/Ch9-svm-lab.ipynb
index 0f272a8..1bb68a3 100644
--- a/Ch9-svm-lab.ipynb
+++ b/Ch9-svm-lab.ipynb
@@ -5,7 +5,9 @@
    "id": "d45c6d2b",
    "metadata": {},
    "source": [
-    "# Chapter 9"
+    "\n",
+    "# Chapter 9\n",
+    "\n"
    ]
   },
   {
@@ -26,18 +28,19 @@
    "id": "eeaa5be0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:31.933940Z",
-     "iopub.status.busy": "2023-07-31T02:15:31.933716Z",
-     "iopub.status.idle": "2023-07-31T02:15:32.774361Z",
-     "shell.execute_reply": "2023-07-31T02:15:32.773846Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:41.109844Z",
+     "iopub.status.busy": "2023-08-06T17:35:41.109434Z",
+     "iopub.status.idle": "2023-08-06T17:35:41.992454Z",
+     "shell.execute_reply": "2023-08-06T17:35:41.991881Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "from matplotlib.pyplot import subplots, cm\n",
     "import sklearn.model_selection as skm\n",
-    "from ISLP import load_data, confusion_table"
+    "from ISLP import load_data, confusion_table\n"
    ]
   },
   {
@@ -55,17 +58,17 @@
    "id": "41a59634",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:32.776623Z",
-     "iopub.status.busy": "2023-07-31T02:15:32.776343Z",
-     "iopub.status.idle": "2023-07-31T02:15:32.807761Z",
-     "shell.execute_reply": "2023-07-31T02:15:32.807471Z"
+     "iopub.execute_input": "2023-08-06T17:35:41.994815Z",
+     "iopub.status.busy": "2023-08-06T17:35:41.994482Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.026716Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.026409Z"
     }
    },
    "outputs": [],
    "source": [
     "from sklearn.svm import SVC\n",
     "from ISLP.svm import plot as plot_svm\n",
-    "from sklearn.metrics import RocCurveDisplay"
+    "from sklearn.metrics import RocCurveDisplay\n"
    ]
   },
   {
@@ -83,15 +86,15 @@
    "id": "c9a175d7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:32.809461Z",
-     "iopub.status.busy": "2023-07-31T02:15:32.809366Z",
-     "iopub.status.idle": "2023-07-31T02:15:32.811185Z",
-     "shell.execute_reply": "2023-07-31T02:15:32.810941Z"
+     "iopub.execute_input": "2023-08-06T17:35:42.028394Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.028303Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.030112Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.029879Z"
     }
    },
    "outputs": [],
    "source": [
-    "roc_curve = RocCurveDisplay.from_estimator # shorthand"
+    "roc_curve = RocCurveDisplay.from_estimator # shorthand\n"
    ]
   },
   {
@@ -123,11 +126,12 @@
    "id": "a7216b47",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:32.812622Z",
-     "iopub.status.busy": "2023-07-31T02:15:32.812526Z",
-     "iopub.status.idle": "2023-07-31T02:15:32.919064Z",
-     "shell.execute_reply": "2023-07-31T02:15:32.918299Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:42.031508Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.031412Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.144107Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.141512Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -150,7 +154,7 @@
     "ax.scatter(X[:,0],\n",
     "           X[:,1],\n",
     "           c=y,\n",
-    "           cmap=cm.coolwarm);"
+    "           cmap=cm.coolwarm);\n"
    ]
   },
   {
@@ -167,11 +171,12 @@
    "id": "ed329198",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:32.924346Z",
-     "iopub.status.busy": "2023-07-31T02:15:32.922626Z",
-     "iopub.status.idle": "2023-07-31T02:15:32.934623Z",
-     "shell.execute_reply": "2023-07-31T02:15:32.934041Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:42.148661Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.148275Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.164597Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.162951Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -190,7 +195,7 @@
    ],
    "source": [
     "svm_linear = SVC(C=10, kernel='linear')\n",
-    "svm_linear.fit(X, y)"
+    "svm_linear.fit(X, y)\n"
    ]
   },
   {
@@ -210,10 +215,10 @@
    "id": "95494b8b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:32.938730Z",
-     "iopub.status.busy": "2023-07-31T02:15:32.937883Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.117216Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.116876Z"
+     "iopub.execute_input": "2023-08-06T17:35:42.170134Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.169857Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.356574Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.356275Z"
     }
    },
    "outputs": [
@@ -233,7 +238,7 @@
     "plot_svm(X,\n",
     "         y,\n",
     "         svm_linear,\n",
-    "         ax=ax)"
+    "         ax=ax)\n"
    ]
   },
   {
@@ -255,11 +260,12 @@
    "id": "98c2236f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.119014Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.118891Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.258092Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.257817Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:42.358304Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.358185Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.497338Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.496986Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -280,7 +286,7 @@
     "plot_svm(X,\n",
     "         y,\n",
     "         svm_linear_small,\n",
-    "         ax=ax)"
+    "         ax=ax)\n"
    ]
   },
   {
@@ -300,11 +306,12 @@
    "id": "b498f594",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.259755Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.259627Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.262432Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.262105Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:42.499133Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.499013Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.501656Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.501370Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -319,7 +326,7 @@
     }
    ],
    "source": [
-    "svm_linear.coef_"
+    "svm_linear.coef_\n"
    ]
   },
   {
@@ -337,11 +344,12 @@
    "id": "b65e80d6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.263978Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.263877Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.290665Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.290398Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:42.503232Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.503114Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.530360Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.530089Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -365,7 +373,7 @@
     "                        cv=kfold,\n",
     "                        scoring='accuracy')\n",
     "grid.fit(X, y)\n",
-    "grid.best_params_"
+    "grid.best_params_\n"
    ]
   },
   {
@@ -384,11 +392,12 @@
    "id": "bba8fad7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.292219Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.292135Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.294550Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.294318Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:42.533039Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.532868Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.535494Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.535174Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -403,7 +412,7 @@
     }
    ],
    "source": [
-    "grid.cv_results_[('mean_test_score')]"
+    "grid.cv_results_[('mean_test_score')]\n"
    ]
   },
   {
@@ -424,17 +433,17 @@
    "id": "ad64269d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.296023Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.295928Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.297787Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.297542Z"
+     "iopub.execute_input": "2023-08-06T17:35:42.537255Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.537133Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.539335Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.538987Z"
     }
    },
    "outputs": [],
    "source": [
     "X_test = rng.standard_normal((20, 2))\n",
     "y_test = np.array([-1]*10+[1]*10)\n",
-    "X_test[y_test==1] += 1"
+    "X_test[y_test==1] += 1\n"
    ]
   },
   {
@@ -453,10 +462,10 @@
    "id": "5107fca1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.299207Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.299107Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.304700Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.304434Z"
+     "iopub.execute_input": "2023-08-06T17:35:42.540795Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.540697Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.546310Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.546056Z"
     }
    },
    "outputs": [
@@ -520,7 +529,7 @@
    "source": [
     "best_ = grid.best_estimator_\n",
     "y_test_hat = best_.predict(X_test)\n",
-    "confusion_table(y_test_hat, y_test)"
+    "confusion_table(y_test_hat, y_test)\n"
    ]
   },
   {
@@ -540,10 +549,10 @@
    "id": "0320d9e0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.306170Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.306087Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.310352Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.310107Z"
+     "iopub.execute_input": "2023-08-06T17:35:42.547949Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.547859Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.552481Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.552209Z"
     }
    },
    "outputs": [
@@ -608,7 +617,7 @@
     "svm_ = SVC(C=0.001,\n",
     "           kernel='linear').fit(X, y)\n",
     "y_test_hat = svm_.predict(X_test)\n",
-    "confusion_table(y_test_hat, y_test)"
+    "confusion_table(y_test_hat, y_test)\n"
    ]
   },
   {
@@ -631,10 +640,10 @@
    "id": "84d7e778",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.311857Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.311774Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.434376Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.434084Z"
+     "iopub.execute_input": "2023-08-06T17:35:42.554007Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.553921Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.677356Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.677072Z"
     }
    },
    "outputs": [
@@ -652,7 +661,7 @@
    "source": [
     "X[y==1] += 1.9;\n",
     "fig, ax = subplots(figsize=(8,8))\n",
-    "ax.scatter(X[:,0], X[:,1], c=y, cmap=cm.coolwarm);"
+    "ax.scatter(X[:,0], X[:,1], c=y, cmap=cm.coolwarm);\n"
    ]
   },
   {
@@ -669,10 +678,10 @@
    "id": "abb1f8be",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.436068Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.435948Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.440665Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.440404Z"
+     "iopub.execute_input": "2023-08-06T17:35:42.679000Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.678886Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.683759Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.683448Z"
     }
    },
    "outputs": [
@@ -736,7 +745,7 @@
    "source": [
     "svm_ = SVC(C=1e5, kernel='linear').fit(X, y)\n",
     "y_hat = svm_.predict(X)\n",
-    "confusion_table(y_hat, y)"
+    "confusion_table(y_hat, y)\n"
    ]
   },
   {
@@ -747,7 +756,7 @@
     "We fit the\n",
     "support vector classifier and plot the resulting hyperplane, using a\n",
     "very large value of `C` so that no observations are\n",
-    "misclassified."
+    "misclassified. "
    ]
   },
   {
@@ -756,11 +765,12 @@
    "id": "2e4ed2f5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.442227Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.442140Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.558482Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.558203Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:42.685443Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.685337Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.800013Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.799680Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -779,7 +789,7 @@
     "plot_svm(X,\n",
     "         y,\n",
     "         svm_,\n",
-    "         ax=ax)"
+    "         ax=ax)\n"
    ]
   },
   {
@@ -800,10 +810,10 @@
    "id": "164a611c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.560147Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.560028Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.565003Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.564720Z"
+     "iopub.execute_input": "2023-08-06T17:35:42.801762Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.801639Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.806674Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.806389Z"
     }
    },
    "outputs": [
@@ -867,7 +877,7 @@
    "source": [
     "svm_ = SVC(C=0.1, kernel='linear').fit(X, y)\n",
     "y_hat = svm_.predict(X)\n",
-    "confusion_table(y_hat, y)"
+    "confusion_table(y_hat, y)\n"
    ]
   },
   {
@@ -887,11 +897,12 @@
    "id": "c67591a1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.566489Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.566402Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.687614Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.687292Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:42.808296Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.808164Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.929878Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.929590Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
@@ -910,7 +921,7 @@
     "plot_svm(X,\n",
     "         y,\n",
     "         svm_,\n",
-    "         ax=ax)"
+    "         ax=ax)\n"
    ]
   },
   {
@@ -939,10 +950,10 @@
    "id": "322be574",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.689275Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.689167Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.691316Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.690975Z"
+     "iopub.execute_input": "2023-08-06T17:35:42.931502Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.931386Z",
+     "iopub.status.idle": "2023-08-06T17:35:42.933592Z",
+     "shell.execute_reply": "2023-08-06T17:35:42.933305Z"
     }
    },
    "outputs": [],
@@ -950,7 +961,7 @@
     "X = rng.standard_normal((200, 2))\n",
     "X[:100] += 2\n",
     "X[100:150] -= 2\n",
-    "y = np.array([1]*150+[2]*50)"
+    "y = np.array([1]*150+[2]*50)\n"
    ]
   },
   {
@@ -967,17 +978,18 @@
    "id": "04fda182",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.692938Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.692827Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.780286Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.779983Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:42.935158Z",
+     "iopub.status.busy": "2023-08-06T17:35:42.935059Z",
+     "iopub.status.idle": "2023-08-06T17:35:43.022251Z",
+     "shell.execute_reply": "2023-08-06T17:35:43.021963Z"
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1474d1090>"
+       "<matplotlib.collections.PathCollection at 0x17fa62650>"
       ]
      },
      "execution_count": 20,
@@ -1000,7 +1012,7 @@
     "ax.scatter(X[:,0],\n",
     "           X[:,1],\n",
     "           c=y,\n",
-    "           cmap=cm.coolwarm)"
+    "           cmap=cm.coolwarm)\n"
    ]
   },
   {
@@ -1019,10 +1031,10 @@
    "id": "0c2690d1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.782021Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.781908Z",
-     "iopub.status.idle": "2023-07-31T02:15:33.786110Z",
-     "shell.execute_reply": "2023-07-31T02:15:33.785823Z"
+     "iopub.execute_input": "2023-08-06T17:35:43.024031Z",
+     "iopub.status.busy": "2023-08-06T17:35:43.023910Z",
+     "iopub.status.idle": "2023-08-06T17:35:43.028070Z",
+     "shell.execute_reply": "2023-08-06T17:35:43.027774Z"
     }
    },
    "outputs": [
@@ -1049,7 +1061,7 @@
     "                                test_size=0.5,\n",
     "                                random_state=0)\n",
     "svm_rbf = SVC(kernel=\"rbf\", gamma=1, C=1)\n",
-    "svm_rbf.fit(X_train, y_train)"
+    "svm_rbf.fit(X_train, y_train)\n"
    ]
   },
   {
@@ -1058,7 +1070,7 @@
    "metadata": {},
    "source": [
     "The plot shows that the resulting SVM has a decidedly non-linear\n",
-    "boundary."
+    "boundary. "
    ]
   },
   {
@@ -1067,10 +1079,10 @@
    "id": "3eb171e8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:33.787740Z",
-     "iopub.status.busy": "2023-07-31T02:15:33.787646Z",
-     "iopub.status.idle": "2023-07-31T02:15:34.044862Z",
-     "shell.execute_reply": "2023-07-31T02:15:34.044553Z"
+     "iopub.execute_input": "2023-08-06T17:35:43.029639Z",
+     "iopub.status.busy": "2023-08-06T17:35:43.029527Z",
+     "iopub.status.idle": "2023-08-06T17:35:43.285558Z",
+     "shell.execute_reply": "2023-08-06T17:35:43.285220Z"
     }
    },
    "outputs": [
@@ -1090,7 +1102,7 @@
     "plot_svm(X_train,\n",
     "         y_train,\n",
     "         svm_rbf,\n",
-    "         ax=ax)"
+    "         ax=ax)\n"
    ]
   },
   {
@@ -1111,10 +1123,10 @@
    "id": "9a6b905b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:34.046569Z",
-     "iopub.status.busy": "2023-07-31T02:15:34.046449Z",
-     "iopub.status.idle": "2023-07-31T02:15:34.197967Z",
-     "shell.execute_reply": "2023-07-31T02:15:34.197661Z"
+     "iopub.execute_input": "2023-08-06T17:35:43.287370Z",
+     "iopub.status.busy": "2023-08-06T17:35:43.287120Z",
+     "iopub.status.idle": "2023-08-06T17:35:43.439266Z",
+     "shell.execute_reply": "2023-08-06T17:35:43.438933Z"
     }
    },
    "outputs": [
@@ -1136,7 +1148,7 @@
     "plot_svm(X_train,\n",
     "         y_train,\n",
     "         svm_rbf,\n",
-    "         ax=ax)"
+    "         ax=ax)\n"
    ]
   },
   {
@@ -1155,10 +1167,10 @@
    "id": "5ab01d6c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:34.199716Z",
-     "iopub.status.busy": "2023-07-31T02:15:34.199590Z",
-     "iopub.status.idle": "2023-07-31T02:15:34.297753Z",
-     "shell.execute_reply": "2023-07-31T02:15:34.297384Z"
+     "iopub.execute_input": "2023-08-06T17:35:43.440929Z",
+     "iopub.status.busy": "2023-08-06T17:35:43.440803Z",
+     "iopub.status.idle": "2023-08-06T17:35:43.533538Z",
+     "shell.execute_reply": "2023-08-06T17:35:43.533266Z"
     }
    },
    "outputs": [
@@ -1184,7 +1196,7 @@
     "                        cv=kfold,\n",
     "                        scoring='accuracy');\n",
     "grid.fit(X_train, y_train)\n",
-    "grid.best_params_"
+    "grid.best_params_\n"
    ]
   },
   {
@@ -1203,10 +1215,10 @@
    "id": "166a6acb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:34.299759Z",
-     "iopub.status.busy": "2023-07-31T02:15:34.299634Z",
-     "iopub.status.idle": "2023-07-31T02:15:34.531852Z",
-     "shell.execute_reply": "2023-07-31T02:15:34.531406Z"
+     "iopub.execute_input": "2023-08-06T17:35:43.535272Z",
+     "iopub.status.busy": "2023-08-06T17:35:43.535151Z",
+     "iopub.status.idle": "2023-08-06T17:35:43.767970Z",
+     "shell.execute_reply": "2023-08-06T17:35:43.767689Z"
     }
    },
    "outputs": [
@@ -1286,7 +1298,7 @@
     "         ax=ax)\n",
     "\n",
     "y_hat_test = best_svm.predict(X_test)\n",
-    "confusion_table(y_hat_test, y_test)"
+    "confusion_table(y_hat_test, y_test)\n"
    ]
   },
   {
@@ -1337,11 +1349,12 @@
    "id": "0607fc41",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:34.533790Z",
-     "iopub.status.busy": "2023-07-31T02:15:34.533662Z",
-     "iopub.status.idle": "2023-07-31T02:15:34.622732Z",
-     "shell.execute_reply": "2023-07-31T02:15:34.622432Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:43.769863Z",
+     "iopub.status.busy": "2023-08-06T17:35:43.769754Z",
+     "iopub.status.idle": "2023-08-06T17:35:43.862697Z",
+     "shell.execute_reply": "2023-08-06T17:35:43.862378Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1362,7 +1375,7 @@
     "          y_train,\n",
     "          name='Training',\n",
     "          color='r',\n",
-    "          ax=ax);"
+    "          ax=ax);\n"
    ]
   },
   {
@@ -1381,10 +1394,10 @@
    "id": "5211a882",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:34.624457Z",
-     "iopub.status.busy": "2023-07-31T02:15:34.624340Z",
-     "iopub.status.idle": "2023-07-31T02:15:34.760006Z",
-     "shell.execute_reply": "2023-07-31T02:15:34.759724Z"
+     "iopub.execute_input": "2023-08-06T17:35:43.864472Z",
+     "iopub.status.busy": "2023-08-06T17:35:43.864353Z",
+     "iopub.status.idle": "2023-08-06T17:35:44.004720Z",
+     "shell.execute_reply": "2023-08-06T17:35:44.004393Z"
     }
    },
    "outputs": [
@@ -1410,7 +1423,7 @@
     "          y_train,\n",
     "          name='Training $\\gamma=50$',\n",
     "          color='r',\n",
-    "          ax=ax);"
+    "          ax=ax);\n"
    ]
   },
   {
@@ -1430,10 +1443,10 @@
    "id": "12acc4ff",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:34.761683Z",
-     "iopub.status.busy": "2023-07-31T02:15:34.761571Z",
-     "iopub.status.idle": "2023-07-31T02:15:34.765670Z",
-     "shell.execute_reply": "2023-07-31T02:15:34.765355Z"
+     "iopub.execute_input": "2023-08-06T17:35:44.006556Z",
+     "iopub.status.busy": "2023-08-06T17:35:44.006435Z",
+     "iopub.status.idle": "2023-08-06T17:35:44.010443Z",
+     "shell.execute_reply": "2023-08-06T17:35:44.010158Z"
     }
    },
    "outputs": [],
@@ -1444,7 +1457,7 @@
     "          name='Test $\\gamma=50$',\n",
     "          color='b',\n",
     "          ax=ax)\n",
-    "fig;"
+    "fig;\n"
    ]
   },
   {
@@ -1461,10 +1474,10 @@
    "id": "21c81913",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:34.767320Z",
-     "iopub.status.busy": "2023-07-31T02:15:34.767196Z",
-     "iopub.status.idle": "2023-07-31T02:15:34.862379Z",
-     "shell.execute_reply": "2023-07-31T02:15:34.862109Z"
+     "iopub.execute_input": "2023-08-06T17:35:44.012048Z",
+     "iopub.status.busy": "2023-08-06T17:35:44.011950Z",
+     "iopub.status.idle": "2023-08-06T17:35:44.108784Z",
+     "shell.execute_reply": "2023-08-06T17:35:44.108364Z"
     }
    },
    "outputs": [
@@ -1492,7 +1505,7 @@
     "              y_,\n",
     "              name=name,\n",
     "              ax=ax,\n",
-    "              color=c)"
+    "              color=c)\n"
    ]
   },
   {
@@ -1516,10 +1529,10 @@
    "id": "2fff4fa8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:34.864078Z",
-     "iopub.status.busy": "2023-07-31T02:15:34.863962Z",
-     "iopub.status.idle": "2023-07-31T02:15:34.950893Z",
-     "shell.execute_reply": "2023-07-31T02:15:34.950574Z"
+     "iopub.execute_input": "2023-08-06T17:35:44.110511Z",
+     "iopub.status.busy": "2023-08-06T17:35:44.110386Z",
+     "iopub.status.idle": "2023-08-06T17:35:44.198408Z",
+     "shell.execute_reply": "2023-08-06T17:35:44.198028Z"
     }
    },
    "outputs": [
@@ -1540,7 +1553,7 @@
     "y = np.hstack([y, [0]*50])\n",
     "X[y==0,1] += 2\n",
     "fig, ax = subplots(figsize=(8,8))\n",
-    "ax.scatter(X[:,0], X[:,1], c=y, cmap=cm.coolwarm);"
+    "ax.scatter(X[:,0], X[:,1], c=y, cmap=cm.coolwarm);\n"
    ]
   },
   {
@@ -1557,11 +1570,12 @@
    "id": "5396f2df",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:34.952587Z",
-     "iopub.status.busy": "2023-07-31T02:15:34.952475Z",
-     "iopub.status.idle": "2023-07-31T02:15:35.530841Z",
-     "shell.execute_reply": "2023-07-31T02:15:35.530380Z"
-    }
+     "iopub.execute_input": "2023-08-06T17:35:44.200116Z",
+     "iopub.status.busy": "2023-08-06T17:35:44.200009Z",
+     "iopub.status.idle": "2023-08-06T17:35:44.778024Z",
+     "shell.execute_reply": "2023-08-06T17:35:44.777739Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1586,7 +1600,7 @@
     "         y,\n",
     "         svm_rbf_3,\n",
     "         scatter_cmap=cm.tab10,\n",
-    "         ax=ax)"
+    "         ax=ax)\n"
    ]
   },
   {
@@ -1620,10 +1634,10 @@
    "id": "f63c575e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:35.532698Z",
-     "iopub.status.busy": "2023-07-31T02:15:35.532574Z",
-     "iopub.status.idle": "2023-07-31T02:15:35.607009Z",
-     "shell.execute_reply": "2023-07-31T02:15:35.606701Z"
+     "iopub.execute_input": "2023-08-06T17:35:44.779762Z",
+     "iopub.status.busy": "2023-08-06T17:35:44.779640Z",
+     "iopub.status.idle": "2023-08-06T17:35:44.856770Z",
+     "shell.execute_reply": "2023-08-06T17:35:44.856276Z"
     }
    },
    "outputs": [
@@ -1640,7 +1654,7 @@
    ],
    "source": [
     "Khan = load_data('Khan')\n",
-    "Khan['xtrain'].shape, Khan['xtest'].shape"
+    "Khan['xtrain'].shape, Khan['xtest'].shape\n"
    ]
   },
   {
@@ -1657,7 +1671,7 @@
     "large number of features relative to the number of observations. This\n",
     "suggests that we should use a linear kernel, because the additional\n",
     "flexibility that will result from using a polynomial or radial kernel \n",
-    "is unnecessary."
+    "is unnecessary. "
    ]
   },
   {
@@ -1666,10 +1680,10 @@
    "id": "32091338",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:35.608711Z",
-     "iopub.status.busy": "2023-07-31T02:15:35.608596Z",
-     "iopub.status.idle": "2023-07-31T02:15:35.637522Z",
-     "shell.execute_reply": "2023-07-31T02:15:35.637236Z"
+     "iopub.execute_input": "2023-08-06T17:35:44.858879Z",
+     "iopub.status.busy": "2023-08-06T17:35:44.858701Z",
+     "iopub.status.idle": "2023-08-06T17:35:44.889028Z",
+     "shell.execute_reply": "2023-08-06T17:35:44.888737Z"
     }
    },
    "outputs": [
@@ -1758,7 +1772,7 @@
     "khan_linear = SVC(kernel='linear', C=10)\n",
     "khan_linear.fit(Khan['xtrain'], Khan['ytrain'])\n",
     "confusion_table(khan_linear.predict(Khan['xtrain']),\n",
-    "                Khan['ytrain'])"
+    "                Khan['ytrain'])\n"
    ]
   },
   {
@@ -1780,10 +1794,10 @@
    "id": "d9058023",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-31T02:15:35.639109Z",
-     "iopub.status.busy": "2023-07-31T02:15:35.639016Z",
-     "iopub.status.idle": "2023-07-31T02:15:35.649929Z",
-     "shell.execute_reply": "2023-07-31T02:15:35.649661Z"
+     "iopub.execute_input": "2023-08-06T17:35:44.891247Z",
+     "iopub.status.busy": "2023-08-06T17:35:44.891089Z",
+     "iopub.status.idle": "2023-08-06T17:35:44.902454Z",
+     "shell.execute_reply": "2023-08-06T17:35:44.902106Z"
     }
    },
    "outputs": [
@@ -1870,7 +1884,7 @@
    ],
    "source": [
     "confusion_table(khan_linear.predict(Khan['xtest']),\n",
-    "                Khan['ytest'])"
+    "                Khan['ytest'])\n"
    ]
   },
   {
@@ -1878,15 +1892,16 @@
    "id": "d0d5aba4",
    "metadata": {},
    "source": [
-    "We see that using `C=10` yields two test set errors on these data."
+    "We see that using `C=10` yields two test set errors on these data.\n",
+    "\n"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "formats": "ipynb,md:myst",
-   "main_language": "python"
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
   },
   "language_info": {
    "codemirror_mode": {