Fix refs again (#76)

* Ch2->Ch02 * fixed latex refs again, somehow crept back in * fixed the page refs, formats synced * unsynced * executed notebook besides 10 * warnings for lasso * allow saving of output in notebooks * Ch10 executed
2026-02-04 17:40:52 -08:00
1 parent 3d9af7c4b0
commit 6bf6160a3d
@@ -36,8 +36,8 @@ Please see the `Python` resources page on the book website [statlearning.com](ht
 You will need to install the `ISLP` package, which provides access to the datasets and custom-built functions that we provide.
 Inside a macOS or Linux terminal type `pip install ISLP`; this also installs most other packages needed in the labs. The `Python` resources page has a link to the `ISLP` documentation website.
-To run this lab, download the file `Ch2-statlearn-lab.ipynb` from the `Python` resources page. 
+To run this lab, download the file `Ch02-statlearn-lab.ipynb` from the `Python` resources page. 
-Now run the following code at the command line: `jupyter lab Ch2-statlearn-lab.ipynb`.
+Now run the following code at the command line: `jupyter lab Ch02-statlearn-lab.ipynb`.
 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.
@@ -72,7 +72,7 @@ print('fit a model with', 11, 'variables')
 The following command will provide information about the `print()` function.
 ```{python}
-print?
+# print?
 ```
@@ -222,7 +222,7 @@ documentation associated with the function `fun`, if it exists.
 We can try this for `np.array()`. 
 ```{python}
-np.array?
+# np.array?
 ```
 This documentation indicates that we could create a floating point array by passing a `dtype` argument into `np.array()`.
@@ -332,7 +332,7 @@ As mentioned above, there is an existing function to add a line to a plot --- `a
 Next we examine some diagnostic plots, several of which were discussed
-in Section~\ref{Ch3:problems.sec}.
+in Section 3.3.3.
 We can find the fitted values and residuals
 of the fit as attributes of the `results` object.
 Various influence measures describing the regression model
@@ -429,7 +429,7 @@ We can access the individual components of `results` by name
 and
 `np.sqrt(results.scale)` gives us the RSE.
-Variance inflation factors (section~\ref{Ch3:problems.sec}) are sometimes useful
+Variance inflation factors (section 3.3.3) are sometimes useful
 to assess the effect of collinearity in the model matrix of a regression model.
 We will compute the VIFs in our multiple regression fit, and use the opportunity to introduce the idea of *list comprehension*.
@@ -5,6 +5,7 @@
   "id": "18156290",
   "metadata": {},
   "source": [
    "\n",
    "# Linear Regression\n",
    "\n",
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch03-linreg-lab.ipynb\">\n",
@@ -30,10 +31,10 @@
   "id": "c5fcfd8f",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:27.021175Z",
+     "iopub.execute_input": "2026-02-05T00:46:33.021645Z",
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@@ -63,10 +64,10 @@
   "id": "42cfb62e",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:27.541322Z",
+     "iopub.execute_input": "2026-02-05T00:46:33.409453Z",
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@@ -96,10 +97,10 @@
   "id": "b387d3ac",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:29.413584Z",
+     "iopub.execute_input": "2026-02-05T00:46:58.508374Z",
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-     "shell.execute_reply": "2026-02-02T23:39:29.415791Z"
+     "shell.execute_reply": "2026-02-05T00:46:58.515799Z"
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   "outputs": [],
@@ -127,10 +128,10 @@
   "id": "307b7e22",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:29.417302Z",
+     "iopub.execute_input": "2026-02-05T00:46:58.518173Z",
-     "iopub.status.busy": "2026-02-02T23:39:29.417209Z",
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+     "iopub.status.idle": "2026-02-05T00:46:58.645367Z",
-     "shell.execute_reply": "2026-02-02T23:39:30.445414Z"
+     "shell.execute_reply": "2026-02-05T00:46:58.644881Z"
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   },
   "outputs": [],
@@ -159,10 +160,10 @@
   "id": "79099069",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.450007Z",
+     "iopub.execute_input": "2026-02-05T00:46:58.647130Z",
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@@ -240,10 +241,10 @@
   "id": "3f99195f",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.456778Z",
+     "iopub.execute_input": "2026-02-05T00:46:58.652673Z",
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@@ -259,12 +260,13 @@
       " '__array_finalize__',\n",
       " '__array_function__',\n",
       " '__array_interface__',\n",
-       " '__array_prepare__',\n",
+       " '__array_namespace__',\n",
       " '__array_priority__',\n",
       " '__array_struct__',\n",
       " '__array_ufunc__',\n",
       " '__array_wrap__',\n",
       " '__bool__',\n",
       " '__buffer__',\n",
       " '__class__',\n",
       " '__class_getitem__',\n",
       " '__complex__',\n",
@@ -366,6 +368,7 @@
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'data',\n",
       " 'device',\n",
       " 'diagonal',\n",
       " 'dot',\n",
       " 'dtype',\n",
@@ -380,6 +383,7 @@
       " 'item',\n",
       " 'itemset',\n",
       " 'itemsize',\n",
       " 'mT',\n",
       " 'max',\n",
       " 'mean',\n",
       " 'min',\n",
@@ -409,10 +413,10 @@
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'to_device',\n",
       " 'tobytes',\n",
       " 'tofile',\n",
       " 'tolist',\n",
       " 'tostring',\n",
       " 'trace',\n",
       " 'transpose',\n",
       " 'var',\n",
@@ -444,10 +448,10 @@
   "id": "f3f913ca",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.462699Z",
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@@ -455,7 +459,7 @@
    {
     "data": {
      "text/plain": [
-       "19"
+       "np.int64(19)"
      ]
     },
     "execution_count": 7,
@@ -469,7 +473,7 @@
  },
  {
   "cell_type": "markdown",
-   "id": "974fad08",
+   "id": "a091c385",
   "metadata": {},
   "source": [
    "    "
@@ -502,10 +506,10 @@
   "id": "d720e2c5",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.466013Z",
+     "iopub.execute_input": "2026-02-05T00:46:58.660790Z",
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+     "shell.execute_reply": "2026-02-05T00:46:58.667528Z"
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   "outputs": [
@@ -514,7 +518,7 @@
      "text/plain": [
       "Index(['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax',\n",
       "       'ptratio', 'lstat', 'medv'],\n",
-       "      dtype='object')"
+       "      dtype='str')"
      ]
     },
     "execution_count": 8,
@@ -546,10 +550,10 @@
   "id": "40e9b0d5",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.474574Z",
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+     "shell.execute_reply": "2026-02-05T00:46:58.675994Z"
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@@ -636,10 +640,10 @@
   "id": "c1d544d6",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.481313Z",
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@@ -671,10 +675,10 @@
   "id": "3d340a07",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.485397Z",
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@@ -782,10 +786,10 @@
   "id": "11b07af0",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.602109Z",
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@@ -877,10 +881,10 @@
   "id": "d5c7b370",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.610774Z",
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@@ -981,10 +985,10 @@
   "id": "5b205c0b",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.618348Z",
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   "outputs": [
@@ -1003,10 +1007,10 @@
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   601.6</td>\n",
       "</tr>\n",
       "<tr>\n",
-       "  <th>Date:</th>             <td>Mon, 02 Feb 2026</td> <th>  Prob (F-statistic):</th> <td>5.08e-88</td>\n",
+       "  <th>Date:</th>             <td>Wed, 04 Feb 2026</td> <th>  Prob (F-statistic):</th> <td>5.08e-88</td>\n",
       "</tr>\n",
       "<tr>\n",
-       "  <th>Time:</th>                 <td>15:39:30</td>     <th>  Log-Likelihood:    </th> <td> -1641.5</td>\n",
+       "  <th>Time:</th>                 <td>16:46:58</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",
@@ -1054,8 +1058,8 @@
       "\\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:}             & Mon, 02 Feb 2026 & \\textbf{  Prob (F-statistic):} &  5.08e-88   \\\\\n",
+       "\\textbf{Date:}             & Wed, 04 Feb 2026 & \\textbf{  Prob (F-statistic):} &  5.08e-88   \\\\\n",
-       "\\textbf{Time:}             &     15:39:30     & \\textbf{  Log-Likelihood:    } &   -1641.5   \\\\\n",
+       "\\textbf{Time:}             &     16:46:58     & \\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",
@@ -1090,8 +1094,8 @@
       "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:                Mon, 02 Feb 2026   Prob (F-statistic):           5.08e-88\n",
+       "Date:                Wed, 04 Feb 2026   Prob (F-statistic):           5.08e-88\n",
-       "Time:                        15:39:30   Log-Likelihood:                -1641.5\n",
+       "Time:                        16:46:58   Log-Likelihood:                -1641.5\n",
       "No. Observations:                 506   AIC:                             3287.\n",
       "Df Residuals:                     504   BIC:                             3295.\n",
       "Df Model:                           1                                         \n",
@@ -1137,10 +1141,10 @@
   "id": "4b024467",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.629123Z",
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@@ -1180,10 +1184,10 @@
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@@ -1264,10 +1268,10 @@
   "id": "768374f6",
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@@ -1302,10 +1306,10 @@
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+     "shell.execute_reply": "2026-02-05T00:46:58.821460Z"
    },
    "lines_to_next_cell": 0
   },
@@ -1341,10 +1345,10 @@
   "id": "ea9727ca",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.647303Z",
+     "iopub.execute_input": "2026-02-05T00:46:58.823291Z",
-     "iopub.status.busy": "2026-02-02T23:39:30.647164Z",
+     "iopub.status.busy": "2026-02-05T00:46:58.823170Z",
-     "iopub.status.idle": "2026-02-02T23:39:30.649480Z",
+     "iopub.status.idle": "2026-02-05T00:46:58.825730Z",
-     "shell.execute_reply": "2026-02-02T23:39:30.649231Z"
+     "shell.execute_reply": "2026-02-05T00:46:58.825179Z"
    },
    "lines_to_next_cell": 0
   },
@@ -1401,10 +1405,10 @@
   "id": "796c20f6",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.650981Z",
+     "iopub.execute_input": "2026-02-05T00:46:58.827130Z",
-     "iopub.status.busy": "2026-02-02T23:39:30.650877Z",
+     "iopub.status.busy": "2026-02-05T00:46:58.827013Z",
-     "iopub.status.idle": "2026-02-02T23:39:30.652739Z",
+     "iopub.status.idle": "2026-02-05T00:46:58.828993Z",
-     "shell.execute_reply": "2026-02-02T23:39:30.652483Z"
+     "shell.execute_reply": "2026-02-05T00:46:58.828629Z"
    },
    "lines_to_next_cell": 0
   },
@@ -1435,10 +1439,10 @@
   "id": "a258b8c0",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.654050Z",
+     "iopub.execute_input": "2026-02-05T00:46:58.830629Z",
-     "iopub.status.busy": "2026-02-02T23:39:30.653935Z",
+     "iopub.status.busy": "2026-02-05T00:46:58.830515Z",
-     "iopub.status.idle": "2026-02-02T23:39:30.655877Z",
+     "iopub.status.idle": "2026-02-05T00:46:58.832427Z",
-     "shell.execute_reply": "2026-02-02T23:39:30.655613Z"
+     "shell.execute_reply": "2026-02-05T00:46:58.831987Z"
    },
    "lines_to_next_cell": 0
   },
@@ -1475,17 +1479,17 @@
   "id": "56b75491",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.657094Z",
+     "iopub.execute_input": "2026-02-05T00:46:58.833622Z",
-     "iopub.status.busy": "2026-02-02T23:39:30.656997Z",
+     "iopub.status.busy": "2026-02-05T00:46:58.833525Z",
-     "iopub.status.idle": "2026-02-02T23:39:30.817942Z",
+     "iopub.status.idle": "2026-02-05T00:46:58.913864Z",
-     "shell.execute_reply": "2026-02-02T23:39:30.809440Z"
+     "shell.execute_reply": "2026-02-05T00:46:58.913175Z"
    },
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
@@ -1523,7 +1527,7 @@
   "metadata": {},
   "source": [
    "Next we examine some diagnostic plots, several of which were discussed\n",
-    "in Section~\\ref{Ch3:problems.sec}.\n",
+    "in Section 3.3.3.\n",
    "We can find the fitted values and residuals\n",
    "of the fit as attributes of the `results` object.\n",
    "Various influence measures describing the regression model\n",
@@ -1539,17 +1543,17 @@
   "id": "74be2100",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.830030Z",
+     "iopub.execute_input": "2026-02-05T00:46:58.915410Z",
-     "iopub.status.busy": "2026-02-02T23:39:30.829002Z",
+     "iopub.status.busy": "2026-02-05T00:46:58.915279Z",
-     "iopub.status.idle": "2026-02-02T23:39:30.947889Z",
+     "iopub.status.idle": "2026-02-05T00:46:58.975204Z",
-     "shell.execute_reply": "2026-02-02T23:39:30.947633Z"
+     "shell.execute_reply": "2026-02-05T00:46:58.974659Z"
    },
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
@@ -1587,10 +1591,10 @@
   "id": "db8bc925",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:30.949366Z",
+     "iopub.execute_input": "2026-02-05T00:46:58.976639Z",
-     "iopub.status.busy": "2026-02-02T23:39:30.949271Z",
+     "iopub.status.busy": "2026-02-05T00:46:58.976522Z",
-     "iopub.status.idle": "2026-02-02T23:39:31.019326Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.029267Z",
-     "shell.execute_reply": "2026-02-02T23:39:31.019079Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.028717Z"
    },
    "lines_to_next_cell": 0
   },
@@ -1598,7 +1602,7 @@
    {
     "data": {
      "text/plain": [
-       "374"
+       "np.int64(374)"
      ]
     },
     "execution_count": 24,
@@ -1607,7 +1611,7 @@
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": 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truncated
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
@@ -1655,10 +1659,10 @@
   "id": "e479373c",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:31.020681Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.030788Z",
-     "iopub.status.busy": "2026-02-02T23:39:31.020598Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.030664Z",
-     "iopub.status.idle": "2026-02-02T23:39:31.030887Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.043708Z",
-     "shell.execute_reply": "2026-02-02T23:39:31.030658Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.043288Z"
    },
    "lines_to_next_cell": 0
   },
@@ -1753,10 +1757,10 @@
   "id": "b903170c",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:31.032055Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.045283Z",
-     "iopub.status.busy": "2026-02-02T23:39:31.031989Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.045156Z",
-     "iopub.status.idle": "2026-02-02T23:39:31.034213Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.047873Z",
-     "shell.execute_reply": "2026-02-02T23:39:31.033996Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.047301Z"
    }
   },
   "outputs": [
@@ -1765,7 +1769,7 @@
      "text/plain": [
       "Index(['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax',\n",
       "       'ptratio', 'lstat'],\n",
-       "      dtype='object')"
+       "      dtype='str')"
      ]
     },
     "execution_count": 26,
@@ -1793,10 +1797,10 @@
   "id": "bbe47670",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:31.035261Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.049180Z",
-     "iopub.status.busy": "2026-02-02T23:39:31.035192Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.049072Z",
-     "iopub.status.idle": "2026-02-02T23:39:31.050874Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.071518Z",
-     "shell.execute_reply": "2026-02-02T23:39:31.050643Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.070822Z"
    }
   },
   "outputs": [
@@ -1969,10 +1973,10 @@
   "id": "a1484d00",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:31.052042Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.073962Z",
-     "iopub.status.busy": "2026-02-02T23:39:31.051970Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.073660Z",
-     "iopub.status.idle": "2026-02-02T23:39:31.067211Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.096434Z",
-     "shell.execute_reply": "2026-02-02T23:39:31.066979Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.095940Z"
    }
   },
   "outputs": [
@@ -2132,7 +2136,7 @@
    "and\n",
    "`np.sqrt(results.scale)` gives us the RSE.\n",
    "\n",
-    "Variance inflation factors (section~\\ref{Ch3:problems.sec}) are sometimes useful\n",
+    "Variance inflation factors (section 3.3.3) are sometimes useful\n",
    "to assess the effect of collinearity in the model matrix of a regression model.\n",
    "We will compute the VIFs in our multiple regression fit, and use the opportunity to introduce the idea of *list comprehension*.\n",
    "\n",
@@ -2157,10 +2161,10 @@
   "id": "46ac946c",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:31.068412Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.097952Z",
-     "iopub.status.busy": "2026-02-02T23:39:31.068335Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.097830Z",
-     "iopub.status.idle": "2026-02-02T23:39:31.074305Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.105816Z",
-     "shell.execute_reply": "2026-02-02T23:39:31.074088Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.105253Z"
    },
    "lines_to_next_cell": 0
   },
@@ -2289,10 +2293,10 @@
   "id": "b626ecb9",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:31.075380Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.107357Z",
-     "iopub.status.busy": "2026-02-02T23:39:31.075309Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.107227Z",
-     "iopub.status.idle": "2026-02-02T23:39:31.079549Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.112960Z",
-     "shell.execute_reply": "2026-02-02T23:39:31.079349Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.112543Z"
    },
    "lines_to_next_cell": 0
   },
@@ -2322,10 +2326,10 @@
   "id": "8bf77477",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:31.080739Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.114374Z",
-     "iopub.status.busy": "2026-02-02T23:39:31.080668Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.114282Z",
-     "iopub.status.idle": "2026-02-02T23:39:31.092282Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.129480Z",
-     "shell.execute_reply": "2026-02-02T23:39:31.092056Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.129041Z"
    },
    "lines_to_next_cell": 2
   },
@@ -2430,10 +2434,10 @@
   "id": "6289c3cb",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:31.093513Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.130928Z",
-     "iopub.status.busy": "2026-02-02T23:39:31.093439Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.130810Z",
-     "iopub.status.idle": "2026-02-02T23:39:31.104646Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.147592Z",
-     "shell.execute_reply": "2026-02-02T23:39:31.104378Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.147101Z"
    },
    "lines_to_next_cell": 0
   },
@@ -2548,10 +2552,10 @@
   "id": "b6542ac6",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:31.105936Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.148985Z",
-     "iopub.status.busy": "2026-02-02T23:39:31.105862Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.148888Z",
-     "iopub.status.idle": "2026-02-02T23:39:31.111397Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.154574Z",
-     "shell.execute_reply": "2026-02-02T23:39:31.111147Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.153934Z"
    },
    "lines_to_next_cell": 0
   },
@@ -2658,17 +2662,17 @@
   "id": "8e4c6004",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:31.112521Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.155864Z",
-     "iopub.status.busy": "2026-02-02T23:39:31.112440Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.155746Z",
-     "iopub.status.idle": "2026-02-02T23:39:31.190330Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.218783Z",
-     "shell.execute_reply": "2026-02-02T23:39:31.189998Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.218372Z"
    },
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
@@ -2714,10 +2718,10 @@
   "id": "a83f4bd2",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:31.191827Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.220662Z",
-     "iopub.status.busy": "2026-02-02T23:39:31.191712Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.220531Z",
-     "iopub.status.idle": "2026-02-02T23:39:31.196704Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.225313Z",
-     "shell.execute_reply": "2026-02-02T23:39:31.196474Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.224873Z"
    },
    "lines_to_next_cell": 0
   },
@@ -2727,7 +2731,7 @@
      "text/plain": [
       "Index(['Sales', 'CompPrice', 'Income', 'Advertising', 'Population', 'Price',\n",
       "       'ShelveLoc', 'Age', 'Education', 'Urban', 'US'],\n",
-       "      dtype='object')"
+       "      dtype='str')"
      ]
     },
     "execution_count": 35,
@@ -2765,10 +2769,10 @@
   "id": "24efc6f1",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:31.198021Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.226697Z",
-     "iopub.status.busy": "2026-02-02T23:39:31.197937Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.226604Z",
-     "iopub.status.idle": "2026-02-02T23:39:31.220399Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.257097Z",
-     "shell.execute_reply": "2026-02-02T23:39:31.220140Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.256625Z"
    },
    "lines_to_next_cell": 0
   },
@@ -2960,8 +2964,7 @@
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
-   "formats": "ipynb",
+   "formats": "ipynb"
   "main_language": "python"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
@@ -2978,7 +2981,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.10"
  }
 },
 "nbformat": 4,
@@ -394,7 +394,7 @@ lda.fit(X_train, L_train)
 ```
 Here we have used the list comprehensions introduced
-in Section~\ref{Ch3-linreg-lab:multivariate-goodness-of-fit}. Looking at our first line above, we see that the right-hand side is a list
+in Section 3.6.4. Looking at our first line above, we see that the right-hand side is a list
 of length two. This is because the code `for M in [X_train, X_test]` iterates over a list
 of length two. While here we loop over a list,
 the list comprehension method works when looping over any iterable object.
@@ -443,7 +443,7 @@ lda.scalings_
 ```
-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 (\ref{Ch4:bayes.multi}).
+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).
  If $-0.64\times `Lag1`  - 0.51 \times `Lag2` $ is large, then the LDA classifier will predict a market increase, and if it is small, then the LDA classifier will predict a market decline.
 ```{python}
@@ -452,7 +452,7 @@ lda_pred = lda.predict(X_test)
 ```
 As we observed in our comparison of classification methods
- (Section~\ref{Ch4:comparison.sec}),  the LDA and logistic
+ (Section 4.5),  the LDA and logistic
 regression predictions are almost identical.
 ```{python}
@@ -511,7 +511,7 @@ The LDA classifier above is the first classifier from the
 `sklearn` library. We will use several other objects
 from this library. The objects
 follow a common structure that simplifies tasks such as cross-validation,
-which we will see in Chapter~\ref{Ch5:resample}. Specifically,
+which we will see in Chapter 5. Specifically,
 the methods first create a generic classifier without
 referring to any data. This classifier is then fit
 to data with the `fit()`  method and predictions are
@@ -797,7 +797,7 @@ feature_std.std()
 ```
-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~\pageref{Ch4-varformula}.
+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 183.
 In this case it does not matter, as long as the variables are all on the same scale.
 Using the function `train_test_split()`  we now split the observations into a test set,
@@ -864,7 +864,7 @@ This is double the rate that one would obtain from random guessing.
 The number of neighbors in KNN is referred to as a *tuning parameter*, also referred to as a *hyperparameter*.
 We do not know *a priori* what value to use. It is therefore of interest
 to see how the classifier performs on test data as we vary these
-parameters. This can be achieved with a `for` loop, described in Section~\ref{Ch2-statlearn-lab:for-loops}.
+parameters. This can be achieved with a `for` loop, described in Section 2.3.8.
 Here we use a for loop to look at the accuracy of our classifier in the group predicted to purchase
 insurance as we vary the number of neighbors from 1 to 5:
@@ -891,7 +891,7 @@ As a comparison, we can also fit a logistic regression model to the
 data. This can also be done
 with `sklearn`, though by default it fits
 something like the *ridge regression* version
-of logistic regression, which we introduce in Chapter~\ref{Ch6:varselect}. This can
+of logistic regression, which we introduce in Chapter 6. This can
 be modified by appropriately setting the argument `C` below. Its default
 value is 1 but by setting it to a very large number, the algorithm converges to the same solution as the usual (unregularized)
 logistic regression estimator discussed above.
@@ -935,7 +935,7 @@ confusion_table(logit_labels, y_test)
 ```
 ## Linear and Poisson Regression on the Bikeshare Data
-Here we fit linear and  Poisson regression models to the `Bikeshare` data, as described in Section~\ref{Ch4:sec:pois}.
+Here we fit linear and  Poisson regression models to the `Bikeshare` data, as described in Section 4.6.
 The response `bikers` measures the number of bike rentals per hour
 in Washington, DC in the period 2010--2012.
@@ -976,7 +976,7 @@ variables constant, there are on average about 7 more riders in
 February than in January. Similarly there are about 16.5 more riders
 in March than in January.
-The results seen in Section~\ref{sec:bikeshare.linear}
+The results seen in Section 4.6.1
 used a slightly different coding of the variables `hr` and `mnth`, as follows:
 ```{python}
@@ -1030,7 +1030,7 @@ np.allclose(M_lm.fittedvalues, M2_lm.fittedvalues)
 ```
-To reproduce the left-hand side of Figure~\ref{Ch4:bikeshare}
+To reproduce the left-hand side of Figure 4.13
 we must first obtain the coefficient estimates associated with
 `mnth`. The coefficients for January through November can be obtained
 directly from the `M2_lm` object. The coefficient for December
@@ -1070,7 +1070,7 @@ ax_month.set_ylabel('Coefficient', fontsize=20);
 ```
-Reproducing the  right-hand plot in Figure~\ref{Ch4:bikeshare}  follows a similar process.
+Reproducing the  right-hand plot in Figure 4.13  follows a similar process.
 ```{python}
 coef_hr = S2[S2.index.str.contains('hr')]['coef']
@@ -1105,7 +1105,7 @@ M_pois = sm.GLM(Y, X2, family=sm.families.Poisson()).fit()
 ```
-We can plot the coefficients associated with `mnth` and `hr`, in order to reproduce  Figure~\ref{Ch4:bikeshare.pois}. We first complete these coefficients as before.
+We can plot the coefficients associated with `mnth` and `hr`, in order to reproduce  Figure 4.15. We first complete these coefficients as before.
 ```{python}
 S_pois = summarize(M_pois)
@@ -226,7 +226,7 @@ for i, d in enumerate(range(1,6)):
 cv_error
 ```
-As in Figure~\ref{Ch5:cvplot}, we see a sharp drop in the estimated test MSE between the linear and
+As in Figure 5.4, we see a sharp drop in the estimated test MSE between the linear and
 quadratic fits, but then no clear improvement from using higher-degree polynomials.
 Above we introduced the `outer()`  method of the `np.power()`
@@ -267,7 +267,7 @@ cv_error
 Notice that the computation time is much shorter than that of LOOCV.
 (In principle, the computation time for LOOCV for a least squares
 linear model should be faster than for $k$-fold CV, due to the
-availability of the formula~(\ref{Ch5:eq:LOOCVform})  for LOOCV;
+availability of the formula~(5.2)  for LOOCV;
 however, the generic `cross_validate()`  function does not make
 use of this formula.)  We still see little evidence that using cubic
 or higher-degree polynomial terms leads to a lower test error than simply
@@ -314,7 +314,7 @@ incurred by picking different random folds.
 ## The Bootstrap
 We illustrate the use of the bootstrap in the simple example
- {of Section~\ref{Ch5:sec:bootstrap},}  as well as on an example involving
+ {of Section 5.2,}  as well as on an example involving
 estimating the accuracy of the linear regression model on the  `Auto`
 data set.
 ### Estimating the Accuracy of a Statistic of Interest
@@ -329,8 +329,8 @@ in a dataframe.
 To illustrate the bootstrap, we
 start with a simple example.
 The  `Portfolio`  data set in the `ISLP` package is described
-in Section~\ref{Ch5:sec:bootstrap}. The goal is to estimate the
+in Section 5.2. The goal is to estimate the
-sampling variance of the parameter $\alpha$ given in formula~(\ref{Ch5:min.var}).  We will
+sampling variance of the parameter $\alpha$ given in formula~(5.7).  We will
 create a function
 `alpha_func()`, which takes as input a dataframe `D` assumed
 to have columns `X` and `Y`, as well as a
@@ -349,7 +349,7 @@ def alpha_func(D, idx):
 ```
 This function returns an estimate for $\alpha$
 based on applying the minimum
-    variance formula (\ref{Ch5:min.var}) to the observations indexed by
+    variance formula (5.7) to the observations indexed by
 the argument `idx`.  For instance, the following command
 estimates $\alpha$ using all 100 observations.
@@ -419,7 +419,7 @@ intercept and slope terms for the linear regression model that uses
 `horsepower` to predict `mpg` in the  `Auto`  data set. We
 will compare the estimates obtained using the bootstrap to those
 obtained using the formulas for ${\rm SE}(\hat{\beta}_0)$ and
-${\rm SE}(\hat{\beta}_1)$ described in Section~\ref{Ch3:secoefsec}.
+${\rm SE}(\hat{\beta}_1)$ described in Section 3.1.2.
 To use our `boot_SE()` function, we must write a function (its
 first argument)
@@ -488,7 +488,7 @@ This indicates that the bootstrap estimate for ${\rm SE}(\hat{\beta}_0)$ is
 0.85, and that the bootstrap
 estimate for ${\rm SE}(\hat{\beta}_1)$ is
 0.0074.  As discussed in
-Section~\ref{Ch3:secoefsec}, standard formulas can be used to compute
+Section 3.1.2, standard formulas can be used to compute
 the standard errors for the regression coefficients in a linear
 model. These can be obtained using the `summarize()`  function
 from `ISLP.sm`.
@@ -502,7 +502,7 @@ model_se
 The standard error estimates for $\hat{\beta}_0$ and $\hat{\beta}_1$
-obtained using the formulas  from Section~\ref{Ch3:secoefsec}  are
+obtained using the formulas  from Section 3.1.2  are
 0.717 for the
 intercept and
 0.006 for the
@@ -510,13 +510,13 @@ slope. Interestingly, these are somewhat different from the estimates
 obtained using the bootstrap.  Does this indicate a problem with the
 bootstrap? In fact, it suggests the opposite.  Recall that the
 standard formulas given in
- {Equation~\ref{Ch3:se.eqn} on page~\pageref{Ch3:se.eqn}}
+Equation 3.8 on page 75
 rely on certain assumptions. For example,
 they depend on the unknown parameter $\sigma^2$, the noise
 variance. We then estimate $\sigma^2$ using the RSS. Now although the
 formulas for the standard errors do not rely on the linear model being
 correct, the estimate for $\sigma^2$ does.  We see
- {in Figure~\ref{Ch3:polyplot} on page~\pageref{Ch3:polyplot}}  that there is
+in Figure 3.8 on page 99  that there is
 a non-linear relationship in the data, and so the residuals from a
 linear fit will be inflated, and so will $\hat{\sigma}^2$.  Secondly,
 the standard formulas assume (somewhat unrealistically) that the $x_i$
@@ -529,7 +529,7 @@ the results from `sm.OLS`.
 Below we compute the bootstrap standard error estimates and the
 standard linear regression estimates that result from fitting the
 quadratic model to the data. Since this model provides a good fit to
-the data (Figure~\ref{Ch3:polyplot}), there is now a better
+the data (Figure 3.8), there is now a better
 correspondence between the bootstrap estimates and the standard
 estimates of ${\rm SE}(\hat{\beta}_0)$, ${\rm SE}(\hat{\beta}_1)$ and
 ${\rm SE}(\hat{\beta}_2)$.
@@ -5,6 +5,7 @@
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   "metadata": {},
   "source": [
    "\n",
    "# Cross-Validation and the Bootstrap\n",
    "\n",
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch05-resample-lab.ipynb\">\n",
@@ -32,10 +33,10 @@
   "id": "f1deb5cc",
   "metadata": {
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@@ -136,10 +137,10 @@
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@@ -166,17 +167,17 @@
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+     "shell.execute_reply": "2026-02-05T00:47:06.536814Z"
    }
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   "outputs": [
    {
     "data": {
      "text/plain": [
-       "23.61661706966988"
+       "np.float64(23.616617069669893)"
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@@ -210,10 +211,10 @@
   "id": "50a66a97",
   "metadata": {
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@@ -253,10 +254,10 @@
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@@ -297,10 +298,10 @@
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@@ -380,10 +381,10 @@
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@@ -391,7 +392,7 @@
    {
     "data": {
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-       "24.23151351792924"
+       "np.float64(24.231513517929226)"
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@@ -448,10 +449,10 @@
   "id": "11226c85",
   "metadata": {
    "execution": {
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@@ -459,7 +460,7 @@
    {
     "data": {
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+       "array([24.23151352, 19.24821312, 19.33498406, 19.4244303 , 19.03321527])"
      ]
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     "execution_count": 10,
@@ -486,7 +487,7 @@
   "id": "a3a920ae",
   "metadata": {},
   "source": [
-    "As in Figure~\\ref{Ch5:cvplot}, we see a sharp drop in the estimated test MSE between the linear and\n",
+    "As in Figure 5.4, we see a sharp drop in the estimated test MSE between the linear and\n",
    "quadratic fits, but then no clear improvement from using higher-degree polynomials.\n",
    "\n",
    "Above we introduced the `outer()`  method of the `np.power()`\n",
@@ -505,10 +506,10 @@
   "id": "64b64d97",
   "metadata": {
    "execution": {
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@@ -547,10 +548,10 @@
   "id": "ca0f972f",
   "metadata": {
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@@ -558,7 +559,7 @@
    {
     "data": {
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-       "array([24.20766449, 19.18533142, 19.27626666, 19.47848403, 19.13720581])"
+       "array([24.20766449, 19.18533142, 19.27626666, 19.47848402, 19.13718373])"
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@@ -589,7 +590,7 @@
    "Notice that the computation time is much shorter than that of LOOCV.\n",
    "(In principle, the computation time for LOOCV for a least squares\n",
    "linear model should be faster than for $k$-fold CV, due to the\n",
-    "availability of the formula~(\\ref{Ch5:eq:LOOCVform})  for LOOCV;\n",
+    "availability of the formula~(5.2)  for LOOCV;\n",
    "however, the generic `cross_validate()`  function does not make\n",
    "use of this formula.)  We still see little evidence that using cubic\n",
    "or higher-degree polynomial terms leads to a lower test error than simply\n",
@@ -613,10 +614,10 @@
   "id": "080cdb29",
   "metadata": {
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-     "iopub.status.idle": "2026-02-02T23:39:40.525494Z",
+     "iopub.status.idle": "2026-02-05T00:47:08.726376Z",
-     "shell.execute_reply": "2026-02-02T23:39:40.525258Z"
+     "shell.execute_reply": "2026-02-05T00:47:08.725862Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "(23.802232661034168, 1.421845094109185)"
+       "(np.float64(23.802232661034168), np.float64(1.4218450941091916))"
      ]
     },
     "execution_count": 14,
@@ -699,7 +700,7 @@
    "\n",
    "## The Bootstrap\n",
    "We illustrate the use of the bootstrap in the simple example\n",
-    " {of Section~\\ref{Ch5:sec:bootstrap},}  as well as on an example involving\n",
+    " {of Section 5.2,}  as well as on an example involving\n",
    "estimating the accuracy of the linear regression model on the  `Auto`\n",
    "data set.\n",
    "### Estimating the Accuracy of a Statistic of Interest\n",
@@ -714,8 +715,8 @@
    "To illustrate the bootstrap, we\n",
    "start with a simple example.\n",
    "The  `Portfolio`  data set in the `ISLP` package is described\n",
-    "in Section~\\ref{Ch5:sec:bootstrap}. The goal is to estimate the\n",
+    "in Section 5.2. The goal is to estimate the\n",
-    "sampling variance of the parameter $\\alpha$ given in formula~(\\ref{Ch5:min.var}).  We will\n",
+    "sampling variance of the parameter $\\alpha$ given in formula~(5.7).  We will\n",
    "create a function\n",
    "`alpha_func()`, which takes as input a dataframe `D` assumed\n",
    "to have columns `X` and `Y`, as well as a\n",
@@ -731,10 +732,10 @@
   "id": "a4b6d9b3",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:40.526599Z",
+     "iopub.execute_input": "2026-02-05T00:47:08.727991Z",
-     "iopub.status.busy": "2026-02-02T23:39:40.526530Z",
+     "iopub.status.busy": "2026-02-05T00:47:08.727860Z",
-     "iopub.status.idle": "2026-02-02T23:39:40.529182Z",
+     "iopub.status.idle": "2026-02-05T00:47:08.731089Z",
-     "shell.execute_reply": "2026-02-02T23:39:40.528979Z"
+     "shell.execute_reply": "2026-02-05T00:47:08.730582Z"
    },
    "lines_to_next_cell": 0
   },
@@ -754,7 +755,7 @@
   "source": [
    "This function returns an estimate for $\\alpha$\n",
    "based on applying the minimum\n",
-    "    variance formula (\\ref{Ch5:min.var}) to the observations indexed by\n",
+    "    variance formula (5.7) to the observations indexed by\n",
    "the argument `idx`.  For instance, the following command\n",
    "estimates $\\alpha$ using all 100 observations."
   ]
@@ -765,17 +766,17 @@
   "id": "81498a11",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:40.530362Z",
+     "iopub.execute_input": "2026-02-05T00:47:08.732467Z",
-     "iopub.status.busy": "2026-02-02T23:39:40.530277Z",
+     "iopub.status.busy": "2026-02-05T00:47:08.732368Z",
-     "iopub.status.idle": "2026-02-02T23:39:40.532680Z",
+     "iopub.status.idle": "2026-02-05T00:47:08.735439Z",
-     "shell.execute_reply": "2026-02-02T23:39:40.532462Z"
+     "shell.execute_reply": "2026-02-05T00:47:08.734958Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "0.57583207459283"
+       "np.float64(0.57583207459283)"
      ]
     },
     "execution_count": 16,
@@ -804,10 +805,10 @@
   "id": "64fe1cb6",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:40.533876Z",
+     "iopub.execute_input": "2026-02-05T00:47:08.736816Z",
-     "iopub.status.busy": "2026-02-02T23:39:40.533810Z",
+     "iopub.status.busy": "2026-02-05T00:47:08.736715Z",
-     "iopub.status.idle": "2026-02-02T23:39:40.536977Z",
+     "iopub.status.idle": "2026-02-05T00:47:08.739777Z",
-     "shell.execute_reply": "2026-02-02T23:39:40.536767Z"
+     "shell.execute_reply": "2026-02-05T00:47:08.739384Z"
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   },
@@ -815,7 +816,7 @@
    {
     "data": {
      "text/plain": [
-       "0.6074452469619004"
+       "np.float64(0.6074452469619002)"
      ]
     },
     "execution_count": 17,
@@ -847,10 +848,10 @@
   "id": "dd16bbae",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:40.538089Z",
+     "iopub.execute_input": "2026-02-05T00:47:08.741328Z",
-     "iopub.status.busy": "2026-02-02T23:39:40.538010Z",
+     "iopub.status.busy": "2026-02-05T00:47:08.741207Z",
-     "iopub.status.idle": "2026-02-02T23:39:40.540142Z",
+     "iopub.status.idle": "2026-02-05T00:47:08.743628Z",
-     "shell.execute_reply": "2026-02-02T23:39:40.539916Z"
+     "shell.execute_reply": "2026-02-05T00:47:08.743222Z"
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    "lines_to_next_cell": 0
   },
@@ -892,17 +893,17 @@
   "id": "b42b4585",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:40.541333Z",
+     "iopub.execute_input": "2026-02-05T00:47:08.744970Z",
-     "iopub.status.busy": "2026-02-02T23:39:40.541248Z",
+     "iopub.status.busy": "2026-02-05T00:47:08.744866Z",
-     "iopub.status.idle": "2026-02-02T23:39:40.761713Z",
+     "iopub.status.idle": "2026-02-05T00:47:09.034568Z",
-     "shell.execute_reply": "2026-02-02T23:39:40.761472Z"
+     "shell.execute_reply": "2026-02-05T00:47:09.034067Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "0.09118176521277699"
+       "np.float64(0.09118176521277699)"
      ]
     },
     "execution_count": 19,
@@ -934,7 +935,7 @@
    "`horsepower` to predict `mpg` in the  `Auto`  data set. We\n",
    "will compare the estimates obtained using the bootstrap to those\n",
    "obtained using the formulas for ${\\rm SE}(\\hat{\\beta}_0)$ and\n",
-    "${\\rm SE}(\\hat{\\beta}_1)$ described in Section~\\ref{Ch3:secoefsec}.\n",
+    "${\\rm SE}(\\hat{\\beta}_1)$ described in Section 3.1.2.\n",
    "\n",
    "To use our `boot_SE()` function, we must write a function (its\n",
    "first argument)\n",
@@ -958,10 +959,10 @@
   "id": "6bc11784",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:40.762911Z",
+     "iopub.execute_input": "2026-02-05T00:47:09.036186Z",
-     "iopub.status.busy": "2026-02-02T23:39:40.762837Z",
+     "iopub.status.busy": "2026-02-05T00:47:09.036064Z",
-     "iopub.status.idle": "2026-02-02T23:39:40.764597Z",
+     "iopub.status.idle": "2026-02-05T00:47:09.038120Z",
-     "shell.execute_reply": "2026-02-02T23:39:40.764401Z"
+     "shell.execute_reply": "2026-02-05T00:47:09.037695Z"
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    "lines_to_next_cell": 0
   },
@@ -993,10 +994,10 @@
   "id": "740cd50c",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:40.765800Z",
+     "iopub.execute_input": "2026-02-05T00:47:09.039473Z",
-     "iopub.status.busy": "2026-02-02T23:39:40.765734Z",
+     "iopub.status.busy": "2026-02-05T00:47:09.039363Z",
-     "iopub.status.idle": "2026-02-02T23:39:40.767191Z",
+     "iopub.status.idle": "2026-02-05T00:47:09.041198Z",
-     "shell.execute_reply": "2026-02-02T23:39:40.766986Z"
+     "shell.execute_reply": "2026-02-05T00:47:09.040802Z"
    },
    "lines_to_next_cell": 0
   },
@@ -1026,10 +1027,10 @@
   "id": "ffb3ec50",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:40.768266Z",
+     "iopub.execute_input": "2026-02-05T00:47:09.042630Z",
-     "iopub.status.busy": "2026-02-02T23:39:40.768204Z",
+     "iopub.status.busy": "2026-02-05T00:47:09.042520Z",
-     "iopub.status.idle": "2026-02-02T23:39:40.789931Z",
+     "iopub.status.idle": "2026-02-05T00:47:09.074556Z",
-     "shell.execute_reply": "2026-02-02T23:39:40.789705Z"
+     "shell.execute_reply": "2026-02-05T00:47:09.074079Z"
    },
    "lines_to_next_cell": 0
   },
@@ -1077,10 +1078,10 @@
   "id": "7d561f70",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:40.791196Z",
+     "iopub.execute_input": "2026-02-05T00:47:09.076044Z",
-     "iopub.status.busy": "2026-02-02T23:39:40.791129Z",
+     "iopub.status.busy": "2026-02-05T00:47:09.075921Z",
-     "iopub.status.idle": "2026-02-02T23:39:42.690473Z",
+     "iopub.status.idle": "2026-02-05T00:47:11.975218Z",
-     "shell.execute_reply": "2026-02-02T23:39:42.690189Z"
+     "shell.execute_reply": "2026-02-05T00:47:11.974653Z"
    },
    "lines_to_next_cell": 2
   },
@@ -1115,7 +1116,7 @@
    "0.85, and that the bootstrap\n",
    "estimate for ${\\rm SE}(\\hat{\\beta}_1)$ is\n",
    "0.0074.  As discussed in\n",
-    "Section~\\ref{Ch3:secoefsec}, standard formulas can be used to compute\n",
+    "Section 3.1.2, standard formulas can be used to compute\n",
    "the standard errors for the regression coefficients in a linear\n",
    "model. These can be obtained using the `summarize()`  function\n",
    "from `ISLP.sm`."
@@ -1127,10 +1128,10 @@
   "id": "3888aa0a",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:42.692058Z",
+     "iopub.execute_input": "2026-02-05T00:47:11.976651Z",
-     "iopub.status.busy": "2026-02-02T23:39:42.691967Z",
+     "iopub.status.busy": "2026-02-05T00:47:11.976530Z",
-     "iopub.status.idle": "2026-02-02T23:39:42.737592Z",
+     "iopub.status.idle": "2026-02-05T00:47:11.995335Z",
-     "shell.execute_reply": "2026-02-02T23:39:42.737346Z"
+     "shell.execute_reply": "2026-02-05T00:47:11.994822Z"
    },
    "lines_to_next_cell": 2
   },
@@ -1160,7 +1161,7 @@
   "metadata": {},
   "source": [
    "The standard error estimates for $\\hat{\\beta}_0$ and $\\hat{\\beta}_1$\n",
-    "obtained using the formulas  from Section~\\ref{Ch3:secoefsec}  are\n",
+    "obtained using the formulas  from Section 3.1.2  are\n",
    "0.717 for the\n",
    "intercept and\n",
    "0.006 for the\n",
@@ -1168,13 +1169,13 @@
    "obtained using the bootstrap.  Does this indicate a problem with the\n",
    "bootstrap? In fact, it suggests the opposite.  Recall that the\n",
    "standard formulas given in\n",
-    " {Equation~\\ref{Ch3:se.eqn} on page~\\pageref{Ch3:se.eqn}}\n",
+    "Equation 3.8 on page 75\n",
    "rely on certain assumptions. For example,\n",
    "they depend on the unknown parameter $\\sigma^2$, the noise\n",
    "variance. We then estimate $\\sigma^2$ using the RSS. Now although the\n",
    "formulas for the standard errors do not rely on the linear model being\n",
    "correct, the estimate for $\\sigma^2$ does.  We see\n",
-    " {in Figure~\\ref{Ch3:polyplot} on page~\\pageref{Ch3:polyplot}}  that there is\n",
+    "in Figure 3.8 on page 99  that there is\n",
    "a non-linear relationship in the data, and so the residuals from a\n",
    "linear fit will be inflated, and so will $\\hat{\\sigma}^2$.  Secondly,\n",
    "the standard formulas assume (somewhat unrealistically) that the $x_i$\n",
@@ -1187,7 +1188,7 @@
    "Below we compute the bootstrap standard error estimates and the\n",
    "standard linear regression estimates that result from fitting the\n",
    "quadratic model to the data. Since this model provides a good fit to\n",
-    "the data (Figure~\\ref{Ch3:polyplot}), there is now a better\n",
+    "the data (Figure 3.8), there is now a better\n",
    "correspondence between the bootstrap estimates and the standard\n",
    "estimates of ${\\rm SE}(\\hat{\\beta}_0)$, ${\\rm SE}(\\hat{\\beta}_1)$ and\n",
    "${\\rm SE}(\\hat{\\beta}_2)$."
@@ -1199,10 +1200,10 @@
   "id": "acc3e32c",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:42.739037Z",
+     "iopub.execute_input": "2026-02-05T00:47:11.996827Z",
-     "iopub.status.busy": "2026-02-02T23:39:42.738882Z",
+     "iopub.status.busy": "2026-02-05T00:47:11.996698Z",
-     "iopub.status.idle": "2026-02-02T23:39:45.405121Z",
+     "iopub.status.idle": "2026-02-05T00:47:16.184970Z",
-     "shell.execute_reply": "2026-02-02T23:39:45.404784Z"
+     "shell.execute_reply": "2026-02-05T00:47:16.184301Z"
    }
   },
   "outputs": [
@@ -1242,10 +1243,10 @@
   "id": "dca5340c",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:39:45.406403Z",
+     "iopub.execute_input": "2026-02-05T00:47:16.186711Z",
-     "iopub.status.busy": "2026-02-02T23:39:45.406328Z",
+     "iopub.status.busy": "2026-02-05T00:47:16.186585Z",
-     "iopub.status.idle": "2026-02-02T23:39:45.416112Z",
+     "iopub.status.idle": "2026-02-05T00:47:16.197450Z",
-     "shell.execute_reply": "2026-02-02T23:39:45.415874Z"
+     "shell.execute_reply": "2026-02-05T00:47:16.197112Z"
    },
    "lines_to_next_cell": 0
   },
@@ -1272,7 +1273,7 @@
  },
  {
   "cell_type": "markdown",
-   "id": "e98297be",
+   "id": "d714b6a3",
   "metadata": {},
   "source": [
    "\n",
@@ -1283,8 +1284,7 @@
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
-   "formats": "ipynb",
+   "formats": "ipynb"
   "main_language": "python"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
@@ -1301,7 +1301,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.10"
  }
 },
 "nbformat": 4,
@@ -86,7 +86,7 @@ Hitters.shape
 ```
-We first choose the best model using forward selection based on $C_p$ (\ref{Ch6:eq:cp}). This score
+We first choose the best model using forward selection based on $C_p$ (6.2). This score
 is not built in as a metric to `sklearn`. We therefore define a function to compute it ourselves, and use
 it as a scorer. By default, `sklearn` tries to maximize a score, hence
  our scoring function  computes the negative $C_p$ statistic.
@@ -111,7 +111,7 @@ sigma2 = OLS(Y,X).fit().scale
 ```
-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~\ref{Ch5-resample-lab:the-bootstrap} to freeze the first argument with our estimate of $\sigma^2$.
+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 $\sigma^2$.
 ```{python}
 neg_Cp = partial(nCp, sigma2)
@@ -363,7 +363,7 @@ Since we
 standardize first, in order to find coefficient
 estimates on the original scale, we must *unstandardize*
 the coefficient estimates. The parameter
-$\lambda$ in (\ref{Ch6:ridge}) and (\ref{Ch6:LASSO}) is called `alphas` in `sklearn`. In order to
+$\lambda$ in (6.5) and (6.7) is called `alphas` in `sklearn`. In order to
 be consistent with the rest of this chapter, we use `lambdas`
 rather than `alphas` in what follows.  {At the time of publication, ridge fits like the one in code chunk [23] issue unwarranted convergence warning messages; we suppressed these when we filtered the warnings above.}
@@ -540,7 +540,7 @@ grid.best_params_['ridge__alpha']
 grid.best_estimator_
 ```
-Recall we set up the `kfold` object for 5-fold cross-validation on page~\pageref{line:choos-among-models}. We now plot the cross-validated MSE as a function of $-\log(\lambda)$, which has shrinkage decreasing from left
+Recall we set up the `kfold` object for 5-fold cross-validation on page 271. We now plot the cross-validated MSE as a function of $-\log(\lambda)$, which has shrinkage decreasing from left
 to right.
 ```{python}
@@ -640,7 +640,7 @@ not perform variable selection!
 ### Evaluating Test Error of Cross-Validated Ridge
 Choosing $\lambda$ using cross-validation provides a single regression
 estimator, similar to fitting a linear regression model as we saw in
-Chapter~\ref{Ch3:linreg}. It is therefore reasonable to estimate what its test error
+Chapter 3. It is therefore reasonable to estimate what its test error
 is. We run into a problem here in that cross-validation will have
 *touched* all of its data in choosing $\lambda$, hence we have no
 further data to estimate test error. A compromise is to do an initial
@@ -728,7 +728,7 @@ ax.set_ylabel('Standardized coefficiients', fontsize=20);
 ```
 The smallest cross-validated error is lower than the test set MSE of the null model
 and of least squares, and very similar to the test MSE of 115526.71 of ridge
-regression (page~\pageref{page:MSECVRidge}) with $\lambda$ chosen by cross-validation.
+regression (page 278) with $\lambda$ chosen by cross-validation.
 ```{python}
 np.min(tuned_lasso.mse_path_.mean(1))
@@ -776,11 +776,11 @@ Principal components regression (PCR) can be performed using
 `PCA()`  from the `sklearn.decomposition`
 module. We now apply PCR to the  `Hitters`  data, in order to
 predict `Salary`. Again, ensure that the missing values have
-been removed from the data, as described in Section~\ref{Ch6-varselect-lab:lab-1-subset-selection-methods}.
+been removed from the data, as described in Section 6.5.1.
 We use `LinearRegression()`  to fit the regression model
 here. Note that it fits an intercept by default, unlike
-the `OLS()` function seen earlier in Section~\ref{Ch6-varselect-lab:lab-1-subset-selection-methods}.
+the `OLS()` function seen earlier in Section 6.5.1.
 ```{python}
 pca = PCA(n_components=2)
@@ -864,7 +864,7 @@ cv_null = skm.cross_validate(linreg,
 The `explained_variance_ratio_`
 attribute of our `PCA` object provides the *percentage of variance explained* in the predictors and in the response using
 different numbers of components. This concept is discussed in greater
-detail in Section~\ref{Ch10:sec:pca}.
+detail in Section 12.2.
 ```{python}
 pipe.named_steps['pca'].explained_variance_ratio_
@@ -47,7 +47,7 @@ from ISLP.pygam import (approx_lam,
 ```
 ## Polynomial Regression and Step Functions
-We start by demonstrating how Figure~\ref{Ch7:fig:poly} can be reproduced.
+We start by demonstrating how Figure 7.1 can be reproduced.
 Let's  begin by loading the data.
 ```{python}
@@ -59,7 +59,7 @@ age = Wage['age']
 Throughout most of this lab, our response is `Wage['wage']`, which
 we have stored as `y` above. 
-As in Section~\ref{Ch3-linreg-lab:non-linear-transformations-of-the-predictors}, we will use the `poly()` function to create a model matrix
+As in Section 3.6.6, we will use the `poly()` function to create a model matrix
 that will fit a $4$th degree polynomial in `age`.
 ```{python}
@@ -73,11 +73,11 @@ summarize(M)
 This polynomial is constructed using the function `poly()`,
 which creates
 a special *transformer* `Poly()` (using `sklearn` terminology
-for feature transformations such as `PCA()` seen in Section \ref{Ch6-varselect-lab:principal-components-regression}) which
+for feature transformations such as `PCA()` seen in Section 6.5.3) which
 allows for easy evaluation of the polynomial at new data points. Here `poly()` is referred to as a *helper* function, and sets up the transformation; `Poly()` is the actual workhorse that computes the transformation. See also 
 the 
 discussion of transformations on
-page~\pageref{Ch3-linreg-lab:using-transformations-fit-and-transform}. 
+page 118.
 In the code above, the first line executes the `fit()` method
 using the dataframe
@@ -140,7 +140,7 @@ def plot_wage_fit(age_df,
 We include an argument `alpha` to `ax.scatter()`
 to add some transparency to the points. This provides a visual indication
 of density. Notice the use of the `zip()` function in the
-`for` loop above (see Section~\ref{Ch2-statlearn-lab:for-loops}).
+`for` loop above (see Section 2.3.8).
 We have three lines to plot, each with different colors and line
 types. Here `zip()` conveniently bundles these together as
 iterators in the loop. {In `Python`{} speak, an "iterator" is an object with a finite number of values, that can be iterated on, as in a loop.}
@@ -243,7 +243,7 @@ anova_lm(*[sm.OLS(y, X_).fit() for X_ in XEs])
 As an alternative to using hypothesis tests and ANOVA, we could choose
-the polynomial degree using cross-validation, as discussed in Chapter~\ref{Ch5:resample}.
+the polynomial degree using cross-validation, as discussed in Chapter 5.
 Next we consider the task of predicting whether an individual earns
 more than $250,000 per year. We proceed much as before, except
@@ -302,7 +302,7 @@ value do not cover each other up. This type of plot is often called a
 *rug plot*.
 In order to fit a step function, as discussed in
-Section~\ref{Ch7:sec:scolstep-function},   we first use the `pd.qcut()`
+Section 7.2,   we first use the `pd.qcut()`
 function to discretize `age` based on quantiles.  Then  we use `pd.get_dummies()` to create the
 columns of the model matrix for this categorical variable. Note that this function will
 include *all* columns for a given categorical, rather than the usual approach which drops one
@@ -334,7 +334,7 @@ evaluation functions are in the `scipy.interpolate` package;
 we have simply wrapped them as transforms
 similar to `Poly()` and `PCA()`.
-In Section~\ref{Ch7:sec:scolr-splin}, we saw
+In Section 7.4, we saw
 that regression splines can be fit by constructing an appropriate
 matrix of basis functions.  The `BSpline()`  function generates the
 entire matrix of basis functions for splines with the specified set of
@@ -349,7 +349,7 @@ bs_age.shape
 ```
 This results in a seven-column matrix, which is what is expected for a cubic-spline basis with 3 interior knots. 
 We can form this same matrix using the `bs()` object,
-which facilitates adding this to a model-matrix builder (as in `poly()` versus its workhorse `Poly()`) described in Section~\ref{Ch7-nonlin-lab:polynomial-regression-and-step-functions}.
+which facilitates adding this to a model-matrix builder (as in `poly()` versus its workhorse `Poly()`) described in Section 7.8.1.
 We now fit a cubic spline model to the `Wage`  data. 
@@ -458,7 +458,7 @@ of a model matrix with a particular smoothing operation:
 `s` for smoothing spline; `l` for linear, and `f` for factor or categorical variables.
 The argument `0` passed to `s` below indicates that this smoother will
 apply to the first column of a feature matrix. Below, we pass it a
-matrix with a single column: `X_age`. The argument `lam` is the penalty parameter $\lambda$ as discussed in Section~\ref{Ch7:sec5.2}.
+matrix with a single column: `X_age`. The argument `lam` is the penalty parameter $\lambda$ as discussed in Section 7.5.2.
 ```{python}
 X_age = np.asarray(age).reshape((-1,1))
@@ -548,7 +548,7 @@ The strength of generalized additive models lies in their ability to fit multiva
 We now fit a GAM by hand to predict
 `wage` using natural spline functions of `year` and `age`,
-treating `education` as a qualitative predictor, as in (\ref{Ch7:nsmod}).
+treating `education` as a qualitative predictor, as in (7.16).
 Since this is just a big linear regression model
 using an appropriate choice of basis functions, we can simply do this
 using the `sm.OLS()`  function.
@@ -631,9 +631,9 @@ ax.set_title('Partial dependence of year on wage', fontsize=20);
 ```
-We now fit the model (\ref{Ch7:nsmod})  using smoothing splines rather
+We now fit the model (7.16)  using smoothing splines rather
 than natural splines.  All of the
-terms in  (\ref{Ch7:nsmod})  are fit simultaneously, taking each other
+terms in  (7.16)  are fit simultaneously, taking each other
 into account to explain the response. The `pygam` package only works with matrices, so we must convert
 the categorical series `education` to its array representation, which can be found
 with the `cat.codes` attribute of `education`. As `year` only has 7 unique values, we
@@ -86,11 +86,11 @@ clf.fit(X, High)
 ```
-In our discussion of qualitative features in Section~\ref{ch3:sec3},
+In our discussion of qualitative features in Section 3.3,
 we noted that for a linear regression model such a feature could be
 represented by including a matrix of dummy variables (one-hot-encoding) in the model
 matrix, using the formula notation of `statsmodels`.
-As mentioned in Section~\ref{Ch8:decison.tree.sec}, there is a more
+As mentioned in Section 8.1, there is a more
 natural way to handle qualitative features when building a decision
 tree, that does not require such dummy variables; each split amounts to partitioning the levels into two groups.
 However, 
@@ -121,7 +121,7 @@ resid_dev
 ```
-This is closely related to the *entropy*, defined in (\ref{Ch8:eq:cross-entropy}).
+This is closely related to the *entropy*, defined in (8.7).
 A small deviance indicates a
 tree that provides a good fit to the (training) data.
@@ -158,7 +158,7 @@ on these data, we must estimate the test error rather than simply
 computing the training error. We split the observations into a
 training set and a test set, build the tree using the training set,
 and evaluate its performance on the test data. This pattern is
-similar to that in Chapter~\ref{Ch6:varselect}, with the linear models
+similar to that in Chapter 6, with the linear models
 replaced here by decision trees --- the code for validation
 is almost identical. This approach leads to correct predictions
 for 68.5% of the locations in the test data set.
@@ -431,7 +431,7 @@ feature_imp.sort_values(by='importance', ascending=False)
 ```
 This
 is a relative measure of the total decrease in node impurity that results from
-splits over that variable, averaged over all trees (this was plotted in Figure~\ref{Ch8:fig:varimp} for a model fit to the `Heart` data). 
+splits over that variable, averaged over all trees (this was plotted in Figure 8.9 for a model fit to the `Heart` data). 
 The results indicate that across all of the trees considered in the
 random forest, the wealth level of the community (`lstat`) and the
@@ -495,7 +495,7 @@ np.mean((y_test - y_hat_boost)**2)
 The test MSE obtained is 14.48,
 similar to the test MSE for bagging. If we want to, we can
 perform boosting with a different value of the shrinkage parameter
-$\lambda$ in  (\ref{Ch8:alphaboost}). The default value is 0.001, but
+$\lambda$ in  (8.10). The default value is 0.001, but
 this is easily modified.  Here we take $\lambda=0.2$.
 ```{python}
@@ -255,9 +255,9 @@ kernel we use `kernel="poly"`, and to fit an SVM with a
 radial kernel  we use
 `kernel="rbf"`.  In the former case we also use the
 `degree` argument to specify a degree for the polynomial kernel
-(this is $d$ in (\ref{Ch9:eq:polyd})), and in the latter case we use
+(this is $d$ in (9.22)), and in the latter case we use
 `gamma` to specify a value of $\gamma$ for the radial basis
-kernel  (\ref{Ch9:eq:radial}).
+kernel  (9.24).
 We first generate some data with a non-linear class boundary, as follows:
@@ -377,7 +377,7 @@ classifier, the fitted value for an observation $X= (X_1, X_2, \ldots,
 X_p)^T$ takes the form $\hat{\beta}_0 + \hat{\beta}_1 X_1 +
 \hat{\beta}_2 X_2 + \ldots + \hat{\beta}_p X_p$. For an SVM with a
 non-linear kernel, the equation that yields the fitted value is given
-in  (\ref{Ch9:eq:svmip}). The sign of the fitted value
+in  (9.23). The sign of the fitted value
 determines on which side of the decision boundary the observation
 lies. Therefore, the relationship between the fitted value and the
 class prediction for a given observation is simple: if the fitted
@@ -1,5 +1,4 @@
 # Deep Learning
 <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch10-deeplearning-lab.ipynb">
@@ -21,7 +20,8 @@ Much of our code is adapted from there, as well as the `pytorch_lightning` docum
 We start with several standard imports that we have  seen before.
 ```{python}
-import numpy as np, pandas as pd
+import numpy as np
 import pandas as pd
 from matplotlib.pyplot import subplots
 from sklearn.linear_model import \
     (LinearRegression,
@@ -57,7 +57,7 @@ the `torchmetrics` package has utilities to compute
 various metrics to evaluate performance when fitting
 a model. The `torchinfo` package provides a useful
 summary of the layers of a model. We use the `read_image()`
-function when loading test images in Section~\ref{Ch13-deeplearning-lab:using-pretrained-cnn-models}.
+function when loading test images in Section 10.9.4.
 If you have not already installed the packages `torchvision`
 and `torchinfo` you can install them by running
@@ -153,17 +153,19 @@ in our example applying the `ResNet50` model
 to some of our own images.
 The `json` module will be used to load
 a JSON file for looking up classes to identify the labels of the
-pictures in the `ResNet50` example.
+pictures in the `ResNet50` example. We'll also import `warnings` to filter
 out warnings when fitting the LASSO to the IMDB data.
 ```{python}
 from glob import glob
 import json
 import warnings
 ```
 ## Single Layer Network on Hitters Data
-We start by fitting the models in Section~\ref{Ch13:sec:when-use-deep} on the `Hitters` data.
+We start by fitting the models in Section 10.6 on the `Hitters` data.
 ```{python}
 Hitters = load_data('Hitters').dropna()
@@ -217,7 +219,7 @@ np.abs(Yhat_test - Y_test).mean()
 Next we fit the lasso using `sklearn`. We are using
 mean absolute error to select and evaluate a model, rather than mean squared error.
-The specialized solver we used in Section~\ref{Ch6-varselect-lab:lab-2-ridge-regression-and-the-lasso} uses only mean squared error. So here, with a bit more work,  we create a cross-validation grid and perform the cross-validation directly.  
+The specialized solver we used in Section 6.5.2 uses only mean squared error. So here, with a bit more work,  we create a cross-validation grid and perform the cross-validation directly.  
 We encode a pipeline with two steps: we first normalize the features using a `StandardScaler()` transform,
 and then fit the lasso without further normalization. 
@@ -439,7 +441,7 @@ hit_module = SimpleModule.regression(hit_model,
 ```
 By using the `SimpleModule.regression()` method,  we indicate that we will use squared-error loss as in
-(\ref{Ch13:eq:4}).
+(10.23).
 We have also asked for mean absolute error to be tracked as well
 in the metrics that are logged.
@@ -476,7 +478,7 @@ hit_trainer = Trainer(deterministic=True,
 hit_trainer.fit(hit_module, datamodule=hit_dm)
 ```
 At each step of SGD, the algorithm randomly selects 32 training observations for
-the computation of the gradient. Recall from Section~\ref{Ch13:sec:fitt-neur-netw}
+the computation of the gradient. Recall from Section 10.7
 that an epoch amounts to the number of SGD steps required to process $n$
 observations. Since the training set has
 $n=175$, and we specified a `batch_size` of 32 in the construction of  `hit_dm`, an epoch is $175/32=5.5$ SGD steps.
@@ -765,8 +767,8 @@ mnist_trainer.test(mnist_module,
                   datamodule=mnist_dm)
 ```
-Table~\ref{Ch13:tab:mnist} also reports the error rates resulting from LDA (Chapter~\ref{Ch4:classification}) and multiclass logistic
+Table 10.1 also reports the error rates resulting from LDA (Chapter 4) and multiclass logistic
-regression. For LDA we refer the reader to Section~\ref{Ch4-classification-lab:linear-discriminant-analysis}.
+regression. For LDA we refer the reader to Section 4.7.3.
 Although we could use the `sklearn` function `LogisticRegression()` to fit  
 multiclass logistic regression, we are set up here to fit such a model
 with `torch`.
@@ -871,7 +873,7 @@ for idx, (X_ ,Y_) in enumerate(cifar_dm.train_dataloader()):
 Before we start, we look at some of the training images; similar code produced
-Figure~\ref{Ch13:fig:cifar100} on page \pageref{Ch13:fig:cifar100}. The example below also illustrates
+Figure 10.5 on page 406. The example below also illustrates
 that `TensorDataset` objects can be indexed with integers --- we are choosing
 random images from the training data by indexing `cifar_train`. In order to display correctly,
 we must reorder the dimensions by a call to `np.transpose()`.
@@ -894,7 +896,7 @@ for i in range(5):
 Here the `imshow()` method recognizes from the shape of its argument that it is a 3-dimensional array, with the last dimension indexing the three RGB color channels.
 We specify a moderately-sized  CNN for
-demonstration purposes, similar in structure to Figure~\ref{Ch13:fig:DeepCNN}.
+demonstration purposes, similar in structure to Figure 10.8.
 We use several layers, each consisting of  convolution, ReLU, and max-pooling steps.
 We first define a module that defines one of these layers. As in our
 previous examples, we overwrite the `__init__()` and `forward()` methods
@@ -1034,7 +1036,7 @@ summary_plot(cifar_results,
             ax,
             col='accuracy',
             ylabel='Accuracy')
-ax.set_xticks(np.linspace(0, 10, 6).astype(int))
+ax.set_xticks(np.linspace(0, 30, 7).astype(int))
 ax.set_ylabel('Accuracy')
 ax.set_ylim([0, 1]);
 ```
@@ -1083,7 +1085,7 @@ clauses; if it works, we get the speedup, if it fails, nothing happens.
 ## Using Pretrained CNN Models
 We now show how to use a CNN pretrained on the  `imagenet` database to classify natural
-images, and demonstrate how we produced Figure~\ref{Ch13:fig:homeimages}.
+images, and demonstrate how we produced Figure 10.10.
 We copied six JPEG images from a digital photo album into the
 directory `book_images`. These images are available
 from the data section of  <www.statlearning.com>, the ISLP book website. Download `book_images.zip`; when
@@ -1192,7 +1194,7 @@ del(cifar_test,
 ## IMDB Document Classification
-We now implement models for sentiment classification (Section~\ref{Ch13:sec:docum-class})  on the `IMDB`
+We now implement models for sentiment classification (Section 10.4)  on the `IMDB`
 dataset. As mentioned above code block~8, we are using
 a preprocessed version of the `IMDB` dataset found in the
 `keras` package. As `keras` uses `tensorflow`, a different
@@ -1346,7 +1348,7 @@ matrix that is recognized by `sklearn.`
 ```
 Similar to what we did in
-Section~\ref{Ch13-deeplearning-lab:single-layer-network-on-hitters-data},
+Section 10.9.1,
 we construct a series of 50 values for the lasso reguralization parameter $\lambda$.
 ```{python}
@@ -1369,16 +1371,20 @@ logit = LogisticRegression(penalty='l1',
 ```
 The path of 50 values takes approximately 40 seconds to run.
 As in Chapter 6, we will filter out warnings, this time using a context manager.
 ```{python}
-coefs = []
+with warnings.catch_warnings():
-intercepts = []
+    warnings.simplefilter("ignore")
-for l in lam_val:
+    coefs = []
-    logit.C = 1/l
+    intercepts = []
-    logit.fit(X_train, Y_train)
+
-    coefs.append(logit.coef_.copy())
+    for l in lam_val:
-    intercepts.append(logit.intercept_)
+        logit.C = 1/l
        logit.fit(X_train, Y_train)
        coefs.append(logit.coef_.copy())
        intercepts.append(logit.intercept_)
 ```
@@ -1454,16 +1460,16 @@ del(imdb_model,
 ## Recurrent Neural Networks
 In this lab we fit the models illustrated in
-Section~\ref{Ch13:sec:recurr-neur-netw}.
+Section 10.5.
 ### Sequential Models for Document Classification
 Here we  fit a simple  LSTM RNN for sentiment prediction to
-the `IMDb` movie-review data, as discussed in Section~\ref{Ch13:sec:sequ-models-docum}.
+the `IMDb` movie-review data, as discussed in Section 10.5.1.
 For an RNN we use the sequence of words in a document, taking their
 order into account. We loaded the preprocessed 
 data at the beginning of
-Section~\ref{Ch13-deeplearning-lab:imdb-document-classification}.
+Section 10.9.5.
 A script that details the preprocessing can be found in the
 `ISLP` library. Notably, since more than 90% of the documents
 had fewer than 500 words, we set the document length to 500. For
@@ -1578,7 +1584,7 @@ del(lstm_model,
 ### Time Series Prediction
-We now show how to fit the models in Section~\ref{Ch13:sec:time-seri-pred}
+We now show how to fit the models in Section 10.5.2
 for  time series prediction.
 We first load and standardize the data.
@@ -10,9 +10,9 @@
 In this lab, we perform survival analyses on three separate data
-sets. In  Section~\ref{brain.cancer.sec} we analyze the  `BrainCancer` 
+sets. In  Section 11.8.1 we analyze the  `BrainCancer` 
-data  that was first described in Section~\ref{sec:KM}. In Section~\ref{time.to.pub.sec}, we examine the  `Publication` 
+data  that was first described in Section 11.3. In Section 11.8.2, we examine the  `Publication` 
-data  from Section~\ref{sec:pub}. Finally,  Section~\ref{call.center.sec}  explores
+data  from Section 11.5.4. Finally,  Section 11.8.3  explores
 a simulated call-center data set.
 We begin by importing some of our libraries at this top
@@ -82,7 +82,7 @@ observation. But some scientists might use the opposite coding. For
 the  `BrainCancer`  data set 35 patients died before the end of
 the study, so we are using the conventional coding.
-To begin the analysis, we re-create  the Kaplan-Meier survival curve shown in Figure~\ref{fig:survbrain}. The main
+To begin the analysis, we re-create  the Kaplan-Meier survival curve shown in Figure 11.2. The main
 package we will use for survival analysis
 is `lifelines`.
 The variable  `time`  corresponds to $y_i$, the time to the $i$th event (either censoring or
@@ -102,7 +102,7 @@ km_brain.plot(label='Kaplan Meier estimate', ax=ax)
 ```
 Next we create Kaplan-Meier survival curves that are stratified by
-`sex`, in order to reproduce  Figure~\ref{fig:survbrain2}.
+`sex`, in order to reproduce  Figure 11.3.
 We  do this using the `groupby()` method of  a  dataframe.
 This  method returns a generator that can
 be  iterated over in the `for` loop. In this case,
@@ -130,7 +130,7 @@ for sex, df in BrainCancer.groupby('sex'):
 ```
-As discussed in Section~\ref{sec:logrank}, we can perform a
+As discussed in Section 11.4, we can perform a
 log-rank test to compare the survival of males to females. We use
 the `logrank_test()` function from the `lifelines.statistics` module.
 The first two arguments are the event times, with the second
@@ -266,9 +266,9 @@ predicted_survival.plot(ax=ax);
 ## Publication Data
-The  `Publication`  data   presented in Section~\ref{sec:pub}  can be
+The  `Publication`  data   presented in Section 11.5.4  can be
 found in the `ISLP` package.
-We first reproduce Figure~\ref{fig:lauersurv}  by plotting the Kaplan-Meier curves
+We first reproduce Figure 11.5  by plotting the Kaplan-Meier curves
 stratified on the  `posres`  variable, which records whether the
 study had a positive or negative result.
@@ -323,7 +323,7 @@ of the study, and whether the study had positive or negative results.
 In this section, we will simulate survival data using the relationship
 between cumulative hazard and
-the survival function explored in Exercise \ref{ex:all3}.
+the survival function explored in Exercise 8.
 Our simulated data will represent the observed
 wait times (in seconds) for 2,000 customers who have phoned a call
 center.  In this context, censoring occurs if a customer hangs up
@@ -394,7 +394,7 @@ that the risk of a call being answered at Center B is 0.74 times the
 risk that it will be answered at Center A; in other words, the wait
 times are a bit longer at Center B.
-Recall from Section~\ref{Ch2-statlearn-lab:loading-data} the use of `lambda`
+Recall from Section 2.3.7 the use of `lambda`
 for creating short functions on the fly.
 We use the function
 `sim_time()` from the `ISLP.survival` package. This function
@@ -5,6 +5,7 @@
   "id": "13a32f39",
   "metadata": {},
   "source": [
    "\n",
    "# Survival Analysis\n",
    "\n",
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch11-surv-lab.ipynb\">\n",
@@ -20,9 +21,9 @@
   "metadata": {},
   "source": [
    " In this lab, we perform survival analyses on three separate data\n",
-    "sets. In  Section~\\ref{brain.cancer.sec} we analyze the  `BrainCancer` \n",
+    "sets. In  Section 11.8.1 we analyze the  `BrainCancer` \n",
-    "data  that was first described in Section~\\ref{sec:KM}. In Section~\\ref{time.to.pub.sec}, we examine the  `Publication` \n",
+    "data  that was first described in Section 11.3. In Section 11.8.2, we examine the  `Publication` \n",
-    "data  from Section~\\ref{sec:pub}. Finally,  Section~\\ref{call.center.sec}  explores\n",
+    "data  from Section 11.5.4. Finally,  Section 11.8.3  explores\n",
    "a simulated call-center data set.\n",
    "\n",
    "We begin by importing some of our libraries at this top\n",
@@ -37,10 +38,10 @@
   "id": "80c238e6",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:28.233456Z",
+     "iopub.execute_input": "2026-02-05T00:46:24.138888Z",
-     "iopub.status.busy": "2026-02-02T23:40:28.233170Z",
+     "iopub.status.busy": "2026-02-05T00:46:24.138775Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.349388Z",
+     "iopub.status.idle": "2026-02-05T00:46:58.531055Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.349073Z"
+     "shell.execute_reply": "2026-02-05T00:46:58.530449Z"
    }
   },
   "outputs": [],
@@ -67,13 +68,22 @@
   "id": "a236fb17",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.351117Z",
+     "iopub.execute_input": "2026-02-05T00:46:58.534797Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.350968Z",
+     "iopub.status.busy": "2026-02-05T00:46:58.534595Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.458922Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.067150Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.458662Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.066449Z"
    }
   },
-   "outputs": [],
+   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jonathantaylor/git-repos/ISLP/ISLP_labs/.venv/lib/python3.12/site-packages/ISLP/survival.py:24: SyntaxWarning: invalid escape sequence '\\c'\n",
      "  H_l(t) = e^l \\cdot H(t)\n"
     ]
    }
   ],
   "source": [
    "from lifelines import \\\n",
    "     (KaplanMeierFitter,\n",
@@ -100,17 +110,17 @@
   "id": "4dd4ddba",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.460452Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.070532Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.460361Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.070384Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.466324Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.081286Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.466096Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.080691Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "Index(['sex', 'diagnosis', 'loc', 'ki', 'gtv', 'stereo', 'status', 'time'], dtype='object')"
+       "Index(['sex', 'diagnosis', 'loc', 'ki', 'gtv', 'stereo', 'status', 'time'], dtype='str')"
      ]
     },
     "execution_count": 3,
@@ -138,10 +148,10 @@
   "id": "ae9435b1",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.467504Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.082752Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.467412Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.082634Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.470004Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.086240Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.469792Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.085810Z"
    },
    "lines_to_next_cell": 2
   },
@@ -149,9 +159,10 @@
    {
     "data": {
      "text/plain": [
       "sex\n",
       "Female    45\n",
       "Male      43\n",
-       "Name: sex, dtype: int64"
+       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
@@ -169,10 +180,10 @@
   "id": "a6e94032",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.471059Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.087801Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.470973Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.087700Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.473403Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.090768Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.473149Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.090351Z"
    },
    "lines_to_next_cell": 2
   },
@@ -180,11 +191,12 @@
    {
     "data": {
      "text/plain": [
       "diagnosis\n",
       "Meningioma    42\n",
       "HG glioma     22\n",
       "Other         14\n",
       "LG glioma      9\n",
-       "Name: diagnosis, dtype: int64"
+       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
@@ -202,10 +214,10 @@
   "id": "afa05da1",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.474413Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.092076Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.474348Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.091980Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.477359Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.094835Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.477157Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.094405Z"
    },
    "lines_to_next_cell": 2
   },
@@ -213,9 +225,10 @@
    {
     "data": {
      "text/plain": [
       "status\n",
       "0    53\n",
       "1    35\n",
-       "Name: status, dtype: int64"
+       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
@@ -240,7 +253,7 @@
    "the  `BrainCancer`  data set 35 patients died before the end of\n",
    "the study, so we are using the conventional coding.\n",
    "\n",
-    "To begin the analysis, we re-create  the Kaplan-Meier survival curve shown in Figure~\\ref{fig:survbrain}. The main\n",
+    "To begin the analysis, we re-create  the Kaplan-Meier survival curve shown in Figure 11.2. The main\n",
    "package we will use for survival analysis\n",
    "is `lifelines`.\n",
    "The variable  `time`  corresponds to $y_i$, the time to the $i$th event (either censoring or\n",
@@ -258,10 +271,10 @@
   "id": "34c7c534",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.478404Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.096438Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.478324Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.096323Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.594965Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.177555Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.592314Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.177119Z"
    }
   },
   "outputs": [
@@ -277,7 +290,7 @@
    },
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
@@ -299,7 +312,7 @@
   "metadata": {},
   "source": [
    "Next we create Kaplan-Meier survival curves that are stratified by\n",
-    "`sex`, in order to reproduce  Figure~\\ref{fig:survbrain2}.\n",
+    "`sex`, in order to reproduce  Figure 11.3.\n",
    "We  do this using the `groupby()` method of  a  dataframe.\n",
    "This  method returns a generator that can\n",
    "be  iterated over in the `for` loop. In this case,\n",
@@ -324,16 +337,16 @@
   "id": "5e742ebe",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.605351Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.185914Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.604913Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.185752Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.756933Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.262531Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.751150Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.262083Z"
    }
   },
   "outputs": [
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": 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truncated
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
@@ -356,7 +369,7 @@
   "id": "2d1575e8",
   "metadata": {},
   "source": [
-    "As discussed in Section~\\ref{sec:logrank}, we can perform a\n",
+    "As discussed in Section 11.4, we can perform a\n",
    "log-rank test to compare the survival of males to females. We use\n",
    "the `logrank_test()` function from the `lifelines.statistics` module.\n",
    "The first two arguments are the event times, with the second\n",
@@ -369,10 +382,10 @@
   "id": "4106e397",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.769387Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.264159Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.769225Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.264034Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.818623Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.358145Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.818386Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.357588Z"
    },
    "lines_to_next_cell": 2
   },
@@ -482,10 +495,10 @@
   "id": "8962942d",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.819898Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.359853Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.819778Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.359712Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.842571Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.392583Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.842339Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.392024Z"
    }
   },
   "outputs": [
@@ -575,10 +588,10 @@
   "id": "d923fe3d",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.844036Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.394108Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.843960Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.393971Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.848436Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.400446Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.848213Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.399897Z"
    }
   },
   "outputs": [
@@ -681,10 +694,10 @@
   "id": "43b23849",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.849715Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.401822Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.849642Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.401708Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.878002Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.440236Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.877763Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.439741Z"
    }
   },
   "outputs": [
@@ -829,10 +842,10 @@
   "id": "20f95f4d",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.879409Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.441882Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.879331Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.441756Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.882681Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.445370Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.882457Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.444972Z"
    }
   },
   "outputs": [],
@@ -862,10 +875,10 @@
   "id": "18ef52a9",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.883885Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.447062Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.883777Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.446962Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.888549Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.453531Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.888317Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.453068Z"
    }
   },
   "outputs": [
@@ -990,10 +1003,10 @@
   "id": "dc826bbc",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.896273Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.455458Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.896164Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.455320Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.904155Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.466192Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.903918Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.465722Z"
    }
   },
   "outputs": [
@@ -1132,10 +1145,10 @@
   "id": "867045f7",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.905454Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.467886Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.905357Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.467750Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.911172Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.474857Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.910926Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.474306Z"
    },
    "lines_to_next_cell": 0
   },
@@ -1293,17 +1306,17 @@
   "id": "9fa60bac",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.912461Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.479066Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.912377Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.478889Z",
-     "iopub.status.idle": "2026-02-02T23:40:29.992285Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.542903Z",
-     "shell.execute_reply": "2026-02-02T23:40:29.992056Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.542497Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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+      "image/png": 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truncated
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
@@ -1323,9 +1336,9 @@
   "metadata": {},
   "source": [
    "## Publication Data\n",
-    "The  `Publication`  data   presented in Section~\\ref{sec:pub}  can be\n",
+    "The  `Publication`  data   presented in Section 11.5.4  can be\n",
    "found in the `ISLP` package.\n",
-    "We first reproduce Figure~\\ref{fig:lauersurv}  by plotting the Kaplan-Meier curves\n",
+    "We first reproduce Figure 11.5  by plotting the Kaplan-Meier curves\n",
    "stratified on the  `posres`  variable, which records whether the\n",
    "study had a positive or negative result."
   ]
@@ -1336,16 +1349,16 @@
   "id": "f8c1a98e",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:29.993691Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.544559Z",
-     "iopub.status.busy": "2026-02-02T23:40:29.993598Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.544431Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.079014Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.618015Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.078774Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.617523Z"
    }
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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+      "image/png": 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truncated
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
@@ -1381,10 +1394,10 @@
   "id": "cd1ed9ac",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.080486Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.619503Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.080397Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.619380Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.111760Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.652204Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.111406Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.651713Z"
    },
    "lines_to_next_cell": 2
   },
@@ -1470,10 +1483,10 @@
   "id": "d53335b0",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.113137Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.653858Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.113022Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.653722Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.146933Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.696330Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.146681Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.695757Z"
    }
   },
   "outputs": [
@@ -1593,7 +1606,7 @@
    "\n",
    "In this section, we will simulate survival data using the relationship\n",
    "between cumulative hazard and\n",
-    "the survival function explored in Exercise \\ref{ex:all3}.\n",
+    "the survival function explored in Exercise 8.\n",
    "Our simulated data will represent the observed\n",
    "wait times (in seconds) for 2,000 customers who have phoned a call\n",
    "center.  In this context, censoring occurs if a customer hangs up\n",
@@ -1614,10 +1627,10 @@
   "id": "582c992b",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.148205Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.697982Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.148127Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.697840Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.151319Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.701582Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.151090Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.701058Z"
    }
   },
   "outputs": [],
@@ -1652,10 +1665,10 @@
   "id": "f3367dad",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.152366Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.702856Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.152298Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.702759Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.159456Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.713355Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.159240Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.712725Z"
    }
   },
   "outputs": [],
@@ -1684,10 +1697,10 @@
   "id": "fdfd2789",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.160732Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.714824Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.160661Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.714732Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.164225Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.719225Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.163999Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.718867Z"
    }
   },
   "outputs": [
@@ -1796,10 +1809,10 @@
   "id": "1a9b68c8",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.165320Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.720840Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.165249Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.720735Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.181030Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.723005Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.179683Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.722537Z"
    }
   },
   "outputs": [],
@@ -1825,7 +1838,7 @@
    "risk that it will be answered at Center A; in other words, the wait\n",
    "times are a bit longer at Center B.\n",
    "\n",
-    "Recall from Section~\\ref{Ch2-statlearn-lab:loading-data} the use of `lambda`\n",
+    "Recall from Section 2.3.7 the use of `lambda`\n",
    "for creating short functions on the fly.\n",
    "We use the function\n",
    "`sim_time()` from the `ISLP.survival` package. This function\n",
@@ -1843,10 +1856,10 @@
   "id": "d2118fdd",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.185644Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.724282Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.185283Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.724172Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.249849Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.726062Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.249050Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.725582Z"
    },
    "lines_to_next_cell": 0
   },
@@ -1874,10 +1887,10 @@
   "id": "68304ba6",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.262130Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.727372Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.261849Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.727267Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.398029Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.827051Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.396818Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.826636Z"
    }
   },
   "outputs": [],
@@ -1903,10 +1916,10 @@
   "id": "becf4cd2",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.410349Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.834720Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.403028Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.834516Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.435600Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.840229Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.435349Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.839638Z"
    },
    "lines_to_next_cell": 2
   },
@@ -2011,17 +2024,17 @@
   "id": "e480331c",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.436950Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.841579Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.436873Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.841464Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.438955Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.844198Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.438757Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.843652Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "0.9075"
+       "np.float64(0.9075)"
      ]
     },
     "execution_count": 28,
@@ -2047,10 +2060,10 @@
   "id": "a194c96e",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.439995Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.845590Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.439929Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.845474Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.560685Z",
+     "iopub.status.idle": "2026-02-05T00:46:59.958564Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.560280Z"
+     "shell.execute_reply": "2026-02-05T00:46:59.957930Z"
    }
   },
   "outputs": [
@@ -2066,7 +2079,7 @@
    },
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
@@ -2099,10 +2112,10 @@
   "id": "3c9eae8a",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.562105Z",
+     "iopub.execute_input": "2026-02-05T00:46:59.960115Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.561994Z",
+     "iopub.status.busy": "2026-02-05T00:46:59.960009Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.721091Z",
+     "iopub.status.idle": "2026-02-05T00:47:00.074116Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.720845Z"
+     "shell.execute_reply": "2026-02-05T00:47:00.073549Z"
    }
   },
   "outputs": [
@@ -2118,7 +2131,7 @@
    },
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
@@ -2155,10 +2168,10 @@
   "id": "bb8a37eb",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.722369Z",
+     "iopub.execute_input": "2026-02-05T00:47:00.075673Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.722280Z",
+     "iopub.status.busy": "2026-02-05T00:47:00.075534Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.737149Z",
+     "iopub.status.idle": "2026-02-05T00:47:00.155654Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.736914Z"
+     "shell.execute_reply": "2026-02-05T00:47:00.155035Z"
    },
    "lines_to_next_cell": 2
   },
@@ -2262,10 +2275,10 @@
   "id": "e3299d19",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.738404Z",
+     "iopub.execute_input": "2026-02-05T00:47:00.157114Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.738333Z",
+     "iopub.status.busy": "2026-02-05T00:47:00.156978Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.751890Z",
+     "iopub.status.idle": "2026-02-05T00:47:00.175601Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.751672Z"
+     "shell.execute_reply": "2026-02-05T00:47:00.175033Z"
    },
    "lines_to_next_cell": 2
   },
@@ -2372,10 +2385,10 @@
   "id": "b9004e25",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.753076Z",
+     "iopub.execute_input": "2026-02-05T00:47:00.177047Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.753002Z",
+     "iopub.status.busy": "2026-02-05T00:47:00.176929Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.859788Z",
+     "iopub.status.idle": "2026-02-05T00:47:00.305419Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.859420Z"
+     "shell.execute_reply": "2026-02-05T00:47:00.304956Z"
    },
    "lines_to_next_cell": 2
   },
@@ -2477,10 +2490,10 @@
   "id": "1780384e",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.861381Z",
+     "iopub.execute_input": "2026-02-05T00:47:00.306869Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.861250Z",
+     "iopub.status.busy": "2026-02-05T00:47:00.306748Z",
-     "iopub.status.idle": "2026-02-02T23:40:30.959129Z",
+     "iopub.status.idle": "2026-02-05T00:47:00.431990Z",
-     "shell.execute_reply": "2026-02-02T23:40:30.958885Z"
+     "shell.execute_reply": "2026-02-05T00:47:00.431526Z"
    },
    "lines_to_next_cell": 2
   },
@@ -2585,10 +2598,10 @@
   "id": "19f93f96",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:30.960384Z",
+     "iopub.execute_input": "2026-02-05T00:47:00.433707Z",
-     "iopub.status.busy": "2026-02-02T23:40:30.960309Z",
+     "iopub.status.busy": "2026-02-05T00:47:00.433586Z",
-     "iopub.status.idle": "2026-02-02T23:40:31.098814Z",
+     "iopub.status.idle": "2026-02-05T00:47:00.570269Z",
-     "shell.execute_reply": "2026-02-02T23:40:31.098085Z"
+     "shell.execute_reply": "2026-02-05T00:47:00.569715Z"
    },
    "lines_to_next_cell": 2
   },
@@ -2719,7 +2732,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.10"
  }
 },
 "nbformat": 4,
@@ -164,7 +164,7 @@ for k in range(pcaUS.components_.shape[1]):
            USArrests.columns[k])
 ```
-Notice that this figure is a reflection of Figure~\ref{Ch10:fig:USArrests:obs} through the $y$-axis. Recall that the
+Notice that this figure is a reflection of Figure 12.1 through the $y$-axis. Recall that the
 principal components are only unique up to a sign change, so we can
 reproduce that figure by flipping the
 signs of the second set of scores and loadings.
@@ -241,7 +241,7 @@ ax.set_xticks(ticks)
 fig
 ```
-The result is similar to that shown in Figure~\ref{Ch10:fig:USArrests:scree}.  Note
+The result is similar to that shown in Figure 12.3.  Note
 that the method `cumsum()`   computes the cumulative sum of
 the elements of a numeric vector. For instance:
@@ -253,15 +253,15 @@ np.cumsum(a)
 ## Matrix Completion
 We now re-create the analysis carried out on the `USArrests` data in
-Section~\ref{Ch10:sec:princ-comp-with}.
+Section 12.3.
-We saw  in Section~\ref{ch10:sec2.2}  that solving the optimization
+We saw  in Section 12.2.2  that solving the optimization
-problem~(\ref{Ch10:eq:mc2})   on a centered data matrix $\bf X$ is
+problem~(12.6)   on a centered data matrix $\bf X$ is
 equivalent to computing the first $M$ principal
 components of the data.  We use our scaled
 and centered `USArrests` data as $\bf X$ below. The *singular value decomposition* 
 (SVD)  is a general algorithm for solving
-(\ref{Ch10:eq:mc2}). 
+(12.6). 
 ```{python}
 X = USArrests_scaled
@@ -319,9 +319,9 @@ Here the array `r_idx`
 contains 20 integers from 0 to 49; this represents the states (rows of `X`) that are selected to contain missing values. And `c_idx` contains
 20 integers from 0 to 3, representing the features (columns in `X`) that contain the missing values for each of the selected states.
-We now write some code to implement Algorithm~\ref{Ch10:alg:hardimpute}. 
+We now write some code to implement Algorithm 12.1. 
 We first write a  function that takes in a matrix, and returns an approximation to the matrix using the `svd()` function.
-This will be needed in Step 2 of Algorithm~\ref{Ch10:alg:hardimpute}.
+This will be needed in Step 2 of Algorithm 12.1.
 ```{python}
 def low_rank(X, M=1):
@@ -330,7 +330,7 @@ def low_rank(X, M=1):
    return L.dot(V[:M])
 ```
-To conduct Step 1 of the algorithm, we initialize `Xhat` --- this is $\tilde{\bf X}$ in Algorithm~\ref{Ch10:alg:hardimpute} ---  by replacing
+To conduct Step 1 of the algorithm, we initialize `Xhat` --- this is $\tilde{\bf X}$ in Algorithm 12.1 ---  by replacing
 the missing values with the column means of the non-missing entries. These are stored in
 `Xbar` below after running `np.nanmean()` over the row axis.
 We make a copy so that when we assign values to `Xhat` below we do not also overwrite the
@@ -360,11 +360,11 @@ a given element is `True` if the corresponding matrix element is missing. The no
 because it allows us to access both the missing and non-missing entries. We store the mean of the squared non-missing elements in `mss0`.
 We store the mean squared error  of the non-missing elements  of the old version of `Xhat` in `mssold` (which currently
 agrees with `mss0`). We plan to store the mean squared error of the non-missing elements of the current version of `Xhat` in `mss`, and will then
-iterate Step 2 of  Algorithm~\ref{Ch10:alg:hardimpute}  until the *relative error*, defined as
+iterate Step 2 of  Algorithm 12.1  until the *relative error*, defined as
 `(mssold - mss) / mss0`, falls below `thresh = 1e-7`.
- {Algorithm~\ref{Ch10:alg:hardimpute} tells us to iterate Step 2 until \eqref{Ch10:eq:mc6} is no longer decreasing. Determining whether \eqref{Ch10:eq:mc6}  is decreasing requires us only to keep track of `mssold - mss`. However, in practice, we keep track of `(mssold - mss) / mss0` instead: this makes it so that the number of iterations required for Algorithm~\ref{Ch10:alg:hardimpute} to converge does not depend on whether we multiplied the raw data $\bf X$ by a constant factor.}
+ {Algorithm 12.1 tells us to iterate Step 2 until 12.14 is no longer decreasing. Determining whether 12.14  is decreasing requires us only to keep track of `mssold - mss`. However, in practice, we keep track of `(mssold - mss) / mss0` instead: this makes it so that the number of iterations required for Algorithm 12.1 to converge does not depend on whether we multiplied the raw data $\bf X$ by a constant factor.}
-In Step 2(a) of Algorithm~\ref{Ch10:alg:hardimpute}, we  approximate `Xhat` using `low_rank()`; we call this `Xapp`. In Step 2(b), we  use `Xapp`  to update the estimates for elements in `Xhat` that are missing in `Xna`. Finally, in Step 2(c), we compute the relative error. These three steps are contained in the following `while` loop:
+In Step 2(a) of Algorithm 12.1, we  approximate `Xhat` using `low_rank()`; we call this `Xapp`. In Step 2(b), we  use `Xapp`  to update the estimates for elements in `Xhat` that are missing in `Xna`. Finally, in Step 2(c), we compute the relative error. These three steps are contained in the following `while` loop:
 ```{python}
 while rel_err > thresh:
@@ -393,7 +393,7 @@ np.corrcoef(Xapp[ismiss], X[ismiss])[0,1]
 ```
-In this lab, we implemented  Algorithm~\ref{Ch10:alg:hardimpute}  ourselves for didactic purposes. However, a reader who wishes to apply matrix completion to their data might look to more specialized `Python`{} implementations.
+In this lab, we implemented  Algorithm 12.1  ourselves for didactic purposes. However, a reader who wishes to apply matrix completion to their data might look to more specialized `Python`{} implementations.
 ## Clustering
@@ -464,7 +464,7 @@ We have used the `n_init` argument to run the $K$-means with 20
 initial cluster assignments (the default is 10). If a
 value of `n_init` greater than one is used, then $K$-means
 clustering will be performed using multiple random assignments in
-Step 1 of  Algorithm~\ref{Ch10:alg:km}, and the `KMeans()` 
+Step 1 of  Algorithm 12.2, and the `KMeans()` 
 function will report only the best results. Here we compare using
 `n_init=1` to `n_init=20`.
@@ -480,7 +480,7 @@ kmeans1.inertia_, kmeans20.inertia_
 ```
 Note that `kmeans.inertia_` is the total within-cluster sum
 of squares, which we seek to minimize by performing $K$-means
-clustering \eqref{Ch10:eq:kmeans}. 
+clustering 12.17. 
 We *strongly* recommend always running $K$-means clustering with
 a large value of `n_init`, such as 20 or 50, since otherwise an
@@ -846,7 +846,7 @@ results in four distinct clusters. It is easy to verify that the
 resulting clusters are the same as the ones we obtained in
 `comp_cut`.
-We claimed earlier in Section~\ref{Ch10:subsec:hc} that
+We claimed earlier in Section 12.4.2 that
 $K$-means clustering and hierarchical clustering with the dendrogram
 cut to obtain the same number of clusters can yield very different
 results.  How do these `NCI60` hierarchical clustering results compare
@@ -91,7 +91,7 @@ truth = pd.Categorical(true_mean == 0,
 ```
 Since this is a simulated data set, we can create a $2 \times 2$ table
-similar to  Table~\ref{Ch12:tab-hypotheses}.
+similar to  Table 13.2.
 ```{python}
 pd.crosstab(decision,
@@ -102,7 +102,7 @@ pd.crosstab(decision,
 ```
 Therefore, at level $\alpha=0.05$, we reject 15 of the 50 false
 null hypotheses, and we incorrectly reject 5 of the true null
-hypotheses. Using the notation from Section~\ref{sec:fwer}, we have
+hypotheses. Using the notation from Section 13.3, we have
 $V=5$, $S=15$, $U=45$ and $W=35$.
 We have set $\alpha=0.05$, which means that we expect to reject around
 5% of the true null hypotheses. This is in line with the $2 \times 2$
@@ -140,12 +140,12 @@ pd.crosstab(decision,
 ## Family-Wise Error Rate
-Recall from  \eqref{eq:FWER.indep}  that if the null hypothesis is true
+Recall from  13.5  that if the null hypothesis is true
 for each of $m$ independent hypothesis tests, then the FWER is equal
 to $1-(1-\alpha)^m$. We can use this expression to compute the FWER
 for $m=1,\ldots, 500$ and $\alpha=0.05$, $0.01$, and $0.001$.
 We plot the FWER for these values of $\alpha$ in order to
-reproduce  Figure~\ref{Ch12:fwer}.
+reproduce  Figure 13.2.
 ```{python}
 m = np.linspace(1, 501)
@@ -263,7 +263,7 @@ However, we decided to perform this test only after examining the data
 and noting that Managers One and Two had the highest and lowest mean
 performances.  In a sense, this means that we have implicitly
 performed ${5 \choose 2} = 5(5-1)/2=10$ hypothesis tests, rather than
-just one, as discussed in  Section~\ref{tukey.sec}.  Hence, we use the
+just one, as discussed in  Section 13.3.2.  Hence, we use the
 `pairwise_tukeyhsd()`  function from
 `statsmodels.stats.multicomp` to apply Tukey’s method
  in order to adjust for multiple testing.  This function takes
@@ -350,7 +350,7 @@ null hypotheses!
 ```
-Figure~\ref{Ch12:fig:BonferroniBenjamini} displays the ordered
+Figure 13.6 displays the ordered
 $p$-values, $p_{(1)} \leq p_{(2)} \leq \cdots \leq p_{(2000)}$, for
 the  `Fund`  dataset, as well as the threshold for rejection by the
 Benjamini--Hochberg procedure.  Recall that the Benjamini--Hochberg
@@ -379,7 +379,7 @@ else:
 ```
-We now reproduce  the middle panel of Figure~\ref{Ch12:fig:BonferroniBenjamini}.
+We now reproduce  the middle panel of Figure 13.6.
 ```{python}
 fig, ax = plt.subplots()
@@ -398,7 +398,7 @@ ax.axline((0, 0), (1,q/m), c='k', ls='--', linewidth=3);
 ## A Re-Sampling Approach
 Here, we implement the re-sampling approach to hypothesis testing
 using the  `Khan`  dataset, which we investigated in
-Section~\ref{sec:permutations}.  First, we merge the training and
+Section 13.5.  First, we merge the training and
 testing data, which results in observations on 83 patients for
 2,308 genes.
@@ -464,7 +464,7 @@ for b in range(B):
 This fraction, 0.0398,
 is our re-sampling-based $p$-value.
 It is almost identical to the $p$-value of  0.0412 obtained using the theoretical null distribution.
-We can plot  a histogram of the re-sampling-based test statistics in order to reproduce  Figure~\ref{Ch12:fig-permp-1}.
+We can plot  a histogram of the re-sampling-based test statistics in order to reproduce  Figure 13.7.
 ```{python}
 fig, ax = plt.subplots(figsize=(8,8))
@@ -487,7 +487,7 @@ ax.set_xlabel("Null Distribution of Test Statistic");
 The re-sampling-based null distribution is almost identical to the theoretical null distribution, which is displayed in red.
 Finally, we implement the plug-in re-sampling FDR approach outlined in
-Algorithm~\ref{Ch12:alg-plugin-fdr}. Depending on the speed of your
+Algorithm 13.4. Depending on the speed of your
 computer, calculating the FDR for all 2,308 genes in the `Khan`
 dataset may take a while.  Hence, we will illustrate the approach on a
 random subset of 100 genes.  For each gene, we first compute the
@@ -520,7 +520,7 @@ for j in range(m):
 Next, we compute the number of rejected null hypotheses $R$, the
 estimated number of false positives $\widehat{V}$, and the estimated
 FDR, for a range of threshold values $c$ in
-Algorithm~\ref{Ch12:alg-plugin-fdr}. The threshold values are chosen
+Algorithm 13.4. The threshold values are chosen
 using the absolute values of the test statistics from the 100 genes.
 ```{python}
@@ -559,8 +559,8 @@ sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.2].min()])
 ```
-The next line  generates  Figure~\ref{fig:labfdr}, which is similar
+The next line  generates  Figure 13.11, which is similar
-to  Figure~\ref{Ch12:fig-plugin-fdr},
+to  Figure 13.9,
 except that it is based on only  a subset of the genes.
 ```{python}
@@ -5,6 +5,7 @@
   "id": "ec6b504d",
   "metadata": {},
   "source": [
    "\n",
    "# Multiple Testing\n",
    "\n",
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch13-multiple-lab.ipynb\">\n",
@@ -31,10 +32,10 @@
   "id": "bd52a066",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:41.491441Z",
+     "iopub.execute_input": "2026-02-05T00:47:11.395883Z",
-     "iopub.status.busy": "2026-02-02T23:40:41.491149Z",
+     "iopub.status.busy": "2026-02-05T00:47:11.395786Z",
-     "iopub.status.idle": "2026-02-02T23:40:42.558004Z",
+     "iopub.status.idle": "2026-02-05T00:47:12.446225Z",
-     "shell.execute_reply": "2026-02-02T23:40:42.557613Z"
+     "shell.execute_reply": "2026-02-05T00:47:12.445607Z"
    }
   },
   "outputs": [],
@@ -61,10 +62,10 @@
   "id": "90117cbf",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:42.560098Z",
+     "iopub.execute_input": "2026-02-05T00:47:12.448067Z",
-     "iopub.status.busy": "2026-02-02T23:40:42.559789Z",
+     "iopub.status.busy": "2026-02-05T00:47:12.447887Z",
-     "iopub.status.idle": "2026-02-02T23:40:42.562039Z",
+     "iopub.status.idle": "2026-02-05T00:47:12.450079Z",
-     "shell.execute_reply": "2026-02-02T23:40:42.561801Z"
+     "shell.execute_reply": "2026-02-05T00:47:12.449643Z"
    },
    "lines_to_next_cell": 2
   },
@@ -100,10 +101,10 @@
   "id": "db60f055",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:42.563289Z",
+     "iopub.execute_input": "2026-02-05T00:47:12.451638Z",
-     "iopub.status.busy": "2026-02-02T23:40:42.563196Z",
+     "iopub.status.busy": "2026-02-05T00:47:12.451522Z",
-     "iopub.status.idle": "2026-02-02T23:40:42.565237Z",
+     "iopub.status.idle": "2026-02-05T00:47:12.453650Z",
-     "shell.execute_reply": "2026-02-02T23:40:42.565023Z"
+     "shell.execute_reply": "2026-02-05T00:47:12.453197Z"
    }
   },
   "outputs": [],
@@ -130,17 +131,17 @@
   "id": "2f769e32",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:42.566311Z",
+     "iopub.execute_input": "2026-02-05T00:47:12.455030Z",
-     "iopub.status.busy": "2026-02-02T23:40:42.566225Z",
+     "iopub.status.busy": "2026-02-05T00:47:12.454914Z",
-     "iopub.status.idle": "2026-02-02T23:40:42.569835Z",
+     "iopub.status.idle": "2026-02-05T00:47:12.458734Z",
-     "shell.execute_reply": "2026-02-02T23:40:42.569565Z"
+     "shell.execute_reply": "2026-02-05T00:47:12.458209Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "0.9307442156164141"
+       "np.float64(0.9307442156164141)"
      ]
     },
     "execution_count": 4,
@@ -177,10 +178,10 @@
   "id": "662e8a87",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:42.570975Z",
+     "iopub.execute_input": "2026-02-05T00:47:12.460253Z",
-     "iopub.status.busy": "2026-02-02T23:40:42.570881Z",
+     "iopub.status.busy": "2026-02-05T00:47:12.460153Z",
-     "iopub.status.idle": "2026-02-02T23:40:42.590875Z",
+     "iopub.status.idle": "2026-02-05T00:47:12.484340Z",
-     "shell.execute_reply": "2026-02-02T23:40:42.590643Z"
+     "shell.execute_reply": "2026-02-05T00:47:12.483841Z"
    },
    "lines_to_next_cell": 0
   },
@@ -204,7 +205,7 @@
   "metadata": {},
   "source": [
    "Since this is a simulated data set, we can create a $2 \\times 2$ table\n",
-    "similar to  Table~\\ref{Ch12:tab-hypotheses}."
+    "similar to  Table 13.2."
   ]
  },
  {
@@ -213,10 +214,10 @@
   "id": "b25f9969",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:42.592212Z",
+     "iopub.execute_input": "2026-02-05T00:47:12.486017Z",
-     "iopub.status.busy": "2026-02-02T23:40:42.592128Z",
+     "iopub.status.busy": "2026-02-05T00:47:12.485891Z",
-     "iopub.status.idle": "2026-02-02T23:40:42.600326Z",
+     "iopub.status.idle": "2026-02-05T00:47:12.494356Z",
-     "shell.execute_reply": "2026-02-02T23:40:42.600082Z"
+     "shell.execute_reply": "2026-02-05T00:47:12.493847Z"
    },
    "lines_to_next_cell": 0
   },
@@ -292,7 +293,7 @@
   "source": [
    "Therefore, at level $\\alpha=0.05$, we reject 15 of the 50 false\n",
    "null hypotheses, and we incorrectly reject 5 of the true null\n",
-    "hypotheses. Using the notation from Section~\\ref{sec:fwer}, we have\n",
+    "hypotheses. Using the notation from Section 13.3, we have\n",
    "$V=5$, $S=15$, $U=45$ and $W=35$.\n",
    "We have set $\\alpha=0.05$, which means that we expect to reject around\n",
    "5% of the true null hypotheses. This is in line with the $2 \\times 2$\n",
@@ -314,10 +315,10 @@
   "id": "4c158b0b",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:42.601626Z",
+     "iopub.execute_input": "2026-02-05T00:47:12.495749Z",
-     "iopub.status.busy": "2026-02-02T23:40:42.601550Z",
+     "iopub.status.busy": "2026-02-05T00:47:12.495629Z",
-     "iopub.status.idle": "2026-02-02T23:40:42.625065Z",
+     "iopub.status.idle": "2026-02-05T00:47:12.524219Z",
-     "shell.execute_reply": "2026-02-02T23:40:42.624786Z"
+     "shell.execute_reply": "2026-02-05T00:47:12.523767Z"
    },
    "lines_to_next_cell": 0
   },
@@ -400,7 +401,7 @@
  },
  {
   "cell_type": "markdown",
-   "id": "4cc20385",
+   "id": "e672cce4",
   "metadata": {},
   "source": [
    "    "
@@ -412,12 +413,12 @@
   "metadata": {},
   "source": [
    "## Family-Wise Error Rate\n",
-    "Recall from  \\eqref{eq:FWER.indep}  that if the null hypothesis is true\n",
+    "Recall from  13.5  that if the null hypothesis is true\n",
    "for each of $m$ independent hypothesis tests, then the FWER is equal\n",
    "to $1-(1-\\alpha)^m$. We can use this expression to compute the FWER\n",
    "for $m=1,\\ldots, 500$ and $\\alpha=0.05$, $0.01$, and $0.001$.\n",
    "We plot the FWER for these values of $\\alpha$ in order to\n",
-    "reproduce  Figure~\\ref{Ch12:fwer}."
+    "reproduce  Figure 13.2."
   ]
  },
  {
@@ -426,16 +427,16 @@
   "id": "093264ed",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:42.626420Z",
+     "iopub.execute_input": "2026-02-05T00:47:12.525786Z",
-     "iopub.status.busy": "2026-02-02T23:40:42.626339Z",
+     "iopub.status.busy": "2026-02-05T00:47:12.525660Z",
-     "iopub.status.idle": "2026-02-02T23:40:42.833591Z",
+     "iopub.status.idle": "2026-02-05T00:47:12.707622Z",
-     "shell.execute_reply": "2026-02-02T23:40:42.832647Z"
+     "shell.execute_reply": "2026-02-05T00:47:12.707134Z"
    }
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
@@ -482,10 +483,10 @@
   "id": "1a3ac106",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:42.838668Z",
+     "iopub.execute_input": "2026-02-05T00:47:12.709270Z",
-     "iopub.status.busy": "2026-02-02T23:40:42.838263Z",
+     "iopub.status.busy": "2026-02-05T00:47:12.709124Z",
-     "iopub.status.idle": "2026-02-02T23:40:42.909564Z",
+     "iopub.status.idle": "2026-02-05T00:47:12.749413Z",
-     "shell.execute_reply": "2026-02-02T23:40:42.909245Z"
+     "shell.execute_reply": "2026-02-05T00:47:12.748911Z"
    }
   },
   "outputs": [
@@ -550,10 +551,10 @@
   "id": "b9f1eac7",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:42.911009Z",
+     "iopub.execute_input": "2026-02-05T00:47:12.750978Z",
-     "iopub.status.busy": "2026-02-02T23:40:42.910932Z",
+     "iopub.status.busy": "2026-02-05T00:47:12.750865Z",
-     "iopub.status.idle": "2026-02-02T23:40:42.913088Z",
+     "iopub.status.idle": "2026-02-05T00:47:12.753234Z",
-     "shell.execute_reply": "2026-02-02T23:40:42.912887Z"
+     "shell.execute_reply": "2026-02-05T00:47:12.752822Z"
    },
    "lines_to_next_cell": 2
   },
@@ -589,10 +590,10 @@
   "id": "0cee9c30",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:42.914211Z",
+     "iopub.execute_input": "2026-02-05T00:47:12.754593Z",
-     "iopub.status.busy": "2026-02-02T23:40:42.914139Z",
+     "iopub.status.busy": "2026-02-05T00:47:12.754488Z",
-     "iopub.status.idle": "2026-02-02T23:40:42.916323Z",
+     "iopub.status.idle": "2026-02-05T00:47:12.756997Z",
-     "shell.execute_reply": "2026-02-02T23:40:42.916073Z"
+     "shell.execute_reply": "2026-02-05T00:47:12.756504Z"
    }
   },
   "outputs": [
@@ -631,10 +632,10 @@
   "id": "1f7cee90",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:42.917445Z",
+     "iopub.execute_input": "2026-02-05T00:47:12.758332Z",
-     "iopub.status.busy": "2026-02-02T23:40:42.917375Z",
+     "iopub.status.busy": "2026-02-05T00:47:12.758215Z",
-     "iopub.status.idle": "2026-02-02T23:40:42.957649Z",
+     "iopub.status.idle": "2026-02-05T00:47:12.803498Z",
-     "shell.execute_reply": "2026-02-02T23:40:42.957396Z"
+     "shell.execute_reply": "2026-02-05T00:47:12.802980Z"
    },
    "lines_to_next_cell": 2
   },
@@ -671,10 +672,10 @@
   "id": "59cdfe23",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:42.958881Z",
+     "iopub.execute_input": "2026-02-05T00:47:12.804910Z",
-     "iopub.status.busy": "2026-02-02T23:40:42.958809Z",
+     "iopub.status.busy": "2026-02-05T00:47:12.804803Z",
-     "iopub.status.idle": "2026-02-02T23:40:42.961378Z",
+     "iopub.status.idle": "2026-02-05T00:47:12.807904Z",
-     "shell.execute_reply": "2026-02-02T23:40:42.961187Z"
+     "shell.execute_reply": "2026-02-05T00:47:12.807476Z"
    },
    "lines_to_next_cell": 2
   },
@@ -715,17 +716,17 @@
   "id": "bf01100e",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:42.962467Z",
+     "iopub.execute_input": "2026-02-05T00:47:12.809286Z",
-     "iopub.status.busy": "2026-02-02T23:40:42.962393Z",
+     "iopub.status.busy": "2026-02-05T00:47:12.809187Z",
-     "iopub.status.idle": "2026-02-02T23:40:42.964706Z",
+     "iopub.status.idle": "2026-02-05T00:47:12.812089Z",
-     "shell.execute_reply": "2026-02-02T23:40:42.964522Z"
+     "shell.execute_reply": "2026-02-05T00:47:12.811573Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "0.038391072368079586"
+       "np.float64(0.038391072368079586)"
      ]
     },
     "execution_count": 14,
@@ -750,7 +751,7 @@
    "and noting that Managers One and Two had the highest and lowest mean\n",
    "performances.  In a sense, this means that we have implicitly\n",
    "performed ${5 \\choose 2} = 5(5-1)/2=10$ hypothesis tests, rather than\n",
-    "just one, as discussed in  Section~\\ref{tukey.sec}.  Hence, we use the\n",
+    "just one, as discussed in  Section 13.3.2.  Hence, we use the\n",
    "`pairwise_tukeyhsd()`  function from\n",
    "`statsmodels.stats.multicomp` to apply Tukey’s method\n",
    "  in order to adjust for multiple testing.  This function takes\n",
@@ -767,10 +768,10 @@
   "id": "60146e08",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:42.965724Z",
+     "iopub.execute_input": "2026-02-05T00:47:12.813422Z",
-     "iopub.status.busy": "2026-02-02T23:40:42.965661Z",
+     "iopub.status.busy": "2026-02-05T00:47:12.813318Z",
-     "iopub.status.idle": "2026-02-02T23:40:43.337336Z",
+     "iopub.status.idle": "2026-02-05T00:47:13.280056Z",
-     "shell.execute_reply": "2026-02-02T23:40:43.337101Z"
+     "shell.execute_reply": "2026-02-05T00:47:13.279564Z"
    },
    "lines_to_next_cell": 2
   },
@@ -827,16 +828,16 @@
   "id": "2ec0aae4",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:43.338635Z",
+     "iopub.execute_input": "2026-02-05T00:47:13.281827Z",
-     "iopub.status.busy": "2026-02-02T23:40:43.338557Z",
+     "iopub.status.busy": "2026-02-05T00:47:13.281689Z",
-     "iopub.status.idle": "2026-02-02T23:40:43.400697Z",
+     "iopub.status.idle": "2026-02-05T00:47:13.334077Z",
-     "shell.execute_reply": "2026-02-02T23:40:43.400471Z"
+     "shell.execute_reply": "2026-02-05T00:47:13.333519Z"
    }
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAy0AAAIQCAYAAACMg4HBAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAMttJREFUeJzt3QmUXFWBBuDbBA2akCAoEkggiAsC4r6gJhpZVFCBiAswCg6uAwLiinNGwS24jCbjgrgMOG6oCDiDAcQFEheURRzEFQ0DiUERJYGogUlqzv9mqqe60p10J+n07e7vO6dOd7+urnrvvlvV9393qZ5Wq9UqAAAAldpqpHcAAABgfYQWAACgakILAABQNaEFAAComtACAABUTWgBAACqJrQAAABVE1oAAICqCS0AAEDVhBYYY0477bTS09MzqPuec845zX1vuummIT/P5Zdf3vxtvrL5PP3pT29uMJTX7UjUm015/xiqu+66q+y4447l85///LAdx9VXX122lNtvv71MmjSpLFy4cIs9J4x2QgtsQe1/jrl997vfXef3rVarzJgxo/n9c57znM32vO95z3vKhRdeWEaDlStXltNPP7088pGPLJMnTy73uc99yj777FPe/OY3l9/97ncjvXtsQBrO7Tqe273vfe+y++67l1e+8pXllltu2ajyy3lPGL/uuuvGRfk/4QlPaMruzDPP3OyPPXPmzD7nJ0Fg1qxZ5YILLig1W7BgQdl2223Li1/84iYkdR7D+m5bIlBtjB122KG8/OUvL//0T/800rsCo8bWI70DMB5ts8025Qtf+EJ56lOf2mf7FVdcUZYuXVomTpy4WZ8voeWII44ohx12WJ/tL3nJS5pGwOZ+vo3129/+thxwwAHl5ptvLi94wQuahm4avf/5n/9ZPv3pTzcNq1/96ldlLPvGN75RRrvp06eXefPmNd/ffffd5Wc/+1n5+Mc/Xi699NLy85//vNz3vvcdcmhJkE2D+1GPelQZy37961+Xq666qjnW9Cq85jWv2ezPkTJ8/etf31u2Z511Vpk7d24Tkl796lcP+nG21PvHPffc04SW173udWXChAnlAQ94QPnsZz/b5z7//M//3Lx3fuhDH+qzPfetVcr6X/7lX8q3v/3t8oxnPGOkdweqJ7TACDj44IPLV77yleYf1tZb///LMEHmsY99bPnjH/+4RfYjDYDcavDf//3fTcPp97//fTPkrDvQvfvd7y7vfe97y1j1l7/8pWnMJ6SNdlOnTi1/93d/12dbeltOOOGE8r3vfa8ceOCBI7Zvtfvc5z7X9H6kEZ4LDekpSIDZnHbZZZc+5+elL31pefCDH9w0+IcSWgbz/pHe47/97W9Nj+nGuuiii8ptt91WXvjCFzY/Z1hVd/0699xzy5///Od1ttfs4Q9/eNOLnB54oQU2zPAwGAFHHnlkM6b5sssu692WK9LnnXdeOeqoowY9f6Q9TCL/9AaS369atap85jOf6R0yceyxxw44Jj0NpAxNyxX/XJFNr9Bee+1Vzj///EEd2w9/+MPyrGc9q2m4phH+tKc9rWmobshXv/rV8pOf/KT84z/+4zqBJaZMmdIEl04Jfgl5aRDd//73bxosy5Yt63OfHGuGmaX3JseV79No++hHP9r8/vrrr28aDGkI7bbbbk1w7NQuo0WLFpVXvepVzbCO7Esaemkkdfra175WDjnkkLLzzjs3V5/32GOP8s53vrOsWbNmnSFUaaxcc801Zfbs2U05vfWtbx1wbsKHP/zhsvfeezf3u9/97lce97jHrbOfP/7xj8uzn/3sZt9yjPvvv3+58sor+z2WnI9TTjmluQqd4z788MObRmGnjO9/5jOf2ZRryjeh4+///u/Lxtppp52ar50hPXK+8rgPfOADmzLLcf7rv/5r7+9T5x//+Mc337/sZS/rrcM5loT+NJrvuOOO3vunsZ/f5/jaUv4ZWpQhhm1r164t8+fPb54vdTzPn/PbfU7j4osvboZQpazyODnHN9xwQ7/1LMeTHs18n/J9wxvesM75X5+c14SV1NW8hrrP83DIuUkDesmSJc3P6dnM8TzoQQ9qyia/zznKe1an9b1/pFct9TR1Jz05kfe7vLa32267pnwe9rCH9db79cnQ1jxuXk9DkX3LsMJueaz2e+BAUg8yTC+9hr/85S+bbatXry5vf/vbm4CXupqhvG9605ua7W15v8vQ1v7kePOa6pQA/x//8R9NuAPWT2iBEZB/mvvtt1/54he/2KdhtGLFima4xeaUYRT5B5tGV77PLY2zDQ1RedGLXtQ0gjPMJw3NDNfqDFn9yTCHNMIzLyX/3DMsLQ3KhIIf/ehH6/3bf//3f+8dcjIYaTDlymsardnHV7ziFU2wSqOosxEbaTTmWNLIeN/73teUf6765zESsNK4Si9OGqQJI+3GW6fcP0Ob0gjKfTJ0J43TzsZGHi+NsTSYM5wlgeptb3tbectb3rLO46UBmH1KMEzjec6cOf0e5yc/+cly4oknNsEx98swqfxNwmFbGtA5vwl9aURlnHyOIeGn835tr33ta5v75hxl+FEaTTm+tj/84Q/loIMOahqj2feEpqOPPnqdEDSQlHd6C3Nbvnx5Uy/ajb2nPOUpvfdLr9qTnvSk8s1vfrN5/pRZ7nPcccc1xxppTL/jHe9ovs9wwXYdTj3LMSd8dM4PW7x4cdlqq62ar52BLhO58zdteQ288Y1vbPYnz5tAlHOaRmWGI7XluRJScl5TR1K2Ge6WetY9XyLHnb9PsP3ABz7QNGAToj7xiU8Mqtxyrm688cbmokZ63NLzOBwTz7vleDPfKPsdeZ1nqGbKJOc+70npyUgP8WAa12nk5xjSIE/Zpr6mjibMpIGf85lyed7znjeoCxrf//73y2Me85iypaTe5j0r9TNDdhM2Us+yvzmvz33uc5tyyes/vVN5r2zL+1dC309/+tM+j5khfxna2t0TlPeIvF91h2CgHy1gizn77LPzH7911VVXtT7ykY+0tt1229Zf/vKX5ncveMELWnPmzGm+32233VqHHHJI79995zvfaf4uXzstWbKk2Z7HbXv729/ebOs0adKk1jHHHDPg/uRx2vLc2fbVr361d9uKFSta06ZNaz360Y8ecJ/Wrl3beshDHtJ65jOf2XzfluPbfffdWwceeOB6yyaPPXXq1NZg3H333a0dd9yxtc8++7T++te/9m6/6KKLmn1629ve1rstx51t73nPe3q3/fnPf27d5z73afX09LTOPffc3u2/+MUvmvumDLvL6LGPfWzzvG3ve9/7mu1f+9rX+hxrt1e96lWt+973vq2//e1vvdue9rSnNX/78Y9/fJ3753e5tR166KGtvffee73lcdhhh7Xufe97t37zm9/0bvvd737X1K/Zs2evcywHHHBAn3P0ute9rjVhwoTWHXfc0fx8wQUX9NbToWofW/ft4Q9/eOu3v/1tn/sed9xxTb364x//2Gf7i1/84qYutMsz+9Fdz2PNmjWtKVOmtN70pjc1P+eYdthhh+a1lOO58847m+0f/OAHW1tttVVz3mPx4sXN433+85/v83iXXHJJn+35++222671ile8os/9br311mb/Ore369k73vGOdep16s5gnHDCCa0ZM2b0nptvfOMbzWP++Mc/3uDrtrveDCSv74MOOqh12223Nbef/OQnTXnn8V772tcOWI+/+MUvNvdZtGjRoN4/UpadPvShDzXb85xDcc899zSv09e//vXrvV/eL/Pcnbpfy5372Pl+2Pm+vHz58ub19qAHPah100039d7ns5/9bFOHUnc65TWcv/3e977X/JzX0DbbbNN685vf3Od+J554YvM+fNddd/XZ/v3vf7/5+y996UuDKg8Yz/S0wAhJL8Ff//rXZrz2nXfe2Xztb2jYSMjwpgwZamsPh8oV61tvvbXfv8nKTumhyTGkF6F9pT1D0zJUKcOrcrVyIOmdSU/HYGToUnoD/uEf/qEZvtKWK+J77rln+frXv77O32SlnrYMT8nV0wz3aY+Tj2zL73KVuVuu8t/rXvfq/Tk9FOmB6lyytHPcfs5pjj+9AZmv8otf/KLP46X3K1eyNyT7kwnGuVLbn1zdz1C+XPXNcJ62adOmNecivRAp2+5j6VwWO/uYx/mv//qv3ueM1MnOXofBSk9Wrtbnlh7E9JqkFzE9S+1haGlTZkhgrlrn+3Z9yS29Fbn/tddeu97nSY/Kk5/85KZuRXrCUvfSO5TH/MEPftBsT69LhuO1jyvDCjP0Kj0Bnc+bq97pUfnOd77T3C/7n6vg6TXovF969574xCf23q9T95yQlG1/9am/OV1f+tKXmqv27XOTq/3Dscxv6kuGruWWoUwpj/QQtOeMddbjzEfJMadHLDZ0TiJDCbuHQbXLPkMo1/c+0O1Pf/pTcy4zLHK45XWW3rHU+dSpDBdtSxml1y/vL511oT0XpV0XUq8OPfTQphe93SuV11bObV6jec/p1D6uLTWPEUYzoQVGSBoMWSkrY9YzrCn/2DKWvQYZotP9WS8PfehDm68DLSGawBLHHHNMb4OoffvUpz7VDAtJQ3QgCUZp6A9Gu3GdkNEtjYr279sSbLpXEUrjIuPVu48z2/ub1/CQhzykz89p3CYYdJZHhngk7OUxcjx5zvZwkO5jz7yawUy6zzyMPFfG12cfjj/++D5DahICEor6K4s0stJA7F5qeNddd+234dQ+7jTcnv/85zdD0TKnJY2ws88+u8/Y/fVJwyx1O7cMvzvppJOa4X8ZNnTGGWf07ncCQYZOddeXdphLMN2QhILMDcoFgISTnJMMJUpjvD1ELMEt9+usqzkfCQTdz51hZO3nbdfpNEy775eGf/f+9VfPUrb91aduebyUSc5zhojlliF+GTaYBvBQGvobksCVQJZheRl6lQbzv/3bv/WGlQSFnLPM88m2HFOCSKzvNdzWvm+nhLEMxcvLine truncated
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
@@ -868,10 +869,10 @@
   "id": "1de94dbc",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:43.401915Z",
+     "iopub.execute_input": "2026-02-05T00:47:13.335629Z",
-     "iopub.status.busy": "2026-02-02T23:40:43.401843Z",
+     "iopub.status.busy": "2026-02-05T00:47:13.335509Z",
-     "iopub.status.idle": "2026-02-02T23:40:43.724524Z",
+     "iopub.status.idle": "2026-02-05T00:47:13.809112Z",
-     "shell.execute_reply": "2026-02-02T23:40:43.724263Z"
+     "shell.execute_reply": "2026-02-05T00:47:13.808624Z"
    }
   },
   "outputs": [],
@@ -897,10 +898,10 @@
   "id": "b0aa032c",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:43.726078Z",
+     "iopub.execute_input": "2026-02-05T00:47:13.810697Z",
-     "iopub.status.busy": "2026-02-02T23:40:43.725996Z",
+     "iopub.status.busy": "2026-02-05T00:47:13.810579Z",
-     "iopub.status.idle": "2026-02-02T23:40:43.728507Z",
+     "iopub.status.idle": "2026-02-05T00:47:13.813539Z",
-     "shell.execute_reply": "2026-02-02T23:40:43.728266Z"
+     "shell.execute_reply": "2026-02-05T00:47:13.813087Z"
    }
   },
   "outputs": [
@@ -942,10 +943,10 @@
   "id": "ba6dcb50",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:43.729744Z",
+     "iopub.execute_input": "2026-02-05T00:47:13.814983Z",
-     "iopub.status.busy": "2026-02-02T23:40:43.729667Z",
+     "iopub.status.busy": "2026-02-05T00:47:13.814869Z",
-     "iopub.status.idle": "2026-02-02T23:40:43.732002Z",
+     "iopub.status.idle": "2026-02-05T00:47:13.817274Z",
-     "shell.execute_reply": "2026-02-02T23:40:43.731704Z"
+     "shell.execute_reply": "2026-02-05T00:47:13.816873Z"
    },
    "lines_to_next_cell": 0
   },
@@ -953,7 +954,7 @@
    {
     "data": {
      "text/plain": [
-       "146"
+       "np.int64(146)"
      ]
     },
     "execution_count": 19,
@@ -986,10 +987,10 @@
   "id": "44c1b26a",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:43.733204Z",
+     "iopub.execute_input": "2026-02-05T00:47:13.818684Z",
-     "iopub.status.busy": "2026-02-02T23:40:43.733131Z",
+     "iopub.status.busy": "2026-02-05T00:47:13.818568Z",
-     "iopub.status.idle": "2026-02-02T23:40:43.735178Z",
+     "iopub.status.idle": "2026-02-05T00:47:13.820908Z",
-     "shell.execute_reply": "2026-02-02T23:40:43.734967Z"
+     "shell.execute_reply": "2026-02-05T00:47:13.820500Z"
    },
    "lines_to_next_cell": 2
   },
@@ -997,7 +998,7 @@
    {
     "data": {
      "text/plain": [
-       "0"
+       "np.int64(0)"
      ]
     },
     "execution_count": 20,
@@ -1014,7 +1015,7 @@
   "id": "bb9aba03",
   "metadata": {},
   "source": [
-    "Figure~\\ref{Ch12:fig:BonferroniBenjamini} displays the ordered\n",
+    "Figure 13.6 displays the ordered\n",
    "$p$-values, $p_{(1)} \\leq p_{(2)} \\leq \\cdots \\leq p_{(2000)}$, for\n",
    "the  `Fund`  dataset, as well as the threshold for rejection by the\n",
    "Benjamini--Hochberg procedure.  Recall that the Benjamini--Hochberg\n",
@@ -1036,10 +1037,10 @@
   "id": "f23a1f9f",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:43.736262Z",
+     "iopub.execute_input": "2026-02-05T00:47:13.822386Z",
-     "iopub.status.busy": "2026-02-02T23:40:43.736193Z",
+     "iopub.status.busy": "2026-02-05T00:47:13.822275Z",
-     "iopub.status.idle": "2026-02-02T23:40:43.738272Z",
+     "iopub.status.idle": "2026-02-05T00:47:13.824875Z",
-     "shell.execute_reply": "2026-02-02T23:40:43.738070Z"
+     "shell.execute_reply": "2026-02-05T00:47:13.824369Z"
    }
   },
   "outputs": [],
@@ -1061,7 +1062,7 @@
   "id": "b84d7916",
   "metadata": {},
   "source": [
-    "We now reproduce  the middle panel of Figure~\\ref{Ch12:fig:BonferroniBenjamini}."
+    "We now reproduce  the middle panel of Figure 13.6."
   ]
  },
  {
@@ -1070,17 +1071,17 @@
   "id": "3c3018cc",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:43.739348Z",
+     "iopub.execute_input": "2026-02-05T00:47:13.826330Z",
-     "iopub.status.busy": "2026-02-02T23:40:43.739279Z",
+     "iopub.status.busy": "2026-02-05T00:47:13.826218Z",
-     "iopub.status.idle": "2026-02-02T23:40:43.914663Z",
+     "iopub.status.idle": "2026-02-05T00:47:14.010340Z",
-     "shell.execute_reply": "2026-02-02T23:40:43.914426Z"
+     "shell.execute_reply": "2026-02-05T00:47:14.009791Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
@@ -1109,7 +1110,7 @@
    "## A Re-Sampling Approach\n",
    "Here, we implement the re-sampling approach to hypothesis testing\n",
    "using the  `Khan`  dataset, which we investigated in\n",
-    "Section~\\ref{sec:permutations}.  First, we merge the training and\n",
+    "Section 13.5.  First, we merge the training and\n",
    "testing data, which results in observations on 83 patients for\n",
    "2,308 genes."
   ]
@@ -1120,22 +1121,31 @@
   "id": "d49b3de7",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:43.916217Z",
+     "iopub.execute_input": "2026-02-05T00:47:14.012072Z",
-     "iopub.status.busy": "2026-02-02T23:40:43.916122Z",
+     "iopub.status.busy": "2026-02-05T00:47:14.011949Z",
-     "iopub.status.idle": "2026-02-02T23:40:43.973817Z",
+     "iopub.status.idle": "2026-02-05T00:47:14.157393Z",
-     "shell.execute_reply": "2026-02-02T23:40:43.973588Z"
+     "shell.execute_reply": "2026-02-05T00:47:14.156590Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_26045/2840220789.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  D['Y'] = pd.concat([Khan['ytrain'], Khan['ytest']])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Y\n",
       "2    29\n",
       "4    25\n",
       "3    18\n",
       "1    11\n",
-       "Name: Y, dtype: int64"
+       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
@@ -1171,10 +1181,10 @@
   "id": "18eaeb65",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:43.975057Z",
+     "iopub.execute_input": "2026-02-05T00:47:14.158842Z",
-     "iopub.status.busy": "2026-02-02T23:40:43.974970Z",
+     "iopub.status.busy": "2026-02-05T00:47:14.158732Z",
-     "iopub.status.idle": "2026-02-02T23:40:43.978337Z",
+     "iopub.status.idle": "2026-02-05T00:47:14.183189Z",
-     "shell.execute_reply": "2026-02-02T23:40:43.978117Z"
+     "shell.execute_reply": "2026-02-05T00:47:14.182446Z"
    },
    "lines_to_next_cell": 2
   },
@@ -1182,7 +1192,7 @@
    {
     "data": {
      "text/plain": [
-       "(-2.0936330736768185, 0.04118643782678394)"
+       "(np.float64(-2.0936330736768185), np.float64(0.04118643782678394))"
      ]
     },
     "execution_count": 24,
@@ -1224,10 +1234,10 @@
   "id": "8e2f821c",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:43.979564Z",
+     "iopub.execute_input": "2026-02-05T00:47:14.184750Z",
-     "iopub.status.busy": "2026-02-02T23:40:43.979493Z",
+     "iopub.status.busy": "2026-02-05T00:47:14.184627Z",
-     "iopub.status.idle": "2026-02-02T23:40:45.736220Z",
+     "iopub.status.idle": "2026-02-05T00:47:17.380369Z",
-     "shell.execute_reply": "2026-02-02T23:40:45.735978Z"
+     "shell.execute_reply": "2026-02-05T00:47:17.379890Z"
    },
    "lines_to_next_cell": 2
   },
@@ -1235,7 +1245,7 @@
    {
     "data": {
      "text/plain": [
-       "0.9602"
+       "np.float64(0.9602)"
      ]
     },
     "execution_count": 25,
@@ -1266,7 +1276,7 @@
    "This fraction, 0.0398,\n",
    "is our re-sampling-based $p$-value.\n",
    "It is almost identical to the $p$-value of  0.0412 obtained using the theoretical null distribution.\n",
-    "We can plot  a histogram of the re-sampling-based test statistics in order to reproduce  Figure~\\ref{Ch12:fig-permp-1}."
+    "We can plot  a histogram of the re-sampling-based test statistics in order to reproduce  Figure 13.7."
   ]
  },
  {
@@ -1275,17 +1285,17 @@
   "id": "f10d3bf0",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:45.737603Z",
+     "iopub.execute_input": "2026-02-05T00:47:17.382043Z",
-     "iopub.status.busy": "2026-02-02T23:40:45.737525Z",
+     "iopub.status.busy": "2026-02-05T00:47:17.381928Z",
-     "iopub.status.idle": "2026-02-02T23:40:45.904098Z",
+     "iopub.status.idle": "2026-02-05T00:47:17.489426Z",
-     "shell.execute_reply": "2026-02-02T23:40:45.903787Z"
+     "shell.execute_reply": "2026-02-05T00:47:17.488927Z"
    },
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
@@ -1320,7 +1330,7 @@
    "The re-sampling-based null distribution is almost identical to the theoretical null distribution, which is displayed in red.\n",
    "\n",
    "Finally, we implement the plug-in re-sampling FDR approach outlined in\n",
-    "Algorithm~\\ref{Ch12:alg-plugin-fdr}. Depending on the speed of your\n",
+    "Algorithm 13.4. Depending on the speed of your\n",
    "computer, calculating the FDR for all 2,308 genes in the `Khan`\n",
    "dataset may take a while.  Hence, we will illustrate the approach on a\n",
    "random subset of 100 genes.  For each gene, we first compute the\n",
@@ -1335,10 +1345,10 @@
   "id": "d37287ae",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:40:45.905463Z",
+     "iopub.execute_input": "2026-02-05T00:47:17.490919Z",
-     "iopub.status.busy": "2026-02-02T23:40:45.905362Z",
+     "iopub.status.busy": "2026-02-05T00:47:17.490801Z",
-     "iopub.status.idle": "2026-02-02T23:43:41.101404Z",
+     "iopub.status.idle": "2026-02-05T00:52:54.178038Z",
-     "shell.execute_reply": "2026-02-02T23:43:41.100635Z"
+     "shell.execute_reply": "2026-02-05T00:52:54.176846Z"
    }
   },
   "outputs": [],
@@ -1371,7 +1381,7 @@
    "Next, we compute the number of rejected null hypotheses $R$, the\n",
    "estimated number of false positives $\\widehat{V}$, and the estimated\n",
    "FDR, for a range of threshold values $c$ in\n",
-    "Algorithm~\\ref{Ch12:alg-plugin-fdr}. The threshold values are chosen\n",
+    "Algorithm 13.4. The threshold values are chosen\n",
    "using the absolute values of the test statistics from the 100 genes."
   ]
  },
@@ -1381,10 +1391,10 @@
   "id": "ecaf03eb",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:43:41.106109Z",
+     "iopub.execute_input": "2026-02-05T00:52:54.181722Z",
-     "iopub.status.busy": "2026-02-02T23:43:41.105886Z",
+     "iopub.status.busy": "2026-02-05T00:52:54.181546Z",
-     "iopub.status.idle": "2026-02-02T23:43:41.236604Z",
+     "iopub.status.idle": "2026-02-05T00:52:54.354719Z",
-     "shell.execute_reply": "2026-02-02T23:43:41.236326Z"
+     "shell.execute_reply": "2026-02-05T00:52:54.354206Z"
    }
   },
   "outputs": [],
@@ -1422,10 +1432,10 @@
   "id": "ceacd8b8",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:43:41.238163Z",
+     "iopub.execute_input": "2026-02-05T00:52:54.356542Z",
-     "iopub.status.busy": "2026-02-02T23:43:41.238070Z",
+     "iopub.status.busy": "2026-02-05T00:52:54.356415Z",
-     "iopub.status.idle": "2026-02-02T23:43:41.241062Z",
+     "iopub.status.idle": "2026-02-05T00:52:54.359797Z",
-     "shell.execute_reply": "2026-02-02T23:43:41.240834Z"
+     "shell.execute_reply": "2026-02-05T00:52:54.359234Z"
    }
   },
   "outputs": [
@@ -1477,10 +1487,10 @@
   "id": "2434f370",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:43:41.242193Z",
+     "iopub.execute_input": "2026-02-05T00:52:54.361441Z",
-     "iopub.status.busy": "2026-02-02T23:43:41.242118Z",
+     "iopub.status.busy": "2026-02-05T00:52:54.361331Z",
-     "iopub.status.idle": "2026-02-02T23:43:41.244401Z",
+     "iopub.status.idle": "2026-02-05T00:52:54.364000Z",
-     "shell.execute_reply": "2026-02-02T23:43:41.244188Z"
+     "shell.execute_reply": "2026-02-05T00:52:54.363553Z"
    }
   },
   "outputs": [
@@ -1534,8 +1544,8 @@
   "id": "117c0723",
   "metadata": {},
   "source": [
-    "The next line  generates  Figure~\\ref{fig:labfdr}, which is similar\n",
+    "The next line  generates  Figure 13.11, which is similar\n",
-    "to  Figure~\\ref{Ch12:fig-plugin-fdr},\n",
+    "to  Figure 13.9,\n",
    "except that it is based on only  a subset of the genes."
   ]
  },
@@ -1545,17 +1555,17 @@
   "id": "34d6a02b",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2026-02-02T23:43:41.245792Z",
+     "iopub.execute_input": "2026-02-05T00:52:54.365271Z",
-     "iopub.status.busy": "2026-02-02T23:43:41.245713Z",
+     "iopub.status.busy": "2026-02-05T00:52:54.365183Z",
-     "iopub.status.idle": "2026-02-02T23:43:41.304373Z",
+     "iopub.status.idle": "2026-02-05T00:52:54.415214Z",
-     "shell.execute_reply": "2026-02-02T23:43:41.304028Z"
+     "shell.execute_reply": "2026-02-05T00:52:54.414710Z"
    },
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
@@ -1573,7 +1583,7 @@
  },
  {
   "cell_type": "markdown",
-   "id": "e672cce4",
+   "id": "9abbfbc2",
   "metadata": {},
   "source": [
    " \n"
@@ -1596,7 +1606,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.10"
  }
 },
 "nbformat": 4,
@@ -23,7 +23,8 @@ def setup_env(outdir,
              uv_executable,
              timeout,
              kernel,
-              allow_errors):
+              allow_errors,
              overwrite):
    """
    Sets up a student environment for ISLP_labs.
@@ -45,6 +46,8 @@ def setup_env(outdir,
        Kernel to use for running notebooks.
    allow_errors : bool
        Allow errors when running notebooks.
    overwrite: bool
        Save notebook outputs in place?
    """
    repo_url = 'https://github.com/intro-stat-learning/ISLP_labs.git'
@@ -110,7 +113,8 @@ def setup_env(outdir,
                    if kernel:
                        nbconvert_command.extend(['--kernel', kernel])
                    nbconvert_command.append('--allow-errors')
-
+                    if overwrite:
                        nbconvert_command.append('--inplace')
                    run_command(nbconvert_command, cwd=str(outdir))
            else:
                run_command([str(uv_bin / 'pip'), 'install', 'pytest', 'nbmake'], cwd=str(outdir))
@@ -128,6 +132,9 @@ def setup_env(outdir,
                                      str(nbfile)]
                    if kernel:
                        pytest_command.append(f'--nbmake-kernel={kernel}')
                    if overwrite:
                        pytest_command.append('--overwrite')
                    # nbmake does not have --allow-errors in the same way as nbconvert
                    # If errors are not allowed, nbmake will fail on first error naturally
@@ -152,7 +159,8 @@ def main():
    parser.add_argument('--uv-executable', default='uv', help='The `uv` executable to use (default: "uv")')
    parser.add_argument('--timeout', type=int, default=3600, help='Timeout for running notebooks (default: 3600)')
    parser.add_argument('--kernel', default=None, help='Kernel to use for running notebooks')
-    parser.add_argument('--allow-errors', action='store_true', help='Allow errors when running notebooks. For older commits, Ch02-statlearn-lab.ipynb raises exceptions. Since v2.2.1 these are handled by appropriate tags in the .ipynb cells.')
+    parser.add_argument('--allow-errors', action='store_true', default=False, help='Allow errors when running notebooks. For older commits, Ch02-statlearn-lab.ipynb raises exceptions. Since v2.2.1 these are handled by appropriate tags in the .ipynb cells.')
    parser.add_argument('--overwrite', action='store_true', default=False, help='If True, store output of cells in notebook(s).')
    parser.add_argument('nbfiles', nargs='*', help='Optional list of notebooks to run.')
@@ -165,7 +173,8 @@ def main():
              args.uv_executable,
              args.timeout,
              args.kernel,
-              args.allow_errors)
+              args.allow_errors,
              args.overwrite)
 if __name__ == '__main__':
    main()