diff --git a/00-Preface.ipynb b/00-Preface.ipynb
index 2afa696..5c6dcb7 100644
--- a/00-Preface.ipynb
+++ b/00-Preface.ipynb
@@ -534,7 +534,7 @@
    "source": [
     "x = np.array([[1., 2.],\n",
     "              [3., 4.]])\n",
-    "print('abddition:\\n', x + x)\n",
+    "print('addition:\\n', x + x)\n",
     "print('\\nelement-wise multiplication\\n', x * x)\n",
     "print('\\nmultiplication\\n', np.dot(x, x))\n",
     "print('\\ndot is also a member of np.array\\n', x.dot(x))"
diff --git a/01-g-h-filter.ipynb b/01-g-h-filter.ipynb
index 190ad20..fff24e0 100644
--- a/01-g-h-filter.ipynb
+++ b/01-g-h-filter.ipynb
@@ -765,7 +765,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "I think this is starting to look really good. Because of the poor initial guess of the weight gain being -1 it takes the filter several days to accurately predict the weight, but once it does that it starts to accurately track the weight. We used no methodology for choosing our scaling factors of $\\frac{4}{10}$ and $\\frac{1}{3}$ (actually, they are poor choices for this problem), but otherwise all of the math followed from very reasonable assumptions. Recall that you can change the value of the parameter `time_step` to a larger value and re-run the cell if you want to see the plot drawn step-by-step.\n",
+    "I think this is starting to look really good. Because of the poor initial guess of the weight gain being 1 it takes the filter several days to accurately predict the weight, but once it does that it starts to accurately track the weight. We used no methodology for choosing our scaling factors of $\\frac{4}{10}$ and $\\frac{1}{3}$ (actually, they are poor choices for this problem), but otherwise all of the math followed from very reasonable assumptions. Recall that you can change the value of the parameter `time_step` to a larger value and re-run the cell if you want to see the plot drawn step-by-step.\n",
     "\n",
     "One final point before we go on. In the prediction step I wrote the line\n",
     "```python\n",
@@ -830,7 +830,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let me introduce some more formal terminology. The *system* is the object that we want to estimate. In this chapter the system is whatever we are trying to weigh. Some texts call this the *plant*. That terminology comes from control system theory. https://en.wikipedia.org/wiki/Plant_(control_theory%29\n",
+    "Let me introduce some more formal terminology. The *system* is the object that we want to estimate. In this chapter the system is whatever we are trying to weigh. Some texts call this the *plant*. That terminology comes from control system theory. https://en.wikipedia.org/wiki/Plant_(control_theory)\n",
     "\n",
     "The *state* of the system is the current configuration or values of that system that is of interest to us. We are interested only in the weight reading. If I put a 100 kg weight on the scale, the state is 100kg. We define the state based on what is relevant to us. The color of the scale is irrelevant to us so we do not include those values in the state. A QA engineer for the manufacturer might include color in the state so that she can track and control the manufacturing process. \n",
     "\n",