diff --git a/08-Designing-Kalman-Filters.ipynb b/08-Designing-Kalman-Filters.ipynb
index 352ee29..0ad4bce 100644
--- a/08-Designing-Kalman-Filters.ipynb
+++ b/08-Designing-Kalman-Filters.ipynb
@@ -2332,7 +2332,7 @@
     "\n",
     "We can then define the *normalized estimated error squared* (NEES) as \n",
     "\n",
-    "$$\\mathbf{\\epsilon} = \\tilde{\\mathbf x}^\\mathsf T\\mathbf P^{-1}\\tilde{\\mathbf x}$$\n",
+    "$$\\epsilon = \\tilde{\\mathbf x}^\\mathsf T\\mathbf P^{-1}\\tilde{\\mathbf x}$$\n",
     "\n",
     "To understand this equation let's look at it if the state's dimension is one. In that case both x and P are scalars, so\n",
     "\n",
@@ -2346,7 +2346,7 @@
     "\n",
     "The math is outside the scope of this book, but a random variable in the form $\\tilde{\\mathbf x}^\\mathsf T\\mathbf P^{-1}\\tilde{\\mathbf x}$ is said to be *chi-squared distributed with n degrees of freedom*, and thus the expected value of the sequence should be $n$. Bar-Shalom [1] has an excellent discussion of this topic. \n",
     "\n",
-    "In plain terms, take the average of all the NESS values, and they should be less then the dimension of x. Let's see that using an example from earlier in the chapter:"
+    "In plain terms, take the average of all the NEES values, and they should be less then the dimension of x. Let's see that using an example from earlier in the chapter:"
    ]
   },
   {
@@ -2399,10 +2399,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`NESS` is implemented FilterPy; access it with\n",
+    "`NEES` is implemented FilterPy; access it with\n",
     "\n",
     "```python\n",
-    "from filterpy.stats import NESS\n",
+    "from filterpy.stats import NEES\n",
     "```\n",
     "This is an excellent measure of the filter's performance, and should be used whenever possible, especially in production code when you need to evaluate the filter while it is running. While designing a filter I still prefer plotting the residauls as it gives me a more intuitive understand what is happening.\n",
     "\n",