diff --git a/03-Gaussians.ipynb b/03-Gaussians.ipynb
index a6c10ed..9762a36 100644
--- a/03-Gaussians.ipynb
+++ b/03-Gaussians.ipynb
@@ -296,7 +296,7 @@
     "\n",
     "### Random Variables\n",
     "\n",
-    "To understand Gaussians we first need to understand a few simple mathematical computations. We start with a **random variable** x. A random variable is a variable whose value depends on some random process. If you flip a coin, you could have a variable $c$, and assign it the value 1 for heads, and 0 for tails. That a random value. It can be the height of the students in a class. That may not seem random to you, but chances are you cannot predict the height of the student Reem Nassar because her height is not deterministically determined.  For a specific classroom perhaps the heights are\n",
+    "To understand Gaussians we first need to understand a few simple mathematical computations. We start with a **random variable** x. A random variable is a variable whose value depends on some random process. If you flip a coin, you could have a variable $c$, and assign it the value 1 for heads, and 0 for tails. That is a random value. It can be the height of the students in a class. That may not seem random to you, but chances are you cannot predict the height of the student Reem Nassar because her height is not deterministically determined.  For a specific classroom perhaps the heights are\n",
     "\n",
     "$$x= [1.8, 2.0, 1.7, 1.9, 1.6]$$\n",
     "\n",
@@ -792,7 +792,7 @@
     "\n",
     "Probably this is immediately recognizable to you as a 'bell curve'. This curve is ubiquitous because under real world conditions many observations are distributed in such a manner. In fact, this is the  curve for the student heights given earlier. I will not use the term 'bell curve' to refer to a Gaussian because several probability distributions have a similar bell curve shape. Non-mathematical sources might not be so precise, so be judicious in what you conclude when you see the term used without definition.\n",
     "\n",
-    "This curve is not unique to heights - a vast amount of natural phenomena exhibits this sort of distribution, including the sensors that we use in filtering problems. As we will see, it also has all the attributes that we are looking for - it represents a unimodal belief or value as a probability, it is continuous, and it is computationally efficient. We will soon discover that it also other desirable qualities which we may not realize we desire.\n",
+    "This curve is not unique to heights - a vast amount of natural phenomena exhibits this sort of distribution, including the sensors that we use in filtering problems. As we will see, it also has all the attributes that we are looking for - it represents a unimodal belief or value as a probability, it is continuous, and it is computationally efficient. We will soon discover that it also has other desirable qualities which we may not realize we desire.\n",
     "\n",
     "To further motivate you, recall the shapes of the probability distributions in the *Discrete Bayes* chapter. They were not perfect Gaussian curves, but they were similar, as in the plot below. We will be using Gaussians to replace the discrete probabilities used in that chapter! Please note that eyeball comparisons of PDF curves is strongly discouraged, as humans have trouble estimating areas; CDFs are usually the preferred choice. "
    ]
@@ -1418,7 +1418,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The area under the curve cannot equal 1, so it is not a probability distribution. What actually happens is that a more students than predicted by a normal distribution get scores nearer the upper end of the range (for example), and that tail becomes 'fat'. Also, the test is probably not able to perfectly distinguish incredibly minute differences in skill in the students, so the distribution to the left of the mean is also probably a bit bunched up in places. The resulting distribution is called a *fat tail distribution*. \n",
+    "The area under the curve cannot equal 1, so it is not a probability distribution. What actually happens is that more students than predicted by a normal distribution get scores nearer the upper end of the range (for example), and that tail becomes 'fat'. Also, the test is probably not able to perfectly distinguish incredibly minute differences in skill in the students, so the distribution to the left of the mean is also probably a bit bunched up in places. The resulting distribution is called a *fat tail distribution*. \n",
     "\n",
     "Kalman filters use sensors to measure the world. The errors in sensor's measurements are rarely truly Gaussian. It is far too early to be talking about the difficulties that this presents to the Kalman filter designer. It is worth keeping in the back of your mind the fact that the Kalman filter math is based on a somewhat idealized model of the world.  For now I will present a bit of code that I will be using later in the book to form fat tail distributions to simulate various processes and sensors. This distribution is called the student's t distribution. \n",
     "\n",
@@ -1532,7 +1532,7 @@
     "\n",
     "It is unlikely that the Student's T distribution is an accurate model of how your sensor (say, a GPS or Doppler) performs, and this is not a book on how to model physical systems. However, it does produce reasonable data to test your filter's performance when presented with real world noise. We will be using distributions like these throughout the rest of the book in our simulations and tests. \n",
     "\n",
-    "This is not an idle concern. The Kalman filter equations assume the noise is normally distributed, and perform sub-optimally if this is not true. Designers for mission critical filters, such as the filters on spacecraft, need to master a lot of theory and emperical knowledge about the performance of the sensors on their spacecraft. \n",
+    "This is not an idle concern. The Kalman filter equations assume the noise is normally distributed, and perform sub-optimally if this is not true. Designers for mission critical filters, such as the filters on spacecraft, need to master a lot of theory and empirical knowledge about the performance of the sensors on their spacecraft. \n",
     "\n",
     "The code for rand_student_t is included in `filterpy.stats`. You may use it with\n",
     "\n",