diff --git a/Gaussians.ipynb b/Gaussians.ipynb
index 16eda12..88516e1 100644
--- a/Gaussians.ipynb
+++ b/Gaussians.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:9184f3be6129a4cf39b259921633ec76099fb8ca364fd39a633da1920257669b"
+  "signature": "sha256:20b4feb883a71cab956a175006257f6c548b074ace2a845bd9dc5c1c1f9b7a91"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -83,7 +83,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from gaussian import gaussian\n",
+      "from stats import gaussian\n",
       "plot_gaussian(22,4,True,xlabel='$^{\\circ}C$',ylabel=\"Percent\")\n",
       "\n",
       "print('Probability of 22 is %.2f' % (gaussian(22,22,4)*100))\n",
@@ -103,7 +103,11 @@
       "\n",
       "> *Important*: I will repeat what I wrote at the top of this section: \"A Gaussian...is completely described with two parameters\"\n",
       "\n",
-      "The standard notation for a normal distribution for a random variable $X$ is $X \\sim\\ \\mathcal{N}(\\mu,\\sigma^2)$. This means I can express the temperature reading of our thermometer as $$temp = \\mathcal{N}(22,4)$$This is an **extremely important** result. Gaussians allow me to capture an infinite number of possible values with only two numbers! With the values $\\mu=22$ and $\\sigma^2=4$ I can compute the probability of the temperature being $22\\,^{\\circ}C$, of $20\\,^{\\circ}C$, of $87.3429\\,^{\\circ}C$, or any other arbitrary value.\n",
+      "The standard notation for a normal distribution for a random variable $X$ is $X \\sim\\ \\mathcal{N}(\\mu,\\sigma^2)$. This means I can express the temperature reading of our thermometer as\n",
+      "\n",
+      "$$temp = \\mathcal{N}(22,4)$$\n",
+      "\n",
+      "This is an **extremely important** result. Gaussians allow me to capture an infinite number of possible values with only two numbers! With the values $\\mu=22$ and $\\sigma^2=4$ I can compute the probability of the temperature being $22\\,^{\\circ}C$, of $20\\,^{\\circ}C$, of $87.3429\\,^{\\circ}C$, or any other arbitrary value.\n",
       "\n",
       "###### The Variance\n",
       "\n",
diff --git a/Kalman Filters.ipynb b/Kalman Filters.ipynb
index 7144ec8..29aa158 100644
--- a/Kalman Filters.ipynb	
+++ b/Kalman Filters.ipynb	
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:91af593ec32629bec5fa5a58b7f5ae29e79fc039fe09b20d37c26dca0ac20430"
+  "signature": "sha256:f5b17ce3aa0755a19e9550b31e0a75c9c255bb570b57e1e26c533de072549821"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -193,8 +193,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import gaussian\n",
-      "gaussian.norm_plot(23, 5)"
+      "import stats\n",
+      "stats.norm_plot(23, 5)"
      ],
      "language": "python",
      "metadata": {},
@@ -289,7 +289,6 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import gaussian\n",
       "import numpy as np\n",
       "\n",
       "def multiply(mu1, sig1, mu2, sig2):\n",
@@ -303,10 +302,10 @@
       "m1,s1 = 23, 5\n",
       "m, s = multiply(m1,s1,m1,s1)\n",
       "\n",
-      "ys = [gaussian.gaussian(x,m1,s1) for x in xs]\n",
+      "ys = [stats.gaussian(x,m1,s1) for x in xs]\n",
       "p1, = plt.plot (xs,ys)\n",
       "\n",
-      "ys = [gaussian.gaussian(x,m,s) for x in xs]\n",
+      "ys = [stats.gaussian(x,m,s) for x in xs]\n",
       "p2, = plt.plot (xs,ys)\n",
       "\n",
       "plt.legend([p1,p2],['original', 'multiply'])\n",
@@ -339,13 +338,13 @@
       "m2,s2 = 25, 5\n",
       "m, s = multiply(m1,s1,m2,s2)\n",
       "\n",
-      "ys = [gaussian.gaussian(x,m1,s1) for x in xs]\n",
+      "ys = [stats.gaussian(x,m1,s1) for x in xs]\n",
       "p1, = plt.plot (xs,ys)\n",
       "\n",
-      "ys = [gaussian.gaussian(x,m2,s2) for x in xs]\n",
+      "ys = [stats.gaussian(x,m2,s2) for x in xs]\n",
       "p2, = plt.plot (xs,ys)\n",
       "\n",
-      "ys = [gaussian.gaussian(x,m,s) for x in xs]\n",
+      "ys = [stats.gaussian(x,m,s) for x in xs]\n",
       "p3, = plt.plot(xs,ys)\n",
       "plt.legend([p1,p2,p3],['measure 1', 'measure 2', 'multiply'])\n",
       "plt.show()"
@@ -379,13 +378,13 @@
       "m2,s2 = 50, 5\n",
       "m, s = multiply(m1,s1,m2,s2)\n",
       "\n",
-      "ys = [gaussian.gaussian(x,m1,s1) for x in xs]\n",
+      "ys = [stats.gaussian(x,m1,s1) for x in xs]\n",
       "p1, = plt.plot (xs,ys)\n",
       "\n",
-      "ys = [gaussian.gaussian(x,m2,s2) for x in xs]\n",
+      "ys = [stats.gaussian(x,m2,s2) for x in xs]\n",
       "p2, = plt.plot (xs,ys)\n",
       "\n",
-      "ys = [gaussian.gaussian(x,m,s) for x in xs]\n",
+      "ys = [stats.gaussian(x,m,s) for x in xs]\n",
       "p3, = plt.plot(xs,ys)\n",
       "plt.legend([p1,p2,p3],['measure 1', 'measure 2', 'multiply'])\n",
       "plt.show()"
@@ -779,7 +778,7 @@
       "\n",
       "    voltage = update(voltage[0], voltage[1], movement, movement_error)\n",
       "\n",
-      "plt.scatter(range(N), zs,marker='+')\n",
+      "plt.scatter(range(N), zs, marker='+')\n",
       "p1, = plt.plot(ps, c='g')\n",
       "plt.legend([p1], ['filter'], 3)\n",
       "plt.xlim((0,N));plt.ylim((0,30))\n",
diff --git a/Multidimensional Kalman Filters.ipynb b/Multidimensional Kalman Filters.ipynb
index 9a25856..1db4a93 100644
--- a/Multidimensional Kalman Filters.ipynb	
+++ b/Multidimensional Kalman Filters.ipynb	
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:c85d72f3d31f37b3edfae8ecdc7e79438bde16b84b6d6d119c4edfc1239913d8"
+  "signature": "sha256:fd542341131473089289548f12ba7f78ac88663ce51e586ff357c5fb48c3eb91"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -81,7 +81,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from gaussian import gaussian, multivariate_gaussian"
+      "from stats import gaussian, multivariate_gaussian"
      ],
      "language": "python",
      "metadata": {},
@@ -241,23 +241,23 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import gaussian as g\n",
+      "import stats\n",
       "\n",
       "cov = np.array([[2,0],[0,2]])\n",
-      "e = g.sigma_ellipse (cov, 2, 7)\n",
+      "e = stats.sigma_ellipse (cov, 2, 7)\n",
       "plt.subplot(131)\n",
-      "g.plot_sigma_ellipse(e, '|2 0|\\n|0 2|')\n",
+      "stats.plot_sigma_ellipse(e, '|2 0|\\n|0 2|')\n",
       "\n",
       "\n",
       "cov = np.array([[2,0],[0,9]])\n",
-      "e = g.sigma_ellipse (cov, 2, 7)\n",
+      "e = stats.sigma_ellipse (cov, 2, 7)\n",
       "plt.subplot(132)\n",
-      "g.plot_sigma_ellipse(e, '|2 0|\\n|0 9|')\n",
+      "stats.plot_sigma_ellipse(e, '|2 0|\\n|0 9|')\n",
       "\n",
       "plt.subplot(133)\n",
       "cov = np.array([[2,1.2],[1.2,3]])\n",
-      "e = g.sigma_ellipse (cov, 2, 7)\n",
-      "g.plot_sigma_ellipse(e,'|2 1.2|\\n|1.2 2|')\n",
+      "e = stats.sigma_ellipse (cov, 2, 7)\n",
+      "stats.plot_sigma_ellipse(e,'|2 1.2|\\n|1.2 2|')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -351,10 +351,10 @@
      "input": [
       "cov = np.array([[0.003,0], [0,12]])\n",
       "sigma=[0.5,1.,1.5,2]\n",
-      "e1 = g.sigma_ellipses(cov, x=1, y=1, sigma=sigma)\n",
-      "e2 = g.sigma_ellipses(cov, x=2, y=2, sigma=sigma)\n",
-      "e3 = g.sigma_ellipses(cov, x=3, y=3, sigma=sigma)\n",
-      "g.plot_sigma_ellipses([e1, e2, e3], axis_equal=True,x_lim=[0,4],y_lim=[0,15])\n",
+      "e1 = stats.sigma_ellipses(cov, x=1, y=1, sigma=sigma)\n",
+      "e2 = stats.sigma_ellipses(cov, x=2, y=2, sigma=sigma)\n",
+      "e3 = stats.sigma_ellipses(cov, x=3, y=3, sigma=sigma)\n",
+      "stats.plot_sigma_ellipses([e1, e2, e3], axis_equal=True,x_lim=[0,4],y_lim=[0,15])\n",
       "plt.ylim([0,11])\n",
       "plt.show()"
      ],
@@ -380,11 +380,11 @@
       "from matplotlib.patches import Ellipse\n",
       "\n",
       "cov = np.array([[1,1],[1,1.1]])\n",
-      "ev = g.sigma_ellipses(cov, x=2, y=2, sigma=sigma)\n",
+      "ev = stats.sigma_ellipses(cov, x=2, y=2, sigma=sigma)\n",
       "\n",
       "isct = Ellipse(xy=(2,2), width=.2, height=1.2, edgecolor='r', fc='None', lw=4)\n",
       "plt.figure().gca().add_artist(isct)\n",
-      "g.plot_sigma_ellipses([e1, e2, e3, ev], axis_equal=True,x_lim=[0,4],y_lim=[0,15])\n",
+      "stats.plot_sigma_ellipses([e1, e2, e3, ev], axis_equal=True,x_lim=[0,4],y_lim=[0,15])\n",
       "plt.ylim([0,11])\n",
       "plt.show()"
      ],
diff --git a/gaussian_internal.py b/gaussian_internal.py
index b0c4490..1c33e6c 100644
--- a/gaussian_internal.py
+++ b/gaussian_internal.py
@@ -9,7 +9,7 @@ import math
 import matplotlib.pylab as pylab
 import matplotlib.pyplot as plt
 import numpy as np
-import gaussian as g
+import stats
 
 def plot_gaussian (mu, variance,
                    mu_line=False,
@@ -17,7 +17,7 @@ def plot_gaussian (mu, variance,
                    xlabel=None,
                    ylabel=None):
     xs = np.arange(mu-variance*2,mu+variance*2,0.1)
-    ys = [g.gaussian (x, mu, variance)*100 for x in xs]
+    ys = [stats.gaussian (x, mu, variance)*100 for x in xs]
     plt.plot (xs, ys)
     if mu_line:
         plt.axvline(mu)
@@ -34,12 +34,12 @@ def display_stddev_plot():
     pylab.rcParams['figure.figsize'] = 12,6
     xs = np.arange(10,30,0.1)
     var = 8; stddev = math.sqrt(var)
-    p2, = plt.plot (xs,[g.gaussian(x, 20, var) for x in xs])
+    p2, = plt.plot (xs,[stats.gaussian(x, 20, var) for x in xs])
     x = 20+stddev
-    y = g.gaussian(x, 20, var)
+    y = stats.gaussian(x, 20, var)
     plt.plot ([x,x], [0,y],'g')
     plt.plot ([20-stddev, 20-stddev], [0,y], 'g')
-    y = g.gaussian(20,20,var)
+    y = stats.gaussian(20,20,var)
     plt.plot ([20,20],[0,y],'b')
     ax = plt.axes()
     ax.annotate('68%', xy=(20.3, 0.045))
diff --git a/gaussian.py b/stats.py
similarity index 100%
rename from gaussian.py
rename to stats.py