diff --git a/charting.ipynb b/charting.ipynb
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+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:2b1b0831c74b585c9d7056bef5a70ccab454f18391545ab1207e05c604c65bdb"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import random\n",
+      "import math\n",
+      "import statistics\n",
+      "import matplotlib.pyplot as plt\n",
+      "import seaborn"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 79
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "My simple coin flip function:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def coin_flip():\n",
+      "    x = random.randint(0,1)\n",
+      "    if x == 0:\n",
+      "        return 'heads'\n",
+      "    else:\n",
+      "        return 'tails'"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 196
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#This was a beautiful function that returned a dictionary.  I did a dictionary comprehension that made me dance when it worked\n",
+      "#It was all for not because of the stupid unordered nature of python dictionaries\n",
+      "\n",
+      "#def flipper(n=100000):\n",
+      "#    flips = []\n",
+      "#    heads = [x for x in flips if x=='heads']\n",
+      "#    tails = [x for x in flips if x=='tails']\n",
+      "#    log_dict = {}\n",
+      "#    for x in range(1,n):\n",
+      "#        flips.append(coin_flip())\n",
+      "#    #print(flips)\n",
+      "#    for y in range (0,n):\n",
+      "#        if 2**y < n:\n",
+      "#            log_dict[2**y]=flips[:2**y]\n",
+      "#    log_dict[n]=flips\n",
+      "#    new_dict = {key: [flips[:key].count('heads'), flips[:key].count('tails')] for key in log_dict}\n",
+      "#    \n",
+      "#    return new_dict\n",
+      "#\n",
+      "#\n",
+      "#flipper()        "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 94
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "My multiple flips function:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def flipper(n=100000):\n",
+      "    \"\"\"flips a coin 100,000 times.  Returns a tuple of the 2**n markers, the breakdown of head and tails,\n",
+      "    a list of the differences and a list of the ratios of x to y\"\"\"\n",
+      "    flips = []\n",
+      "    counts = []\n",
+      "    fliplist = []\n",
+      "    ratios = []\n",
+      "    heads = [x for x in flips if x=='heads']\n",
+      "    tails = [x for x in flips if x=='tails']\n",
+      "    for x in range(1,n):\n",
+      "        flips.append(coin_flip())\n",
+      "    #print(flips)\n",
+      "    for y in range (0,n):\n",
+      "        if 2**y < n:\n",
+      "            counts.append(2**y)\n",
+      "            fliplist.append(flips[:2**y])\n",
+      "    counts.append(n)\n",
+      "    fliplist.append(flips)\n",
+      "    headtails = [(x.count('heads'), x.count('tails')) for x in fliplist]\n",
+      "    difflist = [x[0]-x[1] for x in headtails]\n",
+      "    def ratio(x, y):\n",
+      "        if y > 0:\n",
+      "            return x/y\n",
+      "        else:\n",
+      "            return 0\n",
+      "    ratiolist =[ratio(x[0], x[1]) for x in headtails]\n",
+      "     \n",
+      "    return counts, headtails, difflist, ratiolist\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 155
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "counts, headtails, difflist, ratiolist = flipper()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 156
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print(counts)\n",
+      "print(headtails)\n",
+      "print(difflist)\n",
+      "print(ratiolist)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 100000]\n",
+        "[(1, 0), (1, 1), (3, 1), (7, 1), (13, 3), (23, 9), (38, 26), (70, 58), (148, 108), (270, 242), (542, 482), (1056, 992), (2072, 2024), (4079, 4113), (8096, 8288), (16372, 16396), (32775, 32761), (50065, 49934)]\n",
+        "[1, 0, 2, 6, 10, 14, 12, 12, 40, 28, 60, 64, 48, -34, -192, -24, 14, 131]\n",
+        "[0, 1.0, 3.0, 7.0, 4.333333333333333, 2.5555555555555554, 1.4615384615384615, 1.206896551724138, 1.3703703703703705, 1.115702479338843, 1.1244813278008299, 1.064516129032258, 1.023715415019763, 0.9917335278385606, 0.9768339768339769, 0.9985362283483776, 1.0004273373828638, 1.002623462971122]\n"
+       ]
+      }
+     ],
+     "prompt_number": 157
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "You can see in the early stages that after 8, 16, even 32 flips, it had been really imbalanced towards heads.  More than you'd expect even in such a small sample size.  So it's fun to see especially in the ratio graph of this one, how much of a spike that creates"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.plot(difflist)\n",
+      "plt.xticks(range(len(counts)), counts)\n",
+      "plt.xlabel(\"count\")\n",
+      "plt.ylabel(\"Difference of heads and tails\")\n",
+      "plt.title(\"Heads and Tails over time\")\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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BJ645S8l+jtm4poqRcISnd6tsym8WvRzPzdLhfIivaK/FzH7teyQicpw/PHeQ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\nPWa2HfBVYAxesTaOObPBoAPYXNJk3ACv7I8wSWs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+       "text": [
+        "<matplotlib.figure.Figure at 0x10bd46c88>"
+       ]
+      }
+     ],
+     "prompt_number": 158
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.plot(ratiolist)\n",
+      "plt.xticks(range(len(counts)), counts)\n",
+      "plt.xlabel(\"count\")\n",
+      "plt.ylabel(\"Ratio of heads/tails\")\n",
+      "plt.title(\"Heads and Tails over time\")\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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01sytSUtERGbKhDVo59wh/2Ip8Dbn3EkgOSNFwsxWBB1crpoNY6BTXbSsltLi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\n2JIFs1BxFZgAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10bbd9940>"
+       ]
+      }
+     ],
+     "prompt_number": 159
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.scatter(counts, difflist)\n",
+      "plt.xscale('log')\n",
+      "plt.xlabel(\"count\")\n",
+      "plt.ylabel(\"Difference of heads and tails\")\n",
+      "plt.title(\"Heads and Tails over time\")\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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gh4j4EHBMRHQBXcDg0N+Z+YFWBChJkppvtNsOXwMMLXPcNcwfSZLUIUZsIcjM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\nPnMNXgAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10bf54cf8>"
+       ]
+      }
+     ],
+     "prompt_number": 160
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.scatter(counts, ratiolist)\n",
+      "plt.xscale('log')\n",
+      "plt.xlabel(\"count\")\n",
+      "plt.ylabel(\"Ratio of heads/tails\")\n",
+      "plt.title(\"Heads and Tails over time\")\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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aMdwlSaoZw12SpJox3CVJqhnDXZKkmjHcJUmqGcNdkqSaMdwlSaoZw12SpJox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\nRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10bd31e48>"
+       ]
+      }
+     ],
+     "prompt_number": 161
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def trial_tester(n=20):\n",
+      "    tuple_list = []\n",
+      "    for _ in range(0,n):\n",
+      "        tuple_list.append(flipper())\n",
+      "    return tuple_list\n",
+      "\n",
+      "tuple_list = trial_tester()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 162
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "What is about to follow in terms of finding ratiob is terrible, but my brain broke and I couldn't write it as a function"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ratios = [x[3] for x in tuple_list]\n",
+      "#print(ratios)\n",
+      "\n",
+      "\n",
+      "#def meansgetter(diffs):\n",
+      "#    for lis in diffs:\n",
+      "#        for index in lis:\n",
+      "            \n",
+      "        \n",
+      "ratiob = [[x[0]for x in ratios],\n",
+      "          [x[1] for x in ratios],\n",
+      "          [x[2] for x in ratios],\n",
+      "          [x[3] for x in ratios],\n",
+      "          [x[4] for x in ratios],\n",
+      "          [x[5] for x in ratios],\n",
+      "          [x[6] for x in ratios],\n",
+      "          [x[7] for x in ratios],\n",
+      "          [x[8] for x in ratios],\n",
+      "          [x[9] for x in ratios],\n",
+      "          [x[10] for x in ratios],\n",
+      "          [x[11] for x in ratios],\n",
+      "          [x[12] for x in ratios],\n",
+      "          [x[13] for x in ratios],\n",
+      "          [x[14] for x in ratios],\n",
+      "          [x[15] for x in ratios],\n",
+      "          [x[16] for x in ratios],\n",
+      "          [x[17] for x in ratios]]\n",
+      "\n",
+      "def meansgetter(ratiob):\n",
+      "    meanlist = []\n",
+      "    for index in ratiob:\n",
+      "        newmean = sum(index)/len(index)\n",
+      "        meanlist.append(newmean)\n",
+      "    return meanlist\n",
+      "\n",
+      "meanlist = meansgetter(ratiob)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 171
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.scatter(counts, meanlist)\n",
+      "plt.xscale('log')\n",
+      "plt.xlabel(\"count\")\n",
+      "plt.ylabel(\"Mean of ratio of heads and tails\")\n",
+      "plt.title(\"Heads and Tails over time\")\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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tC3VIkqQuq3J2wDOBtwH3ojilcBGwTWZuW29pkiSpTlWuE3AK8BmKwHA88BPg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\nSPUAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10bd479b0>"
+       ]
+      }
+     ],
+     "prompt_number": 166
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def stdevs(ratiob):\n",
+      "    stdvlist = []\n",
+      "    for index in ratiob:\n",
+      "        stdevs = statistics.stdev(index)\n",
+      "        stdvlist.append(stdevs)\n",
+      "    return stdvlist\n",
+      "\n",
+      "stdvlist = stdevs(ratiob)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 170
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "I like the asymptotic nature of the standard deviation over time"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.scatter(counts, stdvlist)\n",
+      "plt.axhline(y=0, color = 'red')\n",
+      "plt.xscale('log')\n",
+      "plt.xlabel(\"count\")\n",
+      "plt.ylabel(\"Standard Deviation of ratio of heads and tails\")\n",
+      "plt.title(\"Heads and Tails over time\")\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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Yr3X9t9+2KXgGqvrzXwWs6kqE9apa/+/z6NL8bc24loDMPJ9isMeI\necCfWo4fKptIZq2mfwZNrn+T6w7W3/pbf6a4/rPhw/oTxQcxYr3MfLhbwXRJ0z+DJte/yXUH62/9\nrf+k6j8bkoBvA68GiIjdKTYcapqmfwZNrn+T6w7W3/pb/0nVfyaOCRgxsvPRcuCVEfHt8ng29oGP\np+mfQZPr3+S6g/W3/gXrP8n6u4ugJEkNNRu6AyRJ0gSYBEiS1FAmAZIkNZRJgCRJDWUSIElSQ5kE\nSJLUUCYBkiQ1lEmAJEkNZRIgqWMiYpuIOL3bcUgqmARI6qRnAs/udhCSCi4bLOlxIuJY4HUU25ae\nQrFn+anAJsA9wJGZeXVELAUuy8yzytc9nJnrRcSHgS2AbSlu/Kdn5scj4kfANsDSzDyiw9WSNIot\nAZIeIyLeBLwEeCGwK8WmJCuA4zNzR+DvgPMi4ok8upHJWLYHXgnsBrwvIjYGjgCuNgGQpgeTAEmj\n9QPLMvPBzLwHeBmwWWZeAJCZVwJ/AGIt7/PNzFyTmavL658M9NQYt6R1ZBIgabQHeezN+tk8/ubd\nQ7EV+fDIuYiY23J+GLh/1LEJgDTNmARIGm0l8PqIeEJEbAB8BXg4IhYARMTuwObAT4A7gBeUr3td\ny3uMd8NfQ5E8SJoGTAIkPUbZ7P9t4BrgKuAzwEuBI8uBfScAr8/MB4GTgT0i4lqKcQS3lW8zzNjj\nBa4DnhIRZ9VbC0lVODtAkqSGsiVAkqSGMgmQJKmhTAIkSWookwBJkhrKJECSpIYyCZAkqaFMAiRJ\naiiTAEmSGur/A2zpvrOswhQ3AAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10c206c88>"
+       ]
+      }
+     ],
+     "prompt_number": 251
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def flipper100(n=100):\n",
+      "    flips = []\n",
+      "    counts = []\n",
+      "    fliplist = []\n",
+      "    ratios = []\n",
+      "    for x in range(0,n):\n",
+      "        flips.append(coin_flip())\n",
+      "    counts.append(n)\n",
+      "    fliplist.append(flips)\n",
+      "    headtails = [(x.count('heads'), x.count('tails')) for x in fliplist]\n",
+      "    def ratio(x, y):\n",
+      "        if y > 0:\n",
+      "            return x/y\n",
+      "        else:\n",
+      "            return 0\n",
+      "    final_ratio =[ratio(x[0], x[1]) for x in headtails] \n",
+      "    return final_ratio[0]\n",
+      "\n",
+      "def trial_tester(n=100000):\n",
+      "    ratio_list = []\n",
+      "    for _ in range(0,n):\n",
+      "        ratio_list.append(flipper100())\n",
+      "    return ratio_list\n",
+      "\n",
+      "little_ratio_list = trial_tester()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 236
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "little_list_mean =sum(little_ratio_list)/len(little_ratio_list)\n",
+      "little_list_stdev = statistics.stdev(little_ratio_list)\n",
+      "print(little_list_mean)\n",
+      "print(little_list_stdev)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1.0213427421317776\n",
+        "0.21003844571298116\n"
+       ]
+      }
+     ],
+     "prompt_number": 246
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.hist(little_ratio_list, bins=100)\n",
+      "plt.axvline(x=little_list_mean, color = 'red')\n",
+      "plt.axvline(x=little_list_mean+little_list_stdev, color = 'red', linestyle='dashed')\n",
+      "plt.axvline(x=little_list_mean-little_list_stdev, color = 'red', linestyle='dashed')\n",
+      "plt.figtext(x=.57, y=.75, s=\"mean  =  1.02134\")\n",
+      "plt.figtext(x=.57, y=.65, s=\"std dev =  0.21004\")\n",
+      "plt.xlabel(\"ratio\")\n",
+      "plt.ylabel(\"frequency\")\n",
+      "plt.title(\"Distribution of Head/Tail Ratios\")\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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z5m9YsqQH48dfFTfGe+65p0ktAkcffQw33fQzRo48n+LiIlauXMHAgfvXqvfO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\nAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10c4b6588>"
+       ]
+      }
+     ],
+     "prompt_number": 270
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def flipper100(n=1000):\n",
+      "    flips = []\n",
+      "    counts = []\n",
+      "    fliplist = []\n",
+      "    ratios = []\n",
+      "    for x in range(0,n):\n",
+      "        flips.append(coin_flip())\n",
+      "    counts.append(n)\n",
+      "    fliplist.append(flips)\n",
+      "    headtails = [(x.count('heads'), x.count('tails')) for x in fliplist]\n",
+      "    def ratio(x, y):\n",
+      "        if y > 0:\n",
+      "            return x/y\n",
+      "        else:\n",
+      "            return 0\n",
+      "    final_ratio =[ratio(x[0], x[1]) for x in headtails] \n",
+      "    return final_ratio[0]\n",
+      "\n",
+      "def trial_tester(n=100000):\n",
+      "    ratio_list = []\n",
+      "    for _ in range(0,n):\n",
+      "        ratio_list.append(flipper100())\n",
+      "    return ratio_list\n",
+      "\n",
+      "ratio_list = trial_tester()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 233
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ratio_list_mean =sum(ratio_list)/len(ratio_list)\n",
+      "ratio_list_stdev = statistics.stdev(ratio_list)\n",
+      "print(ratio_list_mean)\n",
+      "print(ratio_list_stdev)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1.002040913582857\n",
+        "0.06332705499198464\n"
+       ]
+      }
+     ],
+     "prompt_number": 244
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.hist(ratio_list, bins=200)\n",
+      "plt.axvline(x=ratio_list_mean, color = 'red')\n",
+      "plt.axvline(x=ratio_list_mean+ratio_list_stdev, color = 'red', linestyle='dashed')\n",
+      "plt.axvline(x=ratio_list_mean-ratio_list_stdev, color = 'red', linestyle='dashed')\n",
+      "plt.figtext(x=.67, y=.75, s=\"mean  =  1.00204\")\n",
+      "plt.figtext(x=.67, y=.65, s=\"std dev =  0.06333\")\n",
+      "plt.xlabel(\"ratio\")\n",
+      "plt.ylabel(\"frequency\")\n",
+      "plt.title(\"Distribution of Head/Tail Ratios\")\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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9+PALuP76X3LWWd9jn332Y+eduwPQp89uXHDBRVx++Y+pqamlbdu2jBz5C3be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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10c4bb6a0>"
+       ]
+      }
+     ],
+     "prompt_number": 272
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "\n",
+      "plt.boxplot([little_ratio_list, ratio_list])\n",
+      "plt.ylim(0.25, 2.25)\n",
+      "plt.title(\"Ratios of Coin Flips\")\n",
+      "plt.ylabel(\"ratio\")\n",
+      "plt.xticks(range(1,4), [\"Group 1\", \"Group 2\"])\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x10e642ac8>"
+       ]
+      }
+     ],
+     "prompt_number": 279
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file