unit-project/2/notebooks/unit-project-2-starter-code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DS-SF-30 | Unit Project 2: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, you will implement the exploratory analysis plan developed in Unit Project 1.  This will lay the groundwork for our our first modeling exercise in Unit Project 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, load the python libraries you will need for this project:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "\n",
    "pd.set_option('display.max_rows', 10)\n",
    "pd.set_option('display.max_columns', 10)\n",
    "pd.set_option('display.notebook_repr_html', True)\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, read in your dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admit</th>\n",
       "      <th>gre</th>\n",
       "      <th>gpa</th>\n",
       "      <th>prestige</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>3.61</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>660.0</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>800.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>640.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>2.93</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   admit    gre   gpa  prestige\n",
       "0      0  380.0  3.61       3.0\n",
       "1      1  660.0  3.67       3.0\n",
       "2      1  800.0  4.00       1.0\n",
       "3      1  640.0  3.19       4.0\n",
       "4      0  520.0  2.93       4.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join('..', '..', 'dataset', 'dataset-ucla-admissions.csv'))\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 1.  How many observations are in our dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 2.  Create a summary table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admit</th>\n",
       "      <th>gre</th>\n",
       "      <th>gpa</th>\n",
       "      <th>prestige</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.00000</td>\n",
       "      <td>399.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.317500</td>\n",
       "      <td>588.040201</td>\n",
       "      <td>3.39093</td>\n",
       "      <td>2.486216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.466087</td>\n",
       "      <td>115.628513</td>\n",
       "      <td>0.38063</td>\n",
       "      <td>0.945333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>2.26000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>520.000000</td>\n",
       "      <td>3.13000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>3.39500</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>660.000000</td>\n",
       "      <td>3.67000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            admit         gre        gpa    prestige\n",
       "count  400.000000  398.000000  398.00000  399.000000\n",
       "mean     0.317500  588.040201    3.39093    2.486216\n",
       "std      0.466087  115.628513    0.38063    0.945333\n",
       "min      0.000000  220.000000    2.26000    1.000000\n",
       "25%      0.000000  520.000000    3.13000    2.000000\n",
       "50%      0.000000  580.000000    3.39500    2.000000\n",
       "75%      1.000000  660.000000    3.67000    3.000000\n",
       "max      1.000000  800.000000    4.00000    4.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x117c20990>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x117cb6210>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a251850>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a2d37d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11a4474d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a4c8790>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a62a750>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a6abcd0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11a659ed0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a764f50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a7e5ed0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a849e50>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11a8cbdd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a932bd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a9bbd10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11aa3ec90>]], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7MpESiWmXTSQSOJ1OIfUuEAgECmbWTkZNTQ0vvPDCuMBMgD179tDY2FhUe9NRYZ0tQupd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\n+ewfITgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117c20550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.scatter_matrix(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 3.  Why would `gre` have a larger variance than `gpa`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13369.953039758006"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gre'].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14487948622204228"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gpa'].var()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: variance = std_deviation^2, std deviation is a fraction with gpa (0.38), and a large number with gre (116)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 4.  Drop data points with missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 5.  Confirm that you dropped the correct data.  How can you tell?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# technically, this df has been dropped in frame, but we see these are the same\n",
    "df[ (df.gre >= 0) & (df.gpa >= 0) & (df.admit >= 0) & (df.prestige >= 0) ].shape[0] == df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 6.  Create boxplots for `gre` and `gpa`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11aafaa90>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c3ExZWVlWBQ8MDPiiIxWYYDBIWVmZ81MqUOfn6MVkHQryzRcdqfAYCqTCNtlQ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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ab3b510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.gre.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b732d90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z1HIAscThcPS+Tv/JsWaeOCcnRzk5Odq4caMKCwvl8XhUUVGhefPmSZK8Xq+SkpIUHx+v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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aadfa10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.gpa.plot(kind='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 7.  What do boxplots show?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: the range and quartiles (middle quartile is the mean)\n",
    "\n",
    "gpa mean = 3.4\n",
    "gre mean = ~580"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 8.  Plot the distribution of each variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b531310>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AABghPAAAACO2jm4YDAa1aNEizZw5U8OGDZMkrV+/Xjt27Iha75577tGkSZMkSfv379fG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\n2TEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b531d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.admit.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b766f90>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0w1Dk5eUlqqSryufP4iRTqONkyh4rYTIx2GRazZlWr0TNqULNGSthIaGgoOCi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\nafHLZh8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b7fecd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.gre.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b6983d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+v0/9y8/P18DAgA4dcs4HEwEAcLZL2GsO+vv75XabN2dZlqSvXqgYr6PXsS0a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\nAAPlAAAAGP4/XikufTJ8dP0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bacc650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.gpa.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bd91b10>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L6XT2CCIDicvlUizdRXwJhyMnJXvkcrkS/pstHA5Hukvok8PhsO3PYCb0hJ33\n1857m6xU9oSd99Zqlq/mxo0b9etf/1oLFizQ17/+dUlSUVGRfD5fwnnBYLDHrYzLcelzKgaizk/b\n0l1Cnzy5HhWUlKRsvmT6yM4CHcfTXUKfnE6nvCnc32TYuSfsvL+ZsLfJSkVP2HlvrWZpeNi6dat2\n7NihhQsXxoODJFVWVmr79u0Kh8PxVNvc3Kzq6mrjObq6uhQKhSyrOdPY+Teqi7pD3Wpvb+/3eVwu\nl7xerwKBQFbdAnNEo+kuoU/RaDQl+5uMTOgJO++vnfc2WansCTvvrdUs+5fI5/Pp9ddf17e//W1V\nVVUpGAzGj40dO1bFxcVau3atZs+erb1798Zf1mkqGo0qHA5bVXZGcqa7gC8Ri51P6R5FIpGs6gl3\nzN43pmKxmO3X2849Yef9zYS9TVYqesLOe2s1y8LD3r17df78eb322mt67bXXEo5t2bJFS5Ys0bp1\n61RXV6eysjItWbJExcXFVk0PAABSxLLwMGvWLM2aNesLj5eVlWnZsmVWTQcAANKED8YCAABGCA8A\nAMAI4QEAABghPAAAACOEBwAAYITwAAAAjBAeAACAEcIDAAAwQngAAABGCA8AAMAI4QEAABghPAAA\nACOEBwAAYITwAAAAjBAeAACAEcIDAAAwQngAAABGCA8AAMAI4QEAABghPAAAACOEBwAAYITwAAAA\njBAeAACAEcIDAAAwQngAAABGCA8AAMAI4QEAABhxpXKycDisDRs2aM+ePfJ4PJo5c6buuuuuVJYA\nAACuUErDw6ZNm9TW1qZly5apvb1dzz33nEpKSvSNb3wjlWUAAIArkLLbFqFQSDt37tT3v/99lZeX\na9KkSbr77rv11ltvpaoEAABggZSFh8OHDysajaqqqio+Vl1drdbW1lSVAAAALJCy8BAIBFRQUCCn\n0xkfKywsVHd3t06dOpWqMgAAwBVK2XMeQqGQXK7E6dxut6QLT6S8XBe/ZqByOp2KDbpa7pob0l1K\nrxxX58t1VZ5ceXn9PtfFIJqbm9ujtzJa3iC5K76W7iq+kCtvUEr2NxkZ0RM23l87722yUtoTdt7b\nkeXWPp6lj9YHj8ejSCSSMHYxNOTm5l724wwePNjSujJSQYEKV/483VXYRkFBQbpLsJbXK/30X9Nd\nRUazdU+wv2mRkp4YQHubstsWRUVF6urq0vnz5+NjwWBQHo9HV199darKAAAAVyhl4aG8vFwul0uH\nDh2Kjx0JIblXAAAIHElEQVQ8eFDXXnttqkoAAAAWSFl48Hg8uuWWW7R+/Xr97ne/0549e/Tmm29q\nxowZqSoBAABYwBGLxWKpmqy7u1sbNmzQ+++/r0GDBulb3/qWpk+fnqrpAQCABVIaHgAAQObjg7EA\nAIARwgMAADBCeAAAAEYIDwAAwAjhAQAAGLHVm7+Hw2Ft2LBBe/bskcfj0cyZM3XXXXf1eu6qVav0\nm9/8JmHsscce0w032PMzH5IRDodVV1enefPmaezYsb2e09bWpg0bNuiTTz7RV77yFT344IMaPXp0\niivtf5ezFtncE52dnXrxxRd14MAB5ebm6qabbtJ3v/vdXt+rP5t7wmQdsrkfJOnYsWP6xS9+oebm\nZuXn5+sv/uIv9K1vfavXc7O5J0zWIdt74qIVK1aosLBQtbW1vR63oh9sFR42bdqktrY2LVu2TO3t\n7XruuedUUlKib3zjGz3OPXLkiBYsWKCampr4WDa9zXU4HNaaNWvk8/m+8JxQKKSnnnpKkydP1iOP\nPKL//M//1FNPPaXnnntOHo8nhdX2r8tZCym7e+Lpp59Wfn6+nnzySZ06dUovvPCCcnJydP/99yec\nl+09cbnrIGV3P8RiMT311FO69tprtWrVKh07dkzPPPOMiouLdfPNNyecm809YbIOUnb3xEW7d+/W\nhx9+qFtvvbXX41b1g21uW4RCIe3cuVPf//73VV5erkmTJunuu+/WW2+91ePcSCQiv9+viooKFRYW\nxv/Y9lP0DPl8Pi1dulR+v7/P83bv3i2Px6P7779fw4cP1/e+9z3l5eXpf/7nf1JUaf+73LXI5p44\nevSoWltbVVtbqxEjRqi6ulpz5szR7t27e5ybzT1hsg7Z3A+SdPLkSZWXl+vBBx9UWVmZJkyYoJqa\nGjU1NfU4N5t7wmQdsr0nJOn06dPavHlznx/7YFU/2CY8HD58WNFoVFVVVfGx6upqtba29jj36NGj\ncjgcKi0tTWWJKdPY2KiamhotX768z/NaW1tVXV2dMPa1r31NLS0t/VleSl3uWmRzTwwZMkRLly5N\n+FTAWCyms2fP9jg3m3vCZB2yuR+kC2vxwx/+UFdddZUkqampSQcPHtR1113X49xs74nLXYds7wnp\nwtX7W2+9VSNGjPjCc6zqB9tErkAgoIKCgvhnr0tSYWGhuru7derUKeXn58fHfT6f8vLy9Oyzz6qx\nsVHFxcWaM2eOJkyYkI7SLTdt2rTLOi8QCOgrX/lKwlhhYaE+/fTT/igrLS53LbK5JwYNGqTx48fH\n/x6LxfT2229r3LhxPc7N5p4wWYds7odLPfLIIzpx4oQmTpzY6y3ebO6JP/Vl65DtPbF//341NTVp\n9erVWr9+/ReeZ1U/2ObKQygU6nH5yO12S7pwz/tPHT16VN3d3br++uu1dOlSXX/99Vq5cqU+/vjj\nlNVrB93d3fE1usjtdisSiaSpovQZSD2xadMm/f73v9d9993X49hA6om+1mEg9cOiRYv02GOPqa2t\nTS+99FKP4wOlJ75sHbK5J8LhsNavX6958+b12OtLWdUPtrny4PF4ehR/MTTk5uYmjM+ePVszZszQ\noEGDJElf/epX9fHHH2vHjh2aP39+agq2Abfb3SNYhcPhjH8SVDIGSk9s3rxZv/rVr7Rw4UKNHDmy\nx/GB0hNftg4DpR8kxZ8lP3fuXD377LN64IEHEq7gDpSe+LJ1yOae2Lp1qyoqKhKuzH0Rq/rBNuGh\nqKhIXV1dOn/+vHJyLlwQCQaD8ng8vT4b9mIDXDRixIgvfTZ+tikqKlIwGEwYCwaD8nq9aaoovbK9\nJzZu3Khf//rXWrBggb7+9a/3es5A6InLWQcpu/vh5MmTamlp0aRJk+JjI0eOVCQS0blz5zR48OD4\neDb3hMk6SNnbE++9955OnjypBx54QNLnv3i///77evnllxPOtaofbHPbory8XC6XS4cOHYqPHTx4\nsNdnjT7//PN64YUXEsYOHz7c55NEslFlZWWPJ7k0NzersrIyTRWlT7b3xNatW7Vjxw4tXLhQN910\n0xeel+09cbnrkO394Pf7tXr1agUCgfjYxx9/rIKCgh7/YGZzT5isQzb3xBNPPKHVq1ervr5e9fX1\nuvHGGzVp0iTV19f3ONeqfrBNePB4PLrlllu0fv16/e53v9OePXv05ptvasaMGZIuJKPu7m5J0o03\n3qhdu3bp3Xff1bFjx/Tqq6+qqalJ06dPT+e3kBJ/ug5//ud/rjNnzuill16Sz+fTiy++qFAopG9+\n85tprjI1BkpP+Hw+vf7665o1a5aqqqoUDAbjf6SB0xMm65DN/SBJFRUVqqio0AsvvCCfz6ff/va3\n2rx5s+655x5JA6cnTNYhm3ti6NChGjZsWPxPXl6errrqqvgrS/qjH5xPPPHEE1Z/I8kaN26c2tra\n9Morr+jAgQO655574m90MXfuXF1zzTUqLy/XiBEj5PV6tW3bNm3fvl1nzpzR3//93+urX/1qmr8D\n67366qu67bbbVFJSIilxHdxut6677jq9/fbbeuONNxSNRvXDH/5QxcXFaa66f/S1FtncEzt37tRH\nH32kgwcP6s0330z4c++99w6YnjBZh2zuB0lyOByaOHGi9u3bpy1btuj//u//NH369Pg78g6UnjBZ\nh2zviT/1wQcfSFL8dk5/9IMjFovFLK8cAABkLdvctgAAAJmB8AAAAIwQHgAAgBHCAwAAMEJ4AAAA\nRggPAADACOEBAAAYITwAAAAjhAcAAGCE8AAAAIwQHgAAgJH/D6pFIClm4bOrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bacc890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.prestige.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 9.  If our model had an assumption of a normal distribution for `gre` and `gpa`, would we meet that requirement?  Would their distributions need correction?  If so, why, why not?  How?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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p6F28XHcNVWHBYrE0Sl5GoxGAY+Lj1Wpra7Fp0yaMHDkS8+bNw86dO7Fw4UK8\n/vrriI6OVvyaer2+/jX8Wd3fWyAk10DqC+Af/amutWHa+sPNfv99fSjaibVNfk8GcKRtEp7ddEyj\n6pr27jh+MKDmCF45rxuffwN2ux3SO4shlZyFEJ8A+dB+4NJFSN/uwqVvrzT8aDkw4l4Yx0/9+eDa\n4F4SWtXZ0WQyNboPtS4khISENHhcr9eja9eueOCBBwAAycnJ2LdvH77++mvcc889il8zIiJCTYk+\nLyoqytsluE0g9QXw7f6U1Jxv/KDViqU/vIn4mgq0kSwAHMHg6s/z9UGh/+OeKLMBwelYh29izZ5h\nMOgRGxvrvQIWvA4AqHjvDVzas7Px90UR4tZ1CAsLR9TvngDQzHswiKgKC9HR0aisrIQkSfVDNGaz\nGSaTCW3atGnQtn379khMTGzwWEJCAi5cuKCqwMrKSlgsFlXH+CKDwYCoqChUVFT4/cIfgdQXwD/6\nY7c3vO1r+c4FiLZdahQMKoRQHIrpgY41ZSgO64BXU38LeGnERIbnLnm4C2v2DLtdRGlpqVdrkCQJ\n1k8+cNrm0icfwDpqAnQ6XaP3YLBRdRZJTk6GwWDA0aNH6+ceFBQUoEePHo3apqSkoKCgoMFjP/30\nE4YMGaKqQFEUG13i8Gd2uz1g+hNIfQF8vT8//0JoKigAjhGFKLkWvS6exPRbnvNodUTqyF5/r0n/\n+mfLay+IIqzbNkD/67GAH4Yyd1I1g8NkMmHo0KFYvnw5jh07hvz8fGzevBkjRzoWeTGbzbBarQCA\n4cOH4/Tp01i3bh3OnTuHNWvWoKSkRHVYIKKrWK1NBoU6AoBo2yXgyvuQiJomlyi85fjjFRDf4i2V\nqqd7Tp48Gd26dcOCBQuwcuVKTJgwAQMGDAAAzJw5E7t37wYAdOjQAfPnz8eePXswe/Zs7N27F88+\n+6xPXxcm8nVLv3uzxSvUAoAlP7zliXKI/JYQp3wZaHyfhzbLX9SuGD+g+mKmyWRCZmYmMjMzG31v\nzZo1Db7u2bMnFi9e7Hp1RFRv4rGtSK45p6htB4uyfSKIgtZto4CP31O8U6tpXx4w5B7HkulBiDeS\nEvmBsE2rcO+ZHYrfsBdCIjWth8jf6XQ6CL8eq7i9ACDj2GbtCvJxDAtEPk6SJIRu/1TxDXIygD/9\nYpaWJREFBN39GRBGjFPcfvi5fAws3a9hRb6LYYHIx8nbN0NQOFQqAyg3tgVMJm2LIgoQuvszHJck\nFAiVbJg6T1x2AAAgAElEQVR9IDcoA4PvLllHRJDWrQI+26CobV1Q4G2T5OsMeh1Ka5tZvtwbxk5F\n1FdbFI3e6SFj0vEtyOvQBxD8b0EsVzEsEPkoad0qyAqDAgAs7/wbbOt2m4YVEblHrV3CoxuaX77c\nG/4U0wcDyw4oCgyJNWW4/uJJFLTvqnldvoKXIYh8kCRJkL/YpLi9XdBhW3K6hhURBbYlfScjL6aP\n4qWXoq3BdccRwwKRL/pqi+JbumQAn3YaCnCXPKJWWdJ3Mv58wwxFbTtWl2lcjW/h2YXIByldXU6C\ngI1Jw7C6+0iNKyIKDgejuuNsaEyL7R48+XlQTXRkWCDyRZamt5u+Vk630QwKRO4kCPh791FoaVyv\nbqIj5ODYM4JhgcjHyHt3Azu/bLGdXdBhW6dbPFARUXDJi03DR8kjWmyXWFOG6yuOe6Ai72NYIPIh\nsixDWrey5XbgPAUiLZ0La/lSBAAs2LcME49t1bga7+OZhsiHSId/BEpb3v/h69h+vPxApKGKkAhF\n7fSQce+ZHQEfGBgWiHyE+NYiIPt/FLXdE9tb42qIgtvByK6KJjoCjn0jxhR9DUg+tNCUmzEsEPkA\n8a1FwPd5ituXm7hRFJGm6ic6Klul0SBLuKt4t8ZFeQ/DApGXiaKoKigUh8WgIDJZu4KICIBjouPS\nPpNwSR+iqP2N5b61KqU7MSwQedvHKxQ3FSEgt9uooFqTnsibHHdGDFfU9sbyQwG79gLDApG3ffO5\noma1OiOW9pmEvNg0jQsioqttve4WyDp9i+10QMCuvcCwQORF4ryZgM2qqO3nHW9iUCDyBp0O1beN\nUbRvRKCuvcCwQOQlos0GlCpb1lkGsKr7aG0LIqJmXR6bgf/GXK+obSCuvcCwQOQtr/1ZcVPrDQMB\nfcvDoESknU+TlO3sGohrLzAsEHmBtG4VcPSgssZGEy5Pn6dpPUTUsmBee4FhgcjDpHWrIH+2QfkB\nXXtqVwwRKefK2gs/7dK4KM9gWCDyIEmSIH+xSd1BT76gTTFEpJratRceDpCtrBkWiDzpqy2A1NLm\nt1eJTYDeaNSuHiJSTc3aC23EWsw+kOv3gYFhgciD5C8/Vd44NgH6F/+mXTFE5LKt190Cu6DsV6ge\nst+vv8CwQOQh4luLgAvnlTW+fwqDApEv0+nwaaehitZeAK6sv3DxpJYVaYphgcgDVO3/oNdD+PVY\nbQsiolZb3X0kNiYNUzzhMaNQxciij2FYIPKENcsVNxXuHAudjm9NIn+wuvtIZN0wXVHbHpd+wp9+\nfF/jirRh8HYBRIFOXPQMcPKossYd4qG7P0PTeojIvQ5GdcfZ0Bgk1JY5bScAGFh2ABBFv1tkjR9f\niDSkKigAwC9u0q4YItJG/foLCpoCyDjqf5cjOLJAQeeyKKDadtWqarU2lNSch90uAoqnKylgtyPq\n5FGFVzMdr1xx9xSgtnENkswtqYl8WV5sGk62SUD3yy3v93L3ud2Is1ViSd/JHqjMPRgWKOhU2yRM\nW39Y89eZvf/vGKywrQwgL6YPlnxS2OT33x3Xy211EZE2Ctp3UxQW6i5H/OnH9/0mMPAyBJFGEmou\nKGpXHxT85KRBRE17r9toxWOTDeYv+AGGBSKNnA3roKjdzqjeDApEgUCvR15MH1WBIeP4Fi0rchuG\nBSKNLE19qMWThgzg1T4TPVEOEXnAkr6TVQWGIee/07Qed2FYIHI3WUZv83HcWvYjaoXmpwXJAI60\nTQIMnDpEFEiW9J2MLQnKZiy1t1/2i7UXeJYicqOBpfvxyLEtje63loEGd0XUBYVn+z/uyfKIyEPe\n63E3Rp3d1eLdUP6y9gLDApGbDCzdj9kHcqFvYgBSBnCoXWeESDYUh3XAq6m/5YgCUSC7Mn9hYNkB\nRYEh49hmrOrpu8u88zIEkTvIMh45tqXJoAA43miR9suY3f9JvJo2iUGBKAgs6TsZZkMbRW2Hn8v3\n6W2sGRaI3KD3xRMtLvXq77vOEZF6O+P7KWoXKtkw+0CuzwYG1WHBZrPhnXfewZQpUzBz5kxs3ry5\nxWNKSkrwyCOP4ODBgy4VSeTroiyVitpFWy9qXAkR+RI1ay/oIWPS8S2A7MaVZN1EdVjIzc3FiRMn\nkJWVhWnTpmHt2rXIy3O+9W5OTg4sFovLRRL5uoqQCEXtyk2RGldCRD5F5doLvjoCqSosWCwWbN++\nHVOmTEFycjIGDBiAsWPHYtu2bc0e880336C2trbVhRL5soORXXHR6PzaZHFYDAoikz1TEBH5DLVr\nL4w9s0PLclyiKiycOnUKoiiiZ8+e9Y+lpqaisLDp9eyrqqrw4YcfYsaMGa2rkshXSRJGFv0fZh36\nGOG2mmabiRCQ220UIHBDKKJgtKTvZPz5BmW/CweUFWDisa0aV6SOqinZFRUViIiIgP6qe0EjIyNh\ntVpRVVWFdu3aNWj//vvvIz09HZ06dXJPtUQ+ZOKxrRhT9DUM8s87WMoAavQhCBd/vuxWHBaD3G6j\nkBeb5oUqichXHIzqjrOhMS1OhhYAjCn6Gqu73gXofOM+BFVhwWKxwHDNLV9GoxGAY+Lj1fbt24cj\nR47g0UcfbVWBer2+/jX8Wd3f27V/f/7I7/tSa2u5TQsmHtuKe8/saHT/tAAgTLTg37H9sCe2N8pN\nkY5LDxxRICJBwN+7j8LsA39HS8svGWQJd/20C9uSbvVIaS1RdbY3mUyw2+0NHqsLCSEhIfWPWa1W\nLF++HNOnT2/1L5SICGUTx/xFVFSUt0twG3/tS0nN+dY9gd2OsWf+3exCKwKAWy7sw+vXP+i2TwVC\ni8u6+B7W7Bms2TPcVXNebBq+i+6F/uWHW2z78MnPURHa3idGJVX9Jo+OjkZlZSUkSYLuyknQbDbD\nZDKhTZufJ3cVFhaipKQE2dnZkK+6BeSll15Ceno6pk2bpvg1KysrA+JOCoPBgKioKFRUVDQKXP7G\n3/tit7u+JezEY1sx5swOZZ8KindjW6dbXH6tq8mKp0b5DtbsGazZM9xZ8/cKw0IbsRazD+RiaZ9J\nXg8MqsJCcnIyDAYDjh49il69egEACgoK0KNHjwbtUlJS8MYbbzR47IknnsCjjz6Kvn37qipQFMVG\nlzj8md1uD5j++G9fXHvTN3fpoTmJNRdceh0iCmxbEwcj49jmBvOdmqOHjD8c/AAP3fwCYDJ5oLqm\nqRojNZlMGDp0KJYvX45jx44hPz8fmzdvxsiRIwE4RhmsViuMRiPi4+Mb/AEcw9aBdlmBgoQkYUzR\n16oGIovDOmhWDhH5MZ0On3YaqvhjS6gsYv2u/8Ff//OypmU5o/qC6uTJk9GtWzcsWLAAK1euxIQJ\nEzBgwAAAwMyZM7F79263F0nkbSN/2qnoU0Adu6DDtsSbNayIiPzZ6u4jsTFpGESFH0EEAIm1ZV4L\nDKpnH5pMJmRmZiIzM7PR99asWdPscc6+R+TLBpbux4MnP1fcXgbwaaehPnPLExH5ptXdR2JvVE8s\n3LdMUfu6wACbDfDwXYI8mxE5UbftdFtR2SRbEQI2Jg3D6u4jNa6MiAJB3doLSgkAnt+/QruCmsGw\nQNScFradvpYdwAO3LmJQICLlrqy9oPRyBABcf/GEhgU1jWGBqBlKtp2uIwP4NGkY4K8LVRGR1+TF\npmFpn0moFVq6KdvBANnjcxcYFoiacdOFA4ra8dIDEbVWXmwaHrr5BUXjmA3mLngIwwJREwaW7seo\nom8UtX3uhhkMCkTUeiYTikNjFAcGT85dYFggupYo4tEj61pcpRG4su10VDfNSyKi4PD4oDmK5y/E\n15ZrXM3PeIGV6CqO5Zz/DYOCbC9x22ki0sChyGSkKZjEeD402gPVOHBkgeiKuuWclQQFAPhnp1u9\nvl47EQWe59KmtXgWkgF8ET8Avc3HAVn7vTY4skAEuLScc34HBgUi0oDRiOLQGCTWljnd3faZI47F\nDs+GxuDv3Udp+uGFIwtEAEYW71K1nHNxWAwKIpO1K4iIgtrjg+Y0OdmxqTGEhNoyzD6Qi4Gl+zWr\nh2GBCMCoM/+nuK3IuQpE5AGPD5qD+25ZhP2RXXHeFAmLztDsSIMeMqYe/QSQlH/oUYNhgYLen358\nHx0tymYVXzS28Ym95YkoSBiNeO6Xj+GvvX+LEMnutGkHayWW/+dFTUYYOGeBgpsoYmDZAUVzFewQ\nMGXQ/wB6ZausERG5S5SlUlG7GGslZh/IxUfXxQK4yW2vz5EFCmq/O75ZUVBwLOeczqBARF5RERKh\nuK0eMkYVKb+0qgTDAgW1xOpSRe3Oh0RxlUYi8pqDkV1V7U4ZazG79fUZFig4SRJGFv0f2isc2svv\n0EfjgoiInHBhd0p34pwFCjphm1bh4282Kb5VUgawqtsobYsiImpB3e6UU49+gg5WZR903IUjCxRU\npHWrEPrlRlVBIS+mD+cqEJFPyItNw4xB81BmUj6HwR0YFihoiHY75M83Kh7EqwsKS/pO1rIsIiJ1\ndDrkpNzj0UsSvAxBQUFatwr4bCOaXv+sodNhcfg+uqfj0gNHFIjIB9VdkvjDwQ8RKjtff8EdOLJA\nAU9atwryZxugJCgAwL7oFKxKGcOgQEQ+LS82DV8muG8tBWcYFiigSZIE+YtNqo4pDuugUTVERO71\nXve7FX4Mah2GBQpsX20BJFFxc7ugw7bEmzUsiIjIjfR65MX00TwwMCxQQJNLzipvC+DTTkMBHd8W\nROQ/lvSdDOUfiVzDsyIFJFmWIR/ZD9RcVtRehICNScO4SiMR+aXxPZ7QdHSBd0NQwJH37oa0biVQ\nek5RezuA8bcuAgx8OxCRn+rUCVKhYwRAixsqObJAAUXeuxvSuy8rDgqODaKGMSgQkd97YNgSSFB6\n35c6PENSwJBl2TGioHB1Rrugw6edhvLSAxEFjAeGLQGKivCJ/Jlbn5dhgQLH0QOKRhQs/Qbj/Uux\njrseOJmRiAJNp06YE/cHrHbjUzIsUMCQzeWK2tn6Dca2k3EaV0NEFDj4sYoChtA+WlE7KVJZOyIi\ncmBYoIAhJ6cARpPzRnEJsHfv7ZmCiIgCBMMCBQRZEiG/9xpgszbfSNBBd18GIHhupzYiokDAOQvk\nl2RZBo4ecMxTiIyG/N9vgG93Ob6Z3BO4XAWUXrV6Y1wCdPdlQLjxZqDWEyupExEFDoYF8jtOF11K\n6Q3dky84LkccPQD5YgWEyGggpTcEjigQEbmEYYH8Sv2iS82spSAMGQHBFOL4omeaJiuZEREFG85Z\nIL+hZNElefNHjksURETkNhxZoFa5LAqotilbMbG1DIUHENHSokslZ2E+cAD2Hn2abSLJHG8gIlKD\nYYFapdomYdr6w9q+iCRhZPEu3Fh2CDcqaL78i/3Y+UPz/7TfHdfLfbUREQUB1WHBZrMhJycH+fn5\nMJlMuPvuuzF69Ogm2+7duxcfffQRzp07h/j4eEyYMAH9+/dvddEUPCYe24oxRV/DoHC/BwAoN0Vq\nWBERUfBRPWchNzcXJ06cQFZWFqZNm4a1a9ciLy+vUbtTp04hOzsbt99+O1555RXceeedePXVV3H6\n9Gm3FE6Bb+Kxrbj3zA5VQaE4LAYFkcnaFUVEFIRUhQWLxYLt27djypQpSE5OxoABAzB27Fhs27at\nUdudO3ciLS0Nd911F+Lj4zFixAj06dMHu3btclvxFMDsdow9829VdzOIEJDbbRQXXSIicjNVlyFO\nnToFURTRs2fP+sdSU1OxcePGRm2HDRsGu93e6PHq6moXyqRgMvHYVow5swN6FccUh8Ugt9so5MWm\naVYXEVGwUhUWKioqEBERAb3+59N4ZGQkrFYrqqqq0K5du/rHExMTGxx75swZ7N+/HyNGjGhlyRTI\nJh7bgntVjCh8G9UTG7rc4bj0wBEFIiJNqAoLFosFBkPDQ4xGIwDHxMfmVFZWIjs7G6mpqZzgSM0a\neH6fqqAAAN/GXI+C9l01q4mIiFSGBZPJ1OjSQl1ICAkJafIYs9mMhQsXQhAEPP3006oL1Ov19YHE\nn9WFrGvDlj9q2JfmQ6IaA0v3Y3bBalVBwS7osC3xZre8PhERNU/Vb67o6GhUVlZCkiTodI65kWaz\nGSaTCW3atGnUvry8HC+88AJ0Oh2ysrIaXKZQKiIiQvUxviwqKsrbJbhNVFQUiquc7PKolCRh2tFP\nVM1RkAF82mkooFO/CKngh4tAs2bPYM2ewZr9j6qwkJycDIPBgKNHj6JXL8fCNgUFBejRo0ejthaL\nBYsWLYLBYEBWVpbLv/QrKythsVhcOtaXGAwGREVFoaKiosmJn/6kYV/EVj3XwNL9mHb0E8RYKxUf\nI0LApqR0rO4+0qXXlOF/y0GzZs9gzZ7Bmv2P6ssQQ4cOxfLly/HYY4+hrKwMmzdvxqxZswA4RhnC\nw8NhMpmwYcMGlJSU4Pnnn4ckSTCbzfXPER4ervg1RVF0Oh/C39jt9oDpjyP0uP4GGliyD388uFrV\n/bt2AONvXQQEwOUcIiJ/ofqMO3nyZOTk5GDBggUIDw/HhAkTMGDAAADAzJkzkZmZifT0dOTn58Nq\ntWLevHkNjk9PT0dmZqZ7qie/NbFwC+4tUjeZUQbwadIwBgUiIg9TfdY1mUzIzMxs8hf+mjVr6v//\ntddea11lFLAmHtvqUlDY2IpLD0RE5Dp+RCPPkiSMKfpa5cqMwNLrJyIv/gatqiIiIicYFkh7soze\nF08gylKJXpWnVO31cMEUiRUpY7kyIxGRFzEskKYGlu7HI8e2IKG2TPWxF0wRmDHoWZdujyQiIvdh\nWCDNDCzdj9kHcqF34Y4JCcCKlHsYFIiIfADPxKQNWcYjx7a4FBRkAK/0nshLD0REPoIjC+ReV+Yn\npFUUunTpQQawsVM68uI4mZGIyFcwLJDbtGZ+AuDY6+HTTkN5eyQRkY9hWCC3cGU1RgD453W3QABQ\nHNbBsSkU5ygQEfkchgVqNVdWYwSA4rAYrOwxBhCCe4MWIiJfx49x1Cphm1a5FBRECMjtNopBgYjI\nD3BkgVwmSRJCt3/q0ohCbrdRvNuBiMhPMCyQy6R//ROCpHyL6o+6/Bo/RvVAQWQyRxSIiPwIwwK5\nTCo+rbhtcVgMPk6+kyGBiMgPcc4CuaTm292Q8r9R1FYCOD+BiMiPcWSBVJErK2BduxIX/rNDWXtw\nNUYiIn/HsECKyJIE+f++gLx+FVB92fFgRBQsXXrA9ON/m5zkyNUYiYgCA8MCtUguPg0p922g8GD9\nY21+Mw720b9FuWTCliWvYUzR1w22nuZqjEREgYNhgQAAsiwDRw9ANpdDaB8NpPQB7DbIWz6GvG0D\nINodDROSYPrdHxA9eBhKS0uBKitWdx+J1V3vwsjiXehYU8bVGImIAgzDQpCTZdkRCL7aAlSaHY8B\nQGS04/8uVjgaGowQRk+AMOJe6MLCGz+RToetnW71VNlERORBDAtBTN67G9IH79SHhAYulv/8/9f/\nArqJj0GIS/RccURE5DMYFoKUvHc3pHcW48o4QvMi2kN48gUIvKRARBS0+BsgCMmyDGndSrQYFACg\n0gyhsEDzmoiIyHdxZCHANTlx8egBoPSc8ue4WK56/wciIgocDAsBTN672zGCcCUYyAAQ2xFQOfdA\niIx2f3FEROQ3GBYClLx3N6R3XwauWvsAgCM4qBhVQFwCkNLbvcUREZFf4ZyFAFQ/J+HaoKCWoIPu\nvgwI3NOBiCiocWTBDzU1D6HBL3SlcxIEAZCbmeQYGQXdQ49CuPFm9xRNRER+i2HBzzQ3D0F3/xSg\n30Dg4PeQNuYqe7I7xwA/5AMlZ39+LKI9hNtGQxj1AEcUiIgIAMOCX3E2D0F6ZzEQEdn0AkvN0PUb\nBDzwO8coxcUKx0TGlN4MCURE1ADDgp9oeR6C/HNQaNPO0a5ud8imXJm4KAgC0DONt0YSEVGzOMHR\nXyidhzB6AnSvrIJu8hOA0MyPlxMXiYhIBY4seIkkScBXWyCXnIUQlwDcNgo6nQ6XRQHVtqtGD0Q7\nDEf3I/TLDTApeN5LHZJgFQ1A70EwTv0Twje9D33pz3MSxNgEVI+dDFvvQUCtghUcm1JrQ0nNedjt\nIiSZgYOIKNAxLHiBtG4V5C82AZII4MokxY/fg/TrsagePRmzPv4R/coPY+CFA+hfVoC29hrFz/3S\n95dRcPLwla+igd5PovfFE4iyVqLcFImCyGSgUAAKDzt7GsXeHdfLLc9DRES+i2HBw2wfr4D82YbG\n35BEyJ9tQMTe3VhVdgEhkq3Bty/rQqATZISJ1mafuzgsxhEGriYIONi+mxsqJyKiYBWwYUEQBJ+6\nJi8IAiwWC8TPNjptZyg9W/9DqTC2xX879EFehzT8GNUdvyo7hNkHcqFvYgMoEQJyu41yrJ1ARETk\nRgEbFiptwPr9pd4uA5BlhFVfRGT5T+i5fzu6iGKLh/wQ2R1ruo3AkYjOkK6apJgXm4alfSZh0vEt\nSKwpq3+8OCwGud1GIS82TZMuEBFRcAvYsCDKwMYDng0LbW2X0fnyeXS+fA6dL59H0pX/trNXq3qe\nM2074tC1lxOuyItNQ16HPo3nIXBEgYiINBKwYUFLYfZaJF0dCqod/x9lrXJ6nARl96oWh3Vw3oDz\nEIiIyIMCMizIsgz90QO45fyPqAiJwMHIri598jaJNnSqPt9glKDz5XOItbS8SmJpSHucbhOP0206\n4syV/xaFdsDqXc/D4GSDJ1mvx7ZE7sdARES+I+DCQt3eCeGl5/DMlcfOhsbg792bv6ZvkOxIrL5w\nJRCcQ+dqRzjoWFMOXROTCa9WYWx7JRB0rA8HRW3iUG0Ia7L9p52G4t4zO5pcMVEGUHvbGMDOtbKI\niMh3qA4LNpsNOTk5yM/Ph8lkwt13343Ro0c32fbEiRPIycnB6dOnkZSUhGnTpqFbN+2Gz5vbOyGh\ntgyzD+Qiu8/DONUm4crlg7p5BeeRWFPq9NM+AFQZwhqMEpxuE48z4R1RZWqjqsbV3UcCAMYUfd3g\nNe2CDpbbx8IyNgNY7541EIiIiNxBdVjIzc3FiRMnkJWVhdLSUrz55puIjY3FwIEDG7SzWCxYvHgx\nhgwZglmzZuHzzz/H4sWL8eabb8JkUrIWoTot7Z2gh4w/Hljd4h4INXoTzoTXjRLE148YVJgi3DaJ\ncHX3kVjd9S6MLN6FjjVlKA7rgG2JN+NvY6+H3i2vQERE5D6qwoLFYsH27dsxf/58JCcnIzk5GWPH\njsW2bdsahYWdO3fCZDJh4sSJAICMjAx899132L17N9LT093XgzoK9k64+le9VTCgqE1cw9GC8Hhc\nCG0Pubk9FdxJp8PWTrdq/zpEREStpCosnDp1CqIoomfPnvWPpaamYuPGxgsNFRYWIjU1tcFjvXr1\nwpEjRzQJC7K5XFG7TZ2G4PPEQTgfFtNgDQMiIiJqmqrflhUVFYiIiIBe//NgeWRkJKxWK6qqqhq1\njYqKavBYZGQkysrKoAWhfbSidvkd0nA2PJZBgYiISCHVlyEMhoaHGI1GAI6Jj1ezWq3137u6rd1u\nV1Xgtc/RHPmG/rANuBVyefMLMZWFRiGszy9wo48uYNQ2xDGXo1dcWy9XolyYyehX9QKs2VNYs2ew\nZs/wt5qTo8Pd+nyqwoLJZGr0y74uJISEhDR43Gg0NgoQNptN9eTGtm1V/HCe/1+n3+4I4G+qXt07\nVk+O9XYJqqyeHOftElRjzZ7Bmj2DNXuGP9bsLqrG4qOjo1FZWQlJ+vmOA7PZDJPJhDZt2jRqazY3\nXLzIbDY3ujRBREREvk1VWEhOTobBYMDRo0frHysoKECPHj0atU1JScGRI0caPHb48GGkpKS4WCoR\nERF5g6qwYDKZMHToUCxfvhzHjh1Dfn4+Nm/ejJEjHQsNmc1mWK1WAMCgQYNw+fJlrFq1CkVFRVi5\nciUsFgsGDx7s/l4QERGRZgRZlp2vZ3wNq9WKnJwc5OXlITw8HGPGjMFvfvMbAMCECROQmZlZf2vk\nsWPHsGzZMhQXF6Nz586YMWMGunTp4v5eEBERkWZUhwUiIiIKLlxsgIiIiJxiWCAiIiKnGBaIiIjI\nKYYFIiIicophgYiIiJxStdyzu5WXl2PlypU4cOAAQkJCcPPNN+Ohhx5qtP8EAOzduxcfffQRzp07\nh/j4eEyYMAH9+/f3QtXNU9OfOiUlJZg9ezbmzp2L3r17e7Ba59T05fTp08jJycHx48eRkJCAjIwM\n9OnTxwtVN09Nf/Lz8/GPf/wDZWVl6Nq1KzIyMtC1a1cvVN28c+fOYcWKFTh8+DDatWuHESNGYMyY\nMU22PXHiBHJycnD69GkkJSVh2rRp6Natm4crbp6avvjDeUBNf+r46nkAUNcfXz8XqOmLP5wHrvbS\nSy8hMjISmZmZTX6/tecBr44sZGdnw2az4S9/+Qv+8Ic/4Ntvv8VHH33UqN2pU6eQnZ2N22+/Ha+8\n8gruvPNOvPrqqzh9+rQXqm6e0v5cLScnBxaLxUMVKqe0L9XV1Vi4cCGSkpKQnZ2NAQMGYOnSpais\nrPRC1c1T2p+ioiK88cYbGDduHJYuXYouXbpg8eLF9YuN+QJZlrF48WJERkZiyZIlmD59OjZs2ICd\nO3c2amuxWLB48WJcf/31ePnll9GzZ0+f6o+avvjDeUBNf67mq+cBNf3x9XOBmr74w3ngajt37sT3\n33/f7PfdcR7wWlgoLi5GYWEhMjMzcd111yE1NRXjx49v8ge3c+dOpKWl4a677kJ8fDxGjBiBPn36\nYNeuXV6ovGlq+lPnm2++QW1trQerVEZNX3bs2IGwsDBMnz4d8fHxGD9+PBISEnD8+HEvVN40Nf35\n4YcfkJSUhCFDhiAuLg4PPfQQzGYzioqKvFB50y5evIjk5GRMmzYNHTt2RL9+/ZCWloZDhw41artz\n506YTCZMnDgRiYmJyMjIQFhYGHbv3u2FyhtT2xdfPw+o6U8dXz0PAOr64+vnAjV98YfzQJ1Lly5h\n9QfQqCIAAAbJSURBVOrVTW67UMcd5wGvhYX27dtj/vz5iIiIqH9MlmVUV1c3ajts2DA8/PDDjR5v\nqq23qOkPAFRVVeHDDz/EjBkzPFWiYmr6cvDgwUbDwC+++CL69euneZ1KqelPu3btUFRUhMOHD0OW\nZWzfvh3h4eHo2LGjJ0t2qn379njyyScRGhoKADh06BAKCgqaHO4tLCxEampqg8d69erVaN8Wb1HT\nF385DyjtD+Db5wFAXX98/Vygpi/+cB6ok5ubi/T0dFx33XXNtnHHecBrcxbCw8Nxww031H8tyzI+\n++wz9O3bt1HbxMTEBl+fOXMG+/fvx4gRIzSvUyk1/QGA999/H+np6ejUqZOnSlRMTV9KSkrQo0cP\nLFu2DHv27EFcXBwmTZqEXr16ebJkp9T0Z/DgwdizZw+ee+456HQ66HQ6zJ07F+Hh7t0b3l1mzZqF\nCxcu4Fe/+hUGDhzY6PsVFRVISkpq8FhkZCTOnDnjqRIVa6kv/nAeuFpL/QF8+zxwrZb64w/ngjot\n9cVfzgP79+/HoUOHsHTpUixfvrzZdu44D/jM3RC5ubk4efIkHnzwQaftKisrkZ2djdTUVJ+b2HQ1\nZ/3Zt28fjhw5gvvvv98LlannrC+1tbXYtGkToqKiMG/ePFx//fVYuHAhysvLvVCpMs76U1VVBbPZ\njKlTp+LFF1/E0KFD8fbbb/vMdddrPfPMM5gzZw5OnDiBVatWNfq+1WqF0Whs8JjRaITdbvdQhcq1\n1Jer+cN5oKX++Nt5oKX++NO5oKW++MN5wGazYfny5Zg6dWqj9/i13HEe8ImwsHr1avy///f/8Pjj\njztN2GazGQsWLIAgCHj66ac9WKE6zvpjtVqxfPlyTJs2zeldEr6ipZ+NXq9H165d8cADDyA5ORkP\nP/wwEhMT8fXXX3uh2pa11J8PPvgAnTt3xvDhw9G1a1fMmDEDISEh2LFjh+eLVaBbt2648cYbMXny\nZHz55ZcQRbHB941GI2w2W4PHbDYbTCaTJ8tUpKW+1PGX84Cz/vjbeQBo+efjT+eClvriD+eBtWvX\nonv37g1GTZvjjvOA1/+Vvvfee/jiiy/wxBNP4Kabbmq2XXl5OV544QXodDpkZWWhXbt2HqxSuZb6\nU1hYiJKSEmRnZ+PqPbxeeuklpKenY9q0aZ4s1yklP5v27ds3Gh5OSEjAhQsXPFGiKkr6c/z48fot\n1wFAEAR06dIFpaWlniqzRRcvXsSRI0cwYMCA+sc6deoEu92OmpoatG3btv7x6OhomM3mBsebzWZE\nRUV5rF5n1PQF8P3zgNL++Mt5QM3Px9fPBWr64g/ngV27duHixYt45JFHAKA+DOTl5eH9999v0NYd\n5wGvhoW1a9fiyy+/xFNPPeU0KFgsFixatAgGgwFZWVkNJqr5EiX9SUlJwRtvvNHgsSeeeAKPPvpo\ns/MbvEHpzyYlJQUFBQUNHvvpp58wZMgQrUtURWl/oqKiGs14Li4udjrT2NNKSkqwdOlSvPvuu/Vv\n9uPHjyMiIqLRL9eUlBRs2rSpwWOHDx/GuHHjPFavM2r64g/nAaX98ZfzgNp/a758LlDTF384Dzz/\n/PMNRkRWr14NQRAwceLERm3dcR7w2mWIoqIibNiwAffccw969uwJs9lc/wdwpJ66e0A3bNiAkpIS\nZGZmQpKk+na+NAtaaX+MRiPi4+Mb/AEc/zh95eSn5mczfPhwnD59GuvWrcO5c+ewZs0alJSU+MwJ\nAlDXnzvuuAP/+te/8PXXX+PcuXP44IMPcOHCBaSnp3uzCw10794d3bt3xzvvvIOioiLs3bsXq1ev\nxn333QegYX8GDRqEy5cvY9WqVSgqKsLKlSthsVgwePBgb3ahnpq++MN5QGl//OE8AKj7+fj6uUBN\nX/zhPNChQ4cG/37CwsIQGhqKuLg4AO4/Dwjy1WNgHvTJJ5/gH//4R5PfW7NmDSZMmIDMzEykp6fj\nqaeeQnFxcaN26enpza5W5Wlq+nOtCRMmICsry2dWblPblyNHjuC9995DUVERrrvuOkyZMqXRbTre\npLY/X331FT799FOUl5cjOTkZU6ZMQXJysgcrbpnZbMaKFSuwf/9+hISE4K677sI999wDAI36c+zY\nMSxbtgzFxcXo3LkzZsyYgS5duniz/AaU9sUfzgOAup/N1XztPFBHTX98/Vygpi/+cB642ttvvw0A\n9e8Fd58HvBYWiIiIyD/4xN0QRERE5LsYFoiIiMgphgUiIiJyimGBiIiInGJYICIiIqcYFoiIiMgp\nhgUiIiJyimGBiIiInGJYICIiIqcYFoiIiMgphgUiIiJy6v8DRgXVCtYvQH0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d13d890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import pylab as pl\n",
    "\n",
    "def overlayNormal(series): \n",
    "    arr = sorted(series.values)\n",
    "    fit = stats.norm.pdf(arr, np.mean(arr), np.std(arr))\n",
    "    pl.plot(arr,fit,'-o')\n",
    "    pl.hist(arr,normed=True) \n",
    "\n",
    "overlayNormal(df.gpa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GDx6MlStXIi8vD9deey0+++wz2Gw2DBkyxJcvSUQC1G8/F6r7MmWgzp1QpDAZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d08c510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gpa_norm = (df.gpa - df.gpa.mean()) / (df.gpa.max() - df.gpa.min())\n",
    "\n",
    "overlayNormal(gpa_norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: Not a normal distribution. They are skewed toward higher values. See plot for normal distribution overlaid.\n",
    "\n",
    "They could need to be normalized (bottom graph) but you cannot make the set a normal distribution without changing the underlying data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 10.  Create a correlation matrix for the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admit</th>\n",
       "      <th>gre</th>\n",
       "      <th>gpa</th>\n",
       "      <th>prestige</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>admit</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.181202</td>\n",
       "      <td>0.174116</td>\n",
       "      <td>-0.243563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gre</th>\n",
       "      <td>0.181202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.382408</td>\n",
       "      <td>-0.124533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gpa</th>\n",
       "      <td>0.174116</td>\n",
       "      <td>0.382408</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.060976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prestige</th>\n",
       "      <td>-0.243563</td>\n",
       "      <td>-0.124533</td>\n",
       "      <td>-0.060976</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             admit       gre       gpa  prestige\n",
       "admit     1.000000  0.181202  0.174116 -0.243563\n",
       "gre       0.181202  1.000000  0.382408 -0.124533\n",
       "gpa       0.174116  0.382408  1.000000 -0.060976\n",
       "prestige -0.243563 -0.124533 -0.060976  1.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 11.  What did you find?  Which of our variables are potentially colinear?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: GPA and GRE are potentially co-linear. Prestige is negatively correlated with the other variables (particularly admission, as suggested in unit project 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 12.  Write an analysis plan for exploring the association between graduate school admission rates and prestige of undergraduate schools."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:\n",
    "\n",
    "(Subjective: Create regression models using GPA and GRE, GPA, or GRE, and see what models are generated and the significance of each factor)\n",
    "\n",
    "Assess the covariance\n",
    "\n",
    "Probably need to regularize first, see initial covariance regults below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admit</th>\n",
       "      <th>gre</th>\n",
       "      <th>gpa</th>\n",
       "      <th>prestige</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>admit</th>\n",
       "      <td>0.217197</td>\n",
       "      <td>9.772155</td>\n",
       "      <td>0.030852</td>\n",
       "      <td>-0.107505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gre</th>\n",
       "      <td>9.772155</td>\n",
       "      <td>13390.606315</td>\n",
       "      <td>16.824761</td>\n",
       "      <td>-13.648068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gpa</th>\n",
       "      <td>0.030852</td>\n",
       "      <td>16.824761</td>\n",
       "      <td>0.144558</td>\n",
       "      <td>-0.021957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prestige</th>\n",
       "      <td>-0.107505</td>\n",
       "      <td>-13.648068</td>\n",
       "      <td>-0.021957</td>\n",
       "      <td>0.896967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             admit           gre        gpa   prestige\n",
       "admit     0.217197      9.772155   0.030852  -0.107505\n",
       "gre       9.772155  13390.606315  16.824761 -13.648068\n",
       "gpa       0.030852     16.824761   0.144558  -0.021957\n",
       "prestige -0.107505    -13.648068  -0.021957   0.896967"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cov()"
   ]
  },
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   "source": [
    "> ### Question 13.  What is your hypothesis?"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Answer: \n",
    "\n",
    "GRE and GPA will be largely correlated\n",
    "\n",
    "Admission will be predicted largely by some combination of GRE and GPA, prestige likely would have a (smaller) effect\n",
    "\n",
    "Again, less significantly, prestige will be negatively correlated, because UCLA wants to look good"
   ]
  }
 ],
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