pohzipohzi/Alumni Giving.ipynb

## Alumni Giving.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alumni Giving\n",
    "\n",
    "In this task we attempt to answer Kryswada's questions related to \"uncovering the drivers of alumni giving rate\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "First we load the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns; sns.set(color_codes=True)\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getDataframe(file_name, sheet_name, topLeftVal):\n",
    "    df = pd.read_excel(file_name, sheet_name, header=None)\n",
    "    r, c = getPos(df, topLeftVal)\n",
    "    lastRow = r + 1\n",
    "    while not pd.isna(df.iat[lastRow, c]):\n",
    "        lastRow += 1\n",
    "    ret = df.iloc[r+1:lastRow, c:]\n",
    "    ret.columns = df.iloc[r, c:].values\n",
    "    ret.set_index(topLeftVal, inplace=True)\n",
    "    return ret\n",
    "\n",
    "def getPos(df, val):\n",
    "    for r in range(df.shape[0]):\n",
    "        for c in range(df.shape[1]):\n",
    "            if df.iat[r,c] == val:\n",
    "                return r, c\n",
    "    return 0, 0 # not found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>SFR</th>\n",
       "      <th>LT20</th>\n",
       "      <th>GT50</th>\n",
       "      <th>GRAD</th>\n",
       "      <th>FRR</th>\n",
       "      <th>GIVE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arizona State University</td>\n",
       "      <td>24</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arkansas State University—Jonesboro</td>\n",
       "      <td>19</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Auburn University</td>\n",
       "      <td>18</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United States Air Force Academy</td>\n",
       "      <td>8</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>United States Military Academy</td>\n",
       "      <td>8</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 School SFR  LT20   GT50  GRAD   FRR  GIVE\n",
       "ID                                                                        \n",
       "1              Arizona State University  24  0.42   0.16  0.59  0.81  0.08\n",
       "2   Arkansas State University—Jonesboro  19  0.49   0.04  0.37  0.69  0.11\n",
       "3                     Auburn University  18  0.24   0.17  0.66  0.87  0.31\n",
       "4       United States Air Force Academy   8  0.74  0.001  0.81  0.88  0.11\n",
       "5        United States Military Academy   8  0.95      0  0.86  0.92  0.28"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = getDataframe('./data.xlsx', 'Exhibit 2', 'ID')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We convert numeric columns for convenient processing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 123 entries, 1 to 123\n",
      "Data columns (total 7 columns):\n",
      "School    123 non-null object\n",
      "SFR       123 non-null float64\n",
      "LT20      123 non-null float64\n",
      "GT50      123 non-null float64\n",
      "GRAD      123 non-null float64\n",
      "FRR       123 non-null float64\n",
      "GIVE      123 non-null float64\n",
      "dtypes: float64(6), object(1)\n",
      "memory usage: 7.7+ KB\n"
     ]
    }
   ],
   "source": [
    "cols = df.columns[1:]\n",
    "for col in cols:\n",
    "    df[col] = df[col].astype('float64')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we check to see if it tallies with the summary report generated by StatTools:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SFR</th>\n",
       "      <th>LT20</th>\n",
       "      <th>GT50</th>\n",
       "      <th>GRAD</th>\n",
       "      <th>FRR</th>\n",
       "      <th>GIVE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>17.7724</td>\n",
       "      <td>0.403659</td>\n",
       "      <td>0.136276</td>\n",
       "      <td>0.645203</td>\n",
       "      <td>0.841138</td>\n",
       "      <td>0.141789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variance</th>\n",
       "      <td>20.4068</td>\n",
       "      <td>0.0179283</td>\n",
       "      <td>0.00360768</td>\n",
       "      <td>0.0288301</td>\n",
       "      <td>0.00704623</td>\n",
       "      <td>0.00650825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std. Dev.</th>\n",
       "      <td>4.51739</td>\n",
       "      <td>0.133897</td>\n",
       "      <td>0.0600639</td>\n",
       "      <td>0.169794</td>\n",
       "      <td>0.0839419</td>\n",
       "      <td>0.0806737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skewness</th>\n",
       "      <td>-0.362293</td>\n",
       "      <td>1.10676</td>\n",
       "      <td>-0.0128522</td>\n",
       "      <td>-0.045874</td>\n",
       "      <td>-0.242659</td>\n",
       "      <td>1.10399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median</th>\n",
       "      <td>18</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode</th>\n",
       "      <td>[19.0]</td>\n",
       "      <td>[0.34, 0.38, 0.42]</td>\n",
       "      <td>[0.16]</td>\n",
       "      <td>[0.8]</td>\n",
       "      <td>[0.97]</td>\n",
       "      <td>[0.13]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minimum</th>\n",
       "      <td>6</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum</th>\n",
       "      <td>31</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count</th>\n",
       "      <td>123</td>\n",
       "      <td>123</td>\n",
       "      <td>123</td>\n",
       "      <td>123</td>\n",
       "      <td>123</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                SFR                LT20        GT50       GRAD         FRR  \\\n",
       "Mean        17.7724            0.403659    0.136276   0.645203    0.841138   \n",
       "Variance    20.4068           0.0179283  0.00360768  0.0288301  0.00704623   \n",
       "Std. Dev.   4.51739            0.133897   0.0600639   0.169794   0.0839419   \n",
       "Skewness  -0.362293             1.10676  -0.0128522  -0.045874   -0.242659   \n",
       "Median           18                0.38        0.13       0.64        0.84   \n",
       "Mode         [19.0]  [0.34, 0.38, 0.42]      [0.16]      [0.8]      [0.97]   \n",
       "Minimum           6                0.14           0       0.26        0.58   \n",
       "Maximum          31                0.95        0.31       0.96        0.98   \n",
       "Count           123                 123         123        123         123   \n",
       "\n",
       "                 GIVE  \n",
       "Mean         0.141789  \n",
       "Variance   0.00650825  \n",
       "Std. Dev.   0.0806737  \n",
       "Skewness      1.10399  \n",
       "Median           0.13  \n",
       "Mode           [0.13]  \n",
       "Minimum          0.02  \n",
       "Maximum          0.41  \n",
       "Count             123  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = df.columns[1:]\n",
    "d = {\n",
    "    'Mean': [df[col].mean() for col in cols],\n",
    "    'Variance': [df[col].var() for col in cols],\n",
    "    'Std. Dev.': [df[col].std() for col in cols],\n",
    "    'Skewness': [df[col].skew() for col in cols],\n",
    "    'Median': [df[col].median() for col in cols],\n",
    "    'Mode': [df[col].mode().values for col in cols],\n",
    "    'Minimum': [df[col].min() for col in cols],\n",
    "    'Maximum': [df[col].max() for col in cols],\n",
    "    'Count': [df[col].count() for col in cols],\n",
    "}\n",
    "summary = pd.DataFrame.from_dict(d, orient='index', columns=cols)\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the exception of multiple mode values, the data seems to tally."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "\n",
    "We can try to get a sense of the data using a standard pairplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.pairplot(df.loc[:, cols])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that there is significant correlation between `GRAD` and `FRR`, both of which seem to be positively correlated with `GIVE` (see bottom row)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1\n",
    "\n",
    "Superficially, we can answer this with a simple / multiple linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.regplot(x=\"GRAD\", y=\"GIVE\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.32387485])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# linear regression using only GRAD\n",
    "lr = LinearRegression()\n",
    "lr.fit(df.loc[:, [\"GRAD\"]], df[\"GIVE\"])\n",
    "lr.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that in a simple linear regression model, if school A's graduation rate is 10 points higher than school B's, we expect school A's giving rate to be 3.2 points higher."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.00109935,  0.15065177, -0.03221853,  0.12983038,  0.25690279])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# linear regression using all columns\n",
    "mlr = LinearRegression()\n",
    "mlr.fit(df.loc[:, [\"SFR\", \"LT20\", \"GT50\", \"GRAD\", \"FRR\"]], df[\"GIVE\"])\n",
    "mlr.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that if we use all 5 dependent variables however, we can only expect about 1.3 points higher. However, it is also important to note that significant correlation between GRAD and FRR may affect the results here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2\n",
    "\n",
    "We can answer this by ignoring the SFR column in our multilinear regression analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.1634825 , -0.05032449,  0.14420764,  0.25362669])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# linear regression using all columns except SFR\n",
    "mlr2 = LinearRegression()\n",
    "mlr2.fit(df.loc[:, [\"LT20\", \"GT50\", \"GRAD\", \"FRR\"]], df[\"GIVE\"])\n",
    "mlr2.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We expect about 1.4 points higher this time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3\n",
    "\n",
    "To answer this question, we have to try to define what \"impressive\" means. If we are concerned with giving rates irregardless of other variables, we can simply find the max/min giving rates for our data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>SFR</th>\n",
       "      <th>LT20</th>\n",
       "      <th>GT50</th>\n",
       "      <th>GRAD</th>\n",
       "      <th>FRR</th>\n",
       "      <th>GIVE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>University of Notre Dame</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      School   SFR  LT20  GT50  GRAD   FRR  GIVE\n",
       "ID                                                              \n",
       "73  University of Notre Dame  12.0  0.55   0.1  0.96  0.98  0.41"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['GIVE'] == df['GIVE'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>SFR</th>\n",
       "      <th>LT20</th>\n",
       "      <th>GT50</th>\n",
       "      <th>GRAD</th>\n",
       "      <th>FRR</th>\n",
       "      <th>GIVE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>San Diego State University</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>San Jose State University</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>University of South Alabama</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         School   SFR  LT20  GT50  GRAD   FRR  GIVE\n",
       "ID                                                                 \n",
       "85   San Diego State University  21.0  0.22  0.24  0.66  0.83  0.02\n",
       "86    San Jose State University  25.0  0.25  0.09  0.44  0.82  0.02\n",
       "90  University of South Alabama  22.0  0.42  0.08  0.37  0.68  0.02"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['GIVE'] == df['GIVE'].min()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the data, we can say that `University of Notre Dame` has an extremely impressive giving rate of 0.41, while `San Diego State University`, `San Jose State University` and `University of South Alabama` have an extremely unimpressive giving rate of 0.02."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may also be interested in how well schools have done compared to the variables we have come up with. One way to do so is to find the absolute error of each data point compared to our multilinear regression model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>SFR</th>\n",
       "      <th>LT20</th>\n",
       "      <th>GT50</th>\n",
       "      <th>GRAD</th>\n",
       "      <th>FRR</th>\n",
       "      <th>GIVE</th>\n",
       "      <th>GIVE_PRED</th>\n",
       "      <th>AE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arizona State University</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.121473</td>\n",
       "      <td>-0.041473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arkansas State University—Jonesboro</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.081991</td>\n",
       "      <td>0.028009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Auburn University</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.125132</td>\n",
       "      <td>0.184868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United States Air Force Academy</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.238940</td>\n",
       "      <td>-0.128940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>United States Military Academy</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.287377</td>\n",
       "      <td>-0.007377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 School   SFR  LT20   GT50  GRAD   FRR  GIVE  \\\n",
       "ID                                                                             \n",
       "1              Arizona State University  24.0  0.42  0.160  0.59  0.81  0.08   \n",
       "2   Arkansas State University—Jonesboro  19.0  0.49  0.040  0.37  0.69  0.11   \n",
       "3                     Auburn University  18.0  0.24  0.170  0.66  0.87  0.31   \n",
       "4       United States Air Force Academy   8.0  0.74  0.001  0.81  0.88  0.11   \n",
       "5        United States Military Academy   8.0  0.95  0.000  0.86  0.92  0.28   \n",
       "\n",
       "    GIVE_PRED        AE  \n",
       "ID                       \n",
       "1    0.121473 -0.041473  \n",
       "2    0.081991  0.028009  \n",
       "3    0.125132  0.184868  \n",
       "4    0.238940 -0.128940  \n",
       "5    0.287377 -0.007377  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['GIVE_PRED'] = mlr.predict(df.loc[:, [\"SFR\", \"LT20\", \"GT50\", \"GRAD\", \"FRR\"]])\n",
    "df['AE'] = df['GIVE'] - df['GIVE_PRED']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>SFR</th>\n",
       "      <th>LT20</th>\n",
       "      <th>GT50</th>\n",
       "      <th>GRAD</th>\n",
       "      <th>FRR</th>\n",
       "      <th>GIVE</th>\n",
       "      <th>GIVE_PRED</th>\n",
       "      <th>AE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>University of Alabama</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.153659</td>\n",
       "      <td>0.186341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   School   SFR  LT20  GT50  GRAD   FRR  GIVE  GIVE_PRED  \\\n",
       "ID                                                                         \n",
       "16  University of Alabama  19.0  0.46  0.16  0.67  0.85  0.34   0.153659   \n",
       "\n",
       "          AE  \n",
       "ID            \n",
       "16  0.186341  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['AE'] == df['AE'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>SFR</th>\n",
       "      <th>LT20</th>\n",
       "      <th>GT50</th>\n",
       "      <th>GRAD</th>\n",
       "      <th>FRR</th>\n",
       "      <th>GIVE</th>\n",
       "      <th>GIVE_PRED</th>\n",
       "      <th>AE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United States Air Force Academy</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.23894</td>\n",
       "      <td>-0.12894</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             School  SFR  LT20   GT50  GRAD   FRR  GIVE  \\\n",
       "ID                                                                        \n",
       "4   United States Air Force Academy  8.0  0.74  0.001  0.81  0.88  0.11   \n",
       "\n",
       "    GIVE_PRED       AE  \n",
       "ID                      \n",
       "4     0.23894 -0.12894  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['AE'] == df['AE'].min()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We conclude that `University of Alabama` and `United States Air Force Academy` has the most and least impressive giving rate respectively based on how they outperform / underperform our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4\n",
    "\n",
    "We can again make a prediction based on our multilinear regression model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11497157])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# [\"SFR\", \"LT20\", \"GT50\", \"GRAD\", \"FRR\"]\n",
    "pred = mlr.predict([[17, 0.34, 0.23, 0.67, 0.77]])\n",
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The school's giving rate should be greater than 8%."
   ]
  }
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