{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**10 MINUTES TO APPELPY**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm  # for datasets\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This is a whirlwind tour of Appelpy's functionality:**\n",
    "- Exploratory data analysis\n",
    "- Model estimation\n",
    "- Model diagnostics\n",
    "\n",
    "Note: this notebook was run using Appelpy v0.4.2 and the latest versions of its dependencies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set up model dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore the functionality of Appelpy using one of the most prolific datasets in econometrics: [The California Test Score Data Set](https://rdrr.io/cran/Ecdat/man/Caschool.html) (**Caschool**).\n",
    "\n",
    "Read the dataset with Pandas and obtain the data via a Statsmodels method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "df = sm.datasets.get_rdataset('Caschool', 'Ecdat').data\n",
    "# Add together the two test scores (total score)\n",
    "df['scrtot'] = df['readscr'] + df['mathscr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\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>distcod</th>\n",
       "      <th>county</th>\n",
       "      <th>district</th>\n",
       "      <th>grspan</th>\n",
       "      <th>enrltot</th>\n",
       "      <th>teachers</th>\n",
       "      <th>calwpct</th>\n",
       "      <th>mealpct</th>\n",
       "      <th>computer</th>\n",
       "      <th>testscr</th>\n",
       "      <th>compstu</th>\n",
       "      <th>expnstu</th>\n",
       "      <th>str</th>\n",
       "      <th>avginc</th>\n",
       "      <th>elpct</th>\n",
       "      <th>readscr</th>\n",
       "      <th>mathscr</th>\n",
       "      <th>scrtot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75119</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>Sunol Glen Unified</td>\n",
       "      <td>KK-08</td>\n",
       "      <td>195</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>0.510200</td>\n",
       "      <td>2.040800</td>\n",
       "      <td>67</td>\n",
       "      <td>690.799988</td>\n",
       "      <td>0.343590</td>\n",
       "      <td>6384.911133</td>\n",
       "      <td>17.889910</td>\n",
       "      <td>22.690001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>691.599976</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>1381.599976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61499</td>\n",
       "      <td>Butte</td>\n",
       "      <td>Manzanita Elementary</td>\n",
       "      <td>KK-08</td>\n",
       "      <td>240</td>\n",
       "      <td>11.150000</td>\n",
       "      <td>15.416700</td>\n",
       "      <td>47.916698</td>\n",
       "      <td>101</td>\n",
       "      <td>661.200012</td>\n",
       "      <td>0.420833</td>\n",
       "      <td>5099.380859</td>\n",
       "      <td>21.524664</td>\n",
       "      <td>9.824000</td>\n",
       "      <td>4.583333</td>\n",
       "      <td>660.500000</td>\n",
       "      <td>661.900024</td>\n",
       "      <td>1322.400024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61549</td>\n",
       "      <td>Butte</td>\n",
       "      <td>Thermalito Union Elementary</td>\n",
       "      <td>KK-08</td>\n",
       "      <td>1550</td>\n",
       "      <td>82.900002</td>\n",
       "      <td>55.032299</td>\n",
       "      <td>76.322601</td>\n",
       "      <td>169</td>\n",
       "      <td>643.599976</td>\n",
       "      <td>0.109032</td>\n",
       "      <td>5501.954590</td>\n",
       "      <td>18.697226</td>\n",
       "      <td>8.978000</td>\n",
       "      <td>30.000002</td>\n",
       "      <td>636.299988</td>\n",
       "      <td>650.900024</td>\n",
       "      <td>1287.200012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61457</td>\n",
       "      <td>Butte</td>\n",
       "      <td>Golden Feather Union Elementary</td>\n",
       "      <td>KK-08</td>\n",
       "      <td>243</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>36.475399</td>\n",
       "      <td>77.049202</td>\n",
       "      <td>85</td>\n",
       "      <td>647.700012</td>\n",
       "      <td>0.349794</td>\n",
       "      <td>7101.831055</td>\n",
       "      <td>17.357143</td>\n",
       "      <td>8.978000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>651.900024</td>\n",
       "      <td>643.500000</td>\n",
       "      <td>1295.400024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61523</td>\n",
       "      <td>Butte</td>\n",
       "      <td>Palermo Union Elementary</td>\n",
       "      <td>KK-08</td>\n",
       "      <td>1335</td>\n",
       "      <td>71.500000</td>\n",
       "      <td>33.108601</td>\n",
       "      <td>78.427002</td>\n",
       "      <td>171</td>\n",
       "      <td>640.849976</td>\n",
       "      <td>0.128090</td>\n",
       "      <td>5235.987793</td>\n",
       "      <td>18.671329</td>\n",
       "      <td>9.080333</td>\n",
       "      <td>13.857677</td>\n",
       "      <td>641.799988</td>\n",
       "      <td>639.900024</td>\n",
       "      <td>1281.700012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distcod   county                         district grspan  enrltot  \\\n",
       "0    75119  Alameda               Sunol Glen Unified  KK-08      195   \n",
       "1    61499    Butte             Manzanita Elementary  KK-08      240   \n",
       "2    61549    Butte      Thermalito Union Elementary  KK-08     1550   \n",
       "3    61457    Butte  Golden Feather Union Elementary  KK-08      243   \n",
       "4    61523    Butte         Palermo Union Elementary  KK-08     1335   \n",
       "\n",
       "    teachers    calwpct    mealpct  computer     testscr   compstu  \\\n",
       "0  10.900000   0.510200   2.040800        67  690.799988  0.343590   \n",
       "1  11.150000  15.416700  47.916698       101  661.200012  0.420833   \n",
       "2  82.900002  55.032299  76.322601       169  643.599976  0.109032   \n",
       "3  14.000000  36.475399  77.049202        85  647.700012  0.349794   \n",
       "4  71.500000  33.108601  78.427002       171  640.849976  0.128090   \n",
       "\n",
       "       expnstu        str     avginc      elpct     readscr     mathscr  \\\n",
       "0  6384.911133  17.889910  22.690001   0.000000  691.599976  690.000000   \n",
       "1  5099.380859  21.524664   9.824000   4.583333  660.500000  661.900024   \n",
       "2  5501.954590  18.697226   8.978000  30.000002  636.299988  650.900024   \n",
       "3  7101.831055  17.357143   8.978000   0.000000  651.900024  643.500000   \n",
       "4  5235.987793  18.671329   9.080333  13.857677  641.799988  639.900024   \n",
       "\n",
       "        scrtot  \n",
       "0  1381.599976  \n",
       "1  1322.400024  \n",
       "2  1287.200012  \n",
       "3  1295.400024  \n",
       "4  1281.700012  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspect data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the data, let's find out more about the distributions of some of the key variables before modelling.\n",
    "\n",
    "Appelpy has an **`eda` module** (exploratory data analysis) with handy methods for getting stats about a dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from appelpy.eda import statistical_moments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get all the statistical moments in one place:\n",
    "1. Mean\n",
    "2. Variance\n",
    "3. Skewness (Fisher as default)\n",
    "4. Kurtosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>skew</th>\n",
       "      <th>kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>distcod</th>\n",
       "      <td>67472.8</td>\n",
       "      <td>1.20201e+07</td>\n",
       "      <td>-0.0307844</td>\n",
       "      <td>-1.09626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enrltot</th>\n",
       "      <td>2628.79</td>\n",
       "      <td>1.53124e+07</td>\n",
       "      <td>2.87017</td>\n",
       "      <td>10.2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>teachers</th>\n",
       "      <td>129.067</td>\n",
       "      <td>35311.2</td>\n",
       "      <td>2.93254</td>\n",
       "      <td>11.219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calwpct</th>\n",
       "      <td>13.246</td>\n",
       "      <td>131.213</td>\n",
       "      <td>1.68306</td>\n",
       "      <td>4.58959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mealpct</th>\n",
       "      <td>44.7052</td>\n",
       "      <td>735.678</td>\n",
       "      <td>0.183954</td>\n",
       "      <td>-0.999802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>computer</th>\n",
       "      <td>303.383</td>\n",
       "      <td>194782</td>\n",
       "      <td>2.87169</td>\n",
       "      <td>10.7729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>testscr</th>\n",
       "      <td>654.157</td>\n",
       "      <td>363.03</td>\n",
       "      <td>0.0916151</td>\n",
       "      <td>-0.254288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compstu</th>\n",
       "      <td>0.135927</td>\n",
       "      <td>0.00421926</td>\n",
       "      <td>0.922369</td>\n",
       "      <td>1.43113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>expnstu</th>\n",
       "      <td>5312.41</td>\n",
       "      <td>401876</td>\n",
       "      <td>1.0679</td>\n",
       "      <td>1.87571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>str</th>\n",
       "      <td>19.6404</td>\n",
       "      <td>3.57895</td>\n",
       "      <td>-0.0253655</td>\n",
       "      <td>0.609597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avginc</th>\n",
       "      <td>15.3166</td>\n",
       "      <td>52.2135</td>\n",
       "      <td>2.21516</td>\n",
       "      <td>6.53212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elpct</th>\n",
       "      <td>15.7682</td>\n",
       "      <td>334.375</td>\n",
       "      <td>1.4268</td>\n",
       "      <td>1.4354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>readscr</th>\n",
       "      <td>654.97</td>\n",
       "      <td>404.331</td>\n",
       "      <td>-0.0583962</td>\n",
       "      <td>-0.358049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mathscr</th>\n",
       "      <td>653.343</td>\n",
       "      <td>351.72</td>\n",
       "      <td>0.255082</td>\n",
       "      <td>-0.159811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scrtot</th>\n",
       "      <td>1308.31</td>\n",
       "      <td>1452.12</td>\n",
       "      <td>0.0916149</td>\n",
       "      <td>-0.254288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mean          var       skew  kurtosis\n",
       "distcod    67472.8  1.20201e+07 -0.0307844  -1.09626\n",
       "enrltot    2628.79  1.53124e+07    2.87017   10.2004\n",
       "teachers   129.067      35311.2    2.93254    11.219\n",
       "calwpct     13.246      131.213    1.68306   4.58959\n",
       "mealpct    44.7052      735.678   0.183954 -0.999802\n",
       "computer   303.383       194782    2.87169   10.7729\n",
       "testscr    654.157       363.03  0.0916151 -0.254288\n",
       "compstu   0.135927   0.00421926   0.922369   1.43113\n",
       "expnstu    5312.41       401876     1.0679   1.87571\n",
       "str        19.6404      3.57895 -0.0253655  0.609597\n",
       "avginc     15.3166      52.2135    2.21516   6.53212\n",
       "elpct      15.7682      334.375     1.4268    1.4354\n",
       "readscr     654.97      404.331 -0.0583962 -0.358049\n",
       "mathscr    653.343       351.72   0.255082 -0.159811\n",
       "scrtot     1308.31      1452.12  0.0916149 -0.254288"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistical_moments(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the core of Appelpy is modelling phenomena in datasets.\n",
    "\n",
    "The **`linear_model`** module contains classes for modelling data, e.g. Ordinary Least Squares (OLS)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from appelpy.linear_model import OLS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here's a recipe for estimating a linear model.**\n",
    "\n",
    "1. Make two lists – `y_list` and `X_list` – for your dependent variable (y) and independent variables (X)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_list = ['scrtot']\n",
    "X_list = ['avginc', 'str', 'enrltot', 'expnstu']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Fit a model in one line, by passing the model dataframe and two lists as inputs to the OLS object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_nonrobust = OLS(df, y_list, X_list).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Get the key model results in the `model_selection_stats` attribute, including the Root Mean Square Error (Root MSE)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'root_mse': 25.85923055850994,\n",
       " 'r_squared': 0.5438972040191434,\n",
       " 'r_squared_adj': 0.5395010324916171,\n",
       " 'aic': 3929.1191674301117,\n",
       " 'bic': 3949.320440986499}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_nonrobust.model_selection_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the Statsmodels summary via the `results_output` attribute..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>scrtot</td>      <th>  R-squared:         </th> <td>   0.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.540</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   123.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 18 Feb 2020</td> <th>  Prob (F-statistic):</th> <td>2.05e-69</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:28:04</td>     <th>  Log-Likelihood:    </th> <td> -1959.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   420</td>      <th>  AIC:               </th> <td>   3929.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   415</td>      <th>  BIC:               </th> <td>   3949.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>   <td> 1312.9203</td> <td>   27.833</td> <td>   47.171</td> <td> 0.000</td> <td> 1258.208</td> <td> 1367.632</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>avginc</th>  <td>    3.8640</td> <td>    0.185</td> <td>   20.876</td> <td> 0.000</td> <td>    3.500</td> <td>    4.228</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>str</th>     <td>   -1.3954</td> <td>    0.893</td> <td>   -1.563</td> <td> 0.119</td> <td>   -3.150</td> <td>    0.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>enrltot</th> <td>   -0.0016</td> <td>    0.000</td> <td>   -4.722</td> <td> 0.000</td> <td>   -0.002</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>expnstu</th> <td>   -0.0061</td> <td>    0.003</td> <td>   -2.316</td> <td> 0.021</td> <td>   -0.011</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 7.321</td> <th>  Durbin-Watson:     </th> <td>   0.763</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.026</td> <th>  Jarque-Bera (JB):  </th> <td>   7.438</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.326</td> <th>  Prob(JB):          </th> <td>  0.0243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.969</td> <th>  Cond. No.          </th> <td>1.39e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.39e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 scrtot   R-squared:                       0.544\n",
       "Model:                            OLS   Adj. R-squared:                  0.540\n",
       "Method:                 Least Squares   F-statistic:                     123.7\n",
       "Date:                Tue, 18 Feb 2020   Prob (F-statistic):           2.05e-69\n",
       "Time:                        19:28:04   Log-Likelihood:                -1959.6\n",
       "No. Observations:                 420   AIC:                             3929.\n",
       "Df Residuals:                     415   BIC:                             3949.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const       1312.9203     27.833     47.171      0.000    1258.208    1367.632\n",
       "avginc         3.8640      0.185     20.876      0.000       3.500       4.228\n",
       "str           -1.3954      0.893     -1.563      0.119      -3.150       0.359\n",
       "enrltot       -0.0016      0.000     -4.722      0.000      -0.002      -0.001\n",
       "expnstu       -0.0061      0.003     -2.316      0.021      -0.011      -0.001\n",
       "==============================================================================\n",
       "Omnibus:                        7.321   Durbin-Watson:                   0.763\n",
       "Prob(Omnibus):                  0.026   Jarque-Bera (JB):                7.438\n",
       "Skew:                          -0.326   Prob(JB):                       0.0243\n",
       "Kurtosis:                       2.969   Cond. No.                     1.39e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.39e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_nonrobust.results_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...but now it's finally easy to get the standardized model results via the `results_output_standardized` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87\" ><caption>Unstandardized and Standardized Estimates</caption><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >coef</th>        <th class=\"col_heading level0 col1\" >t</th>        <th class=\"col_heading level0 col2\" >P>|t|</th>        <th class=\"col_heading level0 col3\" >coef_stdX</th>        <th class=\"col_heading level0 col4\" >coef_stdXy</th>        <th class=\"col_heading level0 col5\" >stdev_X</th>    </tr>    <tr>        <th class=\"index_name level0\" >scrtot</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87level0_row0\" class=\"row_heading level0 row0\" >avginc</th>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row0_col0\" class=\"data row0 col0\" >+3.8640</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row0_col1\" class=\"data row0 col1\" >+20.876</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row0_col2\" class=\"data row0 col2\" >0.000</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row0_col3\" class=\"data row0 col3\" >+27.9209</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row0_col4\" class=\"data row0 col4\" >+0.7327</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row0_col5\" class=\"data row0 col5\" >7.2259</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87level0_row1\" class=\"row_heading level0 row1\" >str</th>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row1_col0\" class=\"data row1 col0\" >-1.3954</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row1_col1\" class=\"data row1 col1\" >-1.563</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row1_col2\" class=\"data row1 col2\" >0.119</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row1_col3\" class=\"data row1 col3\" >-2.6399</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row1_col4\" class=\"data row1 col4\" >-0.0693</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row1_col5\" class=\"data row1 col5\" >1.8918</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87level0_row2\" class=\"row_heading level0 row2\" >enrltot</th>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row2_col0\" class=\"data row2 col0\" >-0.0016</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row2_col1\" class=\"data row2 col1\" >-4.722</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row2_col3\" class=\"data row2 col3\" >-6.3035</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row2_col4\" class=\"data row2 col4\" >-0.1654</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row2_col5\" class=\"data row2 col5\" >3913.1050</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87level0_row3\" class=\"row_heading level0 row3\" >expnstu</th>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row3_col0\" class=\"data row3 col0\" >-0.0061</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row3_col1\" class=\"data row3 col1\" >-2.316</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row3_col2\" class=\"data row3 col2\" >0.021</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row3_col3\" class=\"data row3 col3\" >-3.8364</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row3_col4\" class=\"data row3 col4\" >-0.1007</td>\n",
       "                        <td id=\"T_c8db02fa_5284_11ea_92f2_17644cd51b87row3_col5\" class=\"data row3 col5\" >633.9371</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f1c3164ee50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_nonrobust.results_output_standardized\n",
    "# This is a Styler object; add .data to access the underlying Pandas dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output is similar to that of Stata's `listcoef` command."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call the model object's `diagnostic_plot` method to bring up many visual **regression diagnostics:**\n",
    "- `pp_plot`: P-P plot\n",
    "- `qq_plot`: Q-Q plot\n",
    "- `rvp_plot`: Residuals vs Predicted Values plot\n",
    "- `rvf_plot`: Residuals vs Fitted Values plot\n",
    "\n",
    "Appelpy's **`diagnostics`** module has a suite of classes and functions for regression diagnostics:\n",
    "- If the visual diagnostics are not enough, then do formal tests by calling other methods such as `heteroskedasticity_test`.\n",
    "- Are there observations that are stinking up the model?  Pass the model object to the `BadApples` class and examine the influential points, outliers and high-leverage points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here's a recipe for a 2x2 set of diagnostic plots.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(2, 2, figsize=(10, 10))\n",
    "model_nonrobust.diagnostic_plot('pp_plot', ax=axarr[0][0])\n",
    "model_nonrobust.diagnostic_plot('qq_plot', ax=axarr[1][0])\n",
    "model_nonrobust.diagnostic_plot('rvf_plot', ax=axarr[1][1])\n",
    "axarr[0, 1].axis('off')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh dear.  The model specified has particular problems with test scores of 1350 and above.\n",
    "\n",
    "There appears to be non-constance variance in the RVF plot.\n",
    "\n",
    "Perhaps there is heteroskedasticity too, but we could do more specific tests there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heteroskedasticity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from appelpy.diagnostics import heteroskedasticity_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call one of a few **heteroskedasticity tests**, e.g.:\n",
    "- *Breusch-Pagan* (default of Stata's `hettest` command)\n",
    "- *Bresuch-Pagan studentized* (default of R's `bptest` command)\n",
    "- *White* (Stata's `hettest, white` command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breusch-Pagan test :: chi2(1)\n",
      "Test statistic: 2.5579\n",
      "Test p-value: 0.1097\n"
     ]
    }
   ],
   "source": [
    "bp_stats = heteroskedasticity_test('breusch_pagan', model_nonrobust)\n",
    "print('Breusch-Pagan test :: {}'.format(bp_stats['distribution'] + '({})'.format(bp_stats['nu'])))\n",
    "print('Test statistic: {:.4f}'.format(bp_stats['test_stat']))\n",
    "print('Test p-value: {:.4f}'.format(bp_stats['p_value']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breusch-Pagan test (studentized) :: chi2(1)\n",
      "Test statistic: 11.3877\n",
      "Test p-value: 0.0225\n"
     ]
    }
   ],
   "source": [
    "bps_stats = heteroskedasticity_test('breusch_pagan_studentized', model_nonrobust)\n",
    "print('Breusch-Pagan test (studentized) :: {}'.format(bps_stats['distribution'] + '({})'.format(bps_stats['nu'])))\n",
    "print('Test statistic: {:.4f}'.format(bps_stats['test_stat']))\n",
    "print('Test p-value: {:.4f}'.format(bps_stats['p_value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The studentized Breusch-Pagan test is seen as more robust and is showing a significant difference from the initial hypothesis of homoskedasticity, so let's fit a model with heteroskedastic-robust standard errors (`HC1` errors)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_hc1 = OLS(df, y_list, X_list, cov_type='HC1').fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>scrtot</td>      <th>  R-squared:         </th> <td>   0.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.540</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   78.60</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 18 Feb 2020</td> <th>  Prob (F-statistic):</th> <td>1.36e-49</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:28:05</td>     <th>  Log-Likelihood:    </th> <td> -1959.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   420</td>      <th>  AIC:               </th> <td>   3929.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   415</td>      <th>  BIC:               </th> <td>   3949.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>         <td>HC1</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>   <td> 1312.9203</td> <td>   29.364</td> <td>   44.711</td> <td> 0.000</td> <td> 1255.367</td> <td> 1370.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>avginc</th>  <td>    3.8640</td> <td>    0.240</td> <td>   16.119</td> <td> 0.000</td> <td>    3.394</td> <td>    4.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>str</th>     <td>   -1.3954</td> <td>    0.932</td> <td>   -1.498</td> <td> 0.134</td> <td>   -3.221</td> <td>    0.430</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>enrltot</th> <td>   -0.0016</td> <td>    0.000</td> <td>   -5.794</td> <td> 0.000</td> <td>   -0.002</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>expnstu</th> <td>   -0.0061</td> <td>    0.003</td> <td>   -2.166</td> <td> 0.030</td> <td>   -0.012</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 7.321</td> <th>  Durbin-Watson:     </th> <td>   0.763</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.026</td> <th>  Jarque-Bera (JB):  </th> <td>   7.438</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.326</td> <th>  Prob(JB):          </th> <td>  0.0243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.969</td> <th>  Cond. No.          </th> <td>1.39e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors are heteroscedasticity robust (HC1)<br/>[2] The condition number is large, 1.39e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 scrtot   R-squared:                       0.544\n",
       "Model:                            OLS   Adj. R-squared:                  0.540\n",
       "Method:                 Least Squares   F-statistic:                     78.60\n",
       "Date:                Tue, 18 Feb 2020   Prob (F-statistic):           1.36e-49\n",
       "Time:                        19:28:05   Log-Likelihood:                -1959.6\n",
       "No. Observations:                 420   AIC:                             3929.\n",
       "Df Residuals:                     415   BIC:                             3949.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:                  HC1                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const       1312.9203     29.364     44.711      0.000    1255.367    1370.474\n",
       "avginc         3.8640      0.240     16.119      0.000       3.394       4.334\n",
       "str           -1.3954      0.932     -1.498      0.134      -3.221       0.430\n",
       "enrltot       -0.0016      0.000     -5.794      0.000      -0.002      -0.001\n",
       "expnstu       -0.0061      0.003     -2.166      0.030      -0.012      -0.001\n",
       "==============================================================================\n",
       "Omnibus:                        7.321   Durbin-Watson:                   0.763\n",
       "Prob(Omnibus):                  0.026   Jarque-Bera (JB):                7.438\n",
       "Skew:                          -0.326   Prob(JB):                       0.0243\n",
       "Kurtosis:                       2.969   Cond. No.                     1.39e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors are heteroscedasticity robust (HC1)\n",
       "[2] The condition number is large, 1.39e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_hc1.results_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the significant regressors (independent variables) now?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['avginc', 'enrltot', 'expnstu']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_hc1.significant_regressors(0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `str` (student-teacher ratio) and `expnstu` regressors specifically relate to investment in students.  We can do a **Wald test** to test whether the two regressors together are significantly different from 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from appelpy.diagnostics import wald_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wald test :: chi2(2)\n",
      "Test statistic: 4.7352\n",
      "Test p-value: 0.0937\n"
     ]
    }
   ],
   "source": [
    "wald_stats = wald_test(model_hc1, ['str', 'expnstu'])\n",
    "print('Wald test :: {}'.format(wald_stats['distribution'] + '({})'.format(wald_stats['nu'])))\n",
    "print('Test statistic: {:.4f}'.format(wald_stats['test_stat']))\n",
    "print('Test p-value: {:.4f}'.format(wald_stats['p_value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's unsurprising that the standard errors for some of the variables are higher for HC1 errors compared to non-robust errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>std_error_nonrobust</th>\n",
       "      <th>std_error_hc1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>27.833430</td>\n",
       "      <td>29.364467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avginc</th>\n",
       "      <td>0.185093</td>\n",
       "      <td>0.239715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>str</th>\n",
       "      <td>0.892703</td>\n",
       "      <td>0.931541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enrltot</th>\n",
       "      <td>0.000341</td>\n",
       "      <td>0.000278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>expnstu</th>\n",
       "      <td>0.002613</td>\n",
       "      <td>0.002794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         std_error_nonrobust  std_error_hc1\n",
       "const              27.833430      29.364467\n",
       "avginc              0.185093       0.239715\n",
       "str                 0.892703       0.931541\n",
       "enrltot             0.000341       0.000278\n",
       "expnstu             0.002613       0.002794"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([pd.Series(model_nonrobust.results.bse, name='std_error_nonrobust'),\n",
    "           pd.Series(model_hc1.results.bse, name='std_error_hc1')], axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unusual observations: leverage, outliers and influence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's inspect observations that have:\n",
    "- High influence\n",
    "- High leverage\n",
    "- Outlier value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from appelpy.diagnostics import BadApples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up an instance of `BadApples` with the model object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "bad_apples = BadApples(model_hc1).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the leverage values against the normalized residuals squared.\n",
    "\n",
    "Observations in the top-right quadrant have leverage values and residual values higher than the average (i.e. they are the most influential in the model)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(11,7))\n",
    "bad_apples.plot_leverage_vs_residuals_squared(annotate=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Leverage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The leverage values are stored in the `measures_leverage` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48     0.113247\n",
       "34     0.085633\n",
       "404    0.076762\n",
       "413    0.065741\n",
       "159    0.061353\n",
       "Name: leverage, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_apples.measures_leverage.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the five observations with highest leverage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>scrtot</th>\n",
       "      <th>avginc</th>\n",
       "      <th>str</th>\n",
       "      <th>enrltot</th>\n",
       "      <th>expnstu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1261.099976</td>\n",
       "      <td>12.109128</td>\n",
       "      <td>19.017494</td>\n",
       "      <td>27176</td>\n",
       "      <td>5864.366211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1252.200012</td>\n",
       "      <td>11.722225</td>\n",
       "      <td>21.194069</td>\n",
       "      <td>25151</td>\n",
       "      <td>5117.039551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>1387.900024</td>\n",
       "      <td>55.327999</td>\n",
       "      <td>16.262285</td>\n",
       "      <td>1059</td>\n",
       "      <td>6460.657227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1398.200012</td>\n",
       "      <td>50.676998</td>\n",
       "      <td>15.407042</td>\n",
       "      <td>687</td>\n",
       "      <td>7217.263184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>1294.500000</td>\n",
       "      <td>14.298300</td>\n",
       "      <td>20.291372</td>\n",
       "      <td>21338</td>\n",
       "      <td>5123.474121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          scrtot     avginc        str  enrltot      expnstu\n",
       "48   1261.099976  12.109128  19.017494    27176  5864.366211\n",
       "34   1252.200012  11.722225  21.194069    25151  5117.039551\n",
       "404  1387.900024  55.327999  16.262285     1059  6460.657227\n",
       "413  1398.200012  50.676998  15.407042      687  7217.263184\n",
       "159  1294.500000  14.298300  20.291372    21338  5123.474121"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_apples.show_extreme_observations().loc[[48,34,404,413,159]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avginc</th>\n",
       "      <th>str</th>\n",
       "      <th>enrltot</th>\n",
       "      <th>expnstu</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>-0.443884</td>\n",
       "      <td>-0.329278</td>\n",
       "      <td>6.273077</td>\n",
       "      <td>0.870684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>-0.497428</td>\n",
       "      <td>0.821246</td>\n",
       "      <td>5.755585</td>\n",
       "      <td>-0.308182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>5.537230</td>\n",
       "      <td>-1.785664</td>\n",
       "      <td>-0.401163</td>\n",
       "      <td>1.811299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>4.893572</td>\n",
       "      <td>-2.237740</td>\n",
       "      <td>-0.496228</td>\n",
       "      <td>3.004803</td>\n",
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       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>-0.140922</td>\n",
       "      <td>0.344087</td>\n",
       "      <td>4.781167</td>\n",
       "      <td>-0.298032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       avginc       str   enrltot   expnstu\n",
       "48  -0.443884 -0.329278  6.273077  0.870684\n",
       "34  -0.497428  0.821246  5.755585 -0.308182\n",
       "404  5.537230 -1.785664 -0.401163  1.811299\n",
       "413  4.893572 -2.237740 -0.496228  3.004803\n",
       "159 -0.140922  0.344087  4.781167 -0.298032"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_hc1.X_standardized.loc[[48,34,404,413,159]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These high-leverage points have at least one independent variable with a value > 4 standard deviations away from the mean."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The BadApples object calculates heuristics for determining whether values of measures are 'extreme'.\n",
    "\n",
    "The extreme indices are stored in attributes, e.g. outliers stored in `indices_outliers`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6, 7, 8, 9, 10, 13, 14, 37, 38, 47, 134, 370, 392, 403, 404, 415, 417]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_apples.indices_outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The heuristic for outliers: an observation is an outlier if either the absolute value of the standardized residual or absolute value of the studentized residual is higher than 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of observations as outliers: 4.2857%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Proportion of observations as outliers: {len(bad_apples.indices_outliers) / len(df):.4%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By that heuristic, we'd expect (by design) no more than around 5% of the observations to be viewed as outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Influence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also consider a range of influence measures, e.g. Cook's distance, dffits and dfbeta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dfbeta_const</th>\n",
       "      <th>dfbeta_avginc</th>\n",
       "      <th>dfbeta_str</th>\n",
       "      <th>dfbeta_enrltot</th>\n",
       "      <th>dfbeta_expnstu</th>\n",
       "      <th>cooks_d</th>\n",
       "      <th>dffits_internal</th>\n",
       "      <th>dffits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>0.010742</td>\n",
       "      <td>-0.811324</td>\n",
       "      <td>0.068295</td>\n",
       "      <td>0.061925</td>\n",
       "      <td>0.039761</td>\n",
       "      <td>0.152768</td>\n",
       "      <td>-0.873981</td>\n",
       "      <td>-0.882753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>0.024082</td>\n",
       "      <td>-0.529623</td>\n",
       "      <td>0.016131</td>\n",
       "      <td>0.031978</td>\n",
       "      <td>0.026944</td>\n",
       "      <td>0.063718</td>\n",
       "      <td>-0.564440</td>\n",
       "      <td>-0.567338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>0.074297</td>\n",
       "      <td>-0.373064</td>\n",
       "      <td>0.013021</td>\n",
       "      <td>0.033955</td>\n",
       "      <td>-0.099557</td>\n",
       "      <td>0.044116</td>\n",
       "      <td>-0.469661</td>\n",
       "      <td>-0.470876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>-0.277356</td>\n",
       "      <td>-0.193382</td>\n",
       "      <td>0.217223</td>\n",
       "      <td>-0.136237</td>\n",
       "      <td>0.315817</td>\n",
       "      <td>0.030955</td>\n",
       "      <td>-0.393412</td>\n",
       "      <td>-0.397701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>-0.157629</td>\n",
       "      <td>0.099707</td>\n",
       "      <td>0.045684</td>\n",
       "      <td>-0.026836</td>\n",
       "      <td>0.249169</td>\n",
       "      <td>0.025414</td>\n",
       "      <td>0.356468</td>\n",
       "      <td>0.357936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     dfbeta_const  dfbeta_avginc  dfbeta_str  dfbeta_enrltot  dfbeta_expnstu  \\\n",
       "404      0.010742      -0.811324    0.068295        0.061925        0.039761   \n",
       "403      0.024082      -0.529623    0.016131        0.031978        0.026944   \n",
       "413      0.074297      -0.373064    0.013021        0.033955       -0.099557   \n",
       "38      -0.277356      -0.193382    0.217223       -0.136237        0.315817   \n",
       "415     -0.157629       0.099707    0.045684       -0.026836        0.249169   \n",
       "\n",
       "      cooks_d  dffits_internal    dffits  \n",
       "404  0.152768        -0.873981 -0.882753  \n",
       "403  0.063718        -0.564440 -0.567338  \n",
       "413  0.044116        -0.469661 -0.470876  \n",
       "38   0.030955        -0.393412 -0.397701  \n",
       "415  0.025414         0.356468  0.357936  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_apples.measures_influence.sort_values('cooks_d', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Load your dataset and do your pre-processing with Pandas.\n",
    "- Appelpy **`eda`**: inspect your model dataset with handy functions, e.g. statistical moments.\n",
    "- Appelpy **modelling**: `linear_model` contains classes such as OLS and WLS for model estimation.  Use a short recipe for each model and access model estimates, e.g. `results_output` and `results_output_standardized`.\n",
    "- Appelpy **`diagnostics`**: check whether the model estimates are valid with diagnostics, e.g. diagnostic plots, statistical tests and `BadApples` for influence analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other features worth checking out:\n",
    "- Logistic regression (under `discrete_model`)\n",
    "- DummyEncoder and InteractionEncoder classes"
   ]
  }
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