03_Linear_Regression_Applied.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applied"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. This question involves the use of simple linear regression on the Auto data set.\n",
    "    1. Use the `lm()` function to perform a simple linear regression with `mpg` as the response and `horsepower` as the predictor. Use the `summary()` function to print the results. Comment on the output.\n",
    "    \n",
    "     For example:\n",
    "     \n",
    "        1. Is there a relationship between the predictor and the response?\n",
    "        1. How strong is the relationship between the predictor and the response?\n",
    "        1. Is the relationship between the predictor and the response positive or negative?\n",
    "        1. What is the predicted mpg associated with a horsepower of 98? What are the associated 95 % confidence and prediction intervals?\n",
    "        \n",
    "    1. Plot the response and the predictor. Use the abline() function to display the least squares regression line.\n",
    "    1. Use the plot() function to produce diagnostic plots of the least squares regression fit. Comment on any problems you see with the fit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/florian/anaconda3/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "PATH = './data/'\n",
    "auto = pd.read_csv(f'{PATH}Auto.csv',sep=',')\n",
    "auto.loc[auto['horsepower'] == '?','horsepower'] = np.nan\n",
    "auto['horsepower'] = auto['horsepower'].astype(float)\n",
    "auto.dropna(inplace=True)\n",
    "auto.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>mpg</td>       <th>  R-squared:         </th> <td>   0.606</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.605</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   599.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Nov 2018</td> <th>  Prob (F-statistic):</th> <td>7.03e-81</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:23:22</td>     <th>  Log-Likelihood:    </th> <td> -1178.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   392</td>      <th>  AIC:               </th> <td>   2361.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   390</td>      <th>  BIC:               </th> <td>   2369.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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>   39.9359</td> <td>    0.717</td> <td>   55.660</td> <td> 0.000</td> <td>   38.525</td> <td>   41.347</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>horsepower</th> <td>   -0.1578</td> <td>    0.006</td> <td>  -24.489</td> <td> 0.000</td> <td>   -0.171</td> <td>   -0.145</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>16.432</td> <th>  Durbin-Watson:     </th> <td>   0.920</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  17.305</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.492</td> <th>  Prob(JB):          </th> <td>0.000175</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.299</td> <th>  Cond. No.          </th> <td>    322.</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    mpg   R-squared:                       0.606\n",
       "Model:                            OLS   Adj. R-squared:                  0.605\n",
       "Method:                 Least Squares   F-statistic:                     599.7\n",
       "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           7.03e-81\n",
       "Time:                        08:23:22   Log-Likelihood:                -1178.7\n",
       "No. Observations:                 392   AIC:                             2361.\n",
       "Df Residuals:                     390   BIC:                             2369.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         39.9359      0.717     55.660      0.000      38.525      41.347\n",
       "horsepower    -0.1578      0.006    -24.489      0.000      -0.171      -0.145\n",
       "==============================================================================\n",
       "Omnibus:                       16.432   Durbin-Watson:                   0.920\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               17.305\n",
       "Skew:                           0.492   Prob(JB):                     0.000175\n",
       "Kurtosis:                       3.299   Cond. No.                         322.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = auto['mpg']\n",
    "X = auto['horsepower']\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "mod = sm.OLS(y,X).fit()\n",
    "mod.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a strong negative relationship between the predictor and the response. The p-value is close to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.46707715])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([98])\n",
    "x = sm.add_constant(x, has_constant='add')\n",
    "mod.predict(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad442f6908>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad442f60b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='horsepower', y='mpg', data=auto)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad442a0438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.residplot(x=mod.fittedvalues, y=y, lowess=True)\n",
    "ax.set(xlabel='Fitted values',ylabel='Residuals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a strong pattern in the data which indicates a non-linearity. The fit should include a quadratic transformation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. This question involves the use of multiple linear regression on the Auto data set. \n",
    "    1. Produce a scatterplot matrix which includes all of the variables in the data set.\n",
    "    1. Compute the matrix of correlations between the variables using the function cor(). You will need to exclude the name variable, which is qualitative.\n",
    "    1. Use the lm() function to perform a multiple linear regression with mpg as the response and all other variables except name as thepredictors. Use the summary() function to print the results. Comment on the output. For instance:\n",
    "        1. Is there a relationship between the predictors and the response?\n",
    "        1. Which predictors appear to have a statistically significant relationship to the response?\n",
    "        1. What does the coefficient for the year variable suggest?\n",
    "    1. Use the plot() function to produce diagnostic plots of the linear regression fit. Comment on any problems you see with the fit. Do the residual plots suggest any unusually large outliers? Does the leverage plot identify any observations with unusually high leverage?\n",
    "    1. Use the * and : symbols to fit linear regression models with interaction effects. Do any interactions appear to be statistically significant?\n",
    "    1. Try a few different transformations of the variables, such as log(X),√X,X^2. Comment on your findings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fad7c1e3e48>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad442a8c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(auto)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>year</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.777618</td>\n",
       "      <td>-0.805127</td>\n",
       "      <td>-0.778427</td>\n",
       "      <td>-0.832244</td>\n",
       "      <td>0.423329</td>\n",
       "      <td>0.580541</td>\n",
       "      <td>0.565209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>-0.777618</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.950823</td>\n",
       "      <td>0.842983</td>\n",
       "      <td>0.897527</td>\n",
       "      <td>-0.504683</td>\n",
       "      <td>-0.345647</td>\n",
       "      <td>-0.568932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>displacement</th>\n",
       "      <td>-0.805127</td>\n",
       "      <td>0.950823</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.897257</td>\n",
       "      <td>0.932994</td>\n",
       "      <td>-0.543800</td>\n",
       "      <td>-0.369855</td>\n",
       "      <td>-0.614535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>-0.778427</td>\n",
       "      <td>0.842983</td>\n",
       "      <td>0.897257</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.864538</td>\n",
       "      <td>-0.689196</td>\n",
       "      <td>-0.416361</td>\n",
       "      <td>-0.455171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>-0.832244</td>\n",
       "      <td>0.897527</td>\n",
       "      <td>0.932994</td>\n",
       "      <td>0.864538</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.416839</td>\n",
       "      <td>-0.309120</td>\n",
       "      <td>-0.585005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acceleration</th>\n",
       "      <td>0.423329</td>\n",
       "      <td>-0.504683</td>\n",
       "      <td>-0.543800</td>\n",
       "      <td>-0.689196</td>\n",
       "      <td>-0.416839</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.290316</td>\n",
       "      <td>0.212746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0.580541</td>\n",
       "      <td>-0.345647</td>\n",
       "      <td>-0.369855</td>\n",
       "      <td>-0.416361</td>\n",
       "      <td>-0.309120</td>\n",
       "      <td>0.290316</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.181528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <td>0.565209</td>\n",
       "      <td>-0.568932</td>\n",
       "      <td>-0.614535</td>\n",
       "      <td>-0.455171</td>\n",
       "      <td>-0.585005</td>\n",
       "      <td>0.212746</td>\n",
       "      <td>0.181528</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mpg  cylinders  displacement  horsepower    weight  \\\n",
       "mpg           1.000000  -0.777618     -0.805127   -0.778427 -0.832244   \n",
       "cylinders    -0.777618   1.000000      0.950823    0.842983  0.897527   \n",
       "displacement -0.805127   0.950823      1.000000    0.897257  0.932994   \n",
       "horsepower   -0.778427   0.842983      0.897257    1.000000  0.864538   \n",
       "weight       -0.832244   0.897527      0.932994    0.864538  1.000000   \n",
       "acceleration  0.423329  -0.504683     -0.543800   -0.689196 -0.416839   \n",
       "year          0.580541  -0.345647     -0.369855   -0.416361 -0.309120   \n",
       "origin        0.565209  -0.568932     -0.614535   -0.455171 -0.585005   \n",
       "\n",
       "              acceleration      year    origin  \n",
       "mpg               0.423329  0.580541  0.565209  \n",
       "cylinders        -0.504683 -0.345647 -0.568932  \n",
       "displacement     -0.543800 -0.369855 -0.614535  \n",
       "horsepower       -0.689196 -0.416361 -0.455171  \n",
       "weight           -0.416839 -0.309120 -0.585005  \n",
       "acceleration      1.000000  0.290316  0.212746  \n",
       "year              0.290316  1.000000  0.181528  \n",
       "origin            0.212746  0.181528  1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = auto['mpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = auto.drop(['mpg','name'], axis=1)\n",
    "X = sm.add_constant(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod = sm.OLS(y, X).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>mpg</td>       <th>  R-squared:         </th> <td>   0.821</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.818</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   252.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Nov 2018</td> <th>  Prob (F-statistic):</th> <td>2.04e-139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:23:28</td>     <th>  Log-Likelihood:    </th> <td> -1023.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   392</td>      <th>  AIC:               </th> <td>   2063.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   384</td>      <th>  BIC:               </th> <td>   2095.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     7</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>  -17.2184</td> <td>    4.644</td> <td>   -3.707</td> <td> 0.000</td> <td>  -26.350</td> <td>   -8.087</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cylinders</th>    <td>   -0.4934</td> <td>    0.323</td> <td>   -1.526</td> <td> 0.128</td> <td>   -1.129</td> <td>    0.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>displacement</th> <td>    0.0199</td> <td>    0.008</td> <td>    2.647</td> <td> 0.008</td> <td>    0.005</td> <td>    0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>horsepower</th>   <td>   -0.0170</td> <td>    0.014</td> <td>   -1.230</td> <td> 0.220</td> <td>   -0.044</td> <td>    0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>weight</th>       <td>   -0.0065</td> <td>    0.001</td> <td>   -9.929</td> <td> 0.000</td> <td>   -0.008</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>acceleration</th> <td>    0.0806</td> <td>    0.099</td> <td>    0.815</td> <td> 0.415</td> <td>   -0.114</td> <td>    0.275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>year</th>         <td>    0.7508</td> <td>    0.051</td> <td>   14.729</td> <td> 0.000</td> <td>    0.651</td> <td>    0.851</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>origin</th>       <td>    1.4261</td> <td>    0.278</td> <td>    5.127</td> <td> 0.000</td> <td>    0.879</td> <td>    1.973</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>31.906</td> <th>  Durbin-Watson:     </th> <td>   1.309</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  53.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.529</td> <th>  Prob(JB):          </th> <td>2.95e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.460</td> <th>  Cond. No.          </th> <td>8.59e+04</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    mpg   R-squared:                       0.821\n",
       "Model:                            OLS   Adj. R-squared:                  0.818\n",
       "Method:                 Least Squares   F-statistic:                     252.4\n",
       "Date:                Mon, 26 Nov 2018   Prob (F-statistic):          2.04e-139\n",
       "Time:                        08:23:28   Log-Likelihood:                -1023.5\n",
       "No. Observations:                 392   AIC:                             2063.\n",
       "Df Residuals:                     384   BIC:                             2095.\n",
       "Df Model:                           7                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const          -17.2184      4.644     -3.707      0.000     -26.350      -8.087\n",
       "cylinders       -0.4934      0.323     -1.526      0.128      -1.129       0.142\n",
       "displacement     0.0199      0.008      2.647      0.008       0.005       0.035\n",
       "horsepower      -0.0170      0.014     -1.230      0.220      -0.044       0.010\n",
       "weight          -0.0065      0.001     -9.929      0.000      -0.008      -0.005\n",
       "acceleration     0.0806      0.099      0.815      0.415      -0.114       0.275\n",
       "year             0.7508      0.051     14.729      0.000       0.651       0.851\n",
       "origin           1.4261      0.278      5.127      0.000       0.879       1.973\n",
       "==============================================================================\n",
       "Omnibus:                       31.906   Durbin-Watson:                   1.309\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               53.100\n",
       "Skew:                           0.529   Prob(JB):                     2.95e-12\n",
       "Kurtosis:                       4.460   Cond. No.                     8.59e+04\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, 8.59e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The F-statistic is very low - this indicates a relationship between the predictors and the response.  \n",
    "Significant predictors are: _displacement, weight, year, origin_.  \n",
    "The coefficient of the _year_ variable suggests a higher _mpg_ for younger cars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad3c6c74a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.residplot(mod.fittedvalues,y,lowess=True)\n",
    "ax.set(xlabel='Fitted values', ylabel='Residuals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a non linear relationship between the predictors and the response. _TODO_ Add more plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  year  \\\n",
       "0  18.0          8         307.0       130.0  3504.0          12.0    70   \n",
       "1  15.0          8         350.0       165.0  3693.0          11.5    70   \n",
       "2  18.0          8         318.0       150.0  3436.0          11.0    70   \n",
       "3  16.0          8         304.0       150.0  3433.0          12.0    70   \n",
       "4  17.0          8         302.0       140.0  3449.0          10.5    70   \n",
       "\n",
       "   origin                       name  \n",
       "0       1  chevrolet chevelle malibu  \n",
       "1       1          buick skylark 320  \n",
       "2       1         plymouth satellite  \n",
       "3       1              amc rebel sst  \n",
       "4       1                ford torino  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>mpg</td>       <th>  R-squared:         </th> <td>   0.871</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.867</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   214.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Nov 2018</td> <th>  Prob (F-statistic):</th> <td>1.85e-160</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:23:28</td>     <th>  Log-Likelihood:    </th> <td> -959.23</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   392</td>      <th>  AIC:               </th> <td>   1944.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   379</td>      <th>  BIC:               </th> <td>   1996.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    12</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>Intercept</th>               <td>   -6.1550</td> <td>    4.571</td> <td>   -1.347</td> <td> 0.179</td> <td>  -15.142</td> <td>    2.832</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(cylinders)[T.4]</th>       <td>    6.8586</td> <td>    1.518</td> <td>    4.518</td> <td> 0.000</td> <td>    3.874</td> <td>    9.844</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(cylinders)[T.5]</th>       <td>    8.0807</td> <td>    2.313</td> <td>    3.494</td> <td> 0.001</td> <td>    3.533</td> <td>   12.628</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(cylinders)[T.6]</th>       <td>    6.7977</td> <td>    1.723</td> <td>    3.945</td> <td> 0.000</td> <td>    3.410</td> <td>   10.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(cylinders)[T.8]</th>       <td>    8.5930</td> <td>    1.979</td> <td>    4.342</td> <td> 0.000</td> <td>    4.702</td> <td>   12.484</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(origin)[T.2]</th>          <td>    0.7287</td> <td>    0.521</td> <td>    1.400</td> <td> 0.162</td> <td>   -0.295</td> <td>    1.752</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(origin)[T.3]</th>          <td>    1.6098</td> <td>    0.498</td> <td>    3.231</td> <td> 0.001</td> <td>    0.630</td> <td>    2.590</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>displacement</th>            <td>   -0.0647</td> <td>    0.012</td> <td>   -5.458</td> <td> 0.000</td> <td>   -0.088</td> <td>   -0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>horsepower</th>              <td>   -0.1791</td> <td>    0.021</td> <td>   -8.578</td> <td> 0.000</td> <td>   -0.220</td> <td>   -0.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>displacement:horsepower</th> <td>    0.0005</td> <td> 5.35e-05</td> <td>    8.488</td> <td> 0.000</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>weight</th>                  <td>   -0.0031</td> <td>    0.001</td> <td>   -4.643</td> <td> 0.000</td> <td>   -0.004</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>acceleration</th>            <td>   -0.2257</td> <td>    0.090</td> <td>   -2.497</td> <td> 0.013</td> <td>   -0.403</td> <td>   -0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>year</th>                    <td>    0.7237</td> <td>    0.045</td> <td>   16.107</td> <td> 0.000</td> <td>    0.635</td> <td>    0.812</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>62.594</td> <th>  Durbin-Watson:     </th> <td>   1.479</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 138.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.831</td> <th>  Prob(JB):          </th> <td>1.06e-30</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.385</td> <th>  Cond. No.          </th> <td>1.04e+06</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    mpg   R-squared:                       0.871\n",
       "Model:                            OLS   Adj. R-squared:                  0.867\n",
       "Method:                 Least Squares   F-statistic:                     214.0\n",
       "Date:                Mon, 26 Nov 2018   Prob (F-statistic):          1.85e-160\n",
       "Time:                        08:23:28   Log-Likelihood:                -959.23\n",
       "No. Observations:                 392   AIC:                             1944.\n",
       "Df Residuals:                     379   BIC:                             1996.\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===========================================================================================\n",
       "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------------\n",
       "Intercept                  -6.1550      4.571     -1.347      0.179     -15.142       2.832\n",
       "C(cylinders)[T.4]           6.8586      1.518      4.518      0.000       3.874       9.844\n",
       "C(cylinders)[T.5]           8.0807      2.313      3.494      0.001       3.533      12.628\n",
       "C(cylinders)[T.6]           6.7977      1.723      3.945      0.000       3.410      10.186\n",
       "C(cylinders)[T.8]           8.5930      1.979      4.342      0.000       4.702      12.484\n",
       "C(origin)[T.2]              0.7287      0.521      1.400      0.162      -0.295       1.752\n",
       "C(origin)[T.3]              1.6098      0.498      3.231      0.001       0.630       2.590\n",
       "displacement               -0.0647      0.012     -5.458      0.000      -0.088      -0.041\n",
       "horsepower                 -0.1791      0.021     -8.578      0.000      -0.220      -0.138\n",
       "displacement:horsepower     0.0005   5.35e-05      8.488      0.000       0.000       0.001\n",
       "weight                     -0.0031      0.001     -4.643      0.000      -0.004      -0.002\n",
       "acceleration               -0.2257      0.090     -2.497      0.013      -0.403      -0.048\n",
       "year                        0.7237      0.045     16.107      0.000       0.635       0.812\n",
       "==============================================================================\n",
       "Omnibus:                       62.594   Durbin-Watson:                   1.479\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              138.045\n",
       "Skew:                           0.831   Prob(JB):                     1.06e-30\n",
       "Kurtosis:                       5.385   Cond. No.                     1.04e+06\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, 1.04e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = smf.ols(\"mpg ~ C(cylinders) + displacement * horsepower + weight + acceleration + year + C(origin)\", data=auto).fit()\n",
    "mod.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The_ displacement * horsepower_ interaction seems to be statistically significant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### f)"
   ]
  },
  {
   "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>mpg</td>       <th>  R-squared:         </th> <td>   0.668</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   785.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Nov 2018</td> <th>  Prob (F-statistic):</th> <td>1.70e-95</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:23:28</td>     <th>  Log-Likelihood:    </th> <td> -1144.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   392</td>      <th>  AIC:               </th> <td>   2294.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   390</td>      <th>  BIC:               </th> <td>   2302.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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>Intercept</th>          <td>  108.6997</td> <td>    3.050</td> <td>   35.644</td> <td> 0.000</td> <td>  102.704</td> <td>  114.695</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(horsepower)</th> <td>  -18.5822</td> <td>    0.663</td> <td>  -28.034</td> <td> 0.000</td> <td>  -19.885</td> <td>  -17.279</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11.919</td> <th>  Durbin-Watson:     </th> <td>   1.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.003</td> <th>  Jarque-Bera (JB):  </th> <td>  15.557</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.278</td> <th>  Prob(JB):          </th> <td>0.000419</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.803</td> <th>  Cond. No.          </th> <td>    64.6</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    mpg   R-squared:                       0.668\n",
       "Model:                            OLS   Adj. R-squared:                  0.667\n",
       "Method:                 Least Squares   F-statistic:                     785.9\n",
       "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           1.70e-95\n",
       "Time:                        08:23:28   Log-Likelihood:                -1144.9\n",
       "No. Observations:                 392   AIC:                             2294.\n",
       "Df Residuals:                     390   BIC:                             2302.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept            108.6997      3.050     35.644      0.000     102.704     114.695\n",
       "np.log(horsepower)   -18.5822      0.663    -28.034      0.000     -19.885     -17.279\n",
       "==============================================================================\n",
       "Omnibus:                       11.919   Durbin-Watson:                   1.031\n",
       "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               15.557\n",
       "Skew:                           0.278   Prob(JB):                     0.000419\n",
       "Kurtosis:                       3.803   Cond. No.                         64.6\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = smf.ols(\"mpg ~ np.log(horsepower)\", data=auto).fit()\n",
    "mod.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the _log_ of horsepower produces a higher R-squared than using horsepower directly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. This question should be answered using the Carseats data set.\n",
    "    1. Fit a multiple regression model to predict _Sales_ using _Price, Urban, and US._\n",
    "    1. Provide an interpretation of each coefficient in the model. Be careful - some of the variables in the model are qualitative!\n",
    "    1. Write out the model in equation form, being careful to handle the qualitative variables properly.\n",
    "    1. For which of the predictors can you reject the null hypothesis $H_0 : \\beta_j = 0$?\n",
    "    1. On the basis of your response to the previous question, fit a smaller model that only uses the predictors for which there is evidence of association with the outcome.  \n",
    "    1. How well do the models in (a) and (e) fit the data?\n",
    "    1. Using the model from (e), obtain 95 % confidence intervals for the coefficient(s).\n",
    "    1. Is there evidence of outliers or high leverage observations in the model from (e)?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Sales</th>\n",
       "      <th>CompPrice</th>\n",
       "      <th>Income</th>\n",
       "      <th>Advertising</th>\n",
       "      <th>Population</th>\n",
       "      <th>Price</th>\n",
       "      <th>ShelveLoc</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Urban</th>\n",
       "      <th>US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.50</td>\n",
       "      <td>138</td>\n",
       "      <td>73</td>\n",
       "      <td>11</td>\n",
       "      <td>276</td>\n",
       "      <td>120</td>\n",
       "      <td>Bad</td>\n",
       "      <td>42</td>\n",
       "      <td>17</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.22</td>\n",
       "      <td>111</td>\n",
       "      <td>48</td>\n",
       "      <td>16</td>\n",
       "      <td>260</td>\n",
       "      <td>83</td>\n",
       "      <td>Good</td>\n",
       "      <td>65</td>\n",
       "      <td>10</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.06</td>\n",
       "      <td>113</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>269</td>\n",
       "      <td>80</td>\n",
       "      <td>Medium</td>\n",
       "      <td>59</td>\n",
       "      <td>12</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.40</td>\n",
       "      <td>117</td>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "      <td>466</td>\n",
       "      <td>97</td>\n",
       "      <td>Medium</td>\n",
       "      <td>55</td>\n",
       "      <td>14</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.15</td>\n",
       "      <td>141</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>340</td>\n",
       "      <td>128</td>\n",
       "      <td>Bad</td>\n",
       "      <td>38</td>\n",
       "      <td>13</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sales  CompPrice  Income  Advertising  Population  Price ShelveLoc  Age  \\\n",
       "0   9.50        138      73           11         276    120       Bad   42   \n",
       "1  11.22        111      48           16         260     83      Good   65   \n",
       "2  10.06        113      35           10         269     80    Medium   59   \n",
       "3   7.40        117     100            4         466     97    Medium   55   \n",
       "4   4.15        141      64            3         340    128       Bad   38   \n",
       "\n",
       "   Education Urban   US  \n",
       "0         17   Yes  Yes  \n",
       "1         10   Yes  Yes  \n",
       "2         12   Yes  Yes  \n",
       "3         14   Yes  Yes  \n",
       "4         13   Yes   No  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(f\"{PATH}/Carseats.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.239</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   41.52</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Nov 2018</td> <th>  Prob (F-statistic):</th> <td>2.39e-23</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:23:28</td>     <th>  Log-Likelihood:    </th> <td> -927.66</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   400</td>      <th>  AIC:               </th> <td>   1863.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   396</td>      <th>  BIC:               </th> <td>   1879.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</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>Intercept</th>       <td>   13.0435</td> <td>    0.651</td> <td>   20.036</td> <td> 0.000</td> <td>   11.764</td> <td>   14.323</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Urban)[T.Yes]</th> <td>   -0.0219</td> <td>    0.272</td> <td>   -0.081</td> <td> 0.936</td> <td>   -0.556</td> <td>    0.512</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(US)[T.Yes]</th>    <td>    1.2006</td> <td>    0.259</td> <td>    4.635</td> <td> 0.000</td> <td>    0.691</td> <td>    1.710</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Price</th>           <td>   -0.0545</td> <td>    0.005</td> <td>  -10.389</td> <td> 0.000</td> <td>   -0.065</td> <td>   -0.044</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.676</td> <th>  Durbin-Watson:     </th> <td>   1.912</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.713</td> <th>  Jarque-Bera (JB):  </th> <td>   0.758</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.093</td> <th>  Prob(JB):          </th> <td>   0.684</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.897</td> <th>  Cond. No.          </th> <td>    628.</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   R-squared:                       0.239\n",
       "Model:                            OLS   Adj. R-squared:                  0.234\n",
       "Method:                 Least Squares   F-statistic:                     41.52\n",
       "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           2.39e-23\n",
       "Time:                        08:23:28   Log-Likelihood:                -927.66\n",
       "No. Observations:                 400   AIC:                             1863.\n",
       "Df Residuals:                     396   BIC:                             1879.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept          13.0435      0.651     20.036      0.000      11.764      14.323\n",
       "C(Urban)[T.Yes]    -0.0219      0.272     -0.081      0.936      -0.556       0.512\n",
       "C(US)[T.Yes]        1.2006      0.259      4.635      0.000       0.691       1.710\n",
       "Price              -0.0545      0.005    -10.389      0.000      -0.065      -0.044\n",
       "==============================================================================\n",
       "Omnibus:                        0.676   Durbin-Watson:                   1.912\n",
       "Prob(Omnibus):                  0.713   Jarque-Bera (JB):                0.758\n",
       "Skew:                           0.093   Prob(JB):                        0.684\n",
       "Kurtosis:                       2.897   Cond. No.                         628.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = smf.ols(\"Sales ~ Price + C(Urban) + C(US)\", data=data).fit()\n",
    "mod.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad3c035128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, (ax1, ax2) = plt.subplots(1,2,figsize=(16,4))\n",
    "pd.plotting.boxplot(data,column='Sales',by='US',ax=ax1)\n",
    "pd.plotting.boxplot(data,column='Sales',by='Urban',ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "c) - omitted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hypothesis can be rejected for the predictor _Urban_ because of the large p-value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.239</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.235</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   62.43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Nov 2018</td> <th>  Prob (F-statistic):</th> <td>2.66e-24</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:23:28</td>     <th>  Log-Likelihood:    </th> <td> -927.66</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   400</td>      <th>  AIC:               </th> <td>   1861.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   397</td>      <th>  BIC:               </th> <td>   1873.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</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>Intercept</th>    <td>   13.0308</td> <td>    0.631</td> <td>   20.652</td> <td> 0.000</td> <td>   11.790</td> <td>   14.271</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(US)[T.Yes]</th> <td>    1.1996</td> <td>    0.258</td> <td>    4.641</td> <td> 0.000</td> <td>    0.692</td> <td>    1.708</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Price</th>        <td>   -0.0545</td> <td>    0.005</td> <td>  -10.416</td> <td> 0.000</td> <td>   -0.065</td> <td>   -0.044</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.666</td> <th>  Durbin-Watson:     </th> <td>   1.912</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.717</td> <th>  Jarque-Bera (JB):  </th> <td>   0.749</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.092</td> <th>  Prob(JB):          </th> <td>   0.688</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.895</td> <th>  Cond. No.          </th> <td>    607.</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   R-squared:                       0.239\n",
       "Model:                            OLS   Adj. R-squared:                  0.235\n",
       "Method:                 Least Squares   F-statistic:                     62.43\n",
       "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           2.66e-24\n",
       "Time:                        08:23:28   Log-Likelihood:                -927.66\n",
       "No. Observations:                 400   AIC:                             1861.\n",
       "Df Residuals:                     397   BIC:                             1873.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "Intercept       13.0308      0.631     20.652      0.000      11.790      14.271\n",
       "C(US)[T.Yes]     1.1996      0.258      4.641      0.000       0.692       1.708\n",
       "Price           -0.0545      0.005    -10.416      0.000      -0.065      -0.044\n",
       "==============================================================================\n",
       "Omnibus:                        0.666   Durbin-Watson:                   1.912\n",
       "Prob(Omnibus):                  0.717   Jarque-Bera (JB):                0.749\n",
       "Skew:                           0.092   Prob(JB):                        0.688\n",
       "Kurtosis:                       2.895   Cond. No.                         607.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = smf.ols(\"Sales ~ Price + C(US)\", data=data).fit()\n",
    "mod.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The R-squared value is the same for both models, the F-statistic is higher in the second model (62.43 vs 41.52)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model fit in a is the same in a) as in e): R-squared: 0.239."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>11.79032</td>\n",
       "      <td>14.271265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(US)[T.Yes]</th>\n",
       "      <td>0.69152</td>\n",
       "      <td>1.707766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>-0.06476</td>\n",
       "      <td>-0.044195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0          1\n",
       "Intercept     11.79032  14.271265\n",
       "C(US)[T.Yes]   0.69152   1.707766\n",
       "Price         -0.06476  -0.044195"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod.conf_int(alpha=0.05, cols=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad38cc0f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.graphics.influence_plot(mod,plot_alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. In this problem we will investigate the t-statistic for the null hypoth- esis H 0 : β = 0 in simple linear regression without an intercept. To begin, we generate a predictor x and a response y as follows.\n",
    "\n",
    "     ```set . seed (1)\n",
    "     x = rnorm (100)\n",
    "     y =2* x + rnorm (100)```\n",
    "    1.  Perform a simple linear regression of y onto x , without an in- tercept. Report the coefficient estimate β̂, the standard error of this coefficient estimate, and the t-statistic and p-value associ- ated with the null hypothesis H 0 : β = 0. Comment on these results. (You can perform regression without an intercept using the command lm(y∼x+0) .) \n",
    "    1. Now perform a simple linear regression of x onto y without an intercept, and report the coefficient estimate, its standard error, and the corresponding t-statistic and p-values associated with the null hypothesis H 0 : β = 0. Comment on these results.  \n",
    "    1.  What is the relationship between the results obtained in (a) and (b)?\n",
    "    1. For the regression of Y onto X without an intercept, the t- statistic for H 0 : β = 0 takes the form β̂/SE( β̂), where β̂ is given by (3.38), and where\n",
    "     (These formulas are slightly different from those given in Sec- tions 3.1.1 and 3.1.2, since here we are performing regression without an intercept.) Show algebraically, and confirm numeri- cally in R , that the t-statistic can be written as\n",
    "    1. Using the results from (d), argue that the t-statistic for the re- gression of y onto x is the same as the t-statistic for the regression of x onto y .\n",
    "    1. In R , show that when regression is performed with an intercept, the t-statistic for H 0 : β 1 = 0 is the same for the regression of y onto x as it is for the regression of x onto y .\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.normal(size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 2 * x + np.random.normal(size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad38df3cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame({'x':x,'y':y})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.754</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.752</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   303.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Nov 2018</td> <th>  Prob (F-statistic):</th> <td>6.23e-32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:23:29</td>     <th>  Log-Likelihood:    </th> <td> -145.26</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   292.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    99</td>      <th>  BIC:               </th> <td>   295.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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>x</th> <td>    1.9338</td> <td>    0.111</td> <td>   17.430</td> <td> 0.000</td> <td>    1.714</td> <td>    2.154</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.411</td> <th>  Durbin-Watson:     </th> <td>   1.885</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.494</td> <th>  Jarque-Bera (JB):  </th> <td>   0.904</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.016</td> <th>  Prob(JB):          </th> <td>   0.636</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.465</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.754\n",
       "Model:                            OLS   Adj. R-squared:                  0.752\n",
       "Method:                 Least Squares   F-statistic:                     303.8\n",
       "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           6.23e-32\n",
       "Time:                        08:23:29   Log-Likelihood:                -145.26\n",
       "No. Observations:                 100   AIC:                             292.5\n",
       "Df Residuals:                      99   BIC:                             295.1\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x              1.9338      0.111     17.430      0.000       1.714       2.154\n",
       "==============================================================================\n",
       "Omnibus:                        1.411   Durbin-Watson:                   1.885\n",
       "Prob(Omnibus):                  0.494   Jarque-Bera (JB):                0.904\n",
       "Skew:                          -0.016   Prob(JB):                        0.636\n",
       "Kurtosis:                       3.465   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = smf.ols(formula=\"y ~ x - 1\", data=data).fit()\n",
    "\n",
    "mod.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See table. We can reject the hypothesis, both variables are dependent. The coefficient is very close to the \"expected\" coefficient 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>x</td>        <th>  R-squared:         </th> <td>   0.754</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.752</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   303.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Nov 2018</td> <th>  Prob (F-statistic):</th> <td>6.23e-32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:23:29</td>     <th>  Log-Likelihood:    </th> <td> -65.208</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   132.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    99</td>      <th>  BIC:               </th> <td>   135.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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>y</th> <td>    0.3900</td> <td>    0.022</td> <td>   17.430</td> <td> 0.000</td> <td>    0.346</td> <td>    0.434</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.172</td> <th>  Durbin-Watson:     </th> <td>   1.711</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.338</td> <th>  Jarque-Bera (JB):  </th> <td>   1.723</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.160</td> <th>  Prob(JB):          </th> <td>   0.422</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.442</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      x   R-squared:                       0.754\n",
       "Model:                            OLS   Adj. R-squared:                  0.752\n",
       "Method:                 Least Squares   F-statistic:                     303.8\n",
       "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           6.23e-32\n",
       "Time:                        08:23:29   Log-Likelihood:                -65.208\n",
       "No. Observations:                 100   AIC:                             132.4\n",
       "Df Residuals:                      99   BIC:                             135.0\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "y              0.3900      0.022     17.430      0.000       0.346       0.434\n",
       "==============================================================================\n",
       "Omnibus:                        2.172   Durbin-Watson:                   1.711\n",
       "Prob(Omnibus):                  0.338   Jarque-Bera (JB):                1.723\n",
       "Skew:                          -0.160   Prob(JB):                        0.422\n",
       "Kurtosis:                       2.442   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = smf.ols(formula=\"x ~ y - 1\", data=data).fit()\n",
    "\n",
    "mod.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coefficient and standard error are different, the t-statistic and p-value are the same in both cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why is the coefficient ~.04 and not .5?\n",
    "\n",
    "The equation from _a_ can be rewritten which shows the relationship:\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "y &= 2x+\\epsilon \\\\\n",
    "x &= \\frac 1 2 (y- \\epsilon)\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prove that\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "t = \\frac {\\hat\\beta} {SE(\\hat\\beta)} \\stackrel{?}{=} \\frac{\\sqrt{(n-1)}\\sum_{i=1}^n x_iy_i}{\\sqrt{(\\sum_{i=1}^n x_i^2)(\\sum_{i=1}^n y_i^2)-(\\sum_{i=1}^n x_iy_i)^2}}.\n",
    "\\end{align*}\n",
    "$$\n",
    "Proof:\n",
    "$$\n",
    "\\begin{align*}\n",
    "t &= \\frac {\\hat\\beta} {SE(\\hat\\beta)} \\\\\n",
    "&= \\frac{\\frac{\\sum_{i=1}^nx_iy_i}{\\sum_{i=1}^nx_i^2}}{\\sqrt{\\frac{(\\sum_{i=1}^ny_i-x_i\\frac{\\sum_{i=1}^nx_iy_i}{\\sum_{i=1}^nx_i^2})^2}{(n-1)\\sum_{i=1}^nx_i^2}}} \\\\\n",
    "&= \\frac{\\sqrt{n-1}\\sum_{i=1}^nx_iy_i}{\\sqrt{\\sum_{i=1}^nx_i^2}\\sqrt{\\sum_{i=1}^n(y_i^2-2x_iy_i\\frac{\\sum_{i=1}^nx_iy_i}{\\sum_{i=1}^nx_i^2}+x_i^2\\frac{(\\sum_{i=1}^nx_iy_i)^2}{(\\sum_{i=1}^nx_i^2)^2})}} \\\\\n",
    "&= \\frac{\\sqrt{(n-1)}\\sum_{i=1}^n x_iy_i}{\\sqrt{(\\sum_{i=1}^n x_i^2)(\\sum_{i=1}^n y_i^2)-(\\sum_{i=1}^n x_iy_i)^2}}.\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be easily seen from the result above the $x_i$s and $y_i$s only appear as products. Therefor (by commutativity) the results for the t-statistic are the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ommitted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. This problem involves simple linear regression without an intercept.\n",
    "    1. Recall that the coefficient estimate β̂ for the linear regression of Y onto X without an intercept is given by (3.38). Under what circumstance is the coefficient estimate for the regression of X onto Y the same as the coefficient estimate for the regression of Y onto X?\n",
    "    1. Generate an example in R with n = 100 observations in which the coefficient estimate for the regression of X onto Y is different from the coefficient estimate for the regression of Y onto X.\n",
    "    1. Generate an example in R with n = 100 observations in which the coefficient estimate for the regression of X onto Y is the same as the coefficient estimate for the regression of Y onto X.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The estimate $\\hat\\beta$ is to regress $y$ onto $x$ is given by\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\hat\\beta = \\frac{\\sum_{i=1}^nx_iy_i}{\\sum_{i'=1}^nx_{i'}^2}.\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "The answer results from equalling the above formula with the $\\hat\\beta$ needed to regress $x$ onto $y$:\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "\\frac{\\sum_{i=1}^nx_iy_i}{\\sum_{i'=1}^nx_{i'}^2} &= \\frac{\\sum_{i=1}^nx_iy_i}{\\sum_{i'=1}^ny_{i'}^2} \\\\\n",
    "\\sum_{i=1}^nx_i^2 &= \\sum_{i=1}^ny_i^2\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "Based on this equation the coefficient is the same when the sum of the squares of the individual observations are the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>x</td>        <th>  R-squared:         </th> <td>   0.851</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.850</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   565.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Nov 2018</td> <th>  Prob (F-statistic):</th> <td>9.87e-43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:23:29</td>     <th>  Log-Likelihood:    </th> <td> -54.167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   110.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    99</td>      <th>  BIC:               </th> <td>   112.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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>y</th> <td>    0.3932</td> <td>    0.017</td> <td>   23.789</td> <td> 0.000</td> <td>    0.360</td> <td>    0.426</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.847</td> <th>  Durbin-Watson:     </th> <td>   2.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.655</td> <th>  Jarque-Bera (JB):  </th> <td>   0.684</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.202</td> <th>  Prob(JB):          </th> <td>   0.710</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.987</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      x   R-squared:                       0.851\n",
       "Model:                            OLS   Adj. R-squared:                  0.850\n",
       "Method:                 Least Squares   F-statistic:                     565.9\n",
       "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           9.87e-43\n",
       "Time:                        08:23:29   Log-Likelihood:                -54.167\n",
       "No. Observations:                 100   AIC:                             110.3\n",
       "Df Residuals:                      99   BIC:                             112.9\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "y              0.3932      0.017     23.789      0.000       0.360       0.426\n",
       "==============================================================================\n",
       "Omnibus:                        0.847   Durbin-Watson:                   2.058\n",
       "Prob(Omnibus):                  0.655   Jarque-Bera (JB):                0.684\n",
       "Skew:                           0.202   Prob(JB):                        0.710\n",
       "Kurtosis:                       2.987   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.random.normal(size=100)\n",
    "y = 2 * x + np.random.normal(size=100)\n",
    "data = pd.DataFrame({'x':x,'y':y})\n",
    "mod = smf.ols(formula=\"x ~ y - 1\", data=data).fit()\n",
    "mod.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(116.18227835524182, 639.5096823898015)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xSum = np.sum(x**2)\n",
    "ySum = np.sum(y**2)\n",
    "xSum,ySum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(639.5096823898015, 639.5096823898015)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newX = np.append(x,np.sqrt(ySum-xSum))\n",
    "newY = np.append(y,0)\n",
    "np.sum(newX**2),np.sum(newY**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    0.393223\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({'x':newX,'y':newY})\n",
    "smf.ols(formula=\"y ~ x - 1\", data=data).fit().params # returns the coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y    0.393223\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smf.ols(formula=\"x ~ y - 1\", data=data).fit().params # returns the coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. In this exercise you will create some simulated data and will fit simple linear regression models to it. Make sure to use `random.seed(1)` prior to starting part (a) to ensure consistent results. \n",
    "\n",
    "__a.__ Using the `numpy.random.normal`  function, create a vector, $x$ , containing 100 observations drawn from a $N(0, 1)$ distribution. This represents a feature, $X$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(1)\n",
    "x = np.random.normal(size=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__b.__ Using the rnorm() function, create a vector, eps , containing 100 observations drawn from a N (0, 0.25) distribution i.e. a normal distribution with mean zero and variance 0.25."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps = np.random.normal(0,0.25,100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__c.__ Using $x$ and $eps$, generate a vector $y$ according to the model  \n",
    "    $$Y = -1 + 0.5X+\\epsilon$$\n",
    "    What is the length of the vector $y$? What are the values of $\\beta_0$ and $\\beta_1$ in this linear model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = -1 + 0.5*x + eps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The length of $y$ is 100, the values are $\\beta_0 = -1$ and $\\beta_1 = 0.5.$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__d.__ Create a scatterplot displaying the relationship between $x$ and $y$. Comment on what you observe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad38d0fd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__e.__ Fit a least squares linear model to predict y using x . Comment on the model obtained. How do $\\hat\\beta_0$ and $\\hat\\beta_1$ compare to $\\beta_0$ and $\\beta_1$?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept   -1.045736\n",
       "x            0.479943\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'x' : x, 'y':y})\n",
    "mod = smf.ols(formula='y ~ x',data=df).fit()\n",
    "mod.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The estimated parameters $\\hat\\beta_0 = -0.97, \\hat\\beta_1 = 0.51$ are very close to the actual parameters $\\beta_0 = -1, \\beta_1 = 0.5$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__f.__ Display the least squares line on the scatterplot obtained in __(d)__. Draw the population regression line on the plot, in a different color. Use the `plt.legend()` command to create an appropriate legend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad38d3c320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,-1+0.5*x,color='r',label='population regression')\n",
    "plt.plot(x,mod.params[0] + mod.params[1]*x, label='least squares regression')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__g.__ Now fit a polynomial regression model that predicts $y$ using $x$ and $x^2$. Is there evidence that the quadratic term improves the model fit? Explain your answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.843</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.840</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   260.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Nov 2018</td> <th>  Prob (F-statistic):</th> <td>9.38e-40</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:23:30</td>     <th>  Log-Likelihood:    </th> <td> 0.53509</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   4.930</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    97</td>      <th>  BIC:               </th> <td>   12.75</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</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>Intercept</th> <td>   -1.0303</td> <td>    0.030</td> <td>  -34.111</td> <td> 0.000</td> <td>   -1.090</td> <td>   -0.970</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x</th>         <td>    0.4796</td> <td>    0.021</td> <td>   22.802</td> <td> 0.000</td> <td>    0.438</td> <td>    0.521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>        <td>   -0.0115</td> <td>    0.013</td> <td>   -0.872</td> <td> 0.385</td> <td>   -0.038</td> <td>    0.015</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.315</td> <th>  Durbin-Watson:     </th> <td>   2.185</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.518</td> <th>  Jarque-Bera (JB):  </th> <td>   1.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.065</td> <th>  Prob(JB):          </th> <td>   0.564</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.492</td> <th>  Cond. No.          </th> <td>    3.05</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.843\n",
       "Model:                            OLS   Adj. R-squared:                  0.840\n",
       "Method:                 Least Squares   F-statistic:                     260.8\n",
       "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           9.38e-40\n",
       "Time:                        08:23:30   Log-Likelihood:                0.53509\n",
       "No. Observations:                 100   AIC:                             4.930\n",
       "Df Residuals:                      97   BIC:                             12.75\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     -1.0303      0.030    -34.111      0.000      -1.090      -0.970\n",
       "x              0.4796      0.021     22.802      0.000       0.438       0.521\n",
       "x2            -0.0115      0.013     -0.872      0.385      -0.038       0.015\n",
       "==============================================================================\n",
       "Omnibus:                        1.315   Durbin-Watson:                   2.185\n",
       "Prob(Omnibus):                  0.518   Jarque-Bera (JB):                1.144\n",
       "Skew:                           0.065   Prob(JB):                        0.564\n",
       "Kurtosis:                       2.492   Cond. No.                         3.05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['x2'] = df['x']**2\n",
    "mod_quad = smf.ols(formula='y ~ x + x2', data=df).fit()\n",
    "mod_quad.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the p-value there is no evidence of a quadratic relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__h.__ Repeat __(a)–(f)__ after modifying the data generation process in such a way that there is less noise in the data. The model (3.39) should remain the same. You can do this by decreasing the variance of the normal distribution used to generate the error term $\\epsilon$ in __(b)__. Describe your results.\n",
    "\n",
    "__Answer:__ We would excpect the estimated values for the coefficients to be closer to the actual values with less noise in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept   -0.986963\n",
       "x            0.510085\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eps = np.random.normal(0,0.1,size = 100); y = -1 + 0.5 * x + eps\n",
    "mod2 = smf.ols(formula='y ~ x',data=pd.DataFrame({'x' : x, 'y':y})).fit()\n",
    "mod2.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__i.__ Repeat __(a)–(f)__ after modifying the data generation process in such a way that there is more noise in the data. The model (3.39) should remain the same. You can do this by increasing the variance of the normal distribution used to generate the error term $\\epsilon$ in __(b)__. Describe your results.\n",
    "\n",
    "__Answer:__ We would expect the estimated values to be farther away from the actual values of the coefficients with more noise in the data because the true relationship can't be captured so easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept   -0.946890\n",
       "x            0.496754\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eps = np.random.normal(0,1,size = 100); y = -1 + 0.5 * x + eps\n",
    "mod3 = smf.ols(formula='y ~ x',data=pd.DataFrame({'x' : x, 'y':y})).fit()\n",
    "mod3.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__j.__ What are the confidence intervals for $\\beta_0$ and $\\beta_1$ based on the original data set, the noisier data set, and the less noisy data set? Comment on your results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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",
       "    .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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>-1.094183</td>\n",
       "      <td>-0.997289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.438264</td>\n",
       "      <td>0.521621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1\n",
       "Intercept -1.094183 -0.997289\n",
       "x          0.438264  0.521621"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>-1.006795</td>\n",
       "      <td>-0.967131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.493023</td>\n",
       "      <td>0.527146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1\n",
       "Intercept -1.006795 -0.967131\n",
       "x          0.493023  0.527146"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod2.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>-1.132123</td>\n",
       "      <td>-0.761658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.337400</td>\n",
       "      <td>0.656109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1\n",
       "Intercept -1.132123 -0.761658\n",
       "x          0.337400  0.656109"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod3.conf_int()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lengths of the confidence intervals becomes larger as the noise increases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This problem focuses on the collinearity problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__a.__ Perform the following commands in R:  \n",
    "```python\n",
    "random.seed(1)\n",
    "x1 = np.random.uniform(size=100)\n",
    "x2 = 0.5 * x1 + np.random.normal(size=100) / 10\n",
    "y  = 2 + 2 * x1 + 0.3 * x2 + np.random.normal(size=100)```  \n",
    "The last line corresponds to creating a linear model in which $y$ is a function of $x_1$ and $x_2$. Write out the form of the model. What are the regression coefficients?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(1)\n",
    "x1 = np.random.uniform(size=100)\n",
    "x2 = 0.5 * x1 + np.random.normal(size=100) / 10\n",
    "y  = 2 + 2 * x1 + 0.3 * x2 + np.random.normal(size=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is $Y = 2 + 2 X_1 + 0.3 X_2 + \\epsilon$. The regression coefficients are $\\beta_0 = 2$, $\\beta_1 = 2$ and $\\beta_3 = 0.3$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__b.__ What is the correlation between $x_1$ and $x_2$ ? Create a scatterplot displaying the relationship between the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.79679051],\n",
       "       [0.79679051, 1.        ]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(x1,x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fad38b9e320>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad38d0fcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x1,x2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__c.__ Using this data, fit a least squares regression to predict $y$ using $x_1$ and $x_2$. Describe the results obtained. What are $\\hat\\beta_0$, $\\hat\\beta_1$, and $\\hat\\beta_2$? How do these relate to the true $\\beta_0, \\beta_1, \\beta_2$ ? Can you reject the null hypothesis $H_0 : \\beta_1 = 0$? How about the null hypothesis $H_0 : \\beta_2 = 0$?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.264</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.249</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   17.40</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Nov 2018</td> <th>  Prob (F-statistic):</th> <td>3.50e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:23:30</td>     <th>  Log-Likelihood:    </th> <td> -137.99</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   282.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    97</td>      <th>  BIC:               </th> <td>   289.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</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>Intercept</th> <td>    1.9728</td> <td>    0.204</td> <td>    9.674</td> <td> 0.000</td> <td>    1.568</td> <td>    2.378</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>        <td>    2.5894</td> <td>    0.574</td> <td>    4.515</td> <td> 0.000</td> <td>    1.451</td> <td>    3.728</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>        <td>   -1.1111</td> <td>    0.853</td> <td>   -1.303</td> <td> 0.196</td> <td>   -2.803</td> <td>    0.581</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.127</td> <th>  Durbin-Watson:     </th> <td>   1.891</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.569</td> <th>  Jarque-Bera (JB):  </th> <td>   0.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.209</td> <th>  Prob(JB):          </th> <td>   0.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.113</td> <th>  Cond. No.          </th> <td>    11.8</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.264\n",
       "Model:                            OLS   Adj. R-squared:                  0.249\n",
       "Method:                 Least Squares   F-statistic:                     17.40\n",
       "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           3.50e-07\n",
       "Time:                        08:23:30   Log-Likelihood:                -137.99\n",
       "No. Observations:                 100   AIC:                             282.0\n",
       "Df Residuals:                      97   BIC:                             289.8\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      1.9728      0.204      9.674      0.000       1.568       2.378\n",
       "x1             2.5894      0.574      4.515      0.000       1.451       3.728\n",
       "x2            -1.1111      0.853     -1.303      0.196      -2.803       0.581\n",
       "==============================================================================\n",
       "Omnibus:                        1.127   Durbin-Watson:                   1.891\n",
       "Prob(Omnibus):                  0.569   Jarque-Bera (JB):                0.784\n",
       "Skew:                           0.209   Prob(JB):                        0.676\n",
       "Kurtosis:                       3.113   Cond. No.                         11.8\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = smf.ols(formula = 'y ~ x1 + x2',data=pd.DataFrame({'x1':x1, 'x2': x2, 'y' : y})).fit()\n",
    "mod.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values for $\\hat\\beta_0, \\hat\\beta_1 $ and $\\hat\\beta_2$ are $1.96, 2.36$ and $-0.16$ respectively. The hypothesis $H_0 : b_2 = 0$ can be rejected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__d.__ Now fit a least squares regression to predict $y$ using only $x_1$ .Comment on your results. Can you reject the null hypothesis $H_0 : \\beta_1 = 0$?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.251</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   32.86</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Nov 2018</td> <th>  Prob (F-statistic):</th> <td>1.10e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:23:30</td>     <th>  Log-Likelihood:    </th> <td> -138.86</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   281.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    98</td>      <th>  BIC:               </th> <td>   286.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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>Intercept</th> <td>    1.9773</td> <td>    0.205</td> <td>    9.663</td> <td> 0.000</td> <td>    1.571</td> <td>    2.383</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>        <td>    1.9938</td> <td>    0.348</td> <td>    5.732</td> <td> 0.000</td> <td>    1.304</td> <td>    2.684</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.108</td> <th>  Durbin-Watson:     </th> <td>   1.929</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.575</td> <th>  Jarque-Bera (JB):  </th> <td>   0.942</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.237</td> <th>  Prob(JB):          </th> <td>   0.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.973</td> <th>  Cond. No.          </th> <td>    4.56</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.251\n",
       "Model:                            OLS   Adj. R-squared:                  0.243\n",
       "Method:                 Least Squares   F-statistic:                     32.86\n",
       "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           1.10e-07\n",
       "Time:                        08:23:30   Log-Likelihood:                -138.86\n",
       "No. Observations:                 100   AIC:                             281.7\n",
       "Df Residuals:                      98   BIC:                             286.9\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      1.9773      0.205      9.663      0.000       1.571       2.383\n",
       "x1             1.9938      0.348      5.732      0.000       1.304       2.684\n",
       "==============================================================================\n",
       "Omnibus:                        1.108   Durbin-Watson:                   1.929\n",
       "Prob(Omnibus):                  0.575   Jarque-Bera (JB):                0.942\n",
       "Skew:                           0.237   Prob(JB):                        0.624\n",
       "Kurtosis:                       2.973   Cond. No.                         4.56\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = smf.ols(formula = 'y ~ x1',data=pd.DataFrame({'x1':x1, 'x2': x2, 'y' : y})).fit()\n",
    "mod.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hypothesis can't be rejected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__e.__ Now fit a least squares regression to predict $y$ using only $x_2$. Comment on your results. Can you reject the null hypothesis $H_0 : \\beta_1 = 0$?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   12.03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Nov 2018</td> <th>  Prob (F-statistic):</th> <td>0.000779</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:23:30</td>     <th>  Log-Likelihood:    </th> <td> -147.53</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   299.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    98</td>      <th>  BIC:               </th> <td>   304.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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>Intercept</th> <td>    2.4734</td> <td>    0.187</td> <td>   13.205</td> <td> 0.000</td> <td>    2.102</td> <td>    2.845</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>        <td>    1.9556</td> <td>    0.564</td> <td>    3.469</td> <td> 0.001</td> <td>    0.837</td> <td>    3.074</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.338</td> <th>  Durbin-Watson:     </th> <td>   1.928</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.844</td> <th>  Jarque-Bera (JB):  </th> <td>   0.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.122</td> <th>  Prob(JB):          </th> <td>   0.880</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.963</td> <th>  Cond. No.          </th> <td>    5.68</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.109\n",
       "Model:                            OLS   Adj. R-squared:                  0.100\n",
       "Method:                 Least Squares   F-statistic:                     12.03\n",
       "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           0.000779\n",
       "Time:                        08:23:30   Log-Likelihood:                -147.53\n",
       "No. Observations:                 100   AIC:                             299.1\n",
       "Df Residuals:                      98   BIC:                             304.3\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.4734      0.187     13.205      0.000       2.102       2.845\n",
       "x2             1.9556      0.564      3.469      0.001       0.837       3.074\n",
       "==============================================================================\n",
       "Omnibus:                        0.338   Durbin-Watson:                   1.928\n",
       "Prob(Omnibus):                  0.844   Jarque-Bera (JB):                0.255\n",
       "Skew:                           0.122   Prob(JB):                        0.880\n",
       "Kurtosis:                       2.963   Cond. No.                         5.68\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = smf.ols(formula = 'y ~ x2',data=pd.DataFrame({'x1':x1, 'x2': x2, 'y' : y})).fit()\n",
    "mod.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same as above, the hypothesis can't be rejected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__f.__ Do the results obtained in __(c)–(e)__ contradict each other? Explain your answer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results do not contradict each other. Using both, $x_1$ and $x_2$ as predictors makes no sense because both values are highly correlated - the predictive power of the model does not improve. When based only on one predictor each one has a high predictiveness of the outcome."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__g.__ Now suppose we obtain one additional observation, which was unfortunately mismeasured.\n",
    "```python\n",
    "x1 = np.append(x1, 0.1)\n",
    "x2 = np.append(x2, 0.8)\n",
    "y = np.append(y, 6)\n",
    "```\n",
    "\n",
    "Re-fit the linear models from __(c)__ to __(e)__ using this new data. What effect does this new observation have on the each of the models? In each model, is this observation an outlier? A high-leverage point? Both? Explain your answers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.append(x1, 0.1)\n",
    "x2 = np.append(x2, 0.8)\n",
    "y = np.append(y, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ommitted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This problem involves the Boston data set, which we saw in the lab\n",
    "for this chapter. We will now try to predict per capita crime rate\n",
    "using the other variables in this data set. In other words, per capita\n",
    "crime rate is the response, and the other variables are the predictors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__a.__ For each predictor, fit a simple linear regression model to predict\n",
    "the response. Describe your results. In which of the models is\n",
    "there a statistically significant association between the predictor\n",
    "and the response? Create some plots to back up your assertions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>black</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      crim    zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio  \\\n",
       "1  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "2  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "3  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "4  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "5  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "    black  lstat  medv  \n",
       "1  396.90   4.98  24.0  \n",
       "2  396.90   9.14  21.6  \n",
       "3  392.83   4.03  34.7  \n",
       "4  394.63   2.94  33.4  \n",
       "5  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston = pd.read_csv(f'{PATH}Boston.csv',index_col=0)\n",
    "boston.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad38b04748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = boston.drop('crim',axis=1).columns\n",
    "\n",
    "_, axes = plt.subplots(nrows = len(predictors),figsize=(16,12))\n",
    "coefs = {}\n",
    "models = []\n",
    "for i, predictor in enumerate(predictors):\n",
    "    mod = smf.ols(formula = f'crim ~ {predictor}',data = boston).fit()\n",
    "    models.append(mod)\n",
    "    coefs[predictor] = mod.params[1] # store coef for later use\n",
    "    axes[i].scatter(boston[predictor],boston['crim'])\n",
    "    axes[i].set_title(predictor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   crim   R-squared:                       0.040\n",
      "Model:                            OLS   Adj. R-squared:                  0.038\n",
      "Method:                 Least Squares   F-statistic:                     21.10\n",
      "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           5.51e-06\n",
      "Time:                        08:23:31   Log-Likelihood:                -1796.0\n",
      "No. Observations:                 506   AIC:                             3596.\n",
      "Df Residuals:                     504   BIC:                             3604.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      4.4537      0.417     10.675      0.000       3.634       5.273\n",
      "zn            -0.0739      0.016     -4.594      0.000      -0.106      -0.042\n",
      "==============================================================================\n",
      "Omnibus:                      567.443   Durbin-Watson:                   0.857\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            32753.004\n",
      "Skew:                           5.257   Prob(JB):                         0.00\n",
      "Kurtosis:                      40.986   Cond. No.                         28.8\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   crim   R-squared:                       0.165\n",
      "Model:                            OLS   Adj. R-squared:                  0.164\n",
      "Method:                 Least Squares   F-statistic:                     99.82\n",
      "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           1.45e-21\n",
      "Time:                        08:23:31   Log-Likelihood:                -1760.6\n",
      "No. Observations:                 506   AIC:                             3525.\n",
      "Df Residuals:                     504   BIC:                             3534.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -2.0637      0.667     -3.093      0.002      -3.375      -0.753\n",
      "indus          0.5098      0.051      9.991      0.000       0.410       0.610\n",
      "==============================================================================\n",
      "Omnibus:                      585.118   Durbin-Watson:                   0.986\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            41418.938\n",
      "Skew:                           5.449   Prob(JB):                         0.00\n",
      "Kurtosis:                      45.962   Cond. No.                         25.1\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   crim   R-squared:                       0.003\n",
      "Model:                            OLS   Adj. R-squared:                  0.001\n",
      "Method:                 Least Squares   F-statistic:                     1.579\n",
      "Date:                Mon, 26 Nov 2018   Prob (F-statistic):              0.209\n",
      "Time:                        08:23:31   Log-Likelihood:                -1805.6\n",
      "No. Observations:                 506   AIC:                             3615.\n",
      "Df Residuals:                     504   BIC:                             3624.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      3.7444      0.396      9.453      0.000       2.966       4.523\n",
      "chas          -1.8928      1.506     -1.257      0.209      -4.852       1.066\n",
      "==============================================================================\n",
      "Omnibus:                      561.663   Durbin-Watson:                   0.817\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            30645.429\n",
      "Skew:                           5.191   Prob(JB):                         0.00\n",
      "Kurtosis:                      39.685   Cond. No.                         3.96\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   crim   R-squared:                       0.177\n",
      "Model:                            OLS   Adj. R-squared:                  0.176\n",
      "Method:                 Least Squares   F-statistic:                     108.6\n",
      "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           3.75e-23\n",
      "Time:                        08:23:31   Log-Likelihood:                -1757.0\n",
      "No. Observations:                 506   AIC:                             3518.\n",
      "Df Residuals:                     504   BIC:                             3526.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    -13.7199      1.699     -8.073      0.000     -17.059     -10.381\n",
      "nox           31.2485      2.999     10.419      0.000      25.356      37.141\n",
      "==============================================================================\n",
      "Omnibus:                      591.712   Durbin-Watson:                   0.992\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            43138.106\n",
      "Skew:                           5.546   Prob(JB):                         0.00\n",
      "Kurtosis:                      46.852   Cond. No.                         11.3\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   crim   R-squared:                       0.048\n",
      "Model:                            OLS   Adj. R-squared:                  0.046\n",
      "Method:                 Least Squares   F-statistic:                     25.45\n",
      "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           6.35e-07\n",
      "Time:                        08:23:31   Log-Likelihood:                -1793.9\n",
      "No. Observations:                 506   AIC:                             3592.\n",
      "Df Residuals:                     504   BIC:                             3600.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     20.4818      3.364      6.088      0.000      13.872      27.092\n",
      "rm            -2.6841      0.532     -5.045      0.000      -3.729      -1.639\n",
      "==============================================================================\n",
      "Omnibus:                      575.717   Durbin-Watson:                   0.879\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            36658.093\n",
      "Skew:                           5.345   Prob(JB):                         0.00\n",
      "Kurtosis:                      43.305   Cond. No.                         58.4\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   crim   R-squared:                       0.124\n",
      "Model:                            OLS   Adj. R-squared:                  0.123\n",
      "Method:                 Least Squares   F-statistic:                     71.62\n",
      "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           2.85e-16\n",
      "Time:                        08:23:31   Log-Likelihood:                -1772.7\n",
      "No. Observations:                 506   AIC:                             3549.\n",
      "Df Residuals:                     504   BIC:                             3558.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -3.7779      0.944     -4.002      0.000      -5.633      -1.923\n",
      "age            0.1078      0.013      8.463      0.000       0.083       0.133\n",
      "==============================================================================\n",
      "Omnibus:                      574.509   Durbin-Watson:                   0.956\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            36741.903\n",
      "Skew:                           5.322   Prob(JB):                         0.00\n",
      "Kurtosis:                      43.366   Cond. No.                         195.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   crim   R-squared:                       0.144\n",
      "Model:                            OLS   Adj. R-squared:                  0.142\n",
      "Method:                 Least Squares   F-statistic:                     84.89\n",
      "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           8.52e-19\n",
      "Time:                        08:23:31   Log-Likelihood:                -1767.0\n",
      "No. Observations:                 506   AIC:                             3538.\n",
      "Df Residuals:                     504   BIC:                             3546.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      9.4993      0.730     13.006      0.000       8.064      10.934\n",
      "dis           -1.5509      0.168     -9.213      0.000      -1.882      -1.220\n",
      "==============================================================================\n",
      "Omnibus:                      576.519   Durbin-Watson:                   0.952\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            37426.729\n",
      "Skew:                           5.348   Prob(JB):                         0.00\n",
      "Kurtosis:                      43.753   Cond. No.                         9.32\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   crim   R-squared:                       0.391\n",
      "Model:                            OLS   Adj. R-squared:                  0.390\n",
      "Method:                 Least Squares   F-statistic:                     323.9\n",
      "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           2.69e-56\n",
      "Time:                        08:23:31   Log-Likelihood:                -1680.8\n",
      "No. Observations:                 506   AIC:                             3366.\n",
      "Df Residuals:                     504   BIC:                             3374.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -2.2872      0.443     -5.157      0.000      -3.158      -1.416\n",
      "rad            0.6179      0.034     17.998      0.000       0.550       0.685\n",
      "==============================================================================\n",
      "Omnibus:                      656.459   Durbin-Watson:                   1.337\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            75417.007\n",
      "Skew:                           6.478   Prob(JB):                         0.00\n",
      "Kurtosis:                      61.389   Cond. No.                         19.2\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   crim   R-squared:                       0.340\n",
      "Model:                            OLS   Adj. R-squared:                  0.338\n",
      "Method:                 Least Squares   F-statistic:                     259.2\n",
      "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           2.36e-47\n",
      "Time:                        08:23:31   Log-Likelihood:                -1701.4\n",
      "No. Observations:                 506   AIC:                             3407.\n",
      "Df Residuals:                     504   BIC:                             3415.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -8.5284      0.816    -10.454      0.000     -10.131      -6.926\n",
      "tax            0.0297      0.002     16.099      0.000       0.026       0.033\n",
      "==============================================================================\n",
      "Omnibus:                      635.377   Durbin-Watson:                   1.252\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            63763.835\n",
      "Skew:                           6.156   Prob(JB):                         0.00\n",
      "Kurtosis:                      56.599   Cond. No.                     1.16e+03\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, 1.16e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   crim   R-squared:                       0.084\n",
      "Model:                            OLS   Adj. R-squared:                  0.082\n",
      "Method:                 Least Squares   F-statistic:                     46.26\n",
      "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           2.94e-11\n",
      "Time:                        08:23:31   Log-Likelihood:                -1784.1\n",
      "No. Observations:                 506   AIC:                             3572.\n",
      "Df Residuals:                     504   BIC:                             3581.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    -17.6469      3.147     -5.607      0.000     -23.830     -11.464\n",
      "ptratio        1.1520      0.169      6.801      0.000       0.819       1.485\n",
      "==============================================================================\n",
      "Omnibus:                      568.053   Durbin-Watson:                   0.905\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            34221.853\n",
      "Skew:                           5.245   Prob(JB):                         0.00\n",
      "Kurtosis:                      41.899   Cond. No.                         160.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   crim   R-squared:                       0.148\n",
      "Model:                            OLS   Adj. R-squared:                  0.147\n",
      "Method:                 Least Squares   F-statistic:                     87.74\n",
      "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           2.49e-19\n",
      "Time:                        08:23:31   Log-Likelihood:                -1765.8\n",
      "No. Observations:                 506   AIC:                             3536.\n",
      "Df Residuals:                     504   BIC:                             3544.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     16.5535      1.426     11.609      0.000      13.752      19.355\n",
      "black         -0.0363      0.004     -9.367      0.000      -0.044      -0.029\n",
      "==============================================================================\n",
      "Omnibus:                      594.029   Durbin-Watson:                   0.994\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            44041.935\n",
      "Skew:                           5.578   Prob(JB):                         0.00\n",
      "Kurtosis:                      47.323   Cond. No.                     1.49e+03\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, 1.49e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   crim   R-squared:                       0.208\n",
      "Model:                            OLS   Adj. R-squared:                  0.206\n",
      "Method:                 Least Squares   F-statistic:                     132.0\n",
      "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           2.65e-27\n",
      "Time:                        08:23:31   Log-Likelihood:                -1747.5\n",
      "No. Observations:                 506   AIC:                             3499.\n",
      "Df Residuals:                     504   BIC:                             3507.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -3.3305      0.694     -4.801      0.000      -4.694      -1.968\n",
      "lstat          0.5488      0.048     11.491      0.000       0.455       0.643\n",
      "==============================================================================\n",
      "Omnibus:                      601.306   Durbin-Watson:                   1.182\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            49918.826\n",
      "Skew:                           5.645   Prob(JB):                         0.00\n",
      "Kurtosis:                      50.331   Cond. No.                         29.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   crim   R-squared:                       0.151\n",
      "Model:                            OLS   Adj. R-squared:                  0.149\n",
      "Method:                 Least Squares   F-statistic:                     89.49\n",
      "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           1.17e-19\n",
      "Time:                        08:23:31   Log-Likelihood:                -1765.0\n",
      "No. Observations:                 506   AIC:                             3534.\n",
      "Df Residuals:                     504   BIC:                             3542.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     11.7965      0.934     12.628      0.000       9.961      13.632\n",
      "medv          -0.3632      0.038     -9.460      0.000      -0.439      -0.288\n",
      "==============================================================================\n",
      "Omnibus:                      558.880   Durbin-Watson:                   0.996\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            32740.044\n",
      "Skew:                           5.108   Prob(JB):                         0.00\n",
      "Kurtosis:                      41.059   Cond. No.                         64.5\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "for mod in models:\n",
    "    print(mod.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a significant association between 'crim' and:\n",
    "- zn\n",
    "- indus\n",
    "- nox\n",
    "- rm\n",
    "- age\n",
    "- dis\n",
    "- rad\n",
    "- tax\n",
    "- ptratio\n",
    "- black\n",
    "- lstat\n",
    "- medv\n",
    "The is no significant association between 'crim' and 'chas'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__b.__ Fit a multiple regression model to predict the response using all of the predictors. Describe your results. For which predictors can we reject the null hypothesis $H_0 : \\beta_j = 0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>crim</td>       <th>  R-squared:         </th> <td>   0.454</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.440</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   31.47</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Nov 2018</td> <th>  Prob (F-statistic):</th> <td>1.57e-56</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:23:31</td>     <th>  Log-Likelihood:    </th> <td> -1653.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3335.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   492</td>      <th>  BIC:               </th> <td>   3394.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    13</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>Intercept</th> <td>   17.0332</td> <td>    7.235</td> <td>    2.354</td> <td> 0.019</td> <td>    2.818</td> <td>   31.248</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zn</th>        <td>    0.0449</td> <td>    0.019</td> <td>    2.394</td> <td> 0.017</td> <td>    0.008</td> <td>    0.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>indus</th>     <td>   -0.0639</td> <td>    0.083</td> <td>   -0.766</td> <td> 0.444</td> <td>   -0.228</td> <td>    0.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>chas</th>      <td>   -0.7491</td> <td>    1.180</td> <td>   -0.635</td> <td> 0.526</td> <td>   -3.068</td> <td>    1.570</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nox</th>       <td>  -10.3135</td> <td>    5.276</td> <td>   -1.955</td> <td> 0.051</td> <td>  -20.679</td> <td>    0.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rm</th>        <td>    0.4301</td> <td>    0.613</td> <td>    0.702</td> <td> 0.483</td> <td>   -0.774</td> <td>    1.634</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>    0.0015</td> <td>    0.018</td> <td>    0.081</td> <td> 0.935</td> <td>   -0.034</td> <td>    0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dis</th>       <td>   -0.9872</td> <td>    0.282</td> <td>   -3.503</td> <td> 0.001</td> <td>   -1.541</td> <td>   -0.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rad</th>       <td>    0.5882</td> <td>    0.088</td> <td>    6.680</td> <td> 0.000</td> <td>    0.415</td> <td>    0.761</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tax</th>       <td>   -0.0038</td> <td>    0.005</td> <td>   -0.733</td> <td> 0.464</td> <td>   -0.014</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ptratio</th>   <td>   -0.2711</td> <td>    0.186</td> <td>   -1.454</td> <td> 0.147</td> <td>   -0.637</td> <td>    0.095</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>black</th>     <td>   -0.0075</td> <td>    0.004</td> <td>   -2.052</td> <td> 0.041</td> <td>   -0.015</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lstat</th>     <td>    0.1262</td> <td>    0.076</td> <td>    1.667</td> <td> 0.096</td> <td>   -0.023</td> <td>    0.275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>medv</th>      <td>   -0.1989</td> <td>    0.061</td> <td>   -3.287</td> <td> 0.001</td> <td>   -0.318</td> <td>   -0.080</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>666.613</td> <th>  Durbin-Watson:     </th> <td>   1.519</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>84887.625</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 6.617</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>65.058</td>  <th>  Cond. No.          </th> <td>1.58e+04</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   crim   R-squared:                       0.454\n",
       "Model:                            OLS   Adj. R-squared:                  0.440\n",
       "Method:                 Least Squares   F-statistic:                     31.47\n",
       "Date:                Mon, 26 Nov 2018   Prob (F-statistic):           1.57e-56\n",
       "Time:                        08:23:31   Log-Likelihood:                -1653.3\n",
       "No. Observations:                 506   AIC:                             3335.\n",
       "Df Residuals:                     492   BIC:                             3394.\n",
       "Df Model:                          13                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     17.0332      7.235      2.354      0.019       2.818      31.248\n",
       "zn             0.0449      0.019      2.394      0.017       0.008       0.082\n",
       "indus         -0.0639      0.083     -0.766      0.444      -0.228       0.100\n",
       "chas          -0.7491      1.180     -0.635      0.526      -3.068       1.570\n",
       "nox          -10.3135      5.276     -1.955      0.051     -20.679       0.052\n",
       "rm             0.4301      0.613      0.702      0.483      -0.774       1.634\n",
       "age            0.0015      0.018      0.081      0.935      -0.034       0.037\n",
       "dis           -0.9872      0.282     -3.503      0.001      -1.541      -0.433\n",
       "rad            0.5882      0.088      6.680      0.000       0.415       0.761\n",
       "tax           -0.0038      0.005     -0.733      0.464      -0.014       0.006\n",
       "ptratio       -0.2711      0.186     -1.454      0.147      -0.637       0.095\n",
       "black         -0.0075      0.004     -2.052      0.041      -0.015      -0.000\n",
       "lstat          0.1262      0.076      1.667      0.096      -0.023       0.275\n",
       "medv          -0.1989      0.061     -3.287      0.001      -0.318      -0.080\n",
       "==============================================================================\n",
       "Omnibus:                      666.613   Durbin-Watson:                   1.519\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            84887.625\n",
       "Skew:                           6.617   Prob(JB):                         0.00\n",
       "Kurtosis:                      65.058   Cond. No.                     1.58e+04\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, 1.58e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = smf.ols(formula = 'crim ~ zn + indus + chas + nox + rm + age + dis + rad + tax + ptratio + black + lstat + medv', data = boston).fit()\n",
    "mod.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only significant predictors in this model are _dis_, _rad_ and _medv_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__c.__ How do your results from (a) compare to your results from (b)?\n",
    "Create a plot displaying the univariate regression coefficients\n",
    "from (a) on the x-axis, and the multiple regression coefficients\n",
    "from (b) on the y-axis. That is, each predictor is displayed as a\n",
    "single point in the plot. Its coefficient in a simple linear regres-\n",
    "sion model is shown on the x-axis, and its coefficient estimate\n",
    "in the multiple linear regression model is shown on the y-axis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__c.__ How do your results from (a) compare to your results from (b)?\n",
    "Create a plot displaying the univariate regression coefficients\n",
    "from (a) on the x-axis, and the multiple regression coefficients\n",
    "from (b) on the y-axis. That is, each predictor is displayed as a\n",
    "single point in the plot. Its coefficient in a simple linear regres-\n",
    "sion model is shown on the x-axis, and its coefficient estimate\n",
    "in the multiple linear regression model is shown on the y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fad387e3518>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad387e3550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k,v in coefs.items():\n",
    "    plt.scatter(v,mod.params[k],label=k)\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__d.__ Is there evidence of non-linear association between any of the\n",
    "predictors and the response? To answer this question, for each\n",
    "predictor X, fit a model of the form\n",
    "$Y = \\beta_0 + \\beta_1 X + \\beta_2X^2 + \\beta_3X^3 + \\epsilon$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept          4.113000e-26\n",
      "zn                 2.612296e-03\n",
      "np.power(zn, 2)    9.375050e-02\n",
      "np.power(zn, 3)    2.295386e-01\n",
      "dtype: float64\n",
      "Intercept             2.036526e-02\n",
      "indus                 5.297064e-05\n",
      "np.power(indus, 2)    3.420187e-10\n",
      "np.power(indus, 3)    1.196405e-12\n",
      "dtype: float64\n",
      "Intercept            1.239505e-19\n",
      "chas                 2.094345e-01\n",
      "np.power(chas, 2)    2.094345e-01\n",
      "np.power(chas, 3)    2.094345e-01\n",
      "dtype: float64\n",
      "Intercept           1.311939e-11\n",
      "nox                 2.758372e-13\n",
      "np.power(nox, 2)    6.811300e-15\n",
      "np.power(nox, 3)    6.961110e-16\n",
      "dtype: float64\n",
      "Intercept          0.081483\n",
      "rm                 0.211756\n",
      "np.power(rm, 2)    0.364109\n",
      "np.power(rm, 3)    0.508575\n",
      "dtype: float64\n",
      "Intercept           0.357797\n",
      "age                 0.142661\n",
      "np.power(age, 2)    0.047377\n",
      "np.power(age, 3)    0.006680\n",
      "dtype: float64\n",
      "Intercept           1.633088e-30\n",
      "dis                 6.374792e-18\n",
      "np.power(dis, 2)    4.941214e-12\n",
      "np.power(dis, 3)    1.088832e-08\n",
      "dtype: float64\n",
      "Intercept           0.767832\n",
      "rad                 0.623418\n",
      "np.power(rad, 2)    0.613010\n",
      "np.power(rad, 3)    0.482314\n",
      "dtype: float64\n",
      "Intercept           0.104505\n",
      "tax                 0.109708\n",
      "np.power(tax, 2)    0.137468\n",
      "np.power(tax, 3)    0.243851\n",
      "dtype: float64\n",
      "Intercept               0.002462\n",
      "ptratio                 0.003029\n",
      "np.power(ptratio, 2)    0.004120\n",
      "np.power(ptratio, 3)    0.006301\n",
      "dtype: float64\n",
      "Intercept             1.497097e-14\n",
      "black                 1.385871e-01\n",
      "np.power(black, 2)    4.741751e-01\n",
      "np.power(black, 3)    5.436172e-01\n",
      "dtype: float64\n",
      "Intercept             0.554115\n",
      "lstat                 0.334530\n",
      "np.power(lstat, 2)    0.064587\n",
      "np.power(lstat, 3)    0.129891\n",
      "dtype: float64\n",
      "Intercept            3.989088e-46\n",
      "medv                 2.637707e-28\n",
      "np.power(medv, 2)    3.260523e-18\n",
      "np.power(medv, 3)    1.046510e-12\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "for predictor in predictors:\n",
    "    mod = smf.ols(formula=f'crim ~ {predictor} + np.power({predictor},2) + np.power({predictor},3)', data=boston).fit()\n",
    "    print(mod.pvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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