ml_regression_dummy_var.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy Variables\n",
    "\n",
    "You saw in the earlier notebook that you weren't able to directly add a categorical variable to your multiple linear regression model. In this notebook, you will get some practice incorporating categorical data by converting to dummy variables in your models and interpreting the output.\n",
    "\n",
    "Let's start by reading in the necessary libraries and data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>house_id</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>style</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1112</td>\n",
       "      <td>B</td>\n",
       "      <td>1188</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>ranch</td>\n",
       "      <td>598291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>491</td>\n",
       "      <td>B</td>\n",
       "      <td>3512</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>victorian</td>\n",
       "      <td>1744259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5952</td>\n",
       "      <td>B</td>\n",
       "      <td>1134</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>ranch</td>\n",
       "      <td>571669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3525</td>\n",
       "      <td>A</td>\n",
       "      <td>1940</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>ranch</td>\n",
       "      <td>493675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5108</td>\n",
       "      <td>B</td>\n",
       "      <td>2208</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>victorian</td>\n",
       "      <td>1101539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   house_id neighborhood  area  bedrooms  bathrooms      style    price\n",
       "0      1112            B  1188         3          2      ranch   598291\n",
       "1       491            B  3512         5          3  victorian  1744259\n",
       "2      5952            B  1134         3          2      ranch   571669\n",
       "3      3525            A  1940         4          2      ranch   493675\n",
       "4      5108            B  2208         6          4  victorian  1101539"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm;\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv('./data/house_prices.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`1.` Use the [pd.get_dummies](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html) documentation to assist you with obtaining dummy variables for the **neighborhood** column.  Then use [join](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.join.html) to add the dummy variables to your dataframe, **df**, and store the joined results in **df_new**.\n",
    "\n",
    "Fit a linear model using **all three levels** of **neighborhood** to predict the price. Don't forget an intercept.\n",
    "\n",
    "Use your results to answer quiz 1 below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>house_id</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>style</th>\n",
       "      <th>price</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1112</td>\n",
       "      <td>B</td>\n",
       "      <td>1188</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>ranch</td>\n",
       "      <td>598291</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>491</td>\n",
       "      <td>B</td>\n",
       "      <td>3512</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>victorian</td>\n",
       "      <td>1744259</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5952</td>\n",
       "      <td>B</td>\n",
       "      <td>1134</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>ranch</td>\n",
       "      <td>571669</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3525</td>\n",
       "      <td>A</td>\n",
       "      <td>1940</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>ranch</td>\n",
       "      <td>493675</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5108</td>\n",
       "      <td>B</td>\n",
       "      <td>2208</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>victorian</td>\n",
       "      <td>1101539</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   house_id neighborhood  area  bedrooms  bathrooms      style    price  A  B  \\\n",
       "0      1112            B  1188         3          2      ranch   598291  0  1   \n",
       "1       491            B  3512         5          3  victorian  1744259  0  1   \n",
       "2      5952            B  1134         3          2      ranch   571669  0  1   \n",
       "3      3525            A  1940         4          2      ranch   493675  1  0   \n",
       "4      5108            B  2208         6          4  victorian  1101539  0  1   \n",
       "\n",
       "   C  \n",
       "0  0  \n",
       "1  0  \n",
       "2  0  \n",
       "3  0  \n",
       "4  0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = pd.DataFrame()\n",
    "df_new = df\n",
    "df_new[['A','B','C']] = pd.get_dummies(df['neighborhood'])\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.246</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.246</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   655.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 22 Jul 2020</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:07:30</td>     <th>  Log-Likelihood:    </th> <td> -87082.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  6028</td>      <th>  AIC:               </th> <td>1.742e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  6024</td>      <th>  BIC:               </th> <td>1.742e+05</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> -7.01e+17</td> <td> 8.86e+17</td> <td>   -0.792</td> <td> 0.429</td> <td>-2.44e+18</td> <td> 1.04e+18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>A</th>         <td>  7.01e+17</td> <td> 8.86e+17</td> <td>    0.792</td> <td> 0.429</td> <td>-1.04e+18</td> <td> 2.44e+18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>B</th>         <td>  7.01e+17</td> <td> 8.86e+17</td> <td>    0.792</td> <td> 0.429</td> <td>-1.04e+18</td> <td> 2.44e+18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C</th>         <td>  7.01e+17</td> <td> 8.86e+17</td> <td>    0.792</td> <td> 0.429</td> <td>-1.04e+18</td> <td> 2.44e+18</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>682.618</td> <th>  Durbin-Watson:     </th> <td>   1.999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>1140.081</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.788</td>  <th>  Prob(JB):          </th> <td>2.72e-248</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.435</td>  <th>  Cond. No.          </th> <td>3.50e+14</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.246\n",
       "Model:                            OLS   Adj. R-squared:                  0.246\n",
       "Method:                 Least Squares   F-statistic:                     655.0\n",
       "Date:                Wed, 22 Jul 2020   Prob (F-statistic):               0.00\n",
       "Time:                        21:07:30   Log-Likelihood:                -87082.\n",
       "No. Observations:                6028   AIC:                         1.742e+05\n",
       "Df Residuals:                    6024   BIC:                         1.742e+05\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept   -7.01e+17   8.86e+17     -0.792      0.429   -2.44e+18    1.04e+18\n",
       "A            7.01e+17   8.86e+17      0.792      0.429   -1.04e+18    2.44e+18\n",
       "B            7.01e+17   8.86e+17      0.792      0.429   -1.04e+18    2.44e+18\n",
       "C            7.01e+17   8.86e+17      0.792      0.429   -1.04e+18    2.44e+18\n",
       "==============================================================================\n",
       "Omnibus:                      682.618   Durbin-Watson:                   1.999\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1140.081\n",
       "Skew:                           0.788   Prob(JB):                    2.72e-248\n",
       "Kurtosis:                       4.435   Cond. No.                     3.50e+14\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 6.59e-26. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['intercept'] = 1\n",
    "\n",
    "lm = sm.OLS(df_new['price'], df_new[['intercept','A','B','C']])\n",
    "results = lm.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`2.`  Now, fit an appropriate linear model for using **neighborhood** to predict the price of a home. Use **neighborhood A** as your baseline. (And remember that the values shown in the results for the other neighborhoods will be based on comparisons with this baseline neighborhood A then.) Use your resulting model to answer the questions in Quiz 2 and Quiz 3 below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.246</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.246</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   983.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 22 Jul 2020</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:07:52</td>     <th>  Log-Likelihood:    </th> <td> -87082.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  6028</td>      <th>  AIC:               </th> <td>1.742e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  6025</td>      <th>  BIC:               </th> <td>1.742e+05</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> 5.411e+05</td> <td> 1.05e+04</td> <td>   51.537</td> <td> 0.000</td> <td> 5.21e+05</td> <td> 5.62e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>B</th>         <td> 5.295e+05</td> <td>  1.4e+04</td> <td>   37.870</td> <td> 0.000</td> <td> 5.02e+05</td> <td> 5.57e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C</th>         <td> -332.3594</td> <td> 1.52e+04</td> <td>   -0.022</td> <td> 0.983</td> <td>-3.01e+04</td> <td> 2.94e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>689.315</td> <th>  Durbin-Watson:     </th> <td>   1.999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>1154.155</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.793</td>  <th>  Prob(JB):          </th> <td>2.39e-251</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.442</td>  <th>  Cond. No.          </th> <td>    3.88</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.246\n",
       "Model:                            OLS   Adj. R-squared:                  0.246\n",
       "Method:                 Least Squares   F-statistic:                     983.1\n",
       "Date:                Wed, 22 Jul 2020   Prob (F-statistic):               0.00\n",
       "Time:                        21:07:52   Log-Likelihood:                -87082.\n",
       "No. Observations:                6028   AIC:                         1.742e+05\n",
       "Df Residuals:                    6025   BIC:                         1.742e+05\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept   5.411e+05   1.05e+04     51.537      0.000    5.21e+05    5.62e+05\n",
       "B           5.295e+05    1.4e+04     37.870      0.000    5.02e+05    5.57e+05\n",
       "C           -332.3594   1.52e+04     -0.022      0.983   -3.01e+04    2.94e+04\n",
       "==============================================================================\n",
       "Omnibus:                      689.315   Durbin-Watson:                   1.999\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1154.155\n",
       "Skew:                           0.793   Prob(JB):                    2.39e-251\n",
       "Kurtosis:                       4.442   Cond. No.                         3.88\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = sm.OLS(df_new['price'], df_new[['intercept','B','C']])\n",
    "results = lm.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`3.` Run the two cells below to look at the home prices for the A and C neighborhoods. Add neighborhood B. This creates a glimpse into the differences that you found in the previous linear model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff7a8233c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_new.query(\"C == 1\")['price'], alpha = 0.3, label = 'C');\n",
    "plt.hist(df_new.query(\"A == 1\")['price'], alpha = 0.3, label = 'A');\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff7a4ba940>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_new.query(\"C == 1\")['price'], alpha = 0.3, label = 'C')\n",
    "plt.hist(df_new.query(\"A == 1\")['price'], alpha = 0.3, label = 'A')\n",
    "plt.hist(df_new.query(\"B == 1\")['price'], alpha = 0.3, label = 'B')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`4.` Now, add dummy variables for the **style** of house. Create a new linear model using these new dummies, as well as the previous **neighborhood** dummies.  Use **ranch** as the baseline for the **style**.  Additionally, add **bathrooms** and **bedrooms** to your linear model.  Don't forget an intercept.  Use the results of your linear model to answer the last two questions below. **Home prices are measured in dollars, and this dataset is not real.**\n",
    "\n",
    "To minimize scrolling, it might be useful to open another browser window to this concept to answer the quiz questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.809</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.809</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   4250.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 22 Jul 2020</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:02:03</td>     <th>  Log-Likelihood:    </th> <td> -82944.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  6028</td>      <th>  AIC:               </th> <td>1.659e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  6021</td>      <th>  BIC:               </th> <td>1.659e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</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>-2.148e+05</td> <td> 9563.445</td> <td>  -22.456</td> <td> 0.000</td> <td>-2.34e+05</td> <td>-1.96e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>victorian</th> <td>-1.685e+05</td> <td> 9906.629</td> <td>  -17.012</td> <td> 0.000</td> <td>-1.88e+05</td> <td>-1.49e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lodge</th>     <td>-9.797e+04</td> <td> 1.26e+04</td> <td>   -7.780</td> <td> 0.000</td> <td>-1.23e+05</td> <td>-7.33e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th> <td> 9.996e+04</td> <td> 1.09e+04</td> <td>    9.164</td> <td> 0.000</td> <td> 7.86e+04</td> <td> 1.21e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>  <td> 1.732e+05</td> <td> 7677.152</td> <td>   22.558</td> <td> 0.000</td> <td> 1.58e+05</td> <td> 1.88e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>B</th>         <td> 5.229e+05</td> <td> 7040.928</td> <td>   74.271</td> <td> 0.000</td> <td> 5.09e+05</td> <td> 5.37e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C</th>         <td>-7168.6285</td> <td> 7639.254</td> <td>   -0.938</td> <td> 0.348</td> <td>-2.21e+04</td> <td> 7807.045</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>978.611</td> <th>  Durbin-Watson:     </th> <td>   1.993</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>2926.472</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.848</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.962</td>  <th>  Cond. No.          </th> <td>    25.4</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.809\n",
       "Model:                            OLS   Adj. R-squared:                  0.809\n",
       "Method:                 Least Squares   F-statistic:                     4250.\n",
       "Date:                Wed, 22 Jul 2020   Prob (F-statistic):               0.00\n",
       "Time:                        22:02:03   Log-Likelihood:                -82944.\n",
       "No. Observations:                6028   AIC:                         1.659e+05\n",
       "Df Residuals:                    6021   BIC:                         1.659e+05\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept  -2.148e+05   9563.445    -22.456      0.000   -2.34e+05   -1.96e+05\n",
       "victorian  -1.685e+05   9906.629    -17.012      0.000   -1.88e+05   -1.49e+05\n",
       "lodge      -9.797e+04   1.26e+04     -7.780      0.000   -1.23e+05   -7.33e+04\n",
       "bathrooms   9.996e+04   1.09e+04      9.164      0.000    7.86e+04    1.21e+05\n",
       "bedrooms    1.732e+05   7677.152     22.558      0.000    1.58e+05    1.88e+05\n",
       "B           5.229e+05   7040.928     74.271      0.000    5.09e+05    5.37e+05\n",
       "C          -7168.6285   7639.254     -0.938      0.348   -2.21e+04    7807.045\n",
       "==============================================================================\n",
       "Omnibus:                      978.611   Durbin-Watson:                   1.993\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2926.472\n",
       "Skew:                           0.848   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.962   Cond. No.                         25.4\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new[['ranch','victorian','lodge']] = pd.get_dummies(df_new['style'])\n",
    "lm = sm.OLS(df['price'], df[['intercept','victorian','lodge','bathrooms','bedrooms'\n",
    ",'B','C']])\n",
    "results = lm.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8+8+123+7+2+7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05161290322580645"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8/155"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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