Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\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",
    "from pandas_datareader import data as wb\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel(\"F:\\Housing.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>House Price</th>\n",
       "      <th>House Size (sq.ft.)</th>\n",
       "      <th>State</th>\n",
       "      <th>Number of Rooms</th>\n",
       "      <th>Year of Construction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1116000</td>\n",
       "      <td>1940</td>\n",
       "      <td>IN</td>\n",
       "      <td>8</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>860000</td>\n",
       "      <td>1300</td>\n",
       "      <td>IN</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>818400</td>\n",
       "      <td>1420</td>\n",
       "      <td>IN</td>\n",
       "      <td>6</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000000</td>\n",
       "      <td>1680</td>\n",
       "      <td>IN</td>\n",
       "      <td>7</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>640000</td>\n",
       "      <td>1270</td>\n",
       "      <td>IN</td>\n",
       "      <td>5</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1010000</td>\n",
       "      <td>1850</td>\n",
       "      <td>IN</td>\n",
       "      <td>7</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>600000</td>\n",
       "      <td>1000</td>\n",
       "      <td>IN</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>700000</td>\n",
       "      <td>1100</td>\n",
       "      <td>LA</td>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1100000</td>\n",
       "      <td>1600</td>\n",
       "      <td>LA</td>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>570000</td>\n",
       "      <td>1000</td>\n",
       "      <td>NY</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>860000</td>\n",
       "      <td>2150</td>\n",
       "      <td>NY</td>\n",
       "      <td>9</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1085000</td>\n",
       "      <td>1900</td>\n",
       "      <td>NY</td>\n",
       "      <td>9</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1250000</td>\n",
       "      <td>2200</td>\n",
       "      <td>NY</td>\n",
       "      <td>9</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>850000</td>\n",
       "      <td>1100</td>\n",
       "      <td>TX</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>640000</td>\n",
       "      <td>860</td>\n",
       "      <td>TX</td>\n",
       "      <td>4</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>900000</td>\n",
       "      <td>1325</td>\n",
       "      <td>TX</td>\n",
       "      <td>6</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>730000</td>\n",
       "      <td>1350</td>\n",
       "      <td>TX</td>\n",
       "      <td>6</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>750000</td>\n",
       "      <td>1600</td>\n",
       "      <td>TX</td>\n",
       "      <td>6</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>650000</td>\n",
       "      <td>950</td>\n",
       "      <td>TX</td>\n",
       "      <td>2</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>680000</td>\n",
       "      <td>1250</td>\n",
       "      <td>TX</td>\n",
       "      <td>4</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    House Price  House Size (sq.ft.) State  Number of Rooms  \\\n",
       "0       1116000                 1940    IN                8   \n",
       "1        860000                 1300    IN                5   \n",
       "2        818400                 1420    IN                6   \n",
       "3       1000000                 1680    IN                7   \n",
       "4        640000                 1270    IN                5   \n",
       "5       1010000                 1850    IN                7   \n",
       "6        600000                 1000    IN                4   \n",
       "7        700000                 1100   LA                 4   \n",
       "8       1100000                 1600   LA                 7   \n",
       "9        570000                 1000    NY                5   \n",
       "10       860000                 2150    NY                9   \n",
       "11      1085000                 1900    NY                9   \n",
       "12      1250000                 2200    NY                9   \n",
       "13       850000                 1100    TX                4   \n",
       "14       640000                  860    TX                4   \n",
       "15       900000                 1325    TX                6   \n",
       "16       730000                 1350    TX                6   \n",
       "17       750000                 1600    TX                6   \n",
       "18       650000                  950    TX                2   \n",
       "19       680000                 1250    TX                4   \n",
       "\n",
       "    Year of Construction  \n",
       "0                   2002  \n",
       "1                   1992  \n",
       "2                   1987  \n",
       "3                   2000  \n",
       "4                   1995  \n",
       "5                   1998  \n",
       "6                   2015  \n",
       "7                   2014  \n",
       "8                   2017  \n",
       "9                   1997  \n",
       "10                  1997  \n",
       "11                  2000  \n",
       "12                  2014  \n",
       "13                  2017  \n",
       "14                  1997  \n",
       "15                  1997  \n",
       "16                  2000  \n",
       "17                  1992  \n",
       "18                  1987  \n",
       "19                  2000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>House Price</th>\n",
       "      <th>House Size (sq.ft.)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1116000</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>860000</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>818400</td>\n",
       "      <td>1420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000000</td>\n",
       "      <td>1680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>640000</td>\n",
       "      <td>1270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1010000</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>600000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>700000</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1100000</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>570000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>860000</td>\n",
       "      <td>2150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1085000</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1250000</td>\n",
       "      <td>2200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>850000</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>640000</td>\n",
       "      <td>860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>900000</td>\n",
       "      <td>1325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>730000</td>\n",
       "      <td>1350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>750000</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>650000</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>680000</td>\n",
       "      <td>1250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    House Price  House Size (sq.ft.)\n",
       "0       1116000                 1940\n",
       "1        860000                 1300\n",
       "2        818400                 1420\n",
       "3       1000000                 1680\n",
       "4        640000                 1270\n",
       "5       1010000                 1850\n",
       "6        600000                 1000\n",
       "7        700000                 1100\n",
       "8       1100000                 1600\n",
       "9        570000                 1000\n",
       "10       860000                 2150\n",
       "11      1085000                 1900\n",
       "12      1250000                 2200\n",
       "13       850000                 1100\n",
       "14       640000                  860\n",
       "15       900000                 1325\n",
       "16       730000                 1350\n",
       "17       750000                 1600\n",
       "18       650000                  950\n",
       "19       680000                 1250"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['House Price', 'House Size (sq.ft.)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data['House Size (sq.ft.)']\n",
    "y = data['House Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1940\n",
       "1     1300\n",
       "2     1420\n",
       "3     1680\n",
       "4     1270\n",
       "5     1850\n",
       "6     1000\n",
       "7     1100\n",
       "8     1600\n",
       "9     1000\n",
       "10    2150\n",
       "11    1900\n",
       "12    2200\n",
       "13    1100\n",
       "14     860\n",
       "15    1325\n",
       "16    1350\n",
       "17    1600\n",
       "18     950\n",
       "19    1250\n",
       "Name: House Size (sq.ft.), dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1116000\n",
       "1      860000\n",
       "2      818400\n",
       "3     1000000\n",
       "4      640000\n",
       "5     1010000\n",
       "6      600000\n",
       "7      700000\n",
       "8     1100000\n",
       "9      570000\n",
       "10     860000\n",
       "11    1085000\n",
       "12    1250000\n",
       "13     850000\n",
       "14     640000\n",
       "15     900000\n",
       "16     730000\n",
       "17     750000\n",
       "18     650000\n",
       "19     680000\n",
       "Name: House Price, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2bfdfb38940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.axis([0, 2500, 0, 1500000])\n",
    "plt.ylabel('House Price')\n",
    "plt.xlabel('House Size(in sq.ft)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = sm.add_constant(x)\n",
    "reg = sm.OLS(y,x1).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>House Price</td>   <th>  R-squared:         </th> <td>   0.678</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.660</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   37.95</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 03 Apr 2018</td> <th>  Prob (F-statistic):</th> <td>8.13e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:27:41</td>     <th>  Log-Likelihood:    </th> <td> -260.43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    20</td>      <th>  AIC:               </th> <td>   524.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    18</td>      <th>  BIC:               </th> <td>   526.8</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> 2.608e+05</td> <td> 9.76e+04</td> <td>    2.673</td> <td> 0.016</td> <td> 5.58e+04</td> <td> 4.66e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>House Size (sq.ft.)</th> <td>  401.9163</td> <td>   65.243</td> <td>    6.160</td> <td> 0.000</td> <td>  264.846</td> <td>  538.987</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.238</td> <th>  Durbin-Watson:     </th> <td>   1.810</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.538</td> <th>  Jarque-Bera (JB):  </th> <td>   0.715</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.459</td> <th>  Prob(JB):          </th> <td>   0.699</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.884</td> <th>  Cond. No.          </th> <td>5.66e+03</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:            House Price   R-squared:                       0.678\n",
       "Model:                            OLS   Adj. R-squared:                  0.660\n",
       "Method:                 Least Squares   F-statistic:                     37.95\n",
       "Date:                Tue, 03 Apr 2018   Prob (F-statistic):           8.13e-06\n",
       "Time:                        23:27:41   Log-Likelihood:                -260.43\n",
       "No. Observations:                  20   AIC:                             524.9\n",
       "Df Residuals:                      18   BIC:                             526.8\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=======================================================================================\n",
       "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------\n",
       "const                2.608e+05   9.76e+04      2.673      0.016    5.58e+04    4.66e+05\n",
       "House Size (sq.ft.)   401.9163     65.243      6.160      0.000     264.846     538.987\n",
       "==============================================================================\n",
       "Omnibus:                        1.238   Durbin-Watson:                   1.810\n",
       "Prob(Omnibus):                  0.538   Jarque-Bera (JB):                0.715\n",
       "Skew:                          -0.459   Prob(JB):                        0.699\n",
       "Kurtosis:                       2.884   Cond. No.                     5.66e+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, 5.66e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "662800"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "260800 + 402 * 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.678279986579124"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_value**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "401.91628631922595"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260806.2360560964"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.24299510636492"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.129642377231308e-06"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}