wolfers-happiness.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Happiness and the Human Development Index: Australia Is Not a Paradox\n",
    "\n",
    "Justin Wolfers and Andrew Leigh (2006)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recreating paper here: http://users.nber.org/~jwolfers/papers/AustralianHappiness(WP).pdf\n",
    "\n",
    "Download data here: http://users.nber.org/~jwolfers/data/AustralianHappiness(AustER).zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/natashawatkins/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 statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>countryname</th>\n",
       "      <th>cty</th>\n",
       "      <th>happins</th>\n",
       "      <th>lifesat</th>\n",
       "      <th>hdi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2.59013</td>\n",
       "      <td>5.16533</td>\n",
       "      <td>0.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZA</td>\n",
       "      <td>2.96443</td>\n",
       "      <td>5.67455</td>\n",
       "      <td>0.704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>3.12808</td>\n",
       "      <td>7.32543</td>\n",
       "      <td>0.853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>ARM</td>\n",
       "      <td>2.55417</td>\n",
       "      <td>4.31825</td>\n",
       "      <td>0.754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "      <td>3.36505</td>\n",
       "      <td>7.55085</td>\n",
       "      <td>0.946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  countryname  cty  happins  lifesat    hdi\n",
       "0     Albania  ALB  2.59013  5.16533  0.781\n",
       "1     Algeria  DZA  2.96443  5.67455  0.704\n",
       "2   Argentina  ARG  3.12808  7.32543  0.853\n",
       "3     Armenia  ARM  2.55417  4.31825  0.754\n",
       "4   Australia  AUS  3.36505  7.55085  0.946"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_stata('jw_wvs.dta')\n",
    "df.dropna(subset=['hdi','lifesat'], inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEWCAYAAABliCz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTIGwhG+MMQXCEr4xxhSI/w/DDQ8aJKwLDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda5984ca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = df['lifesat']\n",
    "x = df['hdi']\n",
    "df.plot('hdi', 'lifesat', kind='scatter')\n",
    "plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))\n",
    "plt.ylabel('Life satisfaction: world values survey')\n",
    "plt.xlabel('HDI')\n",
    "plt.title('Life satisfaction and development')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = df['lifesat']\n",
    "X = df['hdi']\n",
    "X = sm.add_constant(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "result1 = sm.OLS(Y,X, missing='drop').fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>lifesat</td>     <th>  R-squared:         </th> <td>   0.404</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.395</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   50.06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 25 May 2017</td> <th>  Prob (F-statistic):</th> <td>7.11e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:14:04</td>     <th>  Log-Likelihood:    </th> <td> -100.39</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    76</td>      <th>  AIC:               </th> <td>   204.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    74</td>      <th>  BIC:               </th> <td>   209.4</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>    1.9499</td> <td>    0.653</td> <td>    2.988</td> <td> 0.004</td> <td>    0.650</td> <td>    3.250</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hdi</th>   <td>    5.6791</td> <td>    0.803</td> <td>    7.075</td> <td> 0.000</td> <td>    4.080</td> <td>    7.278</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.387</td> <th>  Durbin-Watson:     </th> <td>   2.134</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.824</td> <th>  Jarque-Bera (JB):  </th> <td>   0.553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.117</td> <th>  Prob(JB):          </th> <td>   0.759</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.654</td> <th>  Cond. No.          </th> <td>    12.6</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                lifesat   R-squared:                       0.404\n",
       "Model:                            OLS   Adj. R-squared:                  0.395\n",
       "Method:                 Least Squares   F-statistic:                     50.06\n",
       "Date:                Thu, 25 May 2017   Prob (F-statistic):           7.11e-10\n",
       "Time:                        13:14:04   Log-Likelihood:                -100.39\n",
       "No. Observations:                  76   AIC:                             204.8\n",
       "Df Residuals:                      74   BIC:                             209.4\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          1.9499      0.653      2.988      0.004       0.650       3.250\n",
       "hdi            5.6791      0.803      7.075      0.000       4.080       7.278\n",
       "==============================================================================\n",
       "Omnibus:                        0.387   Durbin-Watson:                   2.134\n",
       "Prob(Omnibus):                  0.824   Jarque-Bera (JB):                0.553\n",
       "Skew:                          -0.117   Prob(JB):                        0.759\n",
       "Kurtosis:                       2.654   Cond. No.                         12.6\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['resid'] = result1.resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEWCAYAAABmE+CbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRQo43i5+7+YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda59868710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot('hdi', 'resid', kind='scatter')\n",
    "plt.title('Residuals from OLS regression 1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Residuals do not appear to follow a systematic pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cty</th>\n",
       "      <th>country_name</th>\n",
       "      <th>gdp1995</th>\n",
       "      <th>gdp2000</th>\n",
       "      <th>gdp2002</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>1662.906982</td>\n",
       "      <td>1952.422974</td>\n",
       "      <td>2231.718994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2555.547119</td>\n",
       "      <td>3688.650879</td>\n",
       "      <td>4276.209961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>18000.199219</td>\n",
       "      <td>18000.199219</td>\n",
       "      <td>18000.199219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>10351.110352</td>\n",
       "      <td>12252.700195</td>\n",
       "      <td>11085.830078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARM</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>1671.332031</td>\n",
       "      <td>2421.985107</td>\n",
       "      <td>3138.308105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cty          country_name       gdp1995       gdp2000       gdp2002\n",
       "0  AGO                Angola   1662.906982   1952.422974   2231.718994\n",
       "1  ALB               Albania   2555.547119   3688.650879   4276.209961\n",
       "2  ARE  United Arab Emirates  18000.199219  18000.199219  18000.199219\n",
       "3  ARG             Argentina  10351.110352  12252.700195  11085.830078\n",
       "4  ARM               Armenia   1671.332031   2421.985107   3138.308105"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_stata('jw_gdp.dta')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>countryname</th>\n",
       "      <th>cty_x</th>\n",
       "      <th>happins</th>\n",
       "      <th>lifesat</th>\n",
       "      <th>hdi</th>\n",
       "      <th>resid</th>\n",
       "      <th>cty_y</th>\n",
       "      <th>country_name</th>\n",
       "      <th>gdp1995</th>\n",
       "      <th>gdp2000</th>\n",
       "      <th>gdp2002</th>\n",
       "      <th>lngdp2000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2.59013</td>\n",
       "      <td>5.16533</td>\n",
       "      <td>0.781</td>\n",
       "      <td>-1.219956</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2555.547119</td>\n",
       "      <td>3688.650879</td>\n",
       "      <td>4276.209961</td>\n",
       "      <td>8.213017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZA</td>\n",
       "      <td>2.96443</td>\n",
       "      <td>5.67455</td>\n",
       "      <td>0.704</td>\n",
       "      <td>-0.273442</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>4629.817871</td>\n",
       "      <td>5416.995117</td>\n",
       "      <td>5769.333008</td>\n",
       "      <td>8.597297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>3.12808</td>\n",
       "      <td>7.32543</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.531246</td>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>10351.110352</td>\n",
       "      <td>12252.700195</td>\n",
       "      <td>11085.830078</td>\n",
       "      <td>9.413502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>ARM</td>\n",
       "      <td>2.55417</td>\n",
       "      <td>4.31825</td>\n",
       "      <td>0.754</td>\n",
       "      <td>-1.913699</td>\n",
       "      <td>ARM</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>1671.332031</td>\n",
       "      <td>2421.985107</td>\n",
       "      <td>3138.308105</td>\n",
       "      <td>7.792343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "      <td>3.36505</td>\n",
       "      <td>7.55085</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.228505</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>21413.220703</td>\n",
       "      <td>26181.039062</td>\n",
       "      <td>28335.029297</td>\n",
       "      <td>10.172791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  countryname cty_x  happins  lifesat    hdi     resid cty_y country_name  \\\n",
       "0     Albania   ALB  2.59013  5.16533  0.781 -1.219956   ALB      Albania   \n",
       "1     Algeria   DZA  2.96443  5.67455  0.704 -0.273442   DZA      Algeria   \n",
       "2   Argentina   ARG  3.12808  7.32543  0.853  0.531246   ARG    Argentina   \n",
       "3     Armenia   ARM  2.55417  4.31825  0.754 -1.913699   ARM      Armenia   \n",
       "4   Australia   AUS  3.36505  7.55085  0.946  0.228505   AUS    Australia   \n",
       "\n",
       "        gdp1995       gdp2000       gdp2002  lngdp2000  \n",
       "0   2555.547119   3688.650879   4276.209961   8.213017  \n",
       "1   4629.817871   5416.995117   5769.333008   8.597297  \n",
       "2  10351.110352  12252.700195  11085.830078   9.413502  \n",
       "3   1671.332031   2421.985107   3138.308105   7.792343  \n",
       "4  21413.220703  26181.039062  28335.029297  10.172791  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = pd.merge(df, df2, left_on='countryname', right_on='country_name')\n",
    "merged['lngdp2000'] = np.log(merged['gdp2000'])\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEWCAYAAABliCz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda53c79208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = merged['lifesat']\n",
    "x = merged['lngdp2000']\n",
    "merged.plot('lngdp2000', 'lifesat', kind='scatter')\n",
    "plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))\n",
    "plt.ylabel('Life satisfaction: world values survey')\n",
    "plt.xlabel('log(GDP)')\n",
    "plt.title('Life satisfaction and GDP')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y = merged['lifesat']\n",
    "X = merged['lngdp2000']\n",
    "X = sm.add_constant(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result2 = sm.OLS(Y,X, missing='drop').fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>lifesat</td>     <th>  R-squared:         </th> <td>   0.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.548</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   79.65</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 25 May 2017</td> <th>  Prob (F-statistic):</th> <td>7.66e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:14:04</td>     <th>  Log-Likelihood:    </th> <td> -78.970</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    66</td>      <th>  AIC:               </th> <td>   161.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    64</td>      <th>  BIC:               </th> <td>   166.3</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>const</th>     <td>   -1.1512</td> <td>    0.870</td> <td>   -1.323</td> <td> 0.191</td> <td>   -2.890</td> <td>    0.587</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lngdp2000</th> <td>    0.8503</td> <td>    0.095</td> <td>    8.925</td> <td> 0.000</td> <td>    0.660</td> <td>    1.041</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.222</td> <th>  Durbin-Watson:     </th> <td>   1.812</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.329</td> <th>  Jarque-Bera (JB):  </th> <td>   1.578</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.361</td> <th>  Prob(JB):          </th> <td>   0.454</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.228</td> <th>  Cond. No.          </th> <td>    80.4</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                lifesat   R-squared:                       0.554\n",
       "Model:                            OLS   Adj. R-squared:                  0.548\n",
       "Method:                 Least Squares   F-statistic:                     79.65\n",
       "Date:                Thu, 25 May 2017   Prob (F-statistic):           7.66e-13\n",
       "Time:                        13:14:04   Log-Likelihood:                -78.970\n",
       "No. Observations:                  66   AIC:                             161.9\n",
       "Df Residuals:                      64   BIC:                             166.3\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -1.1512      0.870     -1.323      0.191      -2.890       0.587\n",
       "lngdp2000      0.8503      0.095      8.925      0.000       0.660       1.041\n",
       "==============================================================================\n",
       "Omnibus:                        2.222   Durbin-Watson:                   1.812\n",
       "Prob(Omnibus):                  0.329   Jarque-Bera (JB):                1.578\n",
       "Skew:                           0.361   Prob(JB):                        0.454\n",
       "Kurtosis:                       3.228   Cond. No.                         80.4\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda53b8f3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged['resid'] = result2.resid\n",
    "merged.plot('lngdp2000', 'resid', kind='scatter')\n",
    "plt.title('Residuals from OLS regression 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}