causality example.ipynb

{
 "worksheets": [
  {
   "cells": [
    {
     "metadata": {},
     "cell_type": "code",
     "input": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as pp\nfrom causal_analysis.nonparametric import causal_reg\nimport statsmodels.api as sm\n",
     "prompt_number": 2,
     "outputs": [
      {
       "output_type": "pyerr",
       "ename": "ImportError",
       "evalue": "No module named causal_analysis.nonparametric",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-2-037053018986>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mcausal_analysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonparametric\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcausal_reg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mImportError\u001b[0m: No module named causal_analysis.nonparametric"
       ]
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "a = np.random.normal( size=2000)\nA = pd.DataFrame({'a' : a })\nA.hist(bins=20);",
     "prompt_number": 4,
     "outputs": [
      {
       "png": 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3AIcj4grq0yQBMvNoRBwGjgIPA6/2UL0N5v8n4iSV571oOq55zAJ9iS92RrvJtul86wbv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\nqaMs8JLUURZ4SeooC7wkdZQFXpI66v8BrcF5JOUNObEAAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0x112825290>",
       "output_type": "display_data",
       "metadata": {}
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "c = np.random.normal( a)\nb = np.random.normal( a + c)\nB = pd.DataFrame({'b' : b })\nC = pd.DataFrame({'c' : c})\n\nB.hist(bins=20)\nC.hist(bins=20)",
     "prompt_number": 145,
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 145,
       "metadata": {},
       "text": "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1150f8b90>]], dtype=object)"
      },
      {
       "output_type": "display_data",
       "metadata": {},
       "png": 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       "text": "<matplotlib.figure.Figure at 0x114225590>"
      },
      {
       "output_type": "display_data",
       "metadata": {},
       "png": 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SVnqaNvC2hYUFZmdnAZiZmWFubo75+Xlg/1/baV3et25S6hml/t4zuvm+69TL80uW+29n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\n",
       "text": "<matplotlib.figure.Figure at 0x113fec550>"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "d = np.random.normal(c)\nD = pd.DataFrame({'d' : d })\nD.hist(bins=20)",
     "prompt_number": 146,
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 146,
       "metadata": {},
       "text": "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x114a14490>]], dtype=object)"
      },
      {
       "output_type": "display_data",
       "metadata": {},
       "png": 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       "text": "<matplotlib.figure.Figure at 0x114525290>"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "X = pd.DataFrame({ 'a' : a, 'b' : b, 'c' : c, 'd' : d });\nX.plot( x='a', y='b', style='bo'); pp.xlim(-5,5); pp.ylim(-5,5); pp.ylabel('b'); pp.title( 'b vs. a');\nX.plot( x='a', y='c', style='bo'); pp.xlim(-5,5); pp.ylim(-5,5); pp.ylabel('c'); pp.title( 'c vs. a');",
     "prompt_number": 5,
     "outputs": [
      {
       "output_type": "pyerr",
       "ename": "NameError",
       "evalue": "name 'b' is not defined",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-5-ad2e4d0a1bbe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m \u001b[0;34m'a'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'b'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'c'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'd'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m'b vs. a'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m'c vs. a'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mNameError\u001b[0m: name 'b' is not defined"
       ]
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "X.plot( x='b', y='d', style='bo'); pp.xlim(-10,10); pp.ylim(-5,5); pp.ylabel('d'); pp.title( 'd vs. b');\nX.plot( x='c', y='d', style='bo'); pp.xlim(-5,5); pp.ylim(-5,5); pp.ylabel('d'); pp.title( 'd vs. c');",
     "prompt_number": 150,
     "outputs": [
      {
       "output_type": "display_data",
       "metadata": {},
       "png": 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6ycmnyMycQ1ZWOuPGjWLlyqt58MFX9COG4CYdPWZMnItcQQ5Clxiz8ddQajy7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\nLq6GQGAS9llyFVJ9e5RgVc4nCVblBDG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QHgc8ZnUCPmKPSHlY9wBfFW+88T65ucWuueAn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       "text": "<matplotlib.figure.Figure at 0x112bb2210>"
      },
      {
       "output_type": "display_data",
       "metadata": {},
       "png": 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4lubmZOrqYjh+PB04CTyBuIaKbe2EJnqlp/+Up5++vuUp5Fe/KjefgP4XCSmO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\nWI0Y9gXAX5Fg5guIFs4LiNE8grPAOYi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l/gT8qbPbDdA6wjFzwmnkJyfLZ680+UGDmnju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       "text": "<matplotlib.figure.Figure at 0x112cc4390>"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "X.corr()",
     "prompt_number": 151,
     "outputs": [
      {
       "output_type": "pyout",
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>a</th>\n      <td> 1.000000</td>\n      <td> 0.824871</td>\n      <td> 0.723569</td>\n      <td> 0.593919</td>\n    </tr>\n    <tr>\n      <th>b</th>\n      <td> 0.824871</td>\n      <td> 1.000000</td>\n      <td> 0.867877</td>\n      <td> 0.701442</td>\n    </tr>\n    <tr>\n      <th>c</th>\n      <td> 0.723569</td>\n      <td> 0.867877</td>\n      <td> 1.000000</td>\n      <td> 0.817078</td>\n    </tr>\n    <tr>\n      <th>d</th>\n      <td> 0.593919</td>\n      <td> 0.701442</td>\n      <td> 0.817078</td>\n      <td> 1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "prompt_number": 151,
       "text": "          a         b         c         d\na  1.000000  0.824871  0.723569  0.593919\nb  0.824871  1.000000  0.867877  0.701442\nc  0.723569  0.867877  1.000000  0.817078\nd  0.593919  0.701442  0.817078  1.000000"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "regression = sm.OLS(X['b'], X[['c']])\nfit = regression.fit()\nfit.summary()",
     "prompt_number": 152,
     "outputs": [
      {
       "output_type": "pyout",
       "html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>            <td>b</td>        <th>  R-squared:         </th> <td>   0.753</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.753</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6101.</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Sun, 07 Jun 2015</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n</tr>\n<tr>\n  <th>Time:</th>                 <td>17:11:16</td>     <th>  Log-Likelihood:    </th> <td> -3277.1</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>  2000</td>      <th>  AIC:               </th> <td>   6556.</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>  1999</td>      <th>  BIC:               </th> <td>   6562.</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>[95.0% Conf. Int.]</th> \n</tr>\n<tr>\n  <th>c</th> <td>    1.5126</td> <td>    0.019</td> <td>   78.111</td> <td> 0.000</td> <td>    1.475     1.551</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 1.801</td> <th>  Durbin-Watson:     </th> <td>   2.025</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.406</td> <th>  Jarque-Bera (JB):  </th> <td>   1.767</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.073</td> <th>  Prob(JB):          </th> <td>   0.413</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 3.012</td> <th>  Cond. No.          </th> <td>    1.00</td>\n</tr>\n</table>",
       "metadata": {},
       "prompt_number": 152,
       "text": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                      b   R-squared:                       0.753\nModel:                            OLS   Adj. R-squared:                  0.753\nMethod:                 Least Squares   F-statistic:                     6101.\nDate:                Sun, 07 Jun 2015   Prob (F-statistic):               0.00\nTime:                        17:11:16   Log-Likelihood:                -3277.1\nNo. Observations:                2000   AIC:                             6556.\nDf Residuals:                    1999   BIC:                             6562.\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n------------------------------------------------------------------------------\nc              1.5126      0.019     78.111      0.000         1.475     1.551\n==============================================================================\nOmnibus:                        1.801   Durbin-Watson:                   2.025\nProb(Omnibus):                  0.406   Jarque-Bera (JB):                1.767\nSkew:                           0.073   Prob(JB):                        0.413\nKurtosis:                       3.012   Cond. No.                         1.00\n==============================================================================\n\nWarnings:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "regression = sm.OLS(X['b'], X[['c', 'a']])\nfit = regression.fit()\nfit.summary()",
     "prompt_number": 153,
     "outputs": [
      {
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       "html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>            <td>b</td>        <th>  R-squared:         </th> <td>   0.835</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.834</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   5041.</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Sun, 07 Jun 2015</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n</tr>\n<tr>\n  <th>Time:</th>                 <td>17:11:17</td>     <th>  Log-Likelihood:    </th> <td> -2877.0</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>  2000</td>      <th>  AIC:               </th> <td>   5758.</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>  1998</td>      <th>  BIC:               </th> <td>   5769.</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>[95.0% Conf. Int.]</th> \n</tr>\n<tr>\n  <th>c</th> <td>    0.9914</td> <td>    0.023</td> <td>   43.153</td> <td> 0.000</td> <td>    0.946     1.036</td>\n</tr>\n<tr>\n  <th>a</th> <td>    1.0345</td> <td>    0.033</td> <td>   31.351</td> <td> 0.000</td> <td>    0.970     1.099</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 1.791</td> <th>  Durbin-Watson:     </th> <td>   1.914</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.408</td> <th>  Jarque-Bera (JB):  </th> <td>   1.850</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.062</td> <th>  Prob(JB):          </th> <td>   0.396</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 2.918</td> <th>  Cond. No.          </th> <td>    2.72</td>\n</tr>\n</table>",
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       "text": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                      b   R-squared:                       0.835\nModel:                            OLS   Adj. R-squared:                  0.834\nMethod:                 Least Squares   F-statistic:                     5041.\nDate:                Sun, 07 Jun 2015   Prob (F-statistic):               0.00\nTime:                        17:11:17   Log-Likelihood:                -2877.0\nNo. Observations:                2000   AIC:                             5758.\nDf Residuals:                    1998   BIC:                             5769.\nDf Model:                           2                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n------------------------------------------------------------------------------\nc              0.9914      0.023     43.153      0.000         0.946     1.036\na              1.0345      0.033     31.351      0.000         0.970     1.099\n==============================================================================\nOmnibus:                        1.791   Durbin-Watson:                   1.914\nProb(Omnibus):                  0.408   Jarque-Bera (JB):                1.850\nSkew:                           0.062   Prob(JB):                        0.396\nKurtosis:                       2.918   Cond. No.                         2.72\n==============================================================================\n\nWarnings:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
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