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Added Basball Score initial
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‎Analyzing Baseball Scores - v2.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import statsmodels.formula.api as sm\n",
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"import pandas as pd\n",
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"import numpy as np\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"# This data is from pandas/doc/data\n",
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"bb = pd.read_csv('data/baseball.csv', index_col='id')\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<table class=\"simpletable\">\n",
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"<caption>OLS Regression Results</caption>\n",
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"<tr>\n",
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" <th>Dep. Variable:</th> <td>hr</td> <th> R-squared: </th> <td> 0.685</td>\n",
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"</tr>\n",
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"<tr>\n",
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" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.665</td>\n",
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"</tr>\n",
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"<tr>\n",
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" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 34.28</td>\n",
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"</tr>\n",
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"<tr>\n",
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" <th>Date:</th> <td>Mon, 27 Apr 2020</td> <th> Prob (F-statistic):</th> <td>3.48e-15</td>\n",
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"</tr>\n",
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"<tr>\n",
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" <th>Time:</th> <td>09:31:30</td> <th> Log-Likelihood: </th> <td> -205.92</td>\n",
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"</tr>\n",
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"<tr>\n",
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" <th>No. Observations:</th> <td> 68</td> <th> AIC: </th> <td> 421.8</td>\n",
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"</tr>\n",
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"<tr>\n",
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" <th>Df Residuals:</th> <td> 63</td> <th> BIC: </th> <td> 432.9</td>\n",
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"</tr>\n",
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"<tr>\n",
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" <th>Df Model:</th> <td> 4</td> <th> </th> <td> </td> \n",
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"</tr>\n",
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"<tr>\n",
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" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
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"</tr>\n",
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"</table>\n",
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"<table class=\"simpletable\">\n",
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"<tr>\n",
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" <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",
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"</tr>\n",
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"<tr>\n",
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" <th>Intercept</th> <td>-8484.7720</td> <td> 4664.146</td> <td> -1.819</td> <td> 0.074</td> <td>-1.78e+04</td> <td> 835.780</td>\n",
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"</tr>\n",
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"<tr>\n",
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" <th>C(lg)[T.NL]</th> <td> -2.2736</td> <td> 1.325</td> <td> -1.716</td> <td> 0.091</td> <td> -4.922</td> <td> 0.375</td>\n",
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"</tr>\n",
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"<tr>\n",
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" <th>ln_h</th> <td> -1.3542</td> <td> 0.875</td> <td> -1.547</td> <td> 0.127</td> <td> -3.103</td> <td> 0.395</td>\n",
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"</tr>\n",
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"<tr>\n",
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" <th>year</th> <td> 4.2277</td> <td> 2.324</td> <td> 1.819</td> <td> 0.074</td> <td> -0.417</td> <td> 8.872</td>\n",
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"</tr>\n",
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"<tr>\n",
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" <th>g</th> <td> 0.1841</td> <td> 0.029</td> <td> 6.258</td> <td> 0.000</td> <td> 0.125</td> <td> 0.243</td>\n",
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"</tr>\n",
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"</table>\n",
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"<table class=\"simpletable\">\n",
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"<tr>\n",
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" <th>Omnibus:</th> <td>10.875</td> <th> Durbin-Watson: </th> <td> 1.999</td>\n",
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"</tr>\n",
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"<tr>\n",
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" <th>Prob(Omnibus):</th> <td> 0.004</td> <th> Jarque-Bera (JB): </th> <td> 17.298</td>\n",
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"</tr>\n",
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"<tr>\n",
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" <th>Skew:</th> <td> 0.537</td> <th> Prob(JB): </th> <td>0.000175</td>\n",
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"</tr>\n",
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"<tr>\n",
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" <th>Kurtosis:</th> <td> 5.225</td> <th> Cond. No. </th> <td>1.49e+07</td>\n",
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"</tr>\n",
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"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.49e+07. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
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],
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"text/plain": [
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"<class 'statsmodels.iolib.summary.Summary'>\n",
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"\"\"\"\n",
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" OLS Regression Results \n",
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"==============================================================================\n",
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"Dep. Variable: hr R-squared: 0.685\n",
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"Model: OLS Adj. R-squared: 0.665\n",
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"Method: Least Squares F-statistic: 34.28\n",
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"Date: Mon, 27 Apr 2020 Prob (F-statistic): 3.48e-15\n",
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"Time: 09:31:30 Log-Likelihood: -205.92\n",
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"No. Observations: 68 AIC: 421.8\n",
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"Df Residuals: 63 BIC: 432.9\n",
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"Df Model: 4 \n",
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"Covariance Type: nonrobust \n",
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"===============================================================================\n",
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" coef std err t P>|t| [0.025 0.975]\n",
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"-------------------------------------------------------------------------------\n",
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"Intercept -8484.7720 4664.146 -1.819 0.074 -1.78e+04 835.780\n",
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"C(lg)[T.NL] -2.2736 1.325 -1.716 0.091 -4.922 0.375\n",
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"ln_h -1.3542 0.875 -1.547 0.127 -3.103 0.395\n",
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"year 4.2277 2.324 1.819 0.074 -0.417 8.872\n",
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"g 0.1841 0.029 6.258 0.000 0.125 0.243\n",
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"==============================================================================\n",
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"Omnibus: 10.875 Durbin-Watson: 1.999\n",
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"Prob(Omnibus): 0.004 Jarque-Bera (JB): 17.298\n",
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"Skew: 0.537 Prob(JB): 0.000175\n",
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"Kurtosis: 5.225 Cond. No. 1.49e+07\n",
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"==============================================================================\n",
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"\n",
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"Warnings:\n",
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"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
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"[2] The condition number is large, 1.49e+07. This might indicate that there are\n",
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"strong multicollinearity or other numerical problems.\n",
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"\"\"\""
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"bb\n",
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"(bb.query('h > 0')\n",
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".assign(ln_h=lambda df: np.log(df.h))\n",
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".pipe((sm.ols, 'data'), 'hr ~ ln_h + year + g + C(lg)')\n",
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".fit()\n",
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".summary()\n",
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")"
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]
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},
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{
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"cell_type": "code",
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],
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"display_name": "Python 3",
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"language": "python",
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"file_extension": ".py",
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