02_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mod2 Project - Linear Regression Model\n",
    "\n",
    "Second Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis\n",
    "\n",
    "In this notebook, we begin our data analysis. In it we: \n",
    "\n",
    "\n",
    "    a) show distributions\n",
    "    b) confirm that outliers are removed\n",
    "    c) beging to run hypothesis tests on features.\n",
    "\n",
    "The goal is to create a model that can efficiently and accurately predict a players fourth year salary after arbitration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.stats as sms\n",
    "from statsmodels.formula.api import ols\n",
    "# from sklearn.model_selection import train_test_split\n",
    "# from sklearn.linear_model import LinearRegression\n",
    "# from sklearn import metrics\n",
    "# from sklearn.dummy import DummyRegressor\n",
    "# from sklearn import linear_model\n",
    "# from sklearn import metrics\n",
    "# from sklearn.preprocessing import PolynomialFeatures\n",
    "# from sklearn.preprocessing import StandardScaler\n",
    "# from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "import scipy.stats as stats\n",
    "import random\n",
    "from statsmodels.stats import weightstats as stests\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.set(rc={'figure.figsize':(15,10)})\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option('display.max_columns', 300)\n",
    "pd.set_option('display.max_rows', 80)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \n",
    "Below is the dataset created after extensive cleaning in the first notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(994, 71)\n"
     ]
    },
    {
     "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>Unnamed: 0</th>\n",
       "      <th>playerID</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>nameFirst</th>\n",
       "      <th>nameLast</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>debut</th>\n",
       "      <th>finalGame</th>\n",
       "      <th>award_winner</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>DBL</th>\n",
       "      <th>TPL</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "      <th>OBP</th>\n",
       "      <th>BA</th>\n",
       "      <th>TB</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OPS</th>\n",
       "      <th>salary</th>\n",
       "      <th>seasons</th>\n",
       "      <th>G_p</th>\n",
       "      <th>W_p</th>\n",
       "      <th>L_p</th>\n",
       "      <th>GS_p</th>\n",
       "      <th>CG</th>\n",
       "      <th>SHO_p</th>\n",
       "      <th>SV_p</th>\n",
       "      <th>IPouts</th>\n",
       "      <th>ER_p</th>\n",
       "      <th>HR_p</th>\n",
       "      <th>SO_p</th>\n",
       "      <th>IBB_p</th>\n",
       "      <th>BAOpp</th>\n",
       "      <th>ERA</th>\n",
       "      <th>WP</th>\n",
       "      <th>HBP_p</th>\n",
       "      <th>BK</th>\n",
       "      <th>BFP</th>\n",
       "      <th>GF</th>\n",
       "      <th>R_p</th>\n",
       "      <th>SH_p</th>\n",
       "      <th>SF_p</th>\n",
       "      <th>GIDP_p</th>\n",
       "      <th>WHIP</th>\n",
       "      <th>IP</th>\n",
       "      <th>BB_p</th>\n",
       "      <th>throws_R</th>\n",
       "      <th>bats_L</th>\n",
       "      <th>bats_R</th>\n",
       "      <th>prim_pos_2B</th>\n",
       "      <th>prim_pos_3B</th>\n",
       "      <th>prim_pos_C</th>\n",
       "      <th>prim_pos_OF</th>\n",
       "      <th>prim_pos_P</th>\n",
       "      <th>prim_pos_SS</th>\n",
       "      <th>throws</th>\n",
       "      <th>prim_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>aardsda01</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>David</td>\n",
       "      <td>Aardsma</td>\n",
       "      <td>215.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2004-04-06</td>\n",
       "      <td>2015-08-23</td>\n",
       "      <td>0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>419000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.310333</td>\n",
       "      <td>5.742511</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>145.666667</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.948782</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>R</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>abadfe01</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Fernando</td>\n",
       "      <td>Abad</td>\n",
       "      <td>235.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2010-07-28</td>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>0</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>1087500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.666667</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>21.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.279000</td>\n",
       "      <td>5.083699</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>127.666667</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.511177</td>\n",
       "      <td>28.222222</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>L</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>abreuto01</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Tony</td>\n",
       "      <td>Abreu</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2007-05-22</td>\n",
       "      <td>2014-07-28</td>\n",
       "      <td>0</td>\n",
       "      <td>48.666667</td>\n",
       "      <td>122.333333</td>\n",
       "      <td>11.666667</td>\n",
       "      <td>30.666667</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>0.335772</td>\n",
       "      <td>0.251415</td>\n",
       "      <td>53.333333</td>\n",
       "      <td>0.379815</td>\n",
       "      <td>0.715586</td>\n",
       "      <td>745000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>R</td>\n",
       "      <td>2B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>accarje01</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Accardo</td>\n",
       "      <td>195.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2005-05-04</td>\n",
       "      <td>2012-09-27</td>\n",
       "      <td>0</td>\n",
       "      <td>52.333333</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1080000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>52.333333</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>42.333333</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.336667</td>\n",
       "      <td>5.652214</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>27.333333</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.716016</td>\n",
       "      <td>55.333333</td>\n",
       "      <td>17.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>R</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>ackledu01</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Dustin</td>\n",
       "      <td>Ackley</td>\n",
       "      <td>205.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2011-06-17</td>\n",
       "      <td>2016-05-29</td>\n",
       "      <td>0</td>\n",
       "      <td>118.666667</td>\n",
       "      <td>441.333333</td>\n",
       "      <td>54.333333</td>\n",
       "      <td>108.333333</td>\n",
       "      <td>18.666667</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>45.333333</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.320469</td>\n",
       "      <td>0.250526</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>0.431767</td>\n",
       "      <td>0.752236</td>\n",
       "      <td>2600000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>R</td>\n",
       "      <td>1B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   playerID  birthYear nameFirst nameLast  weight  height  \\\n",
       "0           0  aardsda01     1981.0     David  Aardsma   215.0    75.0   \n",
       "1           1   abadfe01     1985.0  Fernando     Abad   235.0    74.0   \n",
       "2           2  abreuto01     1984.0      Tony    Abreu   200.0    70.0   \n",
       "3           3  accarje01     1981.0    Jeremy  Accardo   195.0    72.0   \n",
       "4           4  ackledu01     1988.0    Dustin   Ackley   205.0    73.0   \n",
       "\n",
       "        debut   finalGame  award_winner           G          AB          R  \\\n",
       "0  2004-04-06  2015-08-23             0   27.000000    0.666667   0.000000   \n",
       "1  2010-07-28  2019-09-28             0   29.333333    2.666667   0.000000   \n",
       "2  2007-05-22  2014-07-28             0   48.666667  122.333333  11.666667   \n",
       "3  2005-05-04  2012-09-27             0   52.333333    2.333333   0.000000   \n",
       "4  2011-06-17  2016-05-29             0  118.666667  441.333333  54.333333   \n",
       "\n",
       "            H        DBL       TPL        HR        RBI        SB        CS  \\\n",
       "0    0.000000   0.000000  0.000000  0.000000   0.000000  0.000000  0.000000   \n",
       "1    0.333333   0.000000  0.000000  0.000000   0.000000  0.000000  0.000000   \n",
       "2   30.666667   8.333333  0.666667  1.000000  10.333333  0.666667  0.666667   \n",
       "3    0.333333   0.000000  0.000000  0.000000   0.000000  0.000000  0.000000   \n",
       "4  108.333333  18.666667  3.666667  7.333333  39.000000  7.000000  2.000000   \n",
       "\n",
       "          BB         SO       IBB       HBP        SH        SF      GIDP  \\\n",
       "0   0.000000   0.000000  0.000000  0.000000  0.333333  0.000000  0.000000   \n",
       "1   0.000000   1.333333  0.000000  0.000000  0.000000  0.000000  0.333333   \n",
       "2   4.666667  23.333333  0.333333  1.000000  0.000000  2.000000  4.333333   \n",
       "3   0.000000   0.333333  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "4  45.333333  91.666667  3.000000  0.333333  1.666667  1.666667  4.000000   \n",
       "\n",
       "        OBP        BA          TB       SLG       OPS     salary  seasons  \\\n",
       "0  0.000000  0.000000    0.000000  0.000000  0.000000   419000.0      7.0   \n",
       "1  0.047619  0.047619    0.333333  0.047619  0.095238  1087500.0      5.0   \n",
       "2  0.335772  0.251415   53.333333  0.379815  0.715586   745000.0      4.0   \n",
       "3  0.166667  0.166667    0.333333  0.166667  0.333333  1080000.0      5.0   \n",
       "4  0.320469  0.250526  186.000000  0.431767  0.752236  2600000.0      5.0   \n",
       "\n",
       "         G_p       W_p       L_p  GS_p   CG  SHO_p  SV_p      IPouts  \\\n",
       "0  27.000000  2.000000  0.333333   0.0  0.0    0.0   0.0   96.000000   \n",
       "1  29.333333  0.333333  3.666667   2.0  0.0    0.0   0.0   84.666667   \n",
       "2   0.000000  0.000000  0.000000   0.0  0.0    0.0   0.0    0.000000   \n",
       "3  52.333333  2.333333  4.333333   0.0  0.0    0.0  11.0  166.000000   \n",
       "4   0.000000  0.000000  0.000000   0.0  0.0    0.0   0.0    0.000000   \n",
       "\n",
       "        ER_p      HR_p       SO_p     IBB_p     BAOpp       ERA        WP  \\\n",
       "0  18.333333  4.666667  30.000000  1.000000  0.310333  5.742511  1.000000   \n",
       "1  16.000000  4.666667  21.666667  0.333333  0.279000  5.083699  1.333333   \n",
       "2   0.000000  0.000000   0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "3  23.333333  4.333333  42.333333  2.666667  0.336667  5.652214  1.666667   \n",
       "4   0.000000  0.000000   0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "      HBP_p        BK         BFP         GF        R_p      SH_p      SF_p  \\\n",
       "0  1.333333  0.000000  145.666667   7.000000  19.000000  1.000000  1.666667   \n",
       "1  1.333333  0.000000  127.666667   5.000000  17.000000  1.000000  1.333333   \n",
       "2  0.000000  0.000000    0.000000   0.000000   0.000000  0.000000  0.000000   \n",
       "3  1.333333  0.666667  232.000000  27.333333  24.666667  0.666667  2.000000   \n",
       "4  0.000000  0.000000    0.000000   0.000000   0.000000  0.000000  0.000000   \n",
       "\n",
       "     GIDP_p      WHIP         IP       BB_p  throws_R  bats_L  bats_R  \\\n",
       "0  1.333333  1.948782  32.000000  18.333333         1       0       1   \n",
       "1  1.333333  1.511177  28.222222  11.000000         0       1       0   \n",
       "2  0.000000  0.000000   0.000000   0.000000         1       0       0   \n",
       "3  3.000000  1.716016  55.333333  17.666667         1       0       1   \n",
       "4  0.000000  0.000000   0.000000   0.000000         1       1       0   \n",
       "\n",
       "   prim_pos_2B  prim_pos_3B  prim_pos_C  prim_pos_OF  prim_pos_P  prim_pos_SS  \\\n",
       "0            0            0           0            0           1            0   \n",
       "1            0            0           0            0           1            0   \n",
       "2            1            0           0            0           0            0   \n",
       "3            0            0           0            0           1            0   \n",
       "4            0            0           0            0           0            0   \n",
       "\n",
       "  throws prim_pos  \n",
       "0      R        P  \n",
       "1      L        P  \n",
       "2      R       2B  \n",
       "3      R        P  \n",
       "4      R       1B  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('rookie.csv')\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \n",
    "Some visualizations to show the distributions amongst the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "prim_pos\n",
       "1B    15000000.0\n",
       "2B     6000000.0\n",
       "3B     6916667.0\n",
       "C     12500000.0\n",
       "OF    16083333.0\n",
       "P     14000000.0\n",
       "SS     6000000.0\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('prim_pos').salary.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "prim_pos\n",
       "1B    2.872115e+06\n",
       "2B    1.917065e+06\n",
       "3B    2.620109e+06\n",
       "C     1.983812e+06\n",
       "OF    2.684265e+06\n",
       "P     2.314292e+06\n",
       "SS    2.264286e+06\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('prim_pos').salary.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \n",
    "The distributions of salary differentiated by the primary position of a player."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize = (15,10))\n",
    "sns.boxplot(x = 'prim_pos', y = 'salary', data = df, palette='coolwarm')\n",
    "\n",
    "axes.set_ylabel('Salary in Millions')\n",
    "axes.set_xlabel('Primary Position')\n",
    "axes.set_title('Distribution of Salaries by Position')\n",
    "\n",
    "plt.savefig('PNG/sal_primpos.png', dpi=300, \n",
    "            bbox_inches= 'tight', padding_inches = 4, transparent = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize = (15,10))\n",
    "sns.boxplot(x = 'prim_pos', y = 'H', data = df[df['prim_pos']!='P'], palette='coolwarm')\n",
    "\n",
    "axes.set_ylabel('Average Hits Per season')\n",
    "axes.set_xlabel('Primary Position')\n",
    "axes.set_title('Distribution of Hits Per Season by Position')\n",
    "\n",
    "plt.savefig('PNG/hits_primpos.png', dpi=300, \n",
    "            bbox_inches= 'tight', padding_inches = 4, transparent = True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \n",
    "In the two graphs below, you can see:\n",
    "    \n",
    "    a) The count of left handed batters is far smaller than the count of right handed batters\n",
    "    b) The average salary is higher for a left handed batter\n",
    "\n",
    "Why is that the case?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axes = plt.subplots(figsize = (10,10))\n",
    "\n",
    "\n",
    "sns.countplot(x= 'throws', data = df, palette = 'coolwarm')\n",
    "\n",
    "axes.set_ylabel('Count')\n",
    "axes.set_xlabel('Handedness')\n",
    "axes.set_title('Count of Left vs Right Handed Players')\n",
    "\n",
    "\n",
    "plt.savefig('PNG/right_left_play.png', dpi=300, \n",
    "            bbox_inches= 'tight', padding_inches = 4, transparent = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df.groupby('throws').R.mean().reset_index()\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(figsize = (10,10))\n",
    "\n",
    "sns.barplot(x= 'throws',y = 'R', data = x, palette='coolwarm')\n",
    "\n",
    "\n",
    "axes.set_ylabel('Mean Runs in Millions')\n",
    "axes.set_xlabel('Handedness')\n",
    "axes.set_title('Mean Runs Left vs Right Handed Players')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('PNG/right_left_runs.png', dpi=300, \n",
    "          bbox_inches= 'tight', padding_inches = 4, transparent = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df.groupby('throws').salary.median().reset_index()\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(figsize = (10,10))\n",
    "\n",
    "sns.barplot(x= 'throws',y = 'salary', data = x, palette='coolwarm')\n",
    "\n",
    "\n",
    "axes.set_ylabel('Mean Salary in Millions')\n",
    "axes.set_xlabel('Handedness')\n",
    "axes.set_title('Mean Salary Left vs Right Handed Players')\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('PNG/right_left_meansal.png', dpi=300, \n",
    "            bbox_inches= 'tight', padding_inches = 4, transparent = True )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The convention in baseball is that a pitcher has an advantage when thowing against someone who throws with the same hand.\n",
    "\n",
    "In plainer terms, a lefty pitcher has an advantage against a left handed batter. The same is true with right handed pitchers. \n",
    "\n",
    "The higher average can be attributed to two things:\n",
    "\n",
    "    a) the lack of sampling of left handed hitters shows bias in the data\n",
    "    b) the rarity of left handed hitters and the prevalence of right handed pitchers make them a more precious commodity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R    774\n",
       "L    220\n",
       "Name: throws, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['throws'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['sal_log'] = np.log(df['salary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (15,10))\n",
    "\n",
    "df.salary[df.throws_R == 1].plot(kind = 'hist', bins = 30, alpha = 0.7, label = \"Righty\")\n",
    "df.salary[df.throws_R == 0].plot(kind = 'hist', bins = 30, alpha = 0.7, label = \"Lefty\", color = 'r')\n",
    "plt.legend()\n",
    "plt.title(\"Distribution of Salary per Handedness in Population\")\n",
    "plt.xlabel('Salary')\n",
    "plt.savefig('PNG/hand_salary_dist.png', dpi=300, \n",
    "            bbox_inches= 'tight', padding_inches = 4, transparent = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3MAAAJaCAYAAACFnR4wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde5ikZ10n/G9Vd9dkZrpzGoZTYAku4WZBhAUiIItEg9G8ILCKkAtU5CAIQVh0AQ+IssjLmUUFUUAMqNmIQVBRDoZDOAiR84KLNwE0r3hgkzFDuieT6e7pfv+omtDT6UN1d1V3P92fz3XNNd31PHXf93Oo6vrWc/i15ufnAwAAQLO0t3oAAAAArJ0wBwAA0EDCHAAAQAMJcwAAAA0kzAEAADSQMAcAANBAo1s9AIBBKqWcneRrSb7Ye6idZCrJa2utb+/N8z+SfLXW+rYV2nlhki/UWv9siWk3P7+UMp/kYK31ujWM8dwkT661/kwp5X5JfqHW+uh+n78epZSRJH+a5D8l+c1a6+sWTX9eksclaSUZSfLeJL9Ua51epd01L3+f4z0vyetqrd85yHaHpbfffanWOr7o8V9Lcqta6zMH1M/rklxXa/21NTznfkkur7WePYgxrFUp5c1JLqu1XrGG58wn+VKS40nmk4wl+aNa60uHML7z0se+tvA9oZ/3EIDNIMwBO9HRWuu9T/xSSrlTkg+UUo7XWt9Ra31hH218f5L/s9SEPp+/knskuUOvrU8nGWqQ6zkryQ8m2V9rPb5wQinlx5L81yQPrLUeLaWckuTyJL+W5Jc2YWzsYLXWp6zzqd934kuCUsqpST5fSvlirfXdgxvdmtz8njCA9wCAgRDmgB2v1npN71v15yZ5RynlknSPoryqlPKidIPMdJJDSX4qyY8kuV+SV5ZSjid5ZJIzk/zHJO9OcpsTz+918ZLe0bZ2khfUWt9dSvmpJI+utT48SU78nuTpSf5HktNKKb+f5K3pHRUopZyW5PVJ7p3u0Yj3pHt0bLaUclOSlyW5IMntkryi1vqGxctaSnlwklcm2ddbphck+Xi6R9rGknymlPKjtdavLXja7dI9Grc33SB8UynlmUlu3Wvzrr1xTfTm/XySx9Zab1rQ7/4kb0hyTpIDSSaTPK7WWkspH07y70nuluSPk/x8kjvWWr9VSmklqUl+rNb6heW24aJlvEOvr7PTPZL41lrrKxes519IcjTJB5M8u9Y6uuj5Zye5srdO7t9r45m11o/2pv9ykh9Nd3v+Y5Jn1Fr/ZdFyvKHW+lv9jHdBvw9I8ooke9Jdj39da31ybzwfSPJXvfGckeR5tdZ39kLMm5PcK8m/JplN8rFee2cleV2S/5Dutr2s1vr/9qY9Pclzknwr3z5KfeJI4dm9/u+U5J+T/Hit9V+Xa6+UMprkt5I8KMlMkq8neWKSm5Z6vNY6tWi5P9xr99PLLedq667WekMp5dPprvt3l1KemuRZ6R65+2a62+8rvdf20XRfQ7dO8v4kz6q1ziw+inzi90VjXXJfT/Lk3PI94cR7yC1ec7XW9/b2xf+aZC7d18WNSZ5Qa/3yassL0C/XzAG7xReS3HPhA6WUOyb5b0nOrbXeL90Pfvevtb4+3Q+ez13wQXNfrfUetdbnL9H212ut90ny40neWko5uMQ8SZJa6z8leWGSj9Zan7ho8m+mGyjvme4Hx3sl+e+9aXvSPb3ue9INhf+zdwRt4fIcSPeI2rNrrd+V5AlJ/jDJrZL8P+kdsVwU5JJuoDyc5N9KKZ8opbw6yX+otf5tb/pPpxuYHpDkLknunORhi9q4MMnhWusDa613TfKpJAtPLby+1nr3WuuL0g1Zj+89/n1JDvUb5Hr+KMmHaq33TDdI/Hgp5aJSyt2TvDzJQ2ut/znJDemG1KX8hyRX9o7g/kKSPy6ljJVSfjLd9f/dvWl/lW6YWrwcSwW5vaWUzy/8l+RnFkx/dpIX1lrvn+TuSR5RSrlvb9p3JHlfrfW7e+N5be/xF6UbTu6W5MeSlAXt/UGSt9Ra75vku5M8tJTymFLKvdM9qvq9tdZz0w0YCz043fB8tyRHFoxxyfaSPDDJeUnu1Zv29STftcLjK1luOVdUSilJHpLkylLK9yd5XrpH7u6V5NIk7+p9MZB0g+IPpLuO757kaf300bPkvr7Me8Kyr7lSyp17szwkyc/2TuG8qrfMAAMjzAG7xXy634wv9M/phrzPllJeleTztdZ3LfP8j63Q9u8kSa31S+mehvXAdY7xwnSP0s3XWo/12r1wwfQT1+99Nt1wt3/R8++f7nU8V/XG83fpHpU7b6VOa63fqrVekG5geHO6RzT+spTy8t4sz09ybe+6ujckuX2S8UVtXJ7kklLKz5ZSfqPX58J5Prrg59en+6E56X7QvsURxuX0jgA+qNdGaq3fSnJJuuvpB5O8v9b6jd7sKx05u77Wemmvjfeke4Tnu5I8PMkDkny6F8Z+NicHqI8ubmiBE2H55n/p7Rs9T0hyeinll5L8drpHQk+so5l0g2PS3b5n9n5+aJK39faJa5O8c8F6eEiSF/fG+cl0A+q9k5zfWw//1mvjjYvG+eFa6w29nz+X5MxV2vtib/1cVUp5cZJ31Fr/ZoXHV7Lcci7lQ71Q/MV0v3D477XWTyX5oSR/3FsfqbVeku5pxGf3nndJrXWq9xp6W7r7Rb9W3dcXWe0195kF++NqywuwZk6zBHaLc7PgdLMkqbXOlVIeku5RsIeme7TrvbXW5y3x/KklHjth4TVo7XQ/sM6ne/reCZ0+xtjuPW/h72MLfj/aG/d890DFSe0n3aNQ84seW9zGLfQ+uH6s90H860l+r5TyX9I9DfH5Sf5Xun8v3p7kL9P9kN9a1MbTkzw13dPpLk33dMQ7L5hl4fq7Ism+Usr5Sb433ZDTr/bivvPtZZxdNO14lje7RBvH012HLz9xCmspZU+6pwOesNJ+sJqPJPnf6a7Xt+fbp3gmyXStda738+J9Z+HPJ8Y90nv8e2qtN/bGeqt0T3182jLPOeHogp9P9LVse7XWqVLKvdIN0d+f7lHMV9Zaf3u5x1dYByst52I3XzO3yEhuebSxlW/v5wuX98R2XThfSinLvR5X3deXGMtyr7npLL2uAQbGkTlgx+tdB/MrSV696PF7pXvHvC/X7l3y/me6oS/pfiBcMQQt8FO99u6T7qlZVyW5Nsl3llJOKaWM5eSbnCzX9vuSPLOU0uqFiKcm+es+x5Akn0hyt1LKd/fGc490w9KHV3neviQvK6UsPGpwz3SPJCTdIxv/o9b6x73f759bnr74g+keEfm9dK+B++El5knSDaPpHpl6c5JLF157t5pa62S6R40uTpLedYY/me56el+6pwae1Zt9pRtvHCyl/FCvjR9ON4B/sdfGU3rXqiXd6xv/oN/xLaeUcnq6+9bza61/mu4NcO6S5U8DPeE9SZ5cSmmXUs5I91qt9I6sfTLJzy1o/+O96e9PckHv2sKkt3+uZKX2SikPT/dat7+p3btovi3Jucs9vlpfA/DeJBedOJ25lPLEdE9P/mpv+mNLKXt6pyE/Iclf9B6/Nt0vbpLunVuXstK+vtTrdr2vOYCBcGQO2In29k4VS7o3H7gpyS/WWv9y4Uy11i+UUt6e7il1U+l+i/6s3uQ/T/LSFb7BX+g7SimfS/eb94tqrf9eSnl/ujfZ+Pt0b1zxoXz7eqJPJvnVUsqfpnud3AnPSvfUwC+meyTvvUle0u9C11qvK907U/5WKWVfb9mf2LsxxNkrPPXFvXn/pndTiJF0r3l7TG/6LyV5ZynlSLo31Lgy3SCy0KuSvLGU8uR0jz58IouuUVzkremG699dYZ7/1NsuC52V7vV2r+99iO+keyTwkt4Ry+ckeV/p3jDm87nlqbUn3JTkJ3qnkh5N8qha6/HSvY3+WUk+2VsX/1/6CEOrqbUeLqW8NN1Teo8k+Ua6Yeku6ZbSWM6vpXuq5t8n+b85+ejy45K8rncaYifJ/6q1/lFy89HWD5RSJpP8bfqzZHulW9biwiRf6m2P69M9Tfaflnl8qGqtf11K+Z9JPlhKaacb0h7eO9KedLf5R9M9onp5kt/vPfVZ6e43h9MN//+6RPMr7eu3eE9Y5TX3PQNdcIAltObnF58dAADDVUq5KN07+1246sz9t3nndI/Svbj3wf5H0j0Sdv9F852dJWrC0XxlwZ1qt3osAJvBkTkANlXp3qr+YHqnDA7QN9K9YcUXSymz6R5ZedKA+wCAbcOROQAAgAZyAxQAAIAGEuYAAAAaSJgDAABooG19A5Rrr51c9oK+M87Yl+uvX+6O02wG22BrWf9bzzbYerbB1rMNtp5tsPVsg62109f/wYMTreWmNfbI3OjoanVWGTbbYGtZ/1vPNth6tsHWsw22nm2w9WyDrbWb139jwxwAAMBuJswBAAA0kDAHAADQQMIcAABAAwlzAAAADSTMAQAANNC2rjMHAADsXu32siXW1jXf3NyyZawbSZgDAAC2nXa7lcs+cHWmjs6sOF+nM5Lp6eOrtje+dywXnX/OqoHus5/9dF74wl/M2WffOa1WK0eOHMntb39WHve4n8hVV30iT3ziTy/5vL/6q7/INdf8Y57+9J896fHPf/6zGR+fyF3ucs6qY1wrYQ4AANiWpo7OZPLI9IrzdGZHM31sdqD93ve+98uLXvTSm3//tV/75Xzzm/+2bJBbyV/+5Z/n/PMvEOYAAAA208zMTA4dui4TE6fmV3/1F/OiF7007373u/KOd7w9p556WkZHx3L++T+QJPm7v/tinvOci3P48PV51KMenVL+U6666hP5ylf+Ptdf/+/5+Mc/ml//9ZcnSZ7+9CflxS9+RW51q1ute2zCHAAAwAKf+cyn88xnPjWHD1+fVquVRzziR9Jud+8defjw4fzhH74tl1xyacbGxvKsZ/3Mzc8bHR3Na17zuvzbv/1rnvvcZ+cP//BPcv/7PzDnn39B7n//B+YP/uD3c8MNN+TQoety2mmnbyjIJcIcAADASU6cZvmtbx3Oc55zcW53u9vfPO0b3/in3PnOd84pp5ySJPnO7/yum6fd9a53S6vVyplnHshNN910UputVisXXHBhrrjiffmXf/nnPPzhj9zwOJUmAAAAWMJpp52eX/mVF+flL//1HDp0XZLkDne4Y6655h9z7NhNmZuby5e//Hc3z99q3fKumq1WK/Pzc0mShz3sEfnQh67IF77w2TzgAQ/a8PgcmQMAALal8b1jq87T6YxkenT1Y1T9tLWUO9/5O/LoRz82v/Ebr8p973tuTj/99Dz+8U/IM57x0zn11FNz7NixjI6OZnZ26Zuw3P3u35nf+Z3X5Xa3Oytnn33n7Nu3L/e4xz0zOrrxKCbMAQAA287c3HwuOn/1O0AeODCeQ4em+m5zNfe5z/1yn/vc76THnvCEJ+cJT3hykmR2djbXXXdtfu/3/iBJcvHFP51b3/o2ufe973Pz/Hv27Mnll/9FkuRRj/rRPOpRP3rztPn5+YGcYpkIcwAAwDbVb5HvzSwGPjo6mptuuilPetLjMzo6lrvf/Ttzr3v951Wfd+zYTXn605+S+9//gbnDHe44mLEMpBUAAIBd4mlPuzhPe9rFa3rOnj2n5C1v+cOBjsMNUAAAABpImAMAAGggYQ4AAKCBXDMHAABsS+32Leu2bWS+zbxRymYYapgrpfxikkck6ST57SRXJrkkyXySLyW5uNY6N8wxAAAAzdNut3LdH1+a2akjK853aM9IZo4dX7W90fH9udVjH7dqoPvsZz+dP/uzd+RFL3rpivMdP348z33us3P06NG87GWvzlVXfTIXXPBDq45jkIZ2mmUp5bwk35PkQUkekuSOSV6T5AW11gcnaSUZTIEFAABgx5mdOpKZyckV/81OTq06z8zk5KqhcK0OHbouhw8fzhve8Hv52te+mo9//MqBtt+PYR6Z+8EkX0zyziSnJnlukp9O9+hckrwnyQW96QCwrfV7Cs9G7LTTfwB2is997jN54xt/OyMjI7n97c/K8573y3nFK16Sb3zjn/KKV7wk//Iv/5yvfvXq/Nmf/WkuvfRtedOb3ppTTz0t73zn5Tl69MY87nE/OZRxDTPM3SrJnZI8PMmdk/x5knat9cRfqskkp63UwBln7Mvo6Miy0w8enBjMSFk322BrWf9bzzbYepu1Dd74rv+dI0dnhtb+/r1jeeqjvmto7Q+T18HWsw22nm0wHIf2jKQ1vXpk6exZfZ7RPSM5cGB81flOP31f9uwZu3mbzs/P59WvfmkuvfTSHDhwIK997Wvz0Y/+dV7ykhfn537u5/LKV74sV111VS677LI85SlPyI03fiuf/OSVefzjH58PfvB9ed3rXpdb3Wo4+8cww9yhJH9fa51OUkspN6V7quUJE0kOr9TA9dffuOy0gwcncu21k4MYJ+tkG2wt63/r2QZbb7O2QbvdyvXfuimTR6aH1sf09PEcOjTVuKNzXgdbzzbYerbBcLTbrcwcO56ZY7MrztfZM5rpVeZJkvlOf++zhw/fmGPHZm7eptdf/+/55jf/b57xjGcmSY4dO5bv/u4H5G53u1dmZo7n2msnT3rO933fhfnVX/3F3OUud8/ExOmZn9+zof1jpS8KhhnmPpbk2aWU1yS5XZL9ST5QSjmv1vrhJBcm+dAQ+wcAANiQ0047Pbe+9a3zspe9JuPj4/nYx67M3r37Tpqn3W7fHBJve9vbZnx8Im9961vy8IcP9xYhQwtztdZ3l1K+N8nfpnujlYuT/EOSN5VSOkm+nOTyYfUPAAA02+j4/tXn2TOS+U5/d7Ps19/+7VV58pN/4ubfH/OYx+W5z3125ufns2/f/vzKr7woN910083TzzrrDvn617+at7/90jzmMY/LIx7xqLz2ta/KC1/44r77XI+hliaotT5viYcfMsw+AQCA5pubm8+tHvu4Vec7cGA8hw5N9d3mau5zn/vlPe/54C0ef+Qjf+QWj73xjZckSQ4evHX+6I++fZxqdvZ4HvawR2RkZPn7fwyCouEAAMC21O91xNvpeuPf/d3X5wtf+Fxe9rJXD70vYQ4AAGBAnva0izetr6EVDQcAAGB4hDkAAIAGEuYAAAAaSJgDAABoIGEOAACggYQ5AACABhLmAAAAGkiYAwAAaCBhDgAAoIGEOQAAgAYS5gAAABpImAMAAGggYQ4AAKCBhDkAAIAGEuYAAAAaSJgDAABoIGEOAACggYQ5AACABhLmAAAAGkiYAwAAaCBhDgAAoIGEOQAAgAYS5gAAABpImAMAAGggYQ4AAKCBhDkAAIAGEuYAAAAaSJgDAABoIGEOAACggYQ5AACABhLmAAAAGkiYAwAAaCBhDgAAoIGEOQAAgAYS5gAAABpodKsHAAB0tdutofcxNzc/9D4A2BzCHABsA+P7xnLpFVdn6sbp4fWxdywXnX+OQAewQwhzALBNTB2dyeSR4YU5AHYW18wBAAA0kDAHAADQQMIcAABAAwlzAAAADSTMAQAANJAwBwAA0EDCHAAAQAMJcwAAAA0kzAEAADSQMAcAANBAwhwAAEADCXMAAAANJMwBAAA0kDAHAADQQKNbPQAAYPO0261NaXNubn7g/QBwMmEOAHaJ8X1jufSKqzN14/TA2ux0RjI9ffzkfvaO5aLzzxHoAIZMmAOAXWTq6EwmjwwwzM2OZvrY7MDaA6B/rpkDAABoIGEOAACggYQ5AACABhLmAAAAGkiYAwAAaCBhDgAAoIGEOQAAgAYS5gAAABpImAMAAGggYQ4AAKCBhDkAAIAGEuYAAAAaSJgDAABoIGEOAACggYQ5AACABhLmAAAAGkiYAwAAaCBhDgAAoIGEOQAAgAYS5gAAABpImAMAAGig0WE2Xkr5XJJv9X79hyQvSXJJkvkkX0pyca11bphjAAAA2ImGFuZKKackSa31vAWP/XmSF9RaP1xK+Z0kj0zyzmGNAQAAYKca5pG5eyXZV0p5f6+fX0py3yRX9qa/J8kFEeYAAADWbJhh7sYkr0ry5iTnpBveWrXW+d70ySSnrdTAGWfsy+joyLLTDx6cGMxIWTfbYGtZ/1vPNth6m7UNOp2RdGaH92dzbGwkndm5dPY0r4/F7XU6IzlwYHygfbAy70VbzzbYWrt1/Q8zzH0lyVd74e0rpZRD6R6ZO2EiyeGVGrj++huXnXbw4ESuvXZyEONknWyDrWX9bz3bYOtt1jZot1uZnj6e6WOzQ+tjZuZ4pmfmGtdHZ8/oLdqbHm3n0KGpzM3NL/MsBsl70dazDbbWTl//KwXVYd7N8klJXp0kpZTbJzk1yftLKef1pl+Y5KND7B8AAGDHGuaRud9Lckkp5WPp3r3ySUmuS/KmUkonyZeTXD7E/gEAAHasoYW5Wut0ksctMekhw+oTAABgt1A0HAAAoIGEOQAAgAYS5gAAABpomDdAAYBN0W63Gt0+AKyHMAdAo7XbrVz2gaszdXRmaH3c5sx9SUugA2B7EeYAaLypozOZPDI9tPbH940NrW0AWC/XzAEAADSQMAcAANBAwhwAAEADCXMAAAANJMwBAAA0kDAHAADQQMIcAABAAwlzAAAADSTMAQAANJAwBwAA0EDCHAAAQAMJcwAAAA0kzAEAADSQMAcAANBAo1s9AABg52m3W0PvY25ufuh9AGxnwhwAMFDj+8Zy6RVXZ+rG6eH1sXcsF51/jkAH7GrCHAAwcFNHZzJ5ZHhhDgDXzAEAADSSMAcAANBAwhwAAEADCXMAAAANJMwBAAA0kDAHAADQQMIcAABAAwlzAAAADSTMAQAANJAwBwAA0EDCHAAAQAMJcwAAAA0kzAEAADSQMAcAANBAwhwAAEADCXMAAAANJMwBAAA0kDAHAADQQMIcAABAAwlzAAAADSTMAQAANJAwBwAA0EDCHAAAQAMJcwAAAA0kzAEAADSQMAcAANBAwhwAAEADCXMAAAANJMwBAAA0kDAHAADQQMIcAABAAwlzAAAADSTMAQAANJAwBwAA0EDCHAAAQAMJcwAAAA0kzAEAADSQMAcAANBAwhwAAEADCXMAAAANJMwBAAA0kDAHAADQQMIcAABAAwlzAAAADSTMAQAANJAwBwAA0EDCHAAAQAMJcwAAAA0kzAEAADSQMAcAANBAwhwAAEADCXMAAAANJMwBAAA0kDAHAADQQMIcAABAA40Os/FSyq2TfCbJDySZTXJJkvkkX0pyca11bpj9AwAA7FRDOzJXShlL8rtJjvYeek2SF9RaH5ykleSRw+obAABgpxvmaZavSvI7Sf6l9/t9k1zZ+/k9SR46xL4BAAB2tKGEuVLKTyW5ttb6vgUPt2qt872fJ5OcNoy+2f7a7dbQ/wEAwE43rGvmnpRkvpTy0CT3TvK2JLdeMH0iyeHVGjnjjH0ZHR1ZdvrBgxMbHCYbtZ5t8MZ3/e8cOTozhNF07d87lqc+6ruG1v524jWw9WyDrXfgwHg6nZF0Zod3GfjY2Eg6s3Pp7NHHUha3txnL0umM5MCB8aG13zTei7aebbC1duv6H8q7bK31e0/8XEr5cJKfSfLKUsp5tdYPJ7kwyYdWa+f6629cdtrBgxO59trJDY+V9VvPNmi3W7n+Wzdl8sj0kEaVTE8fz6FDU5mbm1995gbzGth6tsHWO3hwIocOTWV6+nimj80OrZ+ZmeOZnpnTxxI6e0Zv0d5mLMv0aHtXvNf3w3vR1rMNttZOX/8rBdWh3s1ykZ9P8qZSSifJl5Ncvol9AwAA7ChDD3O11vMW/PqQYfcHAACwGygaDgAA0EDCHAAAQAMJcwAAAA20mTdAAbapzajN545z24/tDgDNJszBLtdut3LZB67O1BBr/43vHctF55/jg/02YrsDQPMJc0Cmjs4MtfYf25PtDgDN5po5AACABhLmAAAAGkiYAwAAaCBhDgAAoIGEOQAAgAYS5gAAABpIaQIAoJEUvgd2O2EOAGic8X1jufSKqzN14/BqJSp8D2x3whwA0EgK3wO7nWvmAAAAGkiYAwAAaCBhDgAAoIGEOQAAgAYS5gAAABpImAMAAGggYQ4AAKCBhDkAAIAGEuYAAAAaSJgDAABoIGEOAACggYQ5AACABhLmAAAAGkiYAwAAaCBhDgAAoIGEOQAAgAYS5gAAABpImAMAAGggYQ4AAKCBhDkAAIAGEuYAAAAaSJgDAABoIGEOAACggUa3egAATdNut5b8eZDm5uaH0i4AsHMIcwBr0G63ctkHrs7U0Zl0OiOZnj4+8D7G947lovPPEegAgBUJcwBrNHV0JpNHptOZHc30sdmtHg4AsEu5Zg4AAKCBhDkAAIAGEuYAAAAaSJgDAABooL5ugFJK+askv5/kz2qt08MdEgAAAKvp98jcy5P8UJKvlFJeX0o5d4hjgsZot1tD/wcAAEvp68hcrfXKJFeWUvYmeXSSd5RSbkjy5iRvqLUeG+IYYVtaWG9sWNQbAwBgOX3XmSulnJfkJ5JckOQ9SS5L8gNJ/jzJDw5jcLDdnag3BgAAm63fa+auSfL1dK+be2at9Wjv8Q8n+fTQRgcAAMCS+r1m7vuTPLbW+rYkKaXcJUlqrXO11vsMa3AAAAAsrd8w97Ak7+39fOskf1FKeepwhgQAAMBq+g1zT03y4CSptV6T5L5JfnZYgwIAAGBl/Ya5sSQL71g5ncTt9QAAALZIv3ezfFeSD5ZS3p5uiPvRdO9iCQAAwBbot87c80spj07ykCQzSX6z1vquoY4MYB2GXWhdIXcAYLvou85cki8n+WaSVpKUUr631vqRoYwKYB02o5D7bc7cl7QEOgBg6/VbZ+71SX44ydcWPDyfbskCgG1j2IXcx/eNDa1tAIC16PfI3AVJyoli4QAAAGytfu9m+fX0Tq8EAABg6/V7ZO7fk/yfUsrfJLnpxIO11icNZVQAAACsqN8w997ePwAAALaBfksTvLWUcnaSeyR5X5I71lr/YZgDAwAAYHl9XTNXSnlskr9I8htJzkzyiVLKjw9zYAAAACyv3xugPD/J9ySZrLX+3yT/OckvDm1UAAAArKjfMHe81jp54pda678mmRvOkAAAAFhNvzM89soAAB8aSURBVDdA+btSyjOTjJVS7p3kGUk+P7xhAQAAsJJ+j8xdnOSsJEeTvCXJDekGOgAAALZAv3ezPJLuNXKukwMAANgG+gpzpZS5JPOLHv7XWusdBj8kAAAAVtPvkbmbT8cspYwleVSSBw5rUAAAAKys32vmblZrnam1/kmS7x/CeAAAAOhDv6dZ/uSCX1tJ7pFkZigjAgAAYFX9lib4vgU/zye5LsljBz8cAAAA+tHvNXNPHPZAAAAA6F+/p1n+Q255N8uke8rlfK31OwY6KgAAAFbU72mWlyY5luRN6V4r9/gk5yb55SGNCwAAgBX0G+Z+sNZ6vwW//0Yp5TO11muGMSgAAABW1m+Ya5VSHlprvSJJSikPT3LDSk8opYykeySvJDme5InpnpZ5SbqnbH4pycW11rn1DR0AAGD36rfO3FPTPRp3qJRyXZJfSPKUVZ7zw0lSa31QkhcmeU3v3wtqrQ9ON9g9cl2jBgAA2OX6vZvlZ5Lco5RyqyRHa61H+njOu0op7+79eqck30zysCRX9h57T5ILkrxzzaMGAADY5fo6MldKuVMp5a+TfCLJRCnlg6WUs1d7Xq11tpTy1iS/leTyJK1a64m7Yk4mOW19wwYAANjd+r1m7neTvDLJy9M9wva/krwtyfeu9sRa6xNKKc9PclWSvQsmTSQ5vNJzzzhjX0ZHR5adfvDgxKoDZ7jWsw06nZF0Zvvd9dau0xnJgQPjQ2t/cV9buSyDeg1s9XIMuq9hLsvY2Eg6s3Pp7On2ceL/Qdqs9bVTtvuBA+Obvt31cbLF7TV5WRbazPeujfKZaOvZBltrt67/ft8Bb1VrfX8p5eW9I2tvKqVcvNITSik/keQOtdaXJrkxyVyST5dSzqu1fjjJhUk+tFIb119/47LTDh6cyLXXTvY5fIZhPdug3W5levp4po/NDmlUyfRoO4cOTWVubqnSiIOz1csyqNfAVi/HIG3GsszMHM/0zFymj82ms2d0KH1txvraKdv94MGJHDo0tanbXR8nW+p10NRlWWyz3rs2ymeirWcbbK2dvv5XCqr9hrmjpZQ7pFc4vJTyX9KtO7eSP03y+6WUjyQZS/Lfknw53SDY6f18eZ/9AwAAsEC/Ye45Sd6d5D+WUj6f5MwkP7bSE3o3SXnMEpMesqYRAgAAcAv9hrnbJDk3yV2TjCT5+1rr9NBGBQAAwIr6DXOvqLX+ZZK/G+ZgAAAA6E+/Ye5rpZS3pHtHyqMnHqy1vm0oowIAAGBFK9aZK6Wc1fvxUJJWkgck+b7ev/OGOjIAAACWtdqRub9Icp9a6xNLKT9fa331ZgwKAACAla14ZC7do3EnPH6YAwEAAKB/q4W5hVUyW8vOBQAAwKZaLcwtNL/6LAAAAGyG1a6Zu0cp5eu9n89a8HMryXyt9TuGNzQAAACWs1qYu+umjAIAAIA1WTHM1Vqv2ayBAAAA0L+1XDMHAADANrHaaZbAFmu3l7+R7ErTBtE+AADblzAH29j4vrFcesXVmbpx+hbTOp2RTE8f33AftzlzX9IS6AAAmkaYg21u6uhMJo8sEeZmRzN9bHbD7Y/vG9twGwAAbD7XzAEAADSQMAcAANBAwhwAAEADCXMAAAANJMwBAAA0kDAHAADQQEoTAJtiM4qT76QC6MNelp20rgBgtxLmgKFbqfj5IO2UAuibsb52yroCgN1MmAM2xXLFzwdpJxVAH/b62knrCgB2K9fMAQAANJAwBwAA0EDCHAAAQAMJcwAAAA0kzAEAADSQMAcAANBAwhwAAEADCXMAAAANJMwBAAA0kDAHAADQQMIcAABAAwlzAAAADSTMAQAANJAwBwAA0ECjWz0AAHaudru1I/oAgO1ImANgKMb3jeXSK67O1I3TQ+uj0xnJGeN7kpZAB8DuI8wBMDRTR2cyeWSIYW52NJ1RVwwAsDv5CwgAANBAwhwAAEADCXMAAAANJMwBAAA0kDAHAADQQMIcAABAAylNwEnWWnx32PMDAABLE+a4WbvdymUfuDpTR2f6mr/TGcn09PE19XGbM/cp7gsAAAMgzHGStRT47cyOZvrY7JraH983tp5hAQAAi7hmDgAAoIGEOQAAgAYS5gAAABpImAMAAGggYQ4AAKCB3M0SAIaslWR8f2fFefafMtadc35+3f1MHZnO+p/NUjajPurcnK0GrI8wBwBDNr6/k3Ov+XiO3TC57DydsdHMzc1l9vjcuvrYc+pEPnWnB/VdXobVje8by6VXXJ2pG4e3Tsf3juWi888R6IB1EeYAYBMcu2EyNx2+Ydnp853RHD++/jDHcKyl/irAZnPNHAAAQAMJcwAAAA0kzAEAADSQMAcAANBAwhwAAEADCXMAAAANJMwBAAA0kDAHAADQQMIcAABAAwlzAAAADSTMAQAANJAwBwAA0EDCHAAAQAMJcwAAAA0kzAEAADSQMAcAANBAwhwAAEADCXMAAAANJMwBAAA0kDAHAADQQMIcAABAA40Oo9FSyliStyQ5O8meJL+e5P8kuSTJfJIvJbm41jo3jP4BAAB2umEdmfvxJIdqrQ9OcmGS1yV5TZIX9B5rJXnkkPoGAADY8YYV5v4kya8s+H02yX2TXNn7/T1JHjqkvgEAAHa8oZxmWWudSpJSykSSy5O8IMmraq3zvVkmk5y2WjtnnLEvo6Mjy04/eHBi44PlJJ3OSDqz/e8WnT1r24XGxkbSmZ1b8/PWotMZyYED40Nrf3Ffa1lfa7Xa+hrEetyMbbIZfWxWP4v7GEZfO2Wb7OTtvladzkha7XZa7eW/Q223W5mbb6U1v77vWVvt9qrvScNaV4vba8I22S59DOpvls9EW8822Fq7df0P7d2plHLHJO9M8tu11ktLKa9YMHkiyeHV2rj++huXnXbw4ESuvXZyw+Pk29rtVqanj2f62Gxf83f2jPY97wkzM8czPTO35uetxfRoO4cOTWVubn71mTdgretrPVZaX+tZ/2vtY1A2o4/N6mdhH4PaBiv1MSw7pY/OntFGLMv0aDvzc3OZn1v+UvG5uXbm5+ZXnGcl83Nzq74nDWNdLfU6aMI22S59DOJvls9EW8822Fo7ff2vFFSHcpplKeU2Sd6f5Pm11rf0Hv5cKeW83s8XJvnoMPoGAADYDYZ1ZO6XkpyR5FdKKSeunXt2kt8spXSSfDnd0y8BAABYh2FdM/fsdMPbYg8ZRn8AAAC7jaLhAAAADSTMAQAANJAwBwAA0EDDLf4DAMCK2u3WprQx7JI9wOYT5gAAtsj4vrFcesXVmbpxet1tdDojmZ4+vnI/e8dy0fnnCHSwwwhzAABbaOroTCaPbCDMzd6ycDuwO7hmDgAAoIGEOQAAgAYS5gAAABpImAMAAGggYQ4AAKCB3M2SHWkQNXu2Qx8AALAcYY4dZxA1e/pxmzP3JS2BDgCArSHMsSNttGZPP8b3jQ21fQAAWIlr5gAAABpImAMAAGggYQ4AAKCBhDkAAIAGEuYAAAAaSJgDAABoIKUJAIC+tJKM7++c9FinM5Lp0ZO/G95/ylh37vn5dfUzdWQ663smwO4izAEAfRnf38m513w8x26YvPmxVrud+bm5k+brjI1mbm4us8fnFjexqj2nTuRTd3rQ0GuFAuwEwhwA0LdjN0zmpsM33Pz7UmFuvjOa48fXF+YA6J9r5gAAABpImAMAAGggYQ4AAKCBhDkAAIAGEuYAAAAaSJgDAABoIKUJANjVliqEvdhGi2CP7xtb1/MAYCXCHAC72lKFsBfbSBHsJJk467Y5OuJkGAAGS5gDYNdbXAh7sY0Wwd5z6sR6hwYAy/I1IQAAQAMJcwAAAA0kzAEAADSQMAcAANBAwhwAAEADuZslAOwErdXr2amXx7C1262h9zE3t779F3YiYQ4AdoA9ExO5Z/1Ijhz+1rLzqJfHMLXbrVz2gaszdXRmaH2M7x3LReefI9BBjzAHADvEsUn18thaU0dnMnlkequHAbuGr9cAAAAaSJgDAABoIGEOAACggYQ5AACABhLmAAAAGkiYAwAAaCBhDgAAoIGEOQAAgAYS5gAAABpImAMAAGggYQ4AAKCBhDkAAIAGEuYAAAAaSJgDAABoIGEOAACggUa3egAAa9FKMr6/s+S0/aeMdeeYn99wP1NHprPxVgAAhkeYAxplfH8n517z8Ry7YfIW0zpjo5mbm8vs8bkN9bHn1Il86k4PyuSR6Q21AwAwTMIc0DjHbpjMTYdvuMXj853RHD++8TAHANAErpkDAABoIGEOAACggYQ5AACABhLmAAAAGkiYAwAAaCB3swTYAptRL0+tPADY2YQ5gC0w7Hp5auUBwM4nzAFsEfXyAICNcM0cAABAAwlzAAAADSTMAQAANJAwBwAA0EDCHAAAQAMJcwAAAA2kNMEAtNutTelnbq5Z5X+XKoo8qGLICymMvH0sVwh7kNt9fN/YhtsAANgJhLkNardbuewDV2fq6MxQ+xnfO5aLzj+nUYFuqaLIgyiGvJDCyNvLcoWwB7ndJ866bY6OOKkAAECYG4CpozPCxDIWF0VWDHnnW6oQ9iC3+55TJzbcBgDATuDrbQAAgAYS5gAAABpImAMAAGigoV4zV0q5f5KX11rPK6XcJcklSeaTfCnJxbVWF04BAACsw9COzJVSnpfkzUlO6T30miQvqLU+ON07mD9yWH0DAADsdMM8zfJrSX5kwe/3TXJl7+f3JHnoEPsGAADY0YYW5mqt70iysPhaq9Z6okjaZJLThtU3AAAna7dbQ/8HbK7NrDO38Pq4iSSHV3vCGWfsy+joyLLTDx7cHvWmOp2RdGaHuyo7nZEcODA+1D5O9LOWZensWX7eTmckrXY7rfa3vzNot1uZm2+lNT+Y7xFa7fYtxjw2NpLO7NyKYxuEzehntT4G0fcgl2OpbZ4Mdru32u20Wq1b9DHIfpbar05YvL7Wu96WW1fJYJZjpWVItsf+u136WWlbnLDRbbLSfrsT+hjka361ffeEnbIPD6qP1Z5/xmmn5E+u/FqOHJ1Zcb6NOHjG3qF/Jtqsz0PrsV0+l+5Wu3X9b2aY+1wp5bxa64eTXJjkQ6s94frrb1x22sGDE7n22snBjW6d2u1WpqePZ/rY7FD7mR5t59ChqczNza8+8zqtdVk6e0ZXnHd6tJ35ubnMz307x8/NtTM/N3/SYxsxPzd3izHPzBzP9Mzc0LfJZvSzUh+rrf9B9LFWS23zZLDbfX5uLvPzS7c1qH6W2q9OWLi+NrINlltXyWCWY6VlSLZ+/x2Uzp7RDfez0rY4YaPbZKX9tul9tNrtgb7mV9t3T9gp+/Ag+ujnvWhm5nimjs5mcurYuvtZdRyj7aGvr834PLQe2+Vz6W6109f/SkF1M8Pczyd5Uymlk+TLSS7fxL4BAAB2lKGGuVrrPyZ5QO/nryR5yDD7AwAA2C0UDQcAAGggYQ4AAKCBhDkAAIAG2swboADAmrSSjO/vLDu90xnJ/lPGunPOr+/uduP7xtY3OIaj1d822eh2nzoynWHeD3G1ffeEQSwHsHsJcwBsW+P7Ozn3mo/n2A1L33K61W5nbKSdubm5zB5f3+32J866bY6OOFFlu9gzMZF71o/kyOFvrThfZ2x03dt9z6kT+dSdHpTJIQah1fbdEwaxHMdmB1PuB2geYQ6Abe3YDZO56fANS05rtduZG23n+PH1h7k9p+7OQrPb2bHJ5bf5CfOd0Q1t982w0r57QhOWA9i+fBUJAADQQMIcAABAAwlzAAAADSTMAQAANJAwBwAA0EDCHAAAQAMpTQDbwHLFZVcqJtvpjGR6dG3fx7SSWxTJ3WjB2oUUX95dViuKPIh9yz4FAMsT5mAbWK647ErFZFvtdubn+q9LNHHWbTN35OgtCvFupGDtUn0ovrx7rFYUeRD7ln0KAJYnzME2sVRx2ZWKya41zO05dSLHjxxZUx9rpfjy7rNSUeRB7Fv2KQBYnq87AQAAGkiYAwAAaCBhDgAAoIGEOQAAgAYS5gAAABrI3SwbpN1uNbp9YBO1Vq7RpgYc0FSb8Xllbm7jtVdhMwhzDTG+byyXXnF1pm6cHloftzlzX9IS6GAn2DMxkXvWj9yiruAJasABTbQZn4fG947lovPPEehoBGGuQaaOzmTyyBDfvHzLDjvKsUk14ICdZ9ifh6BJfKUKAADQQMIcAABAAwlzAAAADSTMAQAANJAwBwAA0EDCHAAAQAMpTUCzLVEYeRDFkBebOjId1WZ2kRUKbi/cvzqdkUyPru87MaVAAICNEuZotKUKIw+iGPJJfZw6kU/d6UFq2uwiKxXcXrh/tdrtzM+tbz9TcBsA2ChhjsZbXBh5EMWQYbmC2wv3r42EOQW3AYCN8rUwAABAAwlzAAAADSTMAQAANJAwBwAA0EDCHAAAQAMJcwAAAA2kNME21Eoyvr9z0mMKYQPAgLSS8X1jq862kb+9/bS/Yb3l6MzOZXp05e/nN7IsPi/A9iXMbUPj+zs595qP59gNkzc/phA2AAzGnomJ3LN+JEcOf2vF+Tbyt3firNvm6MhwT4A6sRw33jC5as3L9S6LzwuwvQlz29SxGxTCBoBhOTZ58t/ZpWzkb++eUyfWO7Q1ObEcq4U5nyNgZ3LNHAAAQAMJcwAAAA0kzAEAADSQMAcAANBAwhwAAEADuZvlbrVEjZ211qDpdEZWrGuzKTV2AABglxLmdqmlauystQZNq91e8VbIm1FjBwAAdithbhdbXGNnrTVoVgtzm1VjBwAAdiOHTQAAABpImAMAAGggYQ4AAKCBhDkAAIAGEuYAAAAaSJgDAABoIKUJYDVLFFhP1l5kfSUKrAOwLS3zN3Ap6/27OHVkOhv/SzpY7XZr6M+Zm9tuS71+61lfa7WT1tcgCXOwiqUKrCdrL7K+EgXWAdiOlvsbuJT1/F3cc+pEPnWnB2XyyPRGhjlQ4/vGcukVV2fqxv7H1OmMZHr6eP997B3LReefsyMCSrvdymUfuDpTR2eG1sdOWl+DJsxBHxYXWE/WXmR9JQqsA7BdLfU3cCmD/Lu41aaOzqwpYHZmRzN9bHaII9re1rq+GByHAgAAABpImAMAAGggYQ4AAKCBhDkAAIAGEuYAAAAaSJgDAABoIKUJ1mFhYcR2uzXwgs8KSAMAu8IaipInCpOvx06pzbba+hrE+mziuhLm1qjdbuW6P740s1NHug+0WrnrdVMDramigDQAsBuspSh5srsLk6+rnx1SbHu19bXWou1L9tHQdSXMrcPs1JHMTE52f2m1ctPhyYGGOQWkAYDdot+i5MnuLky+2620vnZz0XaHfwAAABpImAMAAGggYQ4AAKCBhDkAAIAGEuYAAAAayN0sAQDYuTahll2nM5KZ0Xbftew2s17esOvZrbX9VpLx/Z01PWe19dXpjGR69ORjVNuxtuAwCHMAAOxYm1HL7tQ73j7HJ49su3p5m1HP7jZn7kta/Qe68f2dnHvNx3Pshsm+n7Pa+mq125mf+/a07VhbcFiEOQAAdrRh17I75fTTMrtN6+UNu57dWo56nnDshv7XVbL6+loc5nYT18wBAAA0kDAHAADQQMIcAABAA23qNXOllHaS305yryTHkjyl1vrVzRwDAADATrDZR+YeleSUWusDk/xCkldvcv8AAAA7wmaHuf+S5L1JUmv9ZJL7bXL/AAAAO8JmlyY4NcnCAhzHSymjtdbZTR7HhoyO7//2L61W9s2MZG6At0Pdd9pEZsdGTnpsbHQkx+fmB9bPIPpotVqZX6HYZVOWYz19DLqf9fSx2vofRB9rtZV9DLKffvtY6zZYTx/rtVL7O6mPVquV0ZH2hvpZrY9k48uyk/tY6nWwkX76WQ59nNxHP+9F6+2n3+VYbx9raX8797H31P2ZHem/1tp6+tgzMZHbnLlvTaUADp6+N/tPOZ7xvcP7yL/WPvafMpb9p5+W0ZH+jymttr4Wvwb2TEysuWTC+N61l1jYDjY7zN2QZGLB7+2VgtzBgxMrvioOHpxYafLQHHjGU0/6/R5bMgr4/9u7/1ir6zqO488LamTUarp+b9AmvZuxgdoPMCOdklOKCjUQf5W4lGXNatZqTcumqaXlj4mLBHJRRiuWNtNsSSTM0ZyYs/UmIHErsyRaJhAKtz++3zuPXy+dey7fc+499zwf/3Du+X6+3/Ph++Z1Dp/z+dzvV5Ik9ZJZI92BunxwattfYsycqyY6vcxyHXAqQETMAB7t8OtLkiRJ0pjQ6Zm51cDsiFgP9AEf7/DrS5IkSdKY0Dfc3/eQJEmSJI0cbxouSZIkSV3IwZwkSZIkdSEHc5IkSZLUhTp9AZQhi4h3A9dk5vERMR24CdgL/Bc4NzOfqrR/mBfuYffnzPTiKgeoUoMjge9QXLjmEeBTmbm3oe044BZgGkWNLsjMzSPQ7TGllRqU7c1BzRpr0PDcQorzP7PS1hzUrJXzX24zAzWrvA8dDdwF/KncvCQzf9TQ1gy0QSs1KNubg5pVavBaYCnwGmA8xf9LtzS0NQdt0EoNyvY9kYNROZiLiM8D5wDPlk/dQPHBvTEiLgS+AHy2of0EgMYPex2YQWpwFfClzFwbESuAuRRXJx3wYWBCZs4sbztxHfChDnZ5zGm1BuagfoPUgPLLpUUUg+oqc1CjVs+/GajfIDU4Grg+M6/bzy5moGat1sAc1G+QGlwLrMzMVRFxAvA2oHEgYQ5q1moNeikHo3WZ5RZgXsPPCzJzY/n4IGB3pf004NCI+GVE/LoMjg5MtQanlYOIQ4DXA09V2h8H3AOQmQ8C7+hIL8e2VmtgDur3ohpExGHA1cAl+2lvDurV6vk3A/Wrvg8dA8yJiLURcVtEvLLS3gzUr9UamIP6VWvwHuDNEfEr4CxgTaW9OahfqzXomRyMysFcZv4EeK7h5ycBIuJY4GLgW5VddgLfBE4GLgJWRsSonHXsFoPUYG9ETAIeAw4HsrLLq3hhKhtgrzU4MMOogTmoWWMNImI8cBvwGeCZ/exiDmo0jPNvBmpWfR8CNgCXZuYsYCtweWUXM1CzYdTAHNRskBpMBnZk5knAExQrxhqZg5oNowY9k4NROZgbTETMB24F5mTmPyqbNwHfz8z+zNwEbAfe0Ok+jnWZuS0zp1DU4frK5n8Djd8OjsvM5zvWuR7RpAbmoL2OAaYAS4A7gCMj4tuVNuagfYZy/s1A+63OzIcGHgNHVbabgfZrVgNz0H7bgTvLx3fx0pk3c9B+zWrQMznoisFcRJxNMSN3fGZuHaTJ+RTrkYmIN1J8I/Jk53o49kXEnRExpfzxGWBfpck64NSy7Qzg0Q52rycMoQbmoI0yc0Nmvr1cf78A+ENmVpf7mYM2GeL5NwPtd29EvKt8fCLwUGW7GWi/ZjUwB+33AOW/c2AWxYqZRuag/ZrVoGdyMOqnG8ulNTdSTKH+NCIAfpOZl0fE7cCXKZberIiIB4B+4Hy/Aand1RTneA/F1PUFAA01WA3Mjoj1FBcmGJNXDBphzWpgDkaIORhZZqCjFgM3l+9DfwM+AWagw5rVwBy03+eA70bEYorllAvBHHRYsxr0TA76+vv7R7oPkiRJkqQWdcUyS0mSJEnSizmYkyRJkqQu5GBOkiRJkrqQgzlJkiRJ6kIO5iRJkiSpC436WxNIkrQ/EXE68EWKz7NxwO2Z+Y0m+6wBvpKZa2rsx+MU90J9vK5jSpLUjDNzkqSuFBFvorgp7PszcxowE1gQEXNHtmeSJHWGM3OSpG51OHAwcCiwPTP/ExHnAbsBIuIMihvLvhx4GcVNY9cP7BwRBwFLgKnA64DfA2eWj+8BngZ2AROAKzLzvojoAzYB78vMvzbrYETMAG4oj/E0cGFmbo6IqcAKis/h3wKnZOYRB3Y6JEm9xpk5SVJXysxHgJ8BWyNiQ0RcA4wvB0vjgIuAD5SzdtdSLMdsdCywJzNnAkcArwZOLbcFcHZmzgaWAeeUz78X2DzEgdwhwB3AxWUfbgV+WG7+HnBZZk4HtuKXq5KkYXAwJ0nqWpm5GJhMMcM2CXgwIuZl5j7gI8DJEXEF8DFgYmXftcAtEfFJitmzKQ1t/t7w+2+rgNkR8QrgPIoZtaF4K7AjM39Xvt6PgSMiYhIwOTPvLtsta+XvLEnSAAdzkqSuFBFzImJ+Zv4lM5dn5gLg08CiiJgIbADeAqwFbgT6KvvPBVYCO4HlZbuBNrsG2mXms8DdwOnAiRSzgUMx2GdsX+VPSZKGzcGcJKlb7QS+HhGTAcrfZ5sOPEwxK9YPXAXcD8wDxlf2PwlYlZnLgX8BJwzSZsAy4ErgF5m5e4j9S+CwiHhn2b+PAtsycxuwJSJOKdstLPsqSVJLXKMvSepKmXl/RHwV+HlEHFw+fS/wNeB5YCPwR2Bf+fxxlUMsBX4QEWcCe4B1FDN5g73Wuojop5jB25/HyjYD+0yMiPnAzeUSzX8C88vN5wLLIuJKiguv7HrJ0SRJaqKvv98vAyVJ2p9yxm8qxT3sjqrpmJcBSzPzyYiYB5yVmafVcWxJUu9wZk6SpP/vEuBS4Iwaj/kEcF9EPAfsABbVeGxJUo9wZk6SJEmSupAXQJEkSZKkLuRgTpIkSZK6kIM5SZIkSepCDuYkSZIkqQs5mJMkSZKkLuRgTpIkSZK60P8AjaoDujRcncUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (15,10))\n",
    "\n",
    "df.sal_log[df.throws_R == 1].plot(kind = 'hist', bins = 30, alpha = 0.7, label = \"Righty\")\n",
    "df.sal_log[df.throws_R == 0].plot(kind = 'hist', bins = 30, alpha = 0.7, label = \"Lefty\", color = 'r')\n",
    "plt.legend()\n",
    "plt.title(\"Distribution of Salary Log per Handedness in Population\")\n",
    "plt.xlabel('Salary Log')\n",
    "plt.savefig('PNG/hand_sal_log_dist.png', dpi=300, \n",
    "            bbox_inches= 'tight', padding_inches = 4, transparent = True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistic Tests\n",
    "\n",
    "In this section I will perform 3 tests on my data to make sure they have relevance to the question at hand. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test 1: Two sample Proportion Z-test\n",
    "\n",
    "Null hypothesis: Do right and left handed players have different incidents of high earners?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.374274425442938"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sal_log.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7977619026592331"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sal_log.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_left = df[df.throws == 'L']\n",
    "df_right = df[df.throws == 'R']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_high_left = df_left[df_left.sal_log    >=  15.5]\n",
    "df_high_right = df_right[df_right.sal_log >=  15.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is: 0.8732823974971166. So we fail to reject the null hypothesis.\n"
     ]
    }
   ],
   "source": [
    "z, p = sms.proportion.proportions_ztest([len(df_high_left), len(df_high_right)], [len(df_left), len(df_right)])\n",
    "\n",
    "print('The p-value is: ' + str(p) + '. So we fail to reject the null hypothesis.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \n",
    "We fail to reject the null hypothesis that there is no difference between left and right handed players in terms of large salaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test 2: ANOVA\n",
    "\n",
    "Null hypothesis: The means of the samples are equal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>sal_log</td>     <th>  R-squared:         </th> <td>   0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.759</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 14 Dec 2020</td> <th>  Prob (F-statistic):</th>  <td>0.0115</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:51:30</td>     <th>  Log-Likelihood:    </th> <td> -1177.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   994</td>      <th>  AIC:               </th> <td>   2368.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   987</td>      <th>  BIC:               </th> <td>   2402.</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>   14.5513</td> <td>    0.093</td> <td>  156.670</td> <td> 0.000</td> <td>   14.369</td> <td>   14.734</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>prim_pos[T.2B]</th> <td>   -0.3255</td> <td>    0.122</td> <td>   -2.676</td> <td> 0.008</td> <td>   -0.564</td> <td>   -0.087</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>prim_pos[T.3B]</th> <td>   -0.0046</td> <td>    0.150</td> <td>   -0.030</td> <td> 0.976</td> <td>   -0.300</td> <td>    0.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>prim_pos[T.C]</th>  <td>   -0.3427</td> <td>    0.130</td> <td>   -2.644</td> <td> 0.008</td> <td>   -0.597</td> <td>   -0.088</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>prim_pos[T.OF]</th> <td>   -0.0671</td> <td>    0.111</td> <td>   -0.602</td> <td> 0.547</td> <td>   -0.286</td> <td>    0.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>prim_pos[T.P]</th>  <td>   -0.2031</td> <td>    0.099</td> <td>   -2.041</td> <td> 0.041</td> <td>   -0.398</td> <td>   -0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>prim_pos[T.SS]</th> <td>   -0.1262</td> <td>    0.162</td> <td>   -0.781</td> <td> 0.435</td> <td>   -0.443</td> <td>    0.191</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>47.784</td> <th>  Durbin-Watson:     </th> <td>   2.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  21.494</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.121</td> <th>  Prob(JB):          </th> <td>2.15e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.322</td> <th>  Cond. No.          </th> <td>    12.0</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                sal_log   R-squared:                       0.016\n",
       "Model:                            OLS   Adj. R-squared:                  0.011\n",
       "Method:                 Least Squares   F-statistic:                     2.759\n",
       "Date:                Mon, 14 Dec 2020   Prob (F-statistic):             0.0115\n",
       "Time:                        17:51:30   Log-Likelihood:                -1177.1\n",
       "No. Observations:                 994   AIC:                             2368.\n",
       "Df Residuals:                     987   BIC:                             2402.\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept         14.5513      0.093    156.670      0.000      14.369      14.734\n",
       "prim_pos[T.2B]    -0.3255      0.122     -2.676      0.008      -0.564      -0.087\n",
       "prim_pos[T.3B]    -0.0046      0.150     -0.030      0.976      -0.300       0.291\n",
       "prim_pos[T.C]     -0.3427      0.130     -2.644      0.008      -0.597      -0.088\n",
       "prim_pos[T.OF]    -0.0671      0.111     -0.602      0.547      -0.286       0.152\n",
       "prim_pos[T.P]     -0.2031      0.099     -2.041      0.041      -0.398      -0.008\n",
       "prim_pos[T.SS]    -0.1262      0.162     -0.781      0.435      -0.443       0.191\n",
       "==============================================================================\n",
       "Omnibus:                       47.784   Durbin-Watson:                   2.007\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               21.494\n",
       "Skew:                          -0.121   Prob(JB):                     2.15e-05\n",
       "Kurtosis:                       2.322   Cond. No.                         12.0\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# why can we use ols in this case?\n",
    "anova_prim_pos = ols('sal_log~prim_pos', data=df).fit()\n",
    "anova_prim_pos.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=306.0196751487022, pvalue=2.9936528005569697e-221)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(df['sal_log'][df['prim_pos'] == 'P'],\n",
    "                df['sal_log'][df['prim_pos'] == 'C'], \n",
    "                df['sal_log'][df['prim_pos'] == '1B'],\n",
    "                df['salary'][df['prim_pos'] == '2B'],\n",
    "                df['sal_log'][df['prim_pos'] == '3B'],\n",
    "                df['sal_log'][df['prim_pos'] == 'SS'],\n",
    "                df['sal_log'][df['prim_pos'] == 'OF'],\n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The low p-value shows that the means of the samples are not equal. They probably did not come from the same distribution.\n",
    "\n",
    "Further tests will have to be done to see which of these positions do not match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=-1.424, p=0.155\n",
      "C and P probably have the same distribution\n"
     ]
    }
   ],
   "source": [
    "op1 = 'C'\n",
    "op2 = 'P'\n",
    "stat, p = ttest_ind(df.sal_log[df['prim_pos'] == op1], df.sal_log[df['prim_pos'] == op2])\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "    print(op1 + ' and ' + op2 +' probably have the same distribution')\n",
    "else:\n",
    "    print(op1 + ' and ' + op2 +' probably have different distributions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=2.001, p=0.046\n",
      "1B and P probably have different distributions\n"
     ]
    }
   ],
   "source": [
    "op1 = '1B'\n",
    "op2 = 'P'\n",
    "stat, p = ttest_ind(df.sal_log[df['prim_pos'] == op1], df.sal_log[df['prim_pos'] == op2])\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "    print(op1 + ' and ' + op2 +' probably have the same distribution')\n",
    "else:\n",
    "    print(op1 + ' and ' + op2 +' probably have different distributions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=1.866, p=0.062\n",
      "OF and P probably have the same distribution\n"
     ]
    }
   ],
   "source": [
    "op1 = 'OF'\n",
    "op2 = 'P'\n",
    "stat, p = ttest_ind(df.sal_log[df['prim_pos'] == op1], df.sal_log[df['prim_pos'] == op2])\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "    print(op1 + ' and ' + op2 +' probably have the same distribution')\n",
    "else:\n",
    "    print(op1 + ' and ' + op2 +' probably have different distributions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=2.652, p=0.009\n",
      "1B and C probably have different distributions\n"
     ]
    }
   ],
   "source": [
    "op1 = '1B'\n",
    "op2 = 'C'\n",
    "stat, p = ttest_ind(df.sal_log[df['prim_pos'] == op1], df.sal_log[df['prim_pos'] == op2])\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "    print(op1 + ' and ' + op2 +' probably have the same distribution')\n",
    "else:\n",
    "    print(op1 + ' and ' + op2 +' probably have different distributions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=-2.479, p=0.014\n",
      "C and OF probably have different distributions\n"
     ]
    }
   ],
   "source": [
    "op1 = 'C'\n",
    "op2 = 'OF'\n",
    "stat, p = ttest_ind(df.sal_log[df['prim_pos'] == op1], df.sal_log[df['prim_pos'] == op2])\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "    print(op1 + ' and ' + op2 +' probably have the same distribution')\n",
    "else:\n",
    "    print(op1 + ' and ' + op2 +' probably have different distributions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=1.589, p=0.113\n",
      "3B and P probably have the same distribution\n"
     ]
    }
   ],
   "source": [
    "op1 = '3B'\n",
    "op2 = 'P'\n",
    "stat, p = ttest_ind(df.sal_log[df['prim_pos'] == op1], df.sal_log[df['prim_pos'] == op2])\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "    print(op1 + ' and ' + op2 +' probably have the same distribution')\n",
    "else:\n",
    "    print(op1 + ' and ' + op2 +' probably have different distributions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=-1.442, p=0.152\n",
      "C and SS probably have the same distribution\n"
     ]
    }
   ],
   "source": [
    "op1 = 'C'\n",
    "op2 = 'SS'\n",
    "stat, p = ttest_ind(df.sal_log[df['prim_pos'] == op1], df.sal_log[df['prim_pos'] == op2])\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "    print(op1 + ' and ' + op2 +' probably have the same distribution')\n",
    "else:\n",
    "    print(op1 + ' and ' + op2 +' probably have different distributions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test 3: Two Sample T-Test\n",
    "\n",
    "Tests whether the means of two independent samples are significantly different.\n",
    "\n",
    "Assumptions\n",
    "\n",
    "    Observations in each sample are independent and identically distributed\n",
    "    Observations in each sample are normally distributed.\n",
    "    Observations in each sample have the same variance.\n",
    "    \n",
    "Interpretation\n",
    "\n",
    "    H0: the means of the samples are equal.\n",
    "    H1: the means of the samples are unequal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.2567179542004915, pvalue=0.7974497426820828)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(df.sal_log[df.throws == 'L'], df.sal_log[df.throws == 'R'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fail to reject the null hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff068f78bb0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='H',y='sal_log',data=df,col='throws')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation Heatmap')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize = (20,15))\n",
    "\n",
    "sns.heatmap(df.corr(), cbar_kws= {'orientation': 'horizontal'} ,square=True)\n",
    "plt.title(\"Correlation Heatmap\")\n",
    "#plt.savefig('PNG/hand_salary_dist.png', dpi=300, \n",
    "      #      bbox_inches= 'tight', padding_inches = 4, transparent = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}