gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "exp_df = pd.read_excel('ea_test_experiment_analysis.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "exp_df[~exp_df['race4way'].isin(['H', 'W', 'A', 'B'])] = None\n",
    "exp_df['gender'] = exp_df['gender ']\n",
    "exp_df[~exp_df['gender'].isin(['M', 'F'])] = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the dataset 'ea_test_experiment_analysis.xlsx', I was given the task to evaluate whether or not the data showed any causal effects on the insurance status in the treatment group. The treatment group had two values: 1(The group that was contacted) and 0(The group that was left alone for a month). \n",
    "\n",
    "To accomplish this task, I looked at the responses for questions 3, 8, and 12. Questions 3, 8, and 12 were specifically chosen as they presented the largest differences and showed that those in the treatment group were affected. For reference:\n",
    "\n",
    "Question 3 asked: 'Did you successfully enroll in health coverage?\n",
    "\n",
    "Question 8 asked: 'Do you feel like you have enough information about the new health care law and how it affects you and your family?\n",
    "\n",
    "Question 12 asked: 'Did you know that the deadline for signing up for health insurance through HealthCare.gov for most people was on March 31? \n",
    "\n",
    "Each question was given a value of 1(Yes), 2(No), and 3(Other/Don't Know).\n",
    "\n",
    "These responses were split into its respective treatment and 'uninsured_reported' groups. The 'uninsured_reported' values of 0 and 1 represent whether the consumer was/was not insured at that point in time, respectively. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking For Bias "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below plots were created to check to see if there is any bias found between either treatment group. There are slight differences, but not large enough to be concerned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "demo = ['race4way', 'modeled_income_bucket', 'gender', 'age_bucket']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x98107f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa0a5ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xac33f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PCOD9pNmdPklaySyARyLickknAl8gzRj3GLAgIjr6Of49pOkuZ2dJn46Ie7Pp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\n2szMrOAcrM3MzArOwdrMzKzg/j/bOV+2wOMV2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x98e4128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for y in demo:\n",
    "    sns.factorplot(y, data=exp_df, kind='count', hue='treatment', size=3, aspect=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question Values Count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Q3, we see a large increase of people in the treatment/previously uninsured group. The possibility for this increase could come from how well equipped the treatment group is. On Q8, the treatment group also saw a relatively large increase. In each of these questions, those that were previously uninsured/in the treatment group showed the most success and understanding of their health coverage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Column names for the aforementioned questions\n",
    "questions = ['Q3', 'Q8', 'Q12']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Y7qiRbY72xXZHs5E9w7NIRLyP4kPm0En7+4BrgTOAFcDKiBjoeICqyluBbZm5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\nSZJUOybDkiRJkqTa+f8EqMA9ECGbAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3d04080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sjIh9gf2BI4GNe6pobGz7nE48Ojox92jVUqOjE4yMbGvKedQ+mtUu1B3m8gPX\nfqez2edoV+x31CheVJl/mp0Mvxe4oVwgaxOwNjOnIuI6YAPFNOrLMvOpJsclSV1hcnKS4eEtrQ6j\n4Q499DAWLqx/1FaSJKnyZDgzHwROLF9vBk6ZpcxqYHXVsUhStxse3sLlt15J3wEDey7cJiYeG+eq\nt17B4Ycf0epQJElSG2n2yLAkqcX6Dhhg8KAlrQ5DkiSppUyGJUmSJDVcp96aA96e0ylMhiVJkiQ1\nXCfemgPentNJTIYlSZKkFurUEdSHHnrQW3M0r5kMS9IsOvmHiSRpfhke3sKl19zC4sGhVofSUCPf\nSw5Z3uoopF0zGZakWfjDRJLUTIsHhxhYenCrw2ioia0jwCOtDkPaJZNhSdoFf5hIkiR1rgWtDkCS\nJEmSpGYzGZYkSZIkdZ15M006InqA64FjgCeBd2Vm561eI0mSJElqufk0Mnw2sF9mnghcClzb4ngk\nSZIkSR1qPiXDJwFfBsjMe4BXtjYcSZIkSVKnmjfTpIEBYGvN9o6IWJCZOxt1gh9uHWlUVfPGj7aN\nss9j460Oo+EmmvyZOq1t2C4ao9PaBXRm22h2u5gL21B78Ltl73Viu4Dmtg3bRfuYz/2O5qZnamqq\n1TEAEBHXAN/IzLXl9kOZ+bMtDkuSJEmS1IHm0zTprwNvAIiIVwP/0tpwJEmSJEmdaj5Nk/48cFpE\nfL3cfmcrg5EkSZIkda55M01akiRJkqRmmU/TpCVJkiRJagqTYUmSJElS1zEZliRJkiR1nfm0gJbq\nEBHHAx/JzFNn7D8TuBx4GrgpM1e1Ij41X0T0AjcChwL7Aisz8//WHLdtdKGIWADcAASwE3h3Zv5r\nzXHbhepiv6Na9jnaFfsdtSNHhttIRLyP4ktmvxn7e4FrgdcDpwArImKo6QGqVd4BPJaZJwNnAJ+Y\nPmDb6GpnAlOZeRLFj48PTx+wXahe9juahX2OdsV+R23HZLi9fBc4Z5b9RwGbM3M8M58GNgAnNzUy\ntdJnKTodKP6ffrrmmG2jS2XmF4EV5eahwFjNYduF6mW/o5nsczQr+x21I6dJt5HM/HxEvGiWQwPA\n1prtbcBgc6JSq2XmdoCI6AduBT5Yc9i20cUyc2dErAHOBt5Sc8h2obrY72gm+xztjv2O2o0jw51h\nnOJLZlo/8ESLYlELRMQLgXXAzZl5S80h20aXy8zzgJ8HVkXE/uVu24X2lm2oi9nnaHfsd9ROHBlu\nTz0ztjeEzfz9AAACe0lEQVQBL46I5wLbKaadfLTpUaklIuJA4CvABZl554zDto0uFRHvAF6QmR8B\nngQmKRY0AduF5s5+R4B9jnbNfkftyGS4PU0BRMTbgMWZuSoiLgHuoPjBsiozH2llgGqqS4HnApdH\nxBUU7eMGbBvd7nPATRFxF8V3/cXAmyLCdqFnw35H0+xztCv2O2o7PVNTU62OQZIkSZKkpvKeYUmS\nJElS1zEZliRJkiR1HZNhSZIkSVLXMRmWJEmSJHUdk2FJkiRJUtcxGZYkSZIkdR2TYUmSJElS1+lt\ndQCS9l5ELAKuAn4J+BGwFfjdzPxaRDwX+Avg+cCTwIrM/E7LgpUktTX7HEmdwpFhqTN8geLi1ksz\n8xXAxcCfR8RJwCXAdzLz5cCHgE+2LkxJUgewz5HUEUyGpTYXEa8Bfh64JDMnATLznyl+hFxB8f95\nf1m8D9jeijglSe3PPkdSJ3GatNT+jgPum/5RUuNu4CPArwD3RMT3KX6gnNbk+CRJncM+R1LHcGRY\n6lz7U1zw+iTw8cx8PvCLwGfL+70kSWoU+xxJbcdkWGp/9wKviIiFABFxQLn/1eWxNwI3AmTmN4H/\nBI5qQZySpPZnnyOpY5gMS20uMzcA/wZcExG9wHkR8XXgfwNXAt8GzgGIiCOAg4H7WxSuJKmN2edI\n6iQ9U1NTrY5B0l6KiOdQ3Kv1BuDHwBjQA/w9sAr4NPA8isdc/E5m3tmiUCVJbc4+R1KnMBmWOlhE\nvCEz/6bVcUiSOp99jqR2YzIsSZIkSeo63jMsSZIkSeo6JsOSJEmSpK5jMixJkiRJ6jomw5IkSZKk\nrmMyLEmSJEnqOibDkiRJkqSu8/8BpI6Lq9IlvmEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa849d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xbb97c18>"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "for x in questions:\n",
    "    sns.factorplot(x, data=exp_df, kind='count', hue='uninsured_reported', col='treatment', size=3, aspect=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plots show the possibility that outreach efforts did affect the insurace status on the treatment group. There is some cause for concern, which will be spoken in more detail in the next section. Not all questions were answered by the consumer, which may raise flags on the reliability of the answers."
   ]
  }
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