{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heroin and Alcohol: Could There Be a Relationship?\n",
    "*Daphka Alius*<br>\n",
    "*March 14, 2019*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heroin and alcohol are two of the most abused substances in the US. Heroin is a powerful non-medically prescribed opioid drug in the US. Given the current opioid epidemic, which is now a national health crisis, the aim of this project is to determine whether there is a relationship between yearly heroin consumption and yearly alcohol consumption. With that said, the question under evaluation is whether people who heavily consume one substance also consume the other at similar rates.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import thinkstats2\n",
    "import thinkplot\n",
    "import matplotlib.pyplot as plt\n",
    "# import plotly as py\n",
    "import seaborn as sns\n",
    "from scipy.stats import gaussian_kde\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Data Collection from NSDUH for 2017*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "datapath = \"/Users/daphka/Google Drive/ThinkStats2/project1/NSDUH DATA/NSDUH-2017-DS0001-bndl-data-stata/NSDUH_2017.DTA\"\n",
    "df2017 = pd.read_stata(datapath, convert_categoricals=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56276"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df2017\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing Demographics: Can we describe the respondents of the survey?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Let's look at the breakdown of the age groups in the survey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    0.354112\n",
       "2    0.245931\n",
       "1    0.243834\n",
       "3    0.156123\n",
       "Name: catage, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = data[\"catage\"]\n",
    "age.value_counts()/len(age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In summary, we get the breakdown of age to be:\n",
    "* 35.4% were between the age of 12-17\n",
    "* 24.5% between the age of 18-25\n",
    "* 24.3% between the age of 26-34\n",
    "* 15.6% were 35 and above\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This breakdown can be illustrated by a bar graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Bar([\"12-17\", \"18-25\", \"26-34\", \"35+\"], age.value_counts()/len(age))\n",
    "thinkplot.Config(title= \"Distribution of Age\", xlabel = \"Age Groups (year)\", ylabel = \"Percent\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Majority of the respondents were youths and young adults between the age 12-25, which corresponds to 59.9% of the respondents."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Now we will look at the break down of race!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.588475\n",
       "7    0.181534\n",
       "2    0.125222\n",
       "5    0.046752\n",
       "6    0.038364\n",
       "3    0.015033\n",
       "4    0.004620\n",
       "Name: NEWRACE2, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race = data[\"NEWRACE2\"]\n",
    "race.value_counts()/len(race)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In summary, the breakdown of race is the following:\n",
    "* 58.8% Caucasian\n",
    "* 18.1% Hispanic\n",
    "* 12.5% Black or African American\n",
    "* 4.6% Asian\n",
    "* 1.5% Native American and AK Natives\n",
    "* 0.6% HI/Pacific Islander\n",
    "* 3.8% more than one race\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a bar graph representing the breakdown of race:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Bar([\"Caucasian\", \"Hispanic\", \"Black or African\", \"Asian\", \"More Than One Race\", \"Native Americans/AK\", \"HI/Pacific Islander\"], race.value_counts()/len(race))\n",
    "thinkplot.Config(xlabel = \"Race\", ylabel = \"Percent\", title = \"Distribution of Race\")\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The racial makeup of the distribution is remarkable representative of the US."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Finally, we will look at the breakdown of respondent's education."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.253892\n",
       "5    0.243834\n",
       "4    0.206162\n",
       "2    0.200245\n",
       "1    0.095867\n",
       "Name: eduhighcat, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education = data[\"eduhighcat\"]\n",
    "education.value_counts()/len(education)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In summary, we get the following:\n",
    "* 9.59% Less high school\n",
    "* 20.0% High school grad\n",
    "* 25.3% Some coll/Assoc Dg\n",
    "* 20.6% College graduate\n",
    "* 24.38% 12 to 17 year olds\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a bar graph to display the distribution of education."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Bar([\"Less Than High School\", \"High School grad\", \"Some Coll/Assoc Dg\", \"College Grad\", \"K-12\"], education.value_counts().sort_index()/len(education))\n",
    "thinkplot.Config(xlabel = \"Education Level\", ylabel = \"Percent\", title = \"Distribution of Education\")\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the respondents were youths and young adults between the age of 12-25, which corresponds to the high percentage of people in K-12 and have had some college at the time they were surveyed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing variables: \"alcyrtot\" and \"heryrtot\"\n",
    "* alcyrtot: reports the alcohol consumption of a respondent over the  past 12 months or 365 days\n",
    "* heryrtot: reports the heroin consumption of a respondent over the  past 12 months or 365 days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### alcyrtot: past 12 months of alcohol use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look into the variable \"alcyrtot\". But first, we'll remove the non-quantitative variables with np.nan values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"alcyrtot\"].replace([985, 991, 993, 994, 997, 998], np.nan, inplace=True)\n",
    "data[\"heryrtot\"].replace([985, 991, 993, 994, 997, 998], np.nan, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will drop the nan values to do some analysis on the distribution of the reported data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Summary Statistics</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be using Histogram to see the shape of the distribution of alcohol consumption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3839023388653218"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alcoholUsedPast12MO = data[\"alcyrtot\"].dropna()\n",
    "alcoholUsedPast12MO.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alcoholUsedPast12MOHist = thinkstats2.Hist(alcoholUsedPast12MO)\n",
    "thinkplot.Hist(alcoholUsedPast12MOHist)\n",
    "thinkplot.Config(xlabel = \"Total Days of Consumption\", ylabel = \"Frequency\", title = \"Distribution of the Alcohol Consumption\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be observed in this histogram, the share of the distribution of alcohol consumption can be described as being multi-modal, asymmetric and right-skewed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the histogram doesn't really provide much insight into the usage, grouping the data into frequency of use and see what percentage of people fall into each group can give us better results on the respondents' consumption behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0mhT2Ae4ArqBMknc6sG9TQUVERDt6LUldHDjK9pEAkhau+x5sKrCIiJh6vbYUzqYkgRGLAz+b/+FERESbek0K02w/MLJR7y/RTEgREdGWXpPC3yRtOLIh6UXAQ82EFBERbel1TGFv4GRJt9btlYC3NBNSRES0ZcKkIGkhYDFgXWAdypKc19p+pOHYIiJiik2YFGw/LunztjelTKEdERFDqtcxhbMk7SBJjUYTERGt6nVM4YPAksBjkh6idCHZ9jKNRRYREVOu16mzl246kIiIaF+vU2dL0lslfaxuryZp42ZDi4iIqdbrmMJXgE2Bnev2A8BhjUQUERGt6TUpbGL7vcDfAWzfTSlTHZekrSRdJ2m2pH3GOP5BSVdLulzS2XV1t4iIaEmvSeGROgmeASRNBx4f7wH1/MOArYH1gJmS1ht12iXARrbXp6zsdtAkYo+IiPms16TwZeAU4OmSPgmcB3xqgsdsDMy2fUNdoOdE4PXdJ9g+x/bITKu/AVbtOfKIiJjveq0++paki4BXU8pRt7d9zQQPWwW4uWv7FmCTcc7fAzijl3giIqIZ4yYFSdOA9wBrUhbY+ZrtR3t87rEudBtzCU9JbwU2Al4xh+N7AnsCrL766j2+fERETNZE3UfHUj6sr6CMDXxuEs99C7Ba1/aqwK2jT5K0BfD/gO1s/2OsJ7J9hO2NbG80ffr0SYQQERGTMVH30Xq2nw8g6RvAbyfx3BcCa0laA/gTsBNPlLRSn/OFwNeArWz/ZRLPHRERDZiopdCZCXUS3Ubd5+8FnAlcA5xk+ypJB0jarp72WWApyrTcl0o6bTKvERER89dELYUXSLqv3heweN3uae4j26cDp4/at1/X/S0mH3JERDRl3KRge+GpCiQiItrX63UKERGxAEhSiIiIjiSFiIjoSFKIiIiOJIWIiOhIUoiIiI4khYiI6EhSiIiIjiSFiIjoSFKIiIiOJIWIiOhIUoiIiI4khYiI6EhSiIiIjiSFiIjoSFKIiIiOJIWIiOhIUoiIiI4khYiI6EhSiIiIjiSFiIjoSFKIiIiOJIWIiOhIUoiIiI4khYiI6EhSiIiIjiSFiIjoSFKIiIiOJIWIiOhIUoiIiI4khYiI6EhSiIiIjiSFiIjoSFKIiIiORdoOIAbHXgee0HYIrTp035lthxDRuLQUIiKiI0khIiI6Gk0KkraSdJ2k2ZL2GeP4yyVdLOlRSW9qMpaIiJhYY0lB0sLAYcDWwHrATEnrjTrtj8DuwLebiiMiInrX5EDzxsBs2zcASDoReD1w9cgJtm+sxx5vMI6IiOhRk91HqwA3d23fUvdNmqQ9Jc2SNOuOO+6YL8FFRMQ/azIpaIx9npsnsn2E7Y1sbzR9+vR5DCsiIuakyaRwC7Ba1/aqwK0Nvl5ERMyjJpPChcBaktaQtBiwE3Bag68XERHzqLGkYPtRYC/gTOAa4CTbV0k6QNJ2AJJeLOkWYEfga5KuaiqeiIiYWKPTXNg+HTh91L79uu5fSOlWioiIPpArmiMioiNJISIiOpIUIiKiI0khIiI6khQiIqIjSSEiIjqSFCIioiNJISIiOpIUIiKiI0khIiI6khQiIqIjSSEiIjqSFCIioiNJISIiOpIUIiKiI0khIiI6khQiIqIjSSEiIjqSFCIioiNJISIiOpIUIiKiI0khIiI6khQiIqIjSSEiIjqSFCIioiNJISIiOpIUIiKiI0khIiI6khQiIqIjSSEiIjqSFCIioiNJISIiOpIUIiKiI0khIiI6khQiIqJjkbYDiIjo1V4HntB2CK06dN+Zjb9GWgoREdGRpBARER2NJgVJW0m6TtJsSfuMcfwpkr5Tj18gaUaT8URExPgaSwqSFgYOA7YG1gNmSlpv1Gl7AHfbXhP4IvCZpuKJiIiJNdlS2BiYbfsG2w8DJwKvH3XO64Fj6/3vAq+WpAZjioiIcch2M08svQnYyvY76/bbgE1s79V1zpX1nFvq9u/rOXeOeq49gT3r5jrAdY0E3bwVgDsnPCvmJO/fvMt7OG8G+f17pu3pE53UZEnqWN/4R2egXs7B9hHAEfMjqDZJmmV7o7bjGFR5/+Zd3sN5syC8f012H90CrNa1vSpw65zOkbQIsCzw1wZjioiIcTSZFC4E1pK0hqTFgJ2A00adcxqwW73/JuDnbqo/KyIiJtRY95HtRyXtBZwJLAwcZfsqSQcAs2yfBnwDOE7SbEoLYaem4ukTA98F1rK8f/Mu7+G8Gfr3r7GB5oiIGDy5ojkiIjqSFCIioiNJISIiOjJ1doMkbQT8C7Ay8BBwJfAz2ym7nQRJSwJ/t/1Y27EMqryHkydpU+CtlL/hlXjib/jHwPG2720xvMakpdAASbtLuhj4CLA45QrsvwCbAT+VdKyk1duMsZ9JWkjSzpJ+LOkvwLXAbZKukvRZSWu1HWO/y3s4bySdAbyTUj25FSUprAfsC0wDfiBpu/YibE6qjxog6b2UEtyH5nB8A2B522dPbWSDQdK5wM+AHwBX2n687n8a8CpgZ+AU28e3F2V/y3s4byStMHq6nbk5ZxAlKUTfkbSo7Ufm9ZwFWd7D+aN2uz1k+3FJawPrAmcM8/uW7qMGSTpI0jKSFpV0tqQ7Jb217bj6XS9/cMP8Rzk/2H5ExSaS3ijpDfW+us9pM8YB8UtgmqRVgLOBtwPHtBpRw5IUmrWl7fuA11LmeVob+K92Q+p/ktaX9BtJN0s6QtJTu479ts3YBoWkLYHrgf2BbYBtgU8A19dj0RvZfhB4I3CI7TdQxhaGVqqPmrVo/Xcb4ATbf81yET35CuXD7DeUwb7zJG1n+/c88Z7G+A4GtrB9Y/dOSWsApwPPaSOoAaRahbQLZVEwGPLPzaH+z/WBH0q6llLK9u+SpgN/bzmmQbCU7Z/U+5+TdBHwk7omRwbBerMIpXU62p9IYp2MvSlVhKfUudueBZzTckyNykBzw2rXx322H6uDVkvbvr3tuPqZpMuAl3fXgUtaH/ge8DTby7cW3ICQ9BHgzZQVD2+uu1ejTDp5ku1PtxXbIJH0PNtXth3HVEpSaJCkWcBRlK6ju9uOZ1BI2hm4wfZvRu1fHfiY7Xe1E9lgqWuibwesQlnQ6hbgNNtXtxrYAJF0HrAYZXD527bvaTei5iUpNEjSmpRqhbcAs4CjgbOyZkRMpXptgvPFZO7UUtS3AzsCvwWOsX1Wu1E1J0lhCkhaiFKB9FXgcUrr4eBMdzE2SevbvrzeXxT4b2BjyhQDB9ZqkBhHbVUdBGwOjHTDLQv8HNhn9AB0jE/SwsD2wJeB+ygtr4/a/n6rgTUgJakNq33hnwc+S+kTfxPll+rnbcbV547puv+/wJqU93Bx4PA2AhpA3wFOAVayvZbttShTNZxKGWeIHtTy6C8C11AS7OtsP6fe/2KrwTUkLYUG1aqZeygrzH3P9j+6jn3f9htbC66PSbrE9gvr/UuBF49cjAVcZnv9diPsf5Kur4lgUsfiyST9EjgS+O7oaWskvc32ce1E1pyUpDZrR9s3jHUgCWFcy0p6A6Ul+5SRK29tW1K+xfTmIklfAY7lydVHuwGXtBbVgLH98nGODV1CgLQUGidpW+C5lJkVAbB9QHsR9T9JR4/atY/tP0taEfiW7Ve3EdcgkbQY5WKr1zOq+gj4RnerNeaszib7acpVzN1/w89qLaiGJSk0SNLhwBKUWSm/ThlP+K3tPcZ9YET0hVqS+nHK+MHrKFVIsv3xVgNrUAaam/VS27sCd9v+BLAppQkf0ShJS0j6sKT/kjRN0m6STquTNC7VdnwDZPE6xb1s32R7f8og89BKUmjWyMDUg5JWBh4B1mgxnlhwHAM8g/L79mPgxcDnKN1IX20vrIHz91pSfr2kvepY19PbDqpJGWhu1o8kLUcpR72YMm/P19sNKRYQa9t+c63Yuo0yOZ4l/R9wWcuxDZIPULqA3w/8D6WVsFurETUsYwpTRNJTgGnDuq7r/FYvWlvO9h1d+5YGsH1/a4ENCEmX2t6g3j/K9ju6jl1m+wXtRRf9LC2FBkiaY7mpJIbxKsgGLApcIGmdrsVgjqVcUfqL1qIaHLMkLWX7gVEJ4dlAkuoEJP2QcWbktT2U6zNDWgqN6CqpfDrwUp64evlVwC9yjUJvavXW2bZPrtOO/7JeTRrzQJIy/9b4JL2i3n0jsCIwspb1TOBG2x9tJbApkKTQIEk/At5l+7a6vRJwWJJCbyRtBPyP7a0l/QewhO1Pth1XLDgk/XL0BWxj7RsmqT5q1oyRhFD9mbIkZ/TA9izgGXV93LdRJhKMmErT68I6QGfluuktxtO4jCk06xeSzgROoPRP7sSQr9rUgKOBQ4A/jUqwEVPhPyh/xyPT1cwA9mwvnOal+6hhta55pKn5S9untBnPoKkr190KzLR9atvxDJJUcM0ftXJw3bp57bBPEZKkEH2vjsX82fbjbccySCQtQVmDolPBJen7wJdt/6LN2KJ/ZUwh+p7t25IQJq8uRnQWZXEYagXXc5IQYjxJChHD7evAyHUKb+WJ0sqIMWWguWFZHzfaZHuWpO4Krm3bjmlQSFoW2Ioy9bgpY1tn2r6n1cAalpZCAyStLulESXcAFwAXSvpL3Tej3egGk6Sn1qVNY/JSwTVJknalzFf2SsrcR0tSLj69qB4bWhloboCkXwNfoizh91jdtzCwI/AB2y9pM75BIekXwHaUFu2lwB3AubY/2GZcgyYVXJMn6Tpgk9GtgvpeXmB7aK83SkuhGSvY/s5IQgCw/ZjtE4HlW4xr0Cxr+z7KVANH234RsEXLMQ2c2nX5LMqqa9EbMfbcR4/XY0MrYwrNyPq488citRz1zcD/azuYQZZuo0n7JHCxpLN44m94deA1lCm0h1a6jxqQ9XHnD0k7Ah8DzrP973W6gc/a3qHl0GIBULuK/pUn/w2fOexFI0kK0bckPc32X9uOIxZckp5BV/WR7T+3HFLjkhQaUK8k3Yvyi3QI8BZgB+Ba4ADbD7QY3sCQdD1lgPlo4IxM9zxv6jff1Wxf3nYs/U7SBsDhwLKUFoKAVYF7gH+3fXGL4TUqSaEBkk6i9EMuDqwDXAOcBLwOWNH221oMb2DUpSS3oFx8tTHwHeAY279rNbABkgquuSPpUuDdti8Ytf8lwNeGeeW6JIUGjCyF2LU+7kp1fVwBl9lOvf0kSXoV5WrcJSlrDO9j+9ftRtX/JF1i+4WS3klpJXxc0uX5HRyfpOttrzWHY7NtrznVMU2VVB81qCaC00e6Pep2snCPJC1PmZrhbZS1KN5HGazfADgZWKO96AZGKrjmzhmSfgx8kydXEO4K/KS1qKZAkkIzsj7u/PFr4Dhge9u3dO2fVZfqjIkdAJxJqeC6sFZwXd9yTH3P9vslbUPpeuuuPjrM9umtBtewdB9NsayP27u8V/MuFVwxWbmiuSGSlpD0glH7VgdWbimkQbSCpM9KOl3Sz0dubQc1YC6QdLKkbeqYVvRA0oqSvirpMEnLS9pf0uWSTqrdcUMrSaE5jwDfl7Rk176vA0P9CzWffYtSxrsG8AngRuDCNgMaQGsDR1DGZWZL+pSkoZ23Zz46BriaMp5wDvAQ8Frg/yilqkMr3UcNkvQ54GrbR9VWwg9sv7DtuAaFpItsv6i7WkbSubZf0XZsgygVXL0bqdqq9/9oe/WuY5fa3qC96JqVlkKzvg68vd7flXIRVvTukfrvbZK2lfRCygVE0aPa9bG3pFnAf1IquFYAPgR8u9Xg+lv3Z+M3xzk2dFJ91CDb10qiNtdnApu1HdOAObAudPIhypXhywD/0W5IAycVXHPnB10VhPuO7JS0JjDUF0+m+6hhknanXJH7J9szWw4nFjCp4IrJSlJoWJ0H6TZgB9s/azueQSDpEMaeyx4oNeRTGM5AkzQd+DDwXGDayH7bm7cW1ACS9AbgpwvCvGVD3TfWD2w/aHvZJIRJmQVcRPkQ25BysdX1lCuZHxvncfHPUsE1j+pFpydRrq4femkpNGCkL3Jez1nQSToH2NL2I3V7UeAs269qN7LBkQqueSfpk5SW65a2N247nqalpdCMH0j6vKSXd1+nIOlZkvaQdCawVYvxDYqVgaW7tpciF/9NViq45kHX2uqmcKR2AAAXD0lEQVSfAe4dfUHqMEr1UQNsv7rOm/Ju4GV1HvtHgeuAHwO72b69zRgHxP8Cl9QWA8ArgP3bC2cgpYJr3mwDnG/7fklHAe+klPUOrXQfRV+TtCKwSd28IMk0ppKkU4HP2/4/SdOAq4Dn2H645dAak6QQMYRSwTXvJC0HnGr7lV37PgOcY3top89OUogYQpJ2q3dfBqxHWbUOSv/4RbbThRRjSlKIGGKp4Jo7kmbYvnGc4wJWGXWV+FBI9VGDJH1O0nPbjiMWaKngmjuflfQ9SbtKeq6kp0taXdLmkv4H+BXwnLaDbEKqj5p1LXCEpEUok+GdYPvelmMaKJLOs73ZyL9txzOAUsE1F2zvKGk9YBfKNDUrAQ8C1wCnA5+0/fcWQ2xMuo+mgKR1KLOlzqR8wzjS9jnjPyoAJF1se8PuqYxjclLBFZORlkLD6sUv69bbnZR57D8o6d22d2o1uFgg1CTwg7bjiMGQpNAgSV+gLPx9NvAp27+thz4j6br2IouIGFuSQrOuBPa1/eAYx4Z+DpWIGDxJCg2QtGG9eymw7uj10m1fnAHnnmWx+WiNpO8BRwFn2H687XimQgaaG9BV6TEWZy773kl6pe1fjPzbdjyDKBVcc0/SFpQikZcAJwPH2L623aialaTQIEnTRpetjbUvokmp4Jp3dVLBmcD/A24GjgSOH7kocJjk4rVmnd/jvhhF0lMlHThq30xJL20rplgwSVoe2J0yQ+olwMGUxZ9+2mJYjcmYQgNqXfgqwOJ1/vqRfvFlgCVaC2yA2L5b0mskHWN7dt29H/AvbcYVCxZJ36eUkx8HvM72bfXQdyTNai+y5iQpNONfKd8sVgW+0LX/fuCjbQQ0oL5BuZr0o5JeCVxl+852Q4oFzKG2fz7WAdsbTXUwUyFjCg2StIPt77Udx6CStDRlveZ1KdOEnDjMUxY3ZWQsIWMKvZP0xvGO2/7+VMUy1dJSaNaPJO0MzKDrvbZ9QGsRDZC62tX5wFso0zS8veWQBtV/jPo3Jva6cY4ZGNqkkJZCgyT9BLgXuAh4bGS/7c+3FtSAkfQy4IeUZvx+bccTMeySFBok6Urbz2s7jkEn6WDKkoh/bDuWQVLXBv+Q7X279s0EbrKdKrhxSHqr7eMlfXCs47a/MNb+YZCS1GadL+n5bQcx6GzvnYQwebbvBl4jac2u3fsBv2sppEGyZP136TnchlZaCg2SdDWwJvAH4B+U0lTbXr/VwAaApH8FtqeU9hq4FfhBBponR9KewAzbIxVce9l+U8thRR9LUmiQpGeOtd/2TVMdyyCR9CVgbeCbwMhyh6sCuwLX2967rdgGTSq45o2kacAewHOBaSP7bb+jtaAalu6jBtUP/9WAzev9B8l73ottbG9j+0Tb59XbicC2wDZtBzdIbN9PuYp+pILrzHYjGjjHAStSrj06l/Ll5P5WI2pYPqAaJOnjwH8DH6m7FgWOby+igfF3SWNNLf5iIPNGTd7Xga8AJztdA5O1pu2PAX+zfSzli8lQjxPmOoVmvQF4IXAxgO1ba3M+xrc78NX6Xo10H60G3FePxSTY/pWk4yjJISZnZMK7eyQ9D7idct3R0EpSaNbDti3JAJKWnOgBUdabADbpmkNKwC1ZW3juZRxmrh1RS3s/BpwGLFXvD60MNDdI0n8CawGvAT5Nmcfn27YPaTWwAVATArZvlzSdMhHetbavbjeywSFpXUpSvcD2A137t8pgc8xJkkLDJL0G2JLybfdM20M53e78JOndwD6U9+wzlC6jq4CXAQfZ/kZ70Q0GSe8H3gtcA2wA7G37B/XYxbY3HO/xAZLWAfakVG5BeS+PsD3U13kkKUTfkXQFpVJmceAmymDf7bUZf47tDVoNcADU93BT2w9ImgF8FzjO9sGZGG9ikjalzG90BGVMUJTxwXcBb7T9mxbDa1TGFBok6X7KhVcAi1Gqj/5me5n2ohoIj9h+EHhQ0u9HxhLqGgv5FtObhUe6jGzfWC9c+269dibrXk9sP2DmqCVgT5X0c+DjwNatRDUFUpLaINtL216m3qYBOwCHth3XAHhc0qL1/rYjO+uFRPmd7c3tkjotqpogXguswJCXVM4nzx5rTXDb5wLPmvpwpk7+wKaQ7VOBzduOYwC8kdrCsn1L1/7lgQ+1EtHg2ZVSPtlh+1HbuwIvbyekgTLeBWp/m7IoWpDuowaNWqhjIWAjnuhOijm7eayLrGz/CfgTgCTlQqw5G5VMAZD0NNt/tf2rNmIaMKtJ+vIY+0Wp6BpaSQrN6l6o41HgRuD17YQyUM6R9D3KBHid2VElLQZsBuwGnAMc0054/U/SvrYPrPfXA04FFpUk4C22L2g1wP73X+McG8q1mUek+ij6Th07eAewC7AGcA9lMrKFgbOAw2xf2l6E/a+77FTSjymLFJ1Rpw/5ku2Xthth9KskhQbNofnZYfv9UxXLoKoDzisAD9m+p+14BsWopPCkEtSUpMZ40n3UrGnAesB36vaOlKU58y23R7YfAW5rO44B9CxJp1H6wFeVtEQt84VSGh0xpiSFZq0FvKp+sCHpcOAs21lAPZo2euxqIQBJzwC+OvXhDCZJLxs9MD/WvmGS7qMGSbqOclXpX+v2U4Hf2F6n3cgiohdjTQky7NOEpKXQrP8FLpF0Tt1+BbB/e+EMFkl7Ad+qaw1HTJk6zcVLgemSPth1aBlKwcPQSlJokO2jJZ1BmccHYJ9M/zwpKwIXSroYOIoyoWCatjEVFqNMk70I0L0Gyn3AUK9xne6jBtWa8F2AZ9k+QNLqwIq2f9tyaAOjvodbAm+nXPx3EvAN279vNbBYIEh65sia6pIWApayfV/LYTUq01w06yvApsDMun0/cFh74Qye2jK4vd4eBZ5KmdjtoFYD63OS9pX0tHGOby7ptVMZ04D6tKRl6gJZVwPXSRrvwraBl+6jZm1ie0NJl0Bnls/F2g5qUNQ1AXYD7qQsJflfth+p39iuBz7cZnx97grgh5L+Tpn6+Q5KifRalPUVfgZ8qr3wBsZ6tu+TtAtwOmXN9YuAz7YbVnOSFJr1iKSFqfMd1RXEHm83pIGyAmXu+pu6d9p+PN9yx1cX1PmBpLUoixOtROkPPx7Y0/ZDbcY3QBatF1BuT7kq/JFhn749SaFZXwZOAZ4u6ZOUAap92w2p/3V1e3xp1DYAdVK3a6Y8sAFk+3pKqyrmztcoc5ZdBvyyrkcx1GMKGWhuWF0n99WUK0vPzofZxCT9gSdmkx29IIxtD/V89tHfJC1i+9G242hKkkJDar/35baf13YsETF3JC1LWWltZA2Kc4EDbN/bXlTNSvVRQ2w/DlxWy1BjLklaRdJLJb185NZ2TINkvAqk6MlRlKrBN9fbfcDRrUbUsLQUGlTXc30x8Fu6VmuyvV1rQQ0QSZ8B3kIpBXys7nbev95Jup4yAePRwBm5+G9yJF1qe4OJ9g2TDDQ3QNJTbP8D+ETbsQy47YF16nsZc2dtYAvK+hSHSPoOcIzt37Ub1sB4SNJmts+DMhkeMNSVW2kpNGBkwixJx9l+W9vxDKo6RciOddH5mEeSXkUpSV2SUk2zj+1ftxtVf5O0AXAssCyl6OGvwO62L2s1sAalpdCMxSTtBrx01DrNANj+fgsxDQxJh1Cqjx4ELpV0NtBpLWRxot5JWh54K/A24M/A+4DTKBewnUxZ2S7moK7w9wJJy9TtoS5HhSSFpryHMufRcjx5nWYoH3ZJCuMbWQP3IsoHWLc0bSfn18BxwPa2b+naP6uu7xHjkLQcsCswA1ikTMU13F9M0n3UIEl72P5G23EMKkl72z54on0xZ5KUweW5J+l84DeUaUM6sxHYPra1oBqWpNAwSS+lfssY2Wf7m60FNEDmsMBJ1hfugaQfMk6rKhVcvRn2BXXGku6jBkk6Dng2pSSwU1IJJCmMQ9JMYGdgjbrO8IilgbvaiWrgfK7tAIbEcZLeBfyIJ49r/bW9kJqVpNCsjSizLKY5NjnnA7dRJsT7fNf++4HLW4lowNg+F0DSi2xf1H1M0uhxrpizhykzov4/nmh5GRjaqVbSfdQgSScD77d9W9uxxIKprlq3m+0r6vZM4AO2Nxn/kQEg6feUKfDvbDuWqZKWQrNWAK6W9Fue3PRMf24PajnvZ4CnU2rERbmieZlWAxssb6IsSrQLsBmlkmbLdkMaKFdRSqMXGGkpNEjSK8baP9K0j/FJmg28LjPLzhtJawOnAjdTSlOH+orc+UnSKcBzgXNYQK6VSVKIviXpV7Zf1nYcg0jSFTy5+ujpwL3UDzbb67cR16CpF6H+k5SkxqRIup+xywHT/TEJkg4GVqR8y+3+lpaL/yZQF4OZo9Gr2UWMyJhCA2wv3XYMQ2IZSn9udx94rgjvQfeHvqTNgLVsH12XhF2qvcgGwxgtrScZ5pZWWgoRQ0zSxyml0evYXlvSysDJ6ZYb34Lc0soiO9G3JK0q6RRJf5H0Z0nfk7Rq23ENmDcA21HX87B9K+UiwBiH7ZvGugGrAh9uO74mJSlEPzuaMiHeysAqwA8Z8lWvGvBwvXjSAJKWbDmegSNpA0kHSboROBC4tuWQGpUxhehn0213J4FjJH2gtWgG00mSvgYsV6dreAdwZMsx9b1axrsTMJMytcp3KN3tr2o1sCmQMYXoW5J+BhwDnFB3zQTebvvVrQU1gCS9hjJYL+BM2z9tOaS+J+lx4P+APWzPrvtusD2001uMSEsh+tk7gEOBL1K6P86v+2Jyfkcphf6ZpCUkLW37/raD6nM7UFoK50j6CXAiJakOvbQUIoZY7TLaE3ia7WdLWgs4PK2t3tQxmO0prdTNKUtznmL7rFYDa1CSQvQdSV8e7/gwTzEwv0m6FNgYuGBkHQpJV9h+fruRDR5JTwN2BN5ie/O242lKuo+iH70HuBI4CbiVBaTZ3pB/2H54ZBlJSYuQJU3nSl1D4Wv1NrSSFKIfrUT9RgY8Sqn8+J7tu1uNajCdK+mjwOJ1wPnfKaW9EWNK91H0NUmrUPpzPwj8t+3jWg5poEhaCNiDruoj4OtZ+CnmJEkh+pakDSkJ4TXARcDnbV/dblSDQdL2wPm2/9J2LDFYkhSi70j6BPBa4BpKKeBPbD/ablSDRdJ3gU0pEwr+ilLO+yvbV7UaWPS9JIXoO/XCoRuAkcVgRn5JR6YeH9oZKuc3STOAl9bbpsDqwIW2t2kxrOhjGWiOfrRG2wEMC9s3SpoGLF5vI/cjxpSWQsQQqhVHmwLTgeuA39Tb5bYfazO26G9JChFDSNK1wAPAjyjjCRfYvrfdqGIQJClEDKl6Be7IeMJLKCuuXUapSsoU5DGmJIXoW5L2tn3wRPtifPUq5hcBLwfeDaxhe+F2o4p+laQQfUvSxbY3HLXvkpE5fGLOJG1HaSG8DHgucBWlG+l8SkvhjhbDiz6WpBB9R9JMYGdgM8qc9iOWAR61vUUrgQ0QSd+nXpsAXGT74ZZDigGRktToR+cDtwErAJ/v2n8/cHkrEQ0Y229sO4YYTGkpRN+qc9k/ZPvxujziusAZth9pObSIoZWkEH1L0kXAvwBPpdTYzwIetL1Lq4FFDLGF2g4gYhyy/SDwRuAQ228A1ms5poEiae9e9kWMSFKIfiZJmwK7AD+u+zIONjm7jbFv96kOIgZH/sCin+0NfISyJu5Vkp4FnNNyTAOhq4JrDUmndR1aBrirnahiEGRMIWIISXomZWLBTwP7dB26nzL/UaYijzElKUTfkjQd+DDl4qtpI/uHedH0+S0VXDFZGVOIfvYt4FrKN95PADcCF7YZ0AD6JTCtLmt6NvB24JhWI4q+lqQQ/Wx5298AHrF9ru13UCZ2i96lgismJQPN0c9Gujhuk7QtcCuwaovxDKLuCq496r783ccc5Zcj+tmBkpYFPgQcQqmc+Y92Qxo4qeCKSclAc0REdKSlEH2rVst8FXiG7edJWh/YzvaBLYc2MFLBFZOVgeboZ0dSuj4eAbB9ObBTqxENnlRwxaQkKUQ/W8L2b0fty0VXk5MKrpiUdB9FP7tT0rMBA0h6E2WdhehdKrhiUjLQHH2rVsocQVlW8m7gD8Autm9qNbABIum1lNXrVuOJCq5P2D5t3AfGAitJIfpenaphIdv3S9rB9vfajiliWGVMIfqe7b/Zvr9ufrHVYAaMpLUlnS3pyrq9vqR9244r+leSQgwatR3AgEkFV0xKkkIMmvR3Tk4quGJSUn0UfUfSFYz94S/gGVMczqBLBVdMSgaao+/UBWLmKNVHvUsFV0xWkkLEAiAVXNGrJIWIBYykP9peve04oj9loDliwZMKrpijJIXoW5L27mVfTFq6B2KO0n0UfUvSxbY3HLXvEtsvbCumQTFBBdfatp8yxSHFgEhJavQdSTOBnYE1JHXP0bMMcFc7UQ2c17YdQAymJIXoR+dTaulXAD7ftf9+4LJWIhowKTmNuZXuoxgYkl4G7Gz7vW3HEjGs0lKIviZpA0pX0pspF159v92IIoZbqo+i79SZPfeTdA1wKHAzpVX7KtuHtBzeQEkFV0xWuo+i70h6nLIwzB62Z9d9N9h+VruRDZ5UcMVkpfso+tEOlOmdz5H0E+BEcsHVpKSCK+ZWWgrRt+p8PdsDM4HNgWOBU2yf1WpgA6BOKrgG8Glgn65D9wOX2X6slcCi7yUpxECQ9DRgR+AttjdvO55BlQqumEiSQsSQG6uCKwP2MScZU4gYQpLWpozLzKSMIXyHWsHVamDR99JSiBhCqeCKuZXrFCKG0w7A7ZQKriMlvZpUcEUP0lKIGGKp4IrJSlKIWECkgit6kaQQEREdGVOIiIiOJIWIiOhIUoihIOkNkixp3a59MyRdOZfPd6OkFSZx/u6SDh1j//6S/nNenjtiKiUpxLCYCZxHuWArIuZSkkIMPElLAS8D9mAOSUHSwpI+J+kKSZdLel/d/2pJl9T9R0nqXtD+fZIursfWrec/TdKp9Tl+I2n9eYh7SUk/lnSZpCslvaXu30/ShXXfEZJU97+4vu6vJX12pBVU/2+frY+5XNK75zamiCSFGAbbAz+x/Tvgr5I2HOOcPSmzhr7Q9vrAtyRNA46hlGg+nzLty791PebOuhbBV4GRLqBPAJfU5/go8M15iHsr4FbbL7D9POAndf+htl9c9y0OvLbuPxp4j+1Nge5ZTvcA7rX9YuDFwLskrTEPccUCLEkhhsFMypoL1H9njnHOFsDhth8FsP1XYB3gDzWZQLmw6+VdjxlZ+vMiYEa9vxlwXH2OnwPLS1p2nNjmVPNt4ApgC0mfkfQvtu+tx14l6QJJV1AuOHuupOWApW2fX8/5dtdzbQnsKulS4AJgeWCtcWKKmKNMiBcDTdLylA/O50kysDBgSR8efSr//AE90bQP/6j/PsYTfytjPWa8i33uAlYatW9p4B7bd0l6EbAN8GlJZwEHAV8BNrJ9s6T9gWkTxCrgfbbPHO8/E9GLtBRi0L0J+KbtZ9qeYXs1yvTQm4067yzgPZIWgc7VvdcCMyStWc95G3DuBK/3S2CX+hyvpHQx3TfB+dtJWro+5o3URW4krQw8aPt44HPAhpQEAHBnHSt5E4Dtu4H7Jb2kHu8eOzkT+DdJi9bXWLtObxExaWkpxKCbCfzvqH3fo6wf8JmufV8H1gYul/QIcKTtQyW9HTi5JosLgcMneL39gaMlXQ48COw23sm2L6+lqufVlsxfgHfWw88HPltnNH0E+Dfb90g6ktK1dGONacQewJGS/gb8Ahjpbvo6pXvr4joofQdlnCVi0jLNRcSAkLSU7Qfq/X2AlWzv3XJYMWTSUogYHNtK+gjl7/YmYPd2w4lhlJZCRER0ZKA5IiI6khQiIqIjSSEiIjqSFCIioiNJISIiOv4/Yeu39sgTMgcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "infrequent = alcoholUsedPast12MO[alcoholUsedPast12MO < 30]\n",
    "#at least once a month\n",
    "criteria1 = alcoholUsedPast12MO >= 30\n",
    "criteria2 = alcoholUsedPast12MO < 52\n",
    "month_criteria = criteria1 & criteria2\n",
    "monthly = alcoholUsedPast12MO[month_criteria]\n",
    "\n",
    "#at least weekly\n",
    "criteria3 = alcoholUsedPast12MO >= 52\n",
    "criteria4 = alcoholUsedPast12MO < 300\n",
    "weekly_criteria = criteria3 & criteria4\n",
    "weekly = alcoholUsedPast12MO[weekly_criteria]\n",
    "\n",
    "daily = alcoholUsedPast12MO[alcoholUsedPast12MO>360]\n",
    "thinkplot.Bar([\"Infrequently (<30 days)\", \"At Least Monthly (30<= days < 52)\", \"At Least Weekly ( 52<= days < 300)\",\"Almost Daily (>300 days)\"],[len(infrequent)/len(alcoholUsedPast12MO), len(monthly)/len(alcoholUsedPast12MO), len(weekly)/len(alcoholUsedPast12MO), len(daily)/len(alcoholUsedPast12MO)])\n",
    "thinkplot.Config(xlabel = \"Alcohol Usage\", ylabel = \"Percent\", title=\"Categorizing Yearly Consumption\")\n",
    "plt.xticks(rotation = \"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Majority of the respondents can be categorized as either infrequent alcohol drinkers or were consuming alcohol at least weekly during the time they were being surveyed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way of visualizing the distribution of this variable is with its probability mass function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alcoholUsedPast12MOPmf = thinkstats2.Pmf(alcoholUsedPast12MO)\n",
    "thinkplot.Pmf(alcoholUsedPast12MOPmf)\n",
    "thinkplot.Config(xlabel = \"Total Days of Consumption\", ylabel = \"Probability\", title = \"PMF of the Distribution of Alcohol Consumption\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be observed, this probability mass function looks similar to the histogram and it doesn't really tell us more than we had already observed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can the Cumulative Distribution Function tell us a little more about our distribution from a more analytical perspective?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alcoholUsedPast12MOCdf = thinkstats2.Cdf(alcoholUsedPast12MO)\n",
    "thinkplot.Cdf(alcoholUsedPast12MOCdf)\n",
    "thinkplot.Config(xlabel = \"Total Days of Consumption\", ylabel = \"CDF\", title = \"CDF of the Distribution of Alcohol Consumption\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the shape of the CDF, we can hypothesize that the distribution of alcohol consumption looks very similar to to the Weibull analytic distribution model. Can we confirm this hypothesis? We will compare our distribution to a series of analytic distribution to determine if they can be modeled by any of the well-known distribution models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following functions allow you to plot the CDF of the distribution of alcohol consumption against a Gaussian model as well as looking at the Normal Plot to confirm from if the model fits well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MakeNormalModel(values, label=''):\n",
    "    \"\"\"Plots a CDF with a Normal model.\n",
    "\n",
    "    values: sequence\n",
    "    \"\"\"\n",
    "    cdf = thinkstats2.Cdf(values, label=label) #plots percentile of values\n",
    "\n",
    "    mean, var = thinkstats2.TrimmedMeanVar(values)\n",
    "    std = np.sqrt(var)\n",
    "    print('n, mean, std', len(values), mean, std)\n",
    "\n",
    "    xmin = mean - 4 * std\n",
    "    xmax = mean + 4 * std\n",
    "\n",
    "    xs, ps = thinkstats2.RenderNormalCdf(mean, std, xmin, xmax)\n",
    "    thinkplot.Plot(xs, ps, label='model', linewidth=4, color='0.8')\n",
    "    thinkplot.Cdf(cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MakeNormalPlot(values, label=''):\n",
    "    \"\"\"Generates a normal probability plot.\n",
    "\n",
    "    values: sequence\n",
    "    \"\"\"\n",
    "    mean, var = thinkstats2.TrimmedMeanVar(values, p=0.01)\n",
    "    std = np.sqrt(var)\n",
    "\n",
    "    xs = [-5, 5]\n",
    "    xs, ys = thinkstats2.FitLine(xs, mean, std)\n",
    "    thinkplot.Plot(xs, ys, color='0.8', label='model')\n",
    "\n",
    "    xs, ys = thinkstats2.NormalProbability(values)\n",
    "    thinkplot.Plot(xs, ys, '+', alpha=0.3, label=label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n, mean, std 33532 80.33293773963848 87.3373488181064\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MakeNormalModel(alcoholUsedPast12MO, label='')\n",
    "\n",
    "thinkplot.Config(title='Distribution of Alcohol Consumption', \n",
    "         xlabel='Total Days of Consumption', \n",
    "         ylabel='CDF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "MakeNormalPlot(alcoholUsedPast12MO, label='')\n",
    "\n",
    "thinkplot.Config(title='Normal Probability Plot', \n",
    "         xlabel='Standard Normal Sample', \n",
    "         ylabel='Total Days of Consumption')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both the Gaussian CDF and Normal Sample Plot has shown that the data cannot be modeled using a Gaussian analytic model. So can it be modeled using an exponential analytic distribution?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Cdf(alcoholUsedPast12MOCdf, complement=True)\n",
    "thinkplot.Config(title='Distribution of Alcohol Consumption', \n",
    "         xlabel='Total Days of Consumption', \n",
    "         ylabel='CCDF (log)',\n",
    "         yscale = \"log\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the CCDF shows a somewhat linear pattern, which is an indication that the data could potentially be modeled using an exponential analytical distribution, there's a lot of constant intervals. Could there be a better fit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Cdf(alcoholUsedPast12MOCdf, transform = \"weibull\")\n",
    "thinkplot.Config(title = \"Distribution of Alcohol Use\",\n",
    "                 xlabel = \"Total Days of Consumption (log)\",\n",
    "                 ylabel = \"CCDF (log-log)\",\n",
    "                 yscale = \"log\", xscale = \"log\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting this distribution on a log-log y scale and a log x scale, we can see the linear pattern of a typical Weibull distribution. This is another confirmation that the data could be modeled using an exponential distribution since exponential distributions are a special case of Weibull. Exponential Distributions typically measure the inter-arrival time between events of a random variable. In this case, we are measuring the number of days (time) of alcohol consumption which is also a random variable that can take values between 0 - 365 days, making the exponential model a good fit for our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kde = gaussian_kde(alcoholUsedPast12MO, bw_method=0.3)\n",
    "\n",
    "xs = np.linspace(alcoholUsedPast12MO.min(), alcoholUsedPast12MO.max())\n",
    "ds = kde.evaluate(xs)\n",
    "ds /= ds.sum()\n",
    "\n",
    "plt.plot(xs, ds, label='KDE Alcohol Consumption')\n",
    "\n",
    "thinkplot.Config(xlabel='Total Days of Consumption)', ylabel='PDF', title = \"Distribution of Alcohol Consumption\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a smoother version of what the PDF of the distribution of Alcohol consumption looks like using a KDE which is an algorithm use to estimate the densities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    33532.000000\n",
       "mean        82.375880\n",
       "std         91.318878\n",
       "min          1.000000\n",
       "25%         12.000000\n",
       "50%         48.000000\n",
       "75%        120.000000\n",
       "max        365.000000\n",
       "Name: alcyrtot, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alcoholUsedPast12MO.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly by looking at the summary statistics, we can numerically characterize the center and variation of the distribution of alcohol consumption. It is noted that the mean of alcohol consumption over 12 months is 82.4 days and the median is 48 days. Another observations that can be made using the median value which means that 50% of the observations fall below or are equal to 48.0 days is that a good portion of the observations are small, which is another characterization of a Weibull distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## heryrtot: Past 12 Months of Heroin Use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will look into the other variable \"heryrtot\" which looks at the distribution of heroin use over the past 12 months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now look at the histogram of \"heryrtot\" to get a good visualization of the distribution of the observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7344488836309345"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroinUsedPast12MO = data[\"heryrtot\"].dropna()\n",
    "heroinUsedPast12MO.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heroinUsedPast12MOHist = thinkstats2.Hist(heroinUsedPast12MO)\n",
    "thinkplot.Hist(heroinUsedPast12MOHist)\n",
    "thinkplot.Config(xlabel = \"Total Days of Consumption\", ylabel = \"Frequency\", title = \"Distribution of Heroin Consumption\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the graph, we can see that the shape of the distribution is multi-modal, asymmetric and right skewed, which is confirmed by the positive measure of skewness above. Due to the spread of the frequency bins, we can see that there's a lot of variability in the observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again the histogram doesn't really provide much insight into the usage, grouping the data into frequency of use and see what percentage of people fall into each group can give us better results on the respondents' consumption behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w1/Zs4CBJOwIPA3cD+9Vz50k6HbgGWAi83fYj/Yo1IiJG188BbmyfBZw1rO3wjucHj3Luh4EP9y+6iIjoVe7gjoiIrpIsIiKiqySLiIjoKskiIiK6SrKIiIiukiwiIqKrJIuIiOgqySIiIrpKsoiIiK6SLCIioqski4iI6CrJIiIiukqyiIiIrpIsIiKiqySLiIjoKskiIiK6SrKIiIiu+posJO0i6XpJ8yUdOsL+QyRdI+kqSedJekrHvkckXVEfs4efGxER46dvy6pKmgIcB+wELADmSJpt+5qOwy4HZtm+X9L/A44G9qr7HrC9Zb/ii4iI3vXzymIbYL7tm2w/BJwG7N55gO3zbd9fNy8GNupjPBERsYT6mSw2BG7p2F5Q2xbnAODsju2VJc2VdLGkPUY6QdKB9Zi5d9xxx9JHHBERI+pbNxSgEdo84oHSG4BZwAs7mqfZvlXSJsBPJV1t+8ZFvph9AnACwKxZs0b82hERsfT6eWWxANi4Y3sj4NbhB0naEfgvYDfbDw612761/nsT8DNgqz7GGhERo+hnspgDzJQ0Q9KKwN7AIlVNkrYCvkBJFH/saF9b0kr1+XrA84HOgfGIiBhHfeuGsr1Q0kHAOcAU4CTb8yQdBcy1PRv4OLA6cIYkgN/Z3g14OvAFSY9SEtp/D6uiioiIcdTPMQtsnwWcNazt8I7nOy7mvIuAZ/UztoiI6F1P3VCSzuulLSIiBtOoVxaSVgZWBdaTtDaPVzitCWzQ59giImKC6NYN9VbgnZTEcCmPJ4t7KHdnR0TEJDBqsrB9DHCMpH+zfew4xRQRERNMTwPcto+V9Dxgeuc5tr/Sp7giImIC6SlZSDoVeCpwBfBIbTaQZBERMQn0Wjo7C9jCdqbUiIiYhHq9g/vXwPr9DCQiIiauXq8s1gOukfQroHP+pt36ElVEREwovSaLI/sZRERETGy9VkNdUJc8nWn7J5JWpcz3FBERk0Cv0338C/AtygyxUBYx+m6/goqIiIml1wHut1OmCb8HwPYNwBP7FVREREwsvSaLB+s62gBIWp7FrHoXERGDp9dkcYGk9wGrSNoJOAP4fv/CioiIiaTXZHEocAdwNWVywbOAw/oVVERETCy9ls6uQlnp7osAkqbUtvv7FVhEREwcvV5ZnEdJDkNWAX7S7SRJu0i6XtJ8SYeOsP8QSddIukrSebU8d2jffpJuqI/9eowzIiL6oNdksbLt+4Y26vNVRzuhXn0cB7wM2ALYR9IWww67HJhl+9mU0tyj67nrAEcA2wLbAEfUxZciIqIBvSaLv0raemhD0nOAB7qcsw0w3/ZNtZLqNGD3zgNsn297qCvrYmCj+vylwI9t32X7buDHwC49xhoREctYr2MWBwNnSLq1bj8Z2KvLORsCt3RsL6BcKSzOAcDZo5y74fATJB0IHAgwbdq0LuFERMSS6posJC0HrAg8DdicsrTqdbYf7nbqCG0j3psh6Q2UadBfOJZzbZ8AnAAwa9as3PcREdEnXbuhbD8KfNL2w7Z/bfvqHhIFlKuBjTu2NwJuHX6QpB2B/wJ2s/3gWM6NiIjx0euYxbmSXiNppE/8izMHmClphqQVgb2B2Z0HSNqKMt/Ubrb/2LHrHGBnSWvXge2da1tERDSg1zGLQ4DVgEckPUDpJrLtNRd3gu2Fkg6ivMlPodynMU/SUcBc27OBjwOrU8ZDAH5nezfbd0n6ICXhABxl+64l+Q9GRMTS63WK8jWW5IvbPotyt3dn2+Edz3cc5dyTgJOW5PtGRMSy1esU5ZL0Bknvr9sbS9qmv6FFRMRE0euYxeeA7YDX1+37KDfcRUTEJNDrmMW2treWdDmA7bvroHVEREwCvV5ZPFyn7zCApKnAo32LKiIiJpRek8VngO8AT5T0YeBC4CN9iyoiIiaUXquhvibpUuAllLLZPWxf29fIIiJiwhg1WUhaGXgbsCll4aMv2F44HoFFRMTE0a0b6suUOZuupkw1/om+RxQRERNOt26oLWw/C0DSicCv+h9SRERMNN2uLB6bMDDdTxERk1e3K4t/kHRPfS5glbrddW6oiIgYHKMmC9tTxiuQiIiYuHq9zyIiIiaxJIuIiOgqySIiIrpKsoiIiK6SLCIioqu+JgtJu0i6XtJ8SYeOsP8Fki6TtFDSa4fte0TSFfUxe/i5ERExfnpdz2LM6pTmxwE7AQuAOZJm276m47DfAfsD/zHCl3jA9pb9ii8iInrXt2QBbAPMt30TgKTTgN2Bx5KF7ZvrvqyNERExgfWzG2pD4JaO7QW1rVcrS5or6WJJeyzb0CIiYiz6eWWhEdo8hvOn2b5V0ibATyVdbfvGRb6BdCBwIMC0adOWPNKIiBhVP68sFgAbd2xvBNza68m2b63/3gT8DNhqhGNOsD3L9qypU6cuXbQREbFY/UwWc4CZkmZIWhHYG+ipqknS2pJWqs/XA55Px1hHRESMr74lizql+UHAOcC1wOm250k6StJuAJKeK2kBsCfwBUnz6ulPB+ZKuhI4H/jvYVVUERExjvo5ZoHts4CzhrUd3vF8DqV7avh5FwHP6mdsERHRu9zBHRERXSVZREREV0kWERHRVZJFRER0lWQRERFdJVlERERXSRYREdFVkkVERHSVZBEREV0lWURERFdJFhER0VWSRUREdJVkERERXSVZREREV0kWERHRVZJFRER0lWQRERFd9TVZSNpF0vWS5ks6dIT9L5B0maSFkl47bN9+km6oj/36GWdERIyub8lC0hTgOOBlwBbAPpK2GHbY74D9ga8PO3cd4AhgW2Ab4AhJa/cr1oiIGF0/ryy2Aebbvsn2Q8BpwO6dB9i+2fZVwKPDzn0p8GPbd9m+G/gxsEsfY42IiFH0M1lsCNzSsb2gtvX73IiIWMb6mSw0QpuX5bmSDpQ0V9LcO+64Y0zBRURE7/qZLBYAG3dsbwTcuizPtX2C7Vm2Z02dOnWJA42IiNH1M1nMAWZKmiFpRWBvYHaP554D7Cxp7TqwvXNti4iIBvQtWdheCBxEeZO/Fjjd9jxJR0naDUDScyUtAPYEviBpXj33LuCDlIQzBziqtkVERAOW7+cXt30WcNawtsM7ns+hdDGNdO5JwEn9jC8iInrT12QRETEeDvrQN5oOoVGfPWyfvn+PTPcRERFdJVlERERXSRYREdFVkkVERHSVZBEREV0lWURERFdJFhER0VWSRUREdJVkERERXSVZREREV0kWERHRVZJFRER0lWQRERFdJVlERERXSRYREdFVkkVERHSVZBEREV31NVlI2kXS9ZLmSzp0hP0rSfpm3X+JpOm1fbqkByRdUR/H9zPOiIgYXd+WVZU0BTgO2AlYAMyRNNv2NR2HHQDcbXtTSXsDHwP2qvtutL1lv+KLiIje9XMN7m2A+bZvApB0GrA70JksdgeOrM+/BXxWkvoYU/RJ1kDu/xrIEU3qZzfUhsAtHdsLatuIx9heCPwFWLfumyHpckkXSPqnkb6BpAMlzZU094477li20UdExGP6mSxGukJwj8fcBkyzvRVwCPB1SWv+3YH2CbZn2Z41derUpQ44IiJG1s9ksQDYuGN7I+DWxR0jaXlgLeAu2w/a/hOA7UuBG4HN+hhrRESMop/JYg4wU9IMSSsCewOzhx0zG9ivPn8t8FPbljS1DpAjaRNgJnBTH2ONiIhR9G2A2/ZCSQcB5wBTgJNsz5N0FDDX9mzgROBUSfOBuygJBeAFwFGSFgKPAG+zfVe/Yo2IiNH1sxoK22cBZw1rO7zj+d+APUc470zgzH7GFhERvcsd3BER0VWSRUREdJVkERERXSVZREREV0kWERHRVZJFRER0lWQRERFdJVlERERXSRYREdFVkkVERHSVZBEREV0lWURERFdJFhER0VWSRUREdJVkERERXSVZREREV0kWERHRVV+ThaRdJF0vab6kQ0fYv5Kkb9b9l0ia3rHvvbX9ekkv7WecERExur4lC0lTgOOAlwFbAPtI2mLYYQcAd9veFPg08LF67haU9bifAewCfK5+vYiIaEA/ryy2Aebbvsn2Q8BpwO7Djtkd+HJ9/i3gJZJU20+z/aDt3wDz69eLiIgGyHZ/vrD0WmAX2/9ct98IbGv7oI5jfl2PWVC3bwS2BY4ELrb91dp+InC27W8N+x4HAgfWzc2B6/vynxkf6wF3Nh1Ei+X1Wzp5/ZZOm1+/p9ie2u2g5fsYgEZoG56ZFndML+di+wTghLGHNvFImmt7VtNxtFVev6WT12/pTIbXr5/dUAuAjTu2NwJuXdwxkpYH1gLu6vHciIgYJ/1MFnOAmZJmSFqRMmA9e9gxs4H96vPXAj916RebDexdq6VmADOBX/Ux1oiIGEXfuqFsL5R0EHAOMAU4yfY8SUcBc23PBk4ETpU0n3JFsXc9d56k04FrgIXA220/0q9YJ4iB6E5rUF6/pZPXb+kM/OvXtwHuiIgYHLmDOyIiukqyiIiIrpIsIiKiq37eZxGjkDQL+CdgA+AB4NfAT2zf1WhgLSNpNeBvk6AAoi/y+o2dpO2AN1D+fp/M43+/PwS+avsvDYbXN7myGGeS9pd0GfBeYBXKXed/BLYHfizpy5KmNRnjRCZpOUmvl/RDSX8ErgNukzRP0sclzWw6xoksr9/SkXQ28M+UKs9dKMliC+AwYGXge5J2ay7C/kk11DiT9HZKGfEDi9m/JbCu7fPGN7J2kHQB8BPge8CvbT9a29cBXgy8HvjO0FQxsai8fktH0nq2R53Wo5dj2ijJIlpF0gq2H17aYyarvH7LRu2+e8D2o5I2A55Gmb9uYF+3dEM1RNLRktaUtIKk8yTdKekNTcc10fXyxzjIf7BLy/bDKraV9GpJr6rP1XlMkzG2xM+BlSVtCJwHvBk4pdGI+izJojk7274HeAVlLqzNgP9sNqSJT9KzJV0s6RZJJ0hau2NfpoTpQtLOwA2UmZ1fDuwKfAC4oe6L3sj2/cCrgWNtv4oydjGwUg3VnBXqvy8HvmH7ro4Pd7F4n6NOYU8ZaLxQ0m62b+Tx1zQW7xhgR9s3dzbWOdjOAp7eRFAtpFoVtS9lETcY8PfTgf7PTXDfl3QdpezuXyVNBf7WcExtsLrtH9Xnn5B0KfCjul5KBuC6W55yJTvc70myHYuDKRWN36lz2W0CnN9wTH2VAe4G1S6Ue2w/UgfM1rB9e9NxTWSSrgRe0FnLLunZwJnAOrbXbSy4FpD0XuB1lJUrb6nNG1Mm8Tzd9kebiq1NJD3T9q+bjmM8JVk0RNJc4CRKF9TdTcfTFpJeD9xk++Jh7dOA99v+l2Yia4+6xv1uwIaUhcYWALNtX9NoYC0i6UJgRcqg9tdt/7nZiPovyaIhkjalVFDsBcwFTgbOdX4gMU7qvRXOh5UlU0tm3wzsSVlv5xTb5zYbVf8kWTRM0nKUiqjPA49SrjaOybQfI5P0bNtX1ecrAO8BtqFMt/ChWqESi1GvwI4GdgCGuvLWAn4KHDp84DtGJ2kKsAfwGeAeypXa+2x/u9HA+iClsw2qfe2fBD5O6XN/LeUX7qdNxjXBndLx/L+BTSmv4SrA8U0E1DLfBL4DPNn2TNszKVNWfJcyjhE9qCXcnwaupSTeV9p+en3+6UaD65NcWTSkVvH8mbJa4Jm2H+zY923br24suAlM0uW2t6rPrwCeO3SjGXCl7Wc3G+HEJumGmiDGtC8WJennwBeBbw2fukfSG22f2kxk/ZPS2ebsafumkXYkUYxqLUmvolwVrzR0t7FtS8onn+4ulfQ54MssWg21H3B5Y1G1jO0XjLJv4BIF5MqiUZJ2BZ5Bma0SANtHNRfRxCfp5GFNh9r+g6T1ga/ZfkkTcbWFpBUpN5HtzrBqKODEzivcWLw6O+9HKXdtd/79btJYUH2WZNEQSccDq1Jm+vwSZbziV7YPGPXEiGhcLZ09gjI+8UpKVZRsH9FoYH2UAe7mPM/2m4C7bX8A2I7SHRDRN5JWlfRuSf8paWVJ+0maXSe2XL3p+FpklbqMgGz/1vaRlMHtgZVk0ZyhQbH7JW0APAzMaDCemBxOAZ5E+V37IfBc4BOU7qjPNxdW6/ytlr3fIOmgOo72xKaD6qcMcDfnB5KeQCmbvYwyr9GXmg3AB2EjAAAdaklEQVQpJoHNbL+uVo/dRplU0JL+F7iy4dja5J2UbuR3AB+kXFXs12hEfZYxiwlA0krAyoO6du+yVm/Ge4LtOzra1gCwfW9jgbWApCtsb1mfn2T7LR37rrT9D81FFxNZrizGmaTFlsVKYhDv/OyDFYBLJG3esVDPlyl30f6ssajaYa6k1W3fNyxRPBVIou1C0vcZZXZj2wO5/jbkymLcdZR+PhF4Ho/frf1i4Ge5x6I3tZrsPNtn1Ondf17voI0lJEmZm2x0kl5Yn74aWB8YWqt8H+Bm2+9rJLBxkGTREEk/AP7F9m11+8nAcUkWvZE0C/ig7ZdJ+ndgVdsfbjqumBwk/Xz4jXkjtQ2SVEM1Z/pQoqj+QFlaNXpgey7wpLoG8hspEzBGjJepdcEj4LGVBqc2GE/fZcyiOT+TdA7wDUof6N4M+EpbfXAycCzw+2GJN6Lf/p3yNzw0Zc904MDmwum/dEM1qNZmD122/tz2d5qMp23qSoO3AvvY/m7T8bRFqsmWjVrF+LS6ed2gT5WSZBGtVsd6/mD70aZjaQtJq1LW/3ismkzSt4HP2P5Zk7HFxJUxi2g127clUYxNXSDqXMqiPdRqsqcnUcRokiwiJqcvAUP3WbyBx0tAI0aUAe4GZQ3kaIrtuZI6q8l2bTqmtpC0FrALZYp3U8bNzrH950YD67NcWYwzSdMknSbpDuASYI6kP9a26c1G106S1q5L1MbYpJpsjCS9iTKX24soc0OtRrmh9tK6b2BlgHucSfol8D+U5RgfqW1TgD2Bd9r+xybjawtJPwN2o1wdXwHcAVxg+5Am42qTVJONnaTrgW2HX0XU1/IS2wN7r1SuLMbfera/OZQoAGw/Yvs0YN0G42qbtWzfQ5l24WTbzwF2bDimVqndn5tQVsmL3oiR54Z6tO4bWBmzGH9ZA3nZWL6Wzb4O+K+mg2mrdD+N2YeByySdy+N/v9OAnShTlQ+sdEONs6yBvGxI2hN4P3Ch7X+tUy983PZrGg4tBlztcnopi/79njPohSpJFtFKktaxfVfTccTkJOlJdFRD2f5DwyH1XZLFOKt3zx5E+SU7FtgLeA1wHXCU7fsaDK81JN1AGdg+GTg7U2svufpJeWPbVzUdy0QnaUvgeGAtyhWFgI2APwP/avuyBsPrqySLcSbpdEpf5yrA5sC1wOnAK4H1bb+xwfBaoy4LuiPlxrJtgG8Cp9j+v0YDa4lUky0ZSVcAb7V9ybD2fwS+MMgrDSZZjLOhZS071kB+cl0DWcCVtnO/wBhJejHlDuTVKOtIH2r7l81GNbFJutz2VpL+mXJVcYSkq/L7NzpJN9ieuZh9821vOt4xjZdUQzWkJoizhrpP6nYyd48krUuZpuKNlLVA/o1SJLAlcAYwo7noWiHVZEvmbEk/BL7CotWMbwJ+1FhU4yDJYvxlDeRl45fAqcAethd0tM+tS67G6I4CzqFUk82p1WQ3NBzThGf7HZJeTunC66yGOs72WY0G12fphppAsgZy7/JaLZ1Uk8VY5Q7uBkhaVdI/DGubBmzQUEhttJ6kj0s6S9JPhx5NB9Uil0g6Q9LL63hZ9EDS+pI+L+k4SetKOlLSVZJOr916AyvJohkPA9+WtFpH25eAgf5lW8a+Rik3ngF8ALgZmNNkQC2zGXACZcxnvqSPSBrYeY2WoVOAayjjFecDDwCvAP6XUlI7sNIN1RBJnwCusX1Svar4nu2tmo6rLSRdavs5nRU8ki6w/cKmY2ubVJP1bqiKrD7/ne1pHfuusL1lc9H1V64smvMl4M31+ZsoN5dF7x6u/94maVdJW1Fujooe1C6UgyXNBf6DUk22HvAu4OuNBjexdb5nfmWUfQMn1VANsX2dJOql/z7A9k3H1DIfqovQvItyJ/yawL83G1KrpJpsyXyvo5rxsKFGSZsCA31DaLqhGiRpf8odyL+3vU/D4cQkkmqyGKskiwbVeaJuA15j+ydNx9MGko5l5PUEgFIHP47htJakqcC7gWcAKw+1296hsaBaSNKrgB9PhjndBrqPbaKzfb/ttZIoxmQucCnlDW5ryo1kN1Du3H5klPNiUakmW0r1RtrTKTMJDLxcWYyzof7OpT1mspN0PrCz7Yfr9grAubZf3Gxk7ZBqsqUn6cOUq9ydbW/TdDz9liuL8fc9SZ+U9ILO+ywkbSLpAEnnALs0GF9bbACs0bG9OrmpcSxSTbYUJE0B9gQ+Bvxl+E22gyjVUOPM9kvq3DJvBZ5f1xJYCFwP/BDYz/btTcbYEv8NXF6vMABeCBzZXDitk2qypfNy4CLb90o6CfhnSvnxwEo3VLSWpPWBbevmJUmyMV4kfRf4pO3/lbQyMA94uu2HGg6tb5IsIiaRVJMtPUlPAL5r+0UdbR8Dzrc9sNOUJ1lETCKS9qtPnw9sQVlhEEr/+6W20xUVI0qyiJiEUk22ZCRNt33zKPsFbDjsrviBkGqohkj6hKRnNB1HTFqpJlsyH5d0pqQ3SXqGpCdKmiZpB0kfBH4BPL3pIPsh1VDNuQ44QdLylEkEv2H7Lw3H1CqSLrS9/dC/TcfTMqkmWwK295S0BbAvZaqeJwP3A9cCZwEftv23BkPsm3RDNUzS5pTZZ/ehfCr5ou3zRz8rACRdZnvrzmmjo3epJouxyJVFg+qNPU+rjzspawkcIumttvduNLgYeDU5fK/pOKIdkiwaIulTlEXfzwM+YvtXddfHJF3fXGQREX8vyaI5vwYOs33/CPsGfp6ZiGiXJItxJmnr+vQK4Gml0u5xti/LQHfP1P2QiGVP0pnAScDZth9tOp7xkAHucdZRfTISZz2B3kl6ke2fDf3bdDxtk2qyJSdpR0phyj8CZwCn2L6u2aj6K8miIZJWHl5iN1JbRL+kmmzp1ckY9wH+C7gF+CLw1aGbHQdJbsprzkU9tsUwktaW9KFhbftIel5TMcXkI2ldYH/KjLOXA8dQFuT6cYNh9U3GLMZZrW3fEFilriEw1O++JrBqY4G1iO27Je0k6RTb82vz4cA/NRlXTB6Svk0peT8VeKXt2+qub0qa21xk/ZNkMf5eSvk0shHwqY72e4H3NRFQS51IuYP2fZJeBMyzfWezIcUk8lnbPx1ph+1Z4x3MeMiYRUMkvcb2mU3H0VaS1qCsx/00ynQppw3y9ND9MDRWkTGL3kl69Wj7bX97vGIZb7myaM4PJL0emE7Hz8H2UY1F1CJ1hbKLgL0oU1a8ueGQ2ujfh/0b3b1ylH0GBjZZ5MqiIZJ+BPwFuBR4ZKjd9icbC6plJD0f+D6lS+DwpuOJGGRJFg2R9Gvbz2w6jraTdAxlecvfNR1LW9R1399l+7COtn2A39pORd4oJL3B9lclHTLSftufGql9EKR0tjkXSXpW00G0ne2DkyjGxvbdwE6SNu1oPhz4v4ZCapPV6r9rLOYxsHJl0RBJ1wCbAr8BHqSU0Nr2sxsNrAUkvRTYg1KCbOBW4HsZ4O6dpAOB6baHqskOsv3ahsOKCSzJoiGSnjJSu+3fjncsbSLpf4DNgK8AQ0tXbgS8CbjB9sFNxdYmqSZbOpJWBg4AngGsPNRu+y2NBdVn6YZqSE0KGwM71Of3k59HL15u++W2T7N9YX2cBuwKvLzp4NrC9r2UGQOGqsnOaTai1jkVWJ9y39QFlA8s9zYaUZ/lzakhko4A3gO8tzatAHy1uYha42+SRprC/blA5tUamy8BnwPOcLoYxmpT2+8H/mr7y5QPKwM9Bpn7LJrzKmAr4DIA27fWroEY3f7A5+trNdQNtTFwT90XPbL9C0mnUpJGjM3QRIF/lvRM4HbKPVMDK8miOQ/ZtiQDSFqt2wlR1vsAtu2YY0vAgqwfvWQyxrPETqglyO8HZgOr1+cDKwPcDZH0H8BMYCfgo5R5jr5u+9hGA2uBmiiwfbukqZQJBK+zfU2zkbWDpKdREu0ltu/raN8lg9yxOEkWDZK0E7Az5dPxObYHcmrjZUnSW4FDKa/ZxyhdT/OA5wNH2z6xuegmPknvAN4OXAtsCRxs+3t132W2tx7t/ABJmwMHUirJoLyWJ9ge6PtUkiyiVSRdTaneWQX4LWWg8fbaJXC+7S0bDXCCq6/fdrbvkzQd+BZwqu1jMqFgd5K2o8z/dAJlvFGUscd/AV5t++IGw+urjFk0RNK9lBvKAFakVEP91faazUXVCg/bvh+4X9KNQ2MVdY2LfPLpbspQ15Ptm+sNed+q9/1kTfPuDgf2GbaM73cl/RQ4AnhZI1GNg5TONsT2GrbXrI+VgdcAn206rhZ4VNIK9fmuQ431Jqn8Pnd3u6THrr5q4ngFsB4DXvq5jDx1pPXebV8AbDL+4Yyf/HFNELa/C+zQdBwt8GrqFZntBR3t6wLvaiSidnkTpczzMbYX2n4T8IJmQmqV0W68++u4RdGAdEM1ZNgiKssBs3i8WyoW75aRbiCz/Xvg9wCSlJvMRjYswQIgaR3bd9n+RRMxtczGkj4zQrsoFWYDK8miOZ2LqCwEbgZ2byaUVjlf0pmUiQMfm21W0orA9sB+wPnAKc2EN7FJOsz2h+rzLYDvAitIErCX7UsaDXDi+89R9g3k2ttDUg0VrVLHJt4C7AvMAP5MmchtCnAucJztK5qLcGLrLI+V9EPKwlFn1ylU/sf285qNMCaqJIuGLOZS9jG23zFesbRVHeheD3jA9p+bjqcNhiWLRUplUzobo0k3VHNWBrYAvlm396QssZpPxT2y/TBwW9NxtMwmkmZT+tg3krRqLUWGUr4dMaIki+bMBF5c3/CQdDxwru1/bzasGHDDx8WWA5D0JODz4x9OO0l6/vCCgJHaBkm6oRoi6XrKnbR31e21gYttb95sZBHRzUhTowz6dCm5smjOfwOXSzq/br8QOLK5cNpF0kHA1+p60hHjok738TxgqqRDOnatSSmyGFhJFg2xfbKksynzHAEcmmm2x2R9YI6ky4CTKBMx5jI5+m1FynTkywOd68/cAwz0GubphmpIrWvfF9jE9lGSpgHr2/5Vw6G1Rn0NdwbeTLmp8XTgRNs3NhpYDDxJT6nLISNpOWB12/c0HFZfZbqP5nwO2A7Yp27fCxzXXDjtU68kbq+PhcDalEnxjm40sAlM0mGS1hll/w6SXjGeMbXURyWtWRctuwa4XtJoN+y1XrqhmrOt7a0lXQ6PzZq6YtNBtUVdl2E/4E7KsqD/afvh+invBuDdTcY3gV0NfF/S3yhTbN9BKeOeSVnf4ifAR5oLrzW2sH2PpH2Bs4D3UErfP95sWP2TZNGchyVNoc4HVVd8e7TZkFplPcr6Ab/tbLT9aD4ZL15d6Oh7kmZSFox6MqW//avAgbYfaDK+Flmh3hS6B+Uu+IcHfYr8JIvmfAb4DvBESR+mDI4d1mxIE19HF8r/DNsGoE6Id+24B9Yytm+gXIHFkvkCZT63K4Gf1/VABnrMIgPcDaprIb+EcjfteXmT607Sb3h8dt7hi/XY9kCvKRATl6TlbS9sOo5+SbJoQO1Xv8r2M5uOJSLGTtJalJXxhtYAuQA4yvZfmouqv1IN1QDbjwJX1nLZWEKSNpT0PEkvGHo0HVNbjFYRFT05iVLB+Lr6uAc4udGI+ixXFg2pa/Y+F/gVHSts2d6tsaBaRNLHgL0oZYuP1Gbn9euNpBsok1aeDJydGxrHRtIVtrfs1jZIMsA9ziStZPtB4ANNx9JyewCb19cyxm4zYEfK2iDHSvomcIrt/2s2rNZ4QNL2ti+EMokgMNCVZLmyGGdDk41JOtX2G5uOp63qVCl72r6v6VjaTtKLKaWzq1Gqew61/ctmo5rYJG0JfBlYi1JocRewv+0rGw2sj3JlMf5WlLQf8Lxh63ADYPvbDcTUGpKOpVRD3Q9cIek84LGriywa1RtJ6wJvAN4I/AH4N2A25ca8MyirEMZi1NUY/0HSmnV7oMtmIcmiCW+jzAn1BBZdhxvKm2CSxeiG1jm+lPLm1imXyb37JXAqsIftBR3tc+vaKjEKSU8A3gRMB5Yv05QN9oeVdEM1RNIBtk9sOo62knSw7WO6tcXIJCmD2ktO0kXAxZTpUx6becH2lxsLqs+SLBok6XnUTyZDbba/0lhALbKYxWeyhnQXkr7PKFdgqSbrzaAvdDSSdEM1RNKpwFMp5YuPlX4CSRajkLQP8HpgRl1LesgawJ+aiapVPtF0AAPiVEn/AvyARcfM7moupP5KsmjOLMrMlbm0G5uLgNsoEwl+sqP9XuCqRiJqEdsXAEh6ju1LO/dJGj6GFov3EGWG2f/i8Ss1AwM73Uy6oRoi6QzgHbZvazqWmHzqCoP72b66bu8DvNP2tqOfGQCSbqQsM3Bn07GMl1xZNGc94BpJv2LRy9j0Gfeglh1/DHgipc5dlDu412w0sPZ4LWWhqH2B7SmVPTs3G1KrzKOUb08aubJoiKQXjtQ+1E0Qo5M0H3hlZupdcpI2A74L3EIpoR3oO5CXJUnfAZ4BnM8kuc8nySJaSdIvbD+/6TjaRtLVLFoN9UTgL9Q3PNvPbiKutqk31v6dlM7GMiPpXkYuXUw3yhhIOgZYn/LJuPOTXW5qHEVdpGexhq88GDEkYxbjzPYaTccwINak9Bl39rPnDvguOpOBpO2BmbZPrsv6rt5cZO0wwpXZIgb5yixXFhGTkKQjKOXbm9veTNIGwBnp2hvdZL4yy+JH0UqSNpL0HUl/lPQHSWdK2qjpuFrkVcBu1LVUbN9KubExRmH7tyM9gI2AdzcdXz8lWURbnUyZSHADYEPg+wz4SmXL2EP1hlADSFqt4XhaR9KWko6WdDPwIeC6hkPqq4xZRFtNtd2ZHE6R9M7Gommf0yV9AXhCnbbiLcAXG45pwqvlxnsD+1Cml/kmpTv/xY0GNg4yZhGtJOknwCnAN2rTPsCbbb+ksaBaRtJOlAIBAefY/nHDIU14kh4F/hc4wPb82naT7YGd5mNIriyird4CfBb4NKUr5aLaFr37P0q59k8krSppDdv3Nh3UBPcaypXF+ZJ+BJxGSbYDL1cWEZNQ7Xo6EFjH9lMlzQSOz5VZb+oYzx6UK9odKEusfsf2uY0G1kdJFtEqkj4z2v5Bnm5hWZJ0BbANcMnQGiCSrrb9rGYjax9J6wB7AnvZ3qHpePol3VDRNm8Dfg2cDtzKJOkC6IMHbT80tByopOXJsrRLpK5h8YX6GFhJFtE2T6Z+igMWUqpRzrR9d6NRtc8Fkt4HrFIHuv+VUn4cMaJ0Q0VrSdqQ0md8CPAe26c2HFJrSFoOOICOaijgS1mMKxYnySJaSdLWlESxE3Ap8Enb1zQb1cQnaQ/gItt/bDqWaJcki2gVSR8AXgFcSylb/JHthc1G1R6SvgVsR5mE8ReUkuNf2J7XaGAx4SVZRKvUm6JuAoYW6hn6BR6a4n1gZ/1cliRNB55XH9sB04A5tl/eYFgxgWWAO9pmRtMBDALbN0taGVilPoaeR4woVxYRk0itgNoOmApcD1xcH1fZfqTJ2GJiS7KImEQkXQfcB/yAMl5xie2/NBtVtEGSRcQkU+84Hhqv+EfKCnlXUqqkMs17jCjJIlpJ0sG2j+nWFotX79p+DvAC4K3ADNtTmo0qJqoki2glSZfZ3npY2+VD8xzFyCTtRrmieD7wDGAepTvqIsqVxR0NhhcTWJJFtIqkfYDXA9tT1hUYsiaw0PaOjQTWEpK+Tb23ArjU9kMNhxQtkdLZaJuLgNuA9YBPdrTfC1zVSEQtYvvVTccQ7ZQri2ilup7AA7YfrUtdPg042/bDDYcWMZCSLKKVJF0K/BOwNuU+gbnA/bb3bTSwiAG1XNMBRCwh2b4feDVwrO1XAVs0HFNrSDq4l7aIIUkW0VaStB2wL/DD2pYxuN7tN0Lb/uMdRLRH/riirQ4G3ktZ93iepE2A8xuOacLrqCabIWl2x641gT81E1W0QcYsIiYRSU+hTMb4UeDQjl33UuaHynTvMaIki2glSVOBd1NuLFt5qN32Do0F1SKpJouxyphFtNXXgOson5I/ANwMzGkyoJb5ObByXZr2PODNwCmNRhQTWpJFtNW6tk8EHrZ9ge23UCbFi96kmizGJAPc0VZD3SW3SdoVuBXYqMF42qazmuyA2pb3g1is/HJEW31I0lrAu4BjKdU8/95sSK2SarIYkwxwR0REV7myiFaqFTyfB55k+5mSng3sZvtDDYfWCqkmi7HKAHe01Rcp3SgPA9i+Cti70YjaJdVkMSZJFtFWq9r+1bC23FDWu1STxZikGyra6k5JTwUMIOm1lHUuojepJosxyQB3tFKt3jmBskTo3cBvgH1t/7bRwFpC0isoKw1uzOPVZB+wPXvUE2PSSrKIVqvTVixn+15Jr7F9ZtMxRQyijFlEq9n+q+176+anGw2mRSRtJuk8Sb+u28+WdFjTccXElWQRg0RNB9AiqSaLMUmyiEGSPtXepZosxiTVUNEqkq5m5KQg4EnjHE6bpZosxiQD3NEqdfGexUo1VG9STRZjlWQRMYmlmix6lWQREQBI+p3taU3HERNTBrgjYkiqyWKxkiyilSQd3EtbjEm6GWKx0g0VrSTpMttbD2u73PZWTcXUBl2qyTazvdI4hxQtkdLZaBVJ+wCvB2ZI6pzHaE3gT81E1SqvaDqAaKcki2ibiyj3A6wHfLKj/V7gykYiapGUxsaSSjdUDARJzwdeb/vtTccSMYhyZRGtJWlLSpfU6yg3lX272YgiBleqoaJV6myph0u6FvgscAvlCvnFto9tOLzWSDVZjFW6oaJVJD1KWbTnANvza9tNtjdpNrJ2STVZjFW6oaJtXkOZSvt8ST8CTiM3k/Us1WSxpHJlEa1U5zTaA9gH2AH4MvAd2+c2GtgEVydinAF8FDi0Y9e9wJW2H2kksJjwkiyi9SStA+wJ7GV7h6bjaaNUk0U3SRYRk9RI1WQpEojFyZhFxCQiaTPKmM8+lDGKb1KryRoNLCa8XFlETCKpJosllfssIiaX1wC3U6rJvijpJaSaLHqQK4uISSjVZDFWSRYRk1yqyaIXSRYREdFVxiwiIqKrJIuIiOgqySIGnqT7hm3vL+mzy+hrz5L0mYkST0S/5Ka8iC4kLW974Uj7bM8F5o5zSBHjLlcWMalJmirpTElz6uP5tf1ISSdIOhf4iqSVJZ0s6WpJl0t6cT3uRZJ+0HHOSZJ+JukmSe9Ygnj2lPRrSVdK+nltmy7pfyVdVh/Pq+3LSfqcpHmSfiDpLEmvrfueI+kCSZdKOkfSk5fRSxaTVK4sYjJYRdIVHdvrAEPTcx8DfNr2hZKmAecAT6/7ngNsb/sBSe8CsP0sSU8Dzq1TZwz3NODFwBrA9ZI+b/vhMcR6OPBS27+X9ITa9kdgJ9t/kzQT+AYwC3g1MB14FvBE4FrgJEkrAMcCu9u+Q9JewIeBt4whjohFJFnEZPCA7S2HNiTtT3mzBdgR2EJ67CbmNSWtUZ/Ptv1Afb495Q0Y29dJ+i0wUrL4oe0HgQcl/RF4ErCghxiHath/AZwi6XQeXyZ2BeCzdeK/Rzq+7/bAGbYfBW6XdH5t3xx4JvDj+v+aAtzWQwwRi5VkEZPdcsB2HUkBgPom+9fOph6/3oMdzx9h5L+xByStaPuhur0OcCeA7bdJ2hbYFbiiJoh/A/4A/EON929dYhIwz/Z2PcYc0VXGLGKyOxc4aGijvjmP5OfAvvWYzYBpwPVL+D0vAN5Qv9YqlCnCz6/bT7V9ie3D/387d6wLQRSFcfz/RagkChGVJ9Bq0OwzoBVKFQ+hEr3EQ+g8gWyxVJaNxBPQSvRXca9SbjKJyMr/V85MZk735ZwzudQA2QBWgLfWQRxSOwWAMbDfdhfrwKhdfwXWkmy3dy4m2RxYqwQYFtIpsJXkKckLcPLDc1fAQpJn6rHex23cNMQZsNf2KBPqKOmu3btsS/QZNaCm7dtHSSbUEdR3x3NDHXHNgGvgHvhoHcsBcJFkCjwCOwNrlQCP+5DmWpLlUspnklXgAdgtpbz/dV36f9xZSPPttv01tQScGxT6LXYWkqQudxaSpC7DQpLUZVhIkroMC0lSl2EhSer6Ao0ypupfc30pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "infrequent_her = heroinUsedPast12MO[heroinUsedPast12MO < 30]\n",
    "#at least once a month\n",
    "criteria1_her = heroinUsedPast12MO >= 30\n",
    "criteria2_her = heroinUsedPast12MO < 52\n",
    "month_criteria_her = criteria1_her & criteria2_her\n",
    "monthly_her = heroinUsedPast12MO[month_criteria_her]\n",
    "\n",
    "#at least weekly\n",
    "criteria3_her = heroinUsedPast12MO >= 52\n",
    "criteria4_her = heroinUsedPast12MO < 300\n",
    "weekly_criteria_her = criteria3_her & criteria4_her\n",
    "weekly_her = heroinUsedPast12MO[weekly_criteria_her]\n",
    "\n",
    "daily_her = heroinUsedPast12MO[heroinUsedPast12MO>360]\n",
    "\n",
    "thinkplot.Bar([\"Infrequently (<30 days)\", \"At Least Monthly (30<= days < 52)\", \"At Least Weekly ( 52<= days < 300)\",\"Almost Daily (>300 days)\"],[len(infrequent_her)/len(heroinUsedPast12MO), len(monthly_her)/len(heroinUsedPast12MO), len(weekly_her)/len(heroinUsedPast12MO), len(daily_her)/len(heroinUsedPast12MO)])\n",
    "thinkplot.Config(xlabel = \"Heroin Usage\", ylabel = \"Percent\", title=\"Categorizing Yearly Consumption\")\n",
    "plt.xticks(rotation = \"vertical\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can the probability mass function tell us more about this distribution?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heroinUsedPast12MOPmf = thinkstats2.Pmf(heroinUsedPast12MO)\n",
    "thinkplot.Pmf(heroinUsedPast12MOPmf)\n",
    "thinkplot.Config(xlabel = \"Total Days of Consumption\", ylabel = \"Probability\", title = \"Distribution of Heroin Consumption\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The PMF shows the same shape from the histogram where there's a high concentration of small values around the tens and large values around 360 days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about the Cumulative Distribution Function? Can it tell us more?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heroinUsedPast12MOCdf = thinkstats2.Cdf(heroinUsedPast12MO)\n",
    "thinkplot.Cdf(heroinUsedPast12MOCdf)\n",
    "thinkplot.Config(xlabel = \"Total Days of Consumption\", ylabel=\"CDF\", title = \"Distribution of Heroin Consumption\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given that we're measuring time, we see that the CDF looks very similar to the distribution of alcohol consumption. Therefore, we can hypothesize that the dataset can be modeled using an exponential/Weibull distribution. To confirm this, we will plot the distribution against different scales that will easily tell us if that's true."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this plot, we look at the CCDF on a log-y scale and we expect to see a linear pattern if the data can be modeled using an exponential distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Cdf(heroinUsedPast12MOCdf, complement=True, label='')\n",
    "thinkplot.Config(xlabel=\"Total Days of Consumption\", ylabel = \"CCDF (log)\", title = \"Distribution of Heroin Consumption\", yscale='log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the most part, we can see that the data takes on a linear pattern, a marker that it can modeled using an exponential analytical distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we plot it using a Weibull transformation to see the CCDF on a log-log(y) scale and a log(x) scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Cdf(heroinUsedPast12MOCdf, transform = \"weibull\")\n",
    "thinkplot.Config(title = \"Distribution of Heroin Consumption\",\n",
    "                 xlabel = \"Total Days of Consumption\", \n",
    "                 ylabel = \"CCDF (log-log)\",\n",
    "                 yscale = \"log\", xscale = \"log\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we see that the data is linear, another confirmation that the exponential/Weibull distribution might be a great fit for our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kde_her = gaussian_kde(heroinUsedPast12MO, bw_method=0.3)\n",
    "\n",
    "xs_her = np.linspace(heroinUsedPast12MO.min(), heroinUsedPast12MO.max())\n",
    "ds_her = kde.evaluate(xs_her)\n",
    "ds_her /= ds_her.sum()\n",
    "\n",
    "plt.plot(xs_her, ds_her, label='KDE Heroin Consumption')\n",
    "\n",
    "thinkplot.Config(xlabel='Total Days of Consumption)', ylabel='PDF', title = \"Distribution of Alcohol Consumption\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    212.000000\n",
       "mean     125.066038\n",
       "std      128.519331\n",
       "min        1.000000\n",
       "25%       10.000000\n",
       "50%       87.500000\n",
       "75%      216.000000\n",
       "max      365.000000\n",
       "Name: heryrtot, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroinUsedPast12MO.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatterplot and Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will plot the two variables on a scatterplot to see if there exists a linear relationship between them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56276, 56276)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alcoholUsedPast12MONAN = data[\"alcyrtot\"]\n",
    "heroinUsedPast12MONAN = data[\"heryrtot\"]\n",
    "len(alcoholUsedPast12MONAN), len(heroinUsedPast12MONAN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=-0.27983148967809446, intercept=140.3996268371461, rvalue=-0.22416009409678647, pvalue=0.003907859976324315, stderr=0.09558416331698188)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(alcoholUsedPast12MONAN, heroinUsedPast12MONAN, 'o', markersize =1)\n",
    "\n",
    "plt.title(\"Scatterplot of Heroin and Alcohol Consumption\")\n",
    "plt.xlabel('Alcohol Total Days of Consumption')\n",
    "plt.ylabel('Heroin Total Days of Consumption')\n",
    "plt.axis([0, 365, 0, 365]);\n",
    "\n",
    "\n",
    "\n",
    "subset = data.dropna(subset=['alcyrtot', 'heryrtot']) #drops nan from multiple columns using subset as a parameter of dropna()\n",
    "\n",
    "xs = subset[\"alcyrtot\"] #x-values\n",
    "ys = subset[\"heryrtot\"]#y-values\n",
    "\n",
    "res = linregress(xs, ys) #LSE - minimizes the error\n",
    "\n",
    "# plot the line of best fit\n",
    "fx = np.array([xs.min(), xs.max()])\n",
    "fy = res.intercept + res.slope * fx\n",
    "plt.plot(fx, fy, '-', alpha=0.5)\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see there's a weak negative correlation between the two variables. The scatter plot shows that most people who consume alcohol don't typically consume heroin because all the points are fall along the axis of the two variables. Very few points demonstrate the linear relationship between the two. The correlation coefficient is -0.28 and the p-value is 0.003 with a standard error of 0.1. Other than the negative relationship shown by the best fit line, it's hard to determine any relationships between the two variables, so we're going to take another approach to see if there's anything we can say conclusively about the two variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Does a better relationship exists between the two variables?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will group the data by days of consumption of alcohol and we will select the column of heroin consumption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups2 = data.groupby(\"alcyrtot\")[\"heryrtot\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we will group the data into infrequently, almost daily, at least weekly, and at least monthly and take the mean of those groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "infrequent_c=[]\n",
    "monthly_c=[]\n",
    "weekly_c = []\n",
    "daily_c = []\n",
    "for day, df in groups2:\n",
    "    if day < 30:\n",
    "        if not np.isnan(df.mean()):\n",
    "            infrequent_c.append(df.mean())\n",
    "    elif 30 <= day < 52:\n",
    "        if not np.isnan(df.mean()):\n",
    "            monthly_c.append(df.mean())\n",
    "    elif 52 <= day < 300:\n",
    "        if not np.isnan(df.mean()):\n",
    "            weekly_c.append(df.mean())\n",
    "    else:\n",
    "        if not np.isnan(df.mean()):\n",
    "            daily_c.append(df.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then I define a function **meanOfMeans** that will take the mean of these groups to show the average total days of heroin consumption in each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def meanOfMeans(aList):\n",
    "    meanSum=0\n",
    "    listLength = 0\n",
    "    for mean in aList:\n",
    "        meanSum+= mean\n",
    "        listLength+=1\n",
    "    return meanSum/listLength\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.25925925925927"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meansList2 = [meanOfMeans(infrequent_c), meanOfMeans(monthly_c), meanOfMeans(weekly_c), meanOfMeans(daily_c)]\n",
    "meansList2[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, I plot the mean of means, to see how heroin consumption differs in each groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rtwLsZfvi/oYVERFNG6+KaemRRfpsXwRcNN5jIiJiuIw3B3GCpC9LeoGkpUYaJa0naW9Jp/LPe05HRMSQGK+KaVtJ2wP/BmwlaUXKshvXACcBe9i+bTBhRkTEoI07B2H7F5Rd4yIiYgHTS5lrREQsgJIgIiKiqySIiIjoqqcEIWlrSXvV2zMkrdvDcw6TdLukyzvaVpR0uqRr6/cVarskHSjpOkmXSdpiqj9QRETMHxMmCEn7AR8GPlKbFgV+0MNrH8E/l8HuC5xhe33gjHof4BXA+vVrH+DbPbx+RET0US89iNcAOwJ/A7B9K4+v9Dom2+cAd45qfjWPL9NxJLBTR/v3XZwHLC9ptR5ii4iIPuklQTxo25R1mOi8aG4Knmx7DkD9vkptXwO4ueNxt9S2fyJpH0mzJc2eO3fuPIQSERHj6SVBHCvpu5RP9W8Dfgl8bz7HoS5t7vZA2wfbnml75owZM+ZzGBERMaKXxfq+JOmlwD2UzYM+afv0KZ7vz5JWsz2nDiHdXttvAdbqeNyawK1TPEdERMwHvUxSLwX8yvYHKT2HJSQtOsXznQjsUW/vAZzQ0b57rWZ6LnD3yFBUREQ0o5f9IM4Bnl9LUn8JzAbeCOw63pMkHQ28CFhZ0i3AfsDnKENWewM3ATvXh/8C2B64Drgf2GvSP0lEy7zj00c3HUKjvvHxWU2HEBPoJUHI9v31j/pBtr8gacL9IGyP9a+/bZfHGnh7D7FERMSA9DJJLUnPo/QYTqptvSSWiIhosV4SxHsoF8kdb/sKSesBZ/Y3rIiIaFovVUxnA2d33L8eeFc/g4qIiOaNt+Xoz3nitQgG7gDOtN3LUhsREdFi4/UgvtSlbUXgzZI2tb1vl+MRETEkxtty9Oxu7ZJOBC7k8YX2IiJiCE16Pwjbj/QjkIiImF7Gm4NYsUvzCsDuwBV9iygiIqaF8eYgLqRMTI8spDcySX0W8P/6G1ZERDRtvDmICXeNi4iI4ZU9qSMioqskiIiI6CoJIiIiuupp0T1JawBP6Xx83XM6IqIRWS69/8ulT5ggJH2esv/DlcDINRCm7BMRERFDqpcexE7Ahrb/0e9gol3yCS4b3sRw62UO4npgqluMRkRES/XSg7gfuETSGcBjvQjbWfI7ImKI9ZIgTqxf842k9wJvpcxl/J6yB/VqwDGUFWMvAnaz/eD8PG9ERPSulw2DjpyfJ6wVUe8CNrb9gKRjgTcB2wNftX2MpO8AewPfnp/njoiI3o05B1H/cCPp95IuG/01j+ddBFhC0iLAksAcYBvgJ/X4kZTJ8YiIaMh4PYh31+87zM8T2v6TpC8BNwEPAKdRFga8y/bD9WG3AGt0e76kfYB9ANZee+35GVpERHQYswdhe079fiPwd+Bf6tcDtW1KJK0AvBpYF1gdWAp4RbcQxojrYNszbc+cMWPGVMOIiIgJTFjmKukNwAXAzsAbgPMlvX4ezvkS4I+259p+CDgO2BJYvg45AawJ3DoP54iIiHnUSxXTx4Bn2b4dQNIM4Jc8Pl8wWTcBz5W0JGWIaVtgNnAm8HpKJdMewAlTfP2IiJgPerlQbqGR5FD9pcfndWX7fEpyuYhS4roQcDDwYeB9kq4DVgIOneo5IiJi3vXSgzhF0qnAyLoKbwR+MS8ntb0fsN+o5uuBZ8/L60ZExPzTy3UQH5T0WmBryvajB9s+vu+RRUREo8ZNEJIWBk61/RLKZHJERCwgxp1LsP0IcL+k5QYUT0RETBO9zEH8Hfi9pNOBv400ZrG+iIjh1kuCOKl+RUTEAqSnxfokLQGsbfuaAcQUERHTQC9XUr8KuAQ4pd7fXNJ8Xf47IiKmn14ueNufcn3CXQC2L6GsoxQREUOslwTxsO27R7V1XUgvIiKGRy+T1JdL2gVYWNL6lM1+zu1vWBER0bReehDvBDah7Ed9NHAP8J5+BhUREc3rpYrpfsqKrh/rfzgRETFdjJkgJP2cceYabO/Yl4giImJaGK8H8aX6XcD3gLf2P5yIiJguxkwQts8euS3pvs77EREx/Hrd+CdlrRERC5jx5iBW7Li7sKQVKMNNANi+s5+BRUREs8abg7iQ0nMYSQoXdRwzsF6/goqIiOaNNwfRt+U0JC0PHAJsSkk2bwGuAX4ErAPcALzB9l/7FUNERIyv1zmI+e3rwCm2NwKeAVwF7AucYXt94Ix6PyIiGjLwBCFpWeAFwKEAth+0fRfwauDI+rAjgZ0GHVtERDxuzAQhqV9DTOsBc4HDJV0s6RBJSwFPtj0HoH5fZYy49pE0W9LsuXPn9inEiIgYrwfxEwBJZ8zncy4CbAF82/YzKduY9jycZPtg2zNtz5wxY8Z8Di0iIkaMV8W0kKT9gA0kvW/0QdtfmeI5bwFusX1+vf8TSoL4s6TVbM+RtBpw+xRfPyIi5oPxehBvAv5OSSLLdPmaEtu3ATdL2rA2bQtcCZwI7FHb9gBOmOo5IiJi3o1X5noN8HlJl9k+eT6f953ADyUtBlwP7EVJVsdK2hu4Cdh5Pp8zIiImoZcNg86V9BVK5RHA2cCnuuwy17O6benMLoe2neprRkTE/NVLmethwL3AG+rXPcDh/QwqIiKa10sP4qm2X9dx/wBJl/QroIiImB566UE8IGnrkTuStgIe6F9IERExHfTSg/h34PuSlqv3/8rj1UYRETGketmT+lLgGXWJDGzf0/eoIiKicb30IIAkhoiIBU1Tq7lGRMQ0lwQRERFd9TTEJGlLykY+jz3e9vf7FFNEREwDEyYISUcBTwUuAR6pzQaSICIihlgvPYiZwMa23e9gIiJi+uhlDuJyYNV+BxIREdNLLz2IlYErJV0A/GOk0faOfYsqIiIa10uC2L/fQURExPTTy5XUZw8ikIiImF4mnIOQ9FxJv5N0n6QHJT0iKVdVR0QMuV4mqb8BzAKuBZYA3lrbIiJiiPV0oZzt6yQtbPsR4HBJ5/Y5roiIaFgvCeL+unf0JZK+AMwBlprXE0taGJgN/Mn2DpLWBY4BVgQuAnaz/eC8niciIqamlyGm3erj3gH8DVgLeN24z+jNu4GrOu5/Hviq7fUpe07sPR/OERERUzRhgrB9IyBgNdsH2H6f7evm5aSS1gReCRxS7wvYBvhJfciRwE7zco6IiJg3vVQxvYqyDtMp9f7mkk6cx/N+DfgQ8Gi9vxJwl+2H6/1bgDXm8RwRETEPehli2h94NnAXgO1LKCu7TomkHYDbbV/Y2dzloV3XfpK0j6TZkmbPnTt3qmFERMQEekkQD9u+ez6ecytgR0k3UCalt6H0KJaXNDJpviZwa7cn2z7Y9kzbM2fMmDEfw4qIiE49LdYnaRdgYUnrSzoImHKZq+2P2F7T9jrAm4Bf2d4VOBN4fX3YHsAJUz1HRETMu14SxDuBTSgL9R0N3AO8pw+xfBh4n6TrKHMSh/bhHBER0aNe1mK6H/hY/ZqvbJ8FnFVvX0+Z64iIiGlgzAQxUaVSlvuOiBhu4/UgngfcTBlWOp/ulUYRETGkxksQqwIvpSzUtwtwEnC07SsGEVhERDRrzElq24/YPsX2HsBzgeuAsyS9c2DRRUREY8adpJb0JMqSGLMoF8cdCBzX/7AiIqJp401SHwlsCpwMHGD78oFFFRERjRuvB7EbZfXWDYB3lfX0gDJZbdvL9jm2iIho0JgJwnYvF9FFRMSQShKIiIiukiAiIqKrJIiIiOgqCSIiIrpKgoiIiK6SICIioqskiIiI6CoJIiIiukqCiIiIrpIgIiKiq4EnCElrSTpT0lWSrpD07tq+oqTTJV1bv68w6NgiIuJxTfQgHgbeb/vplH0m3i5pY2Bf4Azb6wNn1PsREdGQgScI23NsX1Rv3wtcBawBvBo4sj7sSGCnQccWERGPa3QOQtI6wDMpe14/2fYcKEkEWKW5yCIiorEEIWlp4KfAe2zfM4nn7SNptqTZc+fO7V+AERELuEYShKRFKcnhh7ZHtjD9s6TV6vHVgNu7Pdf2wbZn2p45Y8aMwQQcEbEAaqKKScChwFW2v9Jx6ERgj3p7D+CEQccWERGPG2/L0X7ZirKd6e8lXVLbPgp8DjhW0t7ATcDODcQWERHVwBOE7V9T9rXuZttBxhIREWPLldQREdFVEkRERHSVBBEREV0lQURERFdJEBER0VUSREREdJUEERERXSVBREREV0kQERHRVRJERER0lQQRERFdJUFERERXSRAREdFVEkRERHSVBBEREV0lQURERFdJEBER0VUSREREdJUEERERXU27BCFpO0nXSLpO0r5NxxMRsaCaVglC0sLAN4FXABsDsyRt3GxUERELpmmVIIBnA9fZvt72g8AxwKsbjikiYoEk203H8BhJrwe2s/3Wen834Dm239HxmH2AferdDYFrBh7o/LEycEfTQbRc3sN5k/dv3rT5/XuK7RkTPWiRQUQyCerS9oQMZvtg4ODBhNM/kmbbntl0HG2W93De5P2bNwvC+zfdhphuAdbquL8mcGtDsURELNCmW4L4HbC+pHUlLQa8CTix4ZgiIhZI02qIyfbDkt4BnAosDBxm+4qGw+qX1g+TTQN5D+dN3r95M/Tv37SapI6IiOljug0xRUTENJEEERERXSVBREREV9NqknqYSZoJPB9YHXgAuBz4pe07Gw2shSQtBfzd9iNNx9JGef8mT9LzgDdT/g+vxuP/h08CfmD77gbD65v0IPpM0p6SLgI+AixBufL7dmBr4HRJR0pau8kYpztJC0naRdJJkm4HrgbmSLpC0hclrd90jNNZ3r95I+lk4K2U6srtKAliY+DjwOLACZJ2bC7C/kkVU59JejulXPeBMY5vDqxk+4zBRtYeks4GfgmcAFxu+9HaviLwYmAX4HjbP2guyukr79+8kbSy7XGX1OjlMW2UBBHTnqRFbT80r49ZUOX9mz/q0NwDth+VtAGwEXDyML9vGWIaEElfkLSspEUlnSHpDklvbjquNujlP+Aw/yedV7YfUvEcSa+V9Jp6W52PaTLGljgHWFzSGsAZwF7AEY1G1GdJEIPzMtv3ADtQ1pzaAPhgsyG1g6TNJJ0n6WZJB0taoePYBU3G1gaSXgZcC+wPbA+8EjgAuLYei97I9v3Aa4GDbL+GMhcxtFLFNDiL1u/bA0fbvrPjA1yM71uUP27nUSYLfy1pR9v/x+Pva4zt68BLbN/Q2ShpXeAXwNObCKqFVKuZdgX2rm1D/Td0qH+4aebnkq6mlMf9h6QZwN8bjqktlrZ9Sr39JUkXAqfU/UIyiTaxRSi91tH+RBLsZLybUo14vO0rJK0HnNlwTH2VSeoBqkMj99h+pE54LWP7tqbjmu4kXQq8oLPWXNJmwE+BFW2v1FhwLSDpI8AbKDs03lyb16Kslnys7c82FVubSNrU9uVNxzFISRADImk2cBhleOmvTcfTJpJ2Aa63fd6o9rWBT9h+WzORtUfd231HYA3Kxly3ACfavrLRwFpE0q+BxSgT0/9t+65mI+q/JIgBkfQ0StXDG4HZwOHAac4/QAxQvfbB+ZAyNbW8dS9gZ+AC4AjbpzUbVf8kQQyYpIUolUzfBh6l9Cq+niU3xiZpM9uX1duLAh8Gnk1Z6uDTtbIkxlB7Wl8AtgFGhumWA34F7Dt68jrGJ2lhYCfgQOAeSo/so7aPazSwPkiZ6wDVcfMvA1+kjJ+/nvIL9qsm42qBIzpufw54GuV9XAL4ThMBtcyPgOOB1Wyvb3t9ynIRP6PMS0QParn1V4GrKMn2VbafXm9/tdHg+iQ9iAGplTd3AYcCP7X9j45jx9l+bWPBTXOSLrb9zHr7EuBZIxd/AZfa3qzZCKc3SdfWpDCpY/FEks4Bvgf8ZPTSOZJ2s31UM5H1T8pcB2dn29d3O5DkMKHlJL2G0uN90shVv7YtKZ9wJnahpG8BR/LEKqY9gIsbi6plbL9gnGNDlxwgPYiBkvRKYBPKCpAA2P5UcxG1g6TDRzXta/vPklYFfmh72ybiagtJi1Eu7Ho1o6qYgEM7e7Mxtrrq7WcpV093/h9er7Gg+iwJYkAkfQdYkrJ65iGU+YcLbO897hMjYlqoZa77UeYbXkWpZpLt/RoNrI8yST04W9reHfir7QOA51G6+RF9JWlJSR+S9EHErsw2AAAfE0lEQVRJi0vaQ9KJdQHJpZuOr0WWqMvyy/aNtvenTFAPrSSIwRmZ1Lpf0urAQ8C6DcYTC44jgCdTft9OAp4FfIky1PTt5sJqnb/XMvVrJb2jzout0nRQ/ZRJ6sH5H0nLU0pcL6KsIXRIsyHFAmID22+oVV9zKAv3WdL/Apc2HFubvIcyTPwu4D8pvYc9Go2ozzIH0QBJTwIWH9Z9bPuhXiC3vO25HW3LANi+t7HAWkDSJbY3r7cPs/2WjmOX2n5Gc9HFdJYeRJ9JGrOEVRLDePVlnywKnC9pw47NbY6kXM16VmNRtcNsSUvbvm9UcngqkOQ6AUk/Z5xVg20P5X7UkB5E33WUaK4CbMnjV02/GDgr10D0rlaCnWH7x3W59HPqlawxRZKU9cDGJ+mF9eZrgVWBkb27ZwE32P5oI4ENQBLEgEj6H+BttufU+6sB30yC6J2kmcB/2n6FpPcCS9r+TNNxxYJB0jmjL5br1jZMUsU0OOuMJIfqz5RtR6NHtmcDT657Au9GWegwYlBm1E2CgMd25JvRYDx9lzmIwTlL0qnA0ZTxzDcx5LtR9cnhwEHAn0Yl3Ih+ey/l//HIkjnrAPs0F07/ZYhpgGrd9Eh39BzbxzcZTxvVXfluBWbZ/lnT8bRFqsDmj1qBuFG9e/WwL1OSBBGtU+dv/mz70aZjaQtJS1L2z3isCkzSccCBts9qMraYvjIHEa1je06Sw+TUTZVOo2x0Q60Ce3qSQ4wnCSJiwXEIMHIdxJt5vFwzoqtMUg9Q9gOOJtmeLamzCuyVTcfUFpKWA7ajLJduyjzYqbbvajSwPksPos8krS3pGElzgfOB30m6vbat02x07SVphbqFa0xOqsAmSdLulPXTXkRZi2kpyoWuF9ZjQyuT1H0m6bfA1yjbFD5S2xYGdgbeY/u5TcbXJpLOAnak9HwvAeYCZ9t+X5NxtUmqwCZP0jXAc0b3Fup7eb7tob2eKT2I/lvZ9o9GkgOA7UdsHwOs1GBcbbSc7XsoSx4cbvtfgZc0HFOr1OHN9Si7yUVvRPe1mB6tx4ZW5iD6L/sBzz+L1BLXNwAfazqYtsrQ0qR9BrhI0mk8/n94beCllGW/h1aGmPos+wHPP5J2Bj4B/Nr2f9RlD75o+3UNhxZDrg4nvZwn/h8+ddgLTpIgojUkrWj7zqbjiAWTpCfTUcVk+88Nh9R3SRB9Vq9gfQfll+og4I3A64CrgU/Zvq/B8FpF0rWUyenDgZOzTPXU1U/Ea9m+rOlYpjtJmwPfAZaj9BwErAncBfyH7YsaDK+vkiD6TNKxlHHLJYANgauAY4FXAava3q3B8Fqlbpn5EsrFXs8GfgQcYfsPjQbWEqkCmxpJlwD/Zvv8Ue3PBb47zDvyJUH02ch2jx37Aa9W9wMWcKnt1PJPgaQXU64EXoqyr/K+tn/bbFTTm6SLbT9T0lspvYf9JF2W38HxSbrW9vpjHLvO9tMGHdOgpIppQGpS+MXIsEi9n+w8CZJWoiwRsRtlP413Uib7Nwd+DKzbXHStkCqwqTlZ0knA93liJeLuwCmNRTUASRD9l/2A55/fAkcBO9m+paN9dt2ONMb3KeBUShXY72oV2LUNxzTt2X6XpO0pw3OdVUzftP2LRoPrswwxNSj7AU9O3q95kyqwmKxcST0AkpaU9IxRbWsDqzcUUlutLOmLkn4h6VcjX00H1SLnS/qxpO3rHFj0QNKqkr4t6ZuSVpK0v6TLJB1bh+yGVhLEYDwEHCdpqY62Q4Ch/uXqgx9SyoPXBQ4AbgB+12RALbMBcDBlDuc6Sf8laWjXEZqPjgCupMw/nAk8AOwA/C+l/HVoZYhpQCR9CbjS9mG193CC7Wc2HVebSLrQ9r92Vt5IOtv2C5uOrW1SBda7keqvevsm22t3HLvE9ubNRddf6UEMziHAXvX27pSLvWJyHqrf50h6paRnUi5Yih7U4ZF3S5oNfIBSBbYy8H7gvxsNbnrr/Dv5/XGODZ1UMQ2I7aslUbv0s4Ctm46phT5dN255P+Wq9GWB9zYbUqukCmxqTuioRPz4SKOkpwFDfZFmhpgGSNKelKuA/2R7VsPhxAImVWAxWUkQA1TXZZoDvM72L5uOpy0kHUT39fiBUqc+wHBaS9IM4EPAJsDiI+22t2ksqBaS9Brg9AVhHbWhHj+bbmzfb3u5JIdJmw1cSPmjtgXl4q5rKVdQPzLO8+KJUgU2j+oFrsdSrugfeulB9NnI2OW8PiZA0pnAy2w/VO8vCpxm+8XNRtYOqQKbd5I+Q+nNvsz2s5uOp9/Sg+i/EyR9WdILOq+DkLSepL0lnQps12B8bbI6sEzH/aXJxYaTkSqwedCxl/zngbtHX/w6jFLF1Ge2t63ruPwbsFVdh/9h4BrgJGAP27c1GWOLfA64uPYkAF4I7N9cOK2TKrB5sz1wru17JR0GvJVSKjy0MsQUrSJpVeA59e75Sa4xKJJ+BnzZ9v9KWhy4Ani67QcbDq1vkiAihlyqwOadpOWBn9l+UUfb54EzbQ/tkt9JEBFDTtIe9eZWwMaUnfigjKdfaDvDTNFVEkTEAiJVYFMjaR3bN4xzXMAao65OHwqpYhoQSV+StEnTccQCLVVgU/NFST+VtLukTSStImltSdtI+k/gN8DTmw6yH1LFNDhXAwdLWoSyUN/Rtu9uOKbWkfRr21uPfG86npZJFdgU2N5Z0sbArpSlclYD7geuAn4BfMb23xsMsW8yxDRgkjakrOo6i/LJ43u2zxz/WTFC0kW2t+hcgjl6lyqwmIz0IAaoXmizUf26g7IO//sk/ZvtNzUaXCwQakI4oek4oh2SIAZE0lcom56fAfyX7Qvqoc9Luqa5yCIiukuCGJzLgY/bvr/LsaFf0yUi2icJos8kbVFvXgJsNHqveNsXZbJ6UjTxQyLmP0k/BQ4DTrb9aNPxDEImqfuso2KkG2ct/smR9CLbZ418bzqetkkV2NRJegmlwOS5wI+BI2xf3WxU/ZUEMSCSFh9dCtetLaKfUgU27+qCh7OAjwE3A98DfjByAeIwyYVyg3Nuj23RhaQVJH16VNssSVs2FVMseCStBOxJWcn1YuDrlE2sTm8wrL7JHESf1brzNYAl6vr7I2PoywJLNhZYy9j+q6SXSjrC9nW1+ZPA85uMKxYcko6jlKgfBbzK9px66EeSZjcXWf8kQfTfyymfONYEvtLRfi/w0SYCarFDKVeyflTSi4ArbN/RbEixAPmG7V91O2B75qCDGYTMQQyIpNfZ/mnTcbSZpGUo+1NvRFmu5JhhXmq5H0bmHjIH0TtJrx3vuO3jBhXLoKUHMTj/I2kXYB063nfbn2osopapO3mdC7yRslzEXg2H1EbvHfU9JvaqcY4ZGNoEkR7EgEg6BbgbuBB4ZKTd9pcbC6qFJG0F/JzS3f9k0/FEDLMkiAGRdLntTZuOYxhI+jpl68ebmo6lLepe6O+3/fGOtlnAjbZTTTcOSW+2/QNJ7+t23PZXurUPg5S5Ds65kv6l6SCGge13JzlMju2/Ai+V9LSO5k8Cf2gopDZZqn5fZoyvoZUexIBIuhJ4GvBH4B+Uclfb3qzRwFpC0suBnSglwwZuBU7IJHXvJO0DrGN7pArsHbZf33BYMY0lQQyIpKd0a7d946BjaRtJXwM2AL4PjGzruCawO3Ct7Xc3FVubpAps3khaHNgb2ARYfKTd9lsaC6rPMsQ0IDURrAVsU2/fT97/Xm1ve3vbx9j+df06BnglsH3TwbWF7XspV++PVIGd2mxErXMUsCrl2qazKR9S7m00oj7LH6gBkbQf8GHgI7VpUeAHzUXUKn+X1G1J9GcBWctqcg4BvgX82Bk+mKyn2f4E8DfbR1I+oAz1vGKugxic1wDPBC4CsH1r7fLHxPYEvl3fr5EhprWAe+qx6JHt30g6ipIoYnJGFuO7S9KmwG2U65qGVhLE4Dxo25IMIGmpiZ4Qhe2LgOd0rGsl4Jbspzw1mbOZsoNrufAngBOBpevtoZVJ6gGR9AFgfeClwGcpawr9t+2DGg2sJWpywPZtkmZQFum72vaVzUbWDpI2oiTX823f19G+XSaqYyxJEAMk6aXAyyifgE+1PZRLBM9vkv4N2Jfyvn2eMqx0BbAV8AXbhzYX3fQn6V3A24GrgM2Bd9s+oR67yPYW4z0/QNKGwD6UCjAo7+XBtof6OpIkiJj2JP2eUnWzBHAjZbLwttrdP9P25o0GOM3V9+95tu+TtA7wE+Ao21/Pon0Tk/Q8ynpLB1PmEEWZT3wb8Frb5zUYXl9lDmJAJN1LucALYDFKFdPfbC/bXFSt8ZDt+4H7Jf3fyNxD3SMin3AmtvDIsJLtG+pFcj+p1+Zkj++JfRKYNWqL259J+hWwH/CKRqIagJS5DojtZWwvW78WB14HfKPpuFriUUmL1tuvHGmsFy7ld3hit0l6rJdVk8UOwMoMeZnmfPLUbvuf2z4bWG/w4QxO/nM1xPbPgG2ajqMlXkvtfdm+paN9JeD9jUTULrtTSjIfY/th27sDL2gmpFYZ72K4vw0sigZkiGlARm06shAwk8eHnGJ8N3e7qMv2n4A/AUhSLvzqblRSBUDSirbvtP2bJmJqmbUkHdilXZTKsKGVBDE4nZuOPAzcALy6mVBa50xJP6UszvfYKq6SFgO2BvYAzgSOaCa86U3Sx21/ut7eGPgZsKgkAW+0fX6jAU5/Hxzn2FDuRT0iVUwx7dW5hrcAuwLrAndRFktbGDgN+KbtS5qLcHrrLGWVdBJls6WT6/IlX7O9ZbMRxnSVBDEgY3RRH2P7XYOKpc3qZPXKwAO272o6njYYlSCeUNaaMtcYT4aYBmdxYGPgR/X+zpTtR/PJdxJsPwTMaTqOlllP0omUMfM1JS1Zy4ahlFtHdJUEMTjrAy+uf+CQ9B3gNNvZPD76bfRc10IAkp4MfHvw4bSTpK1GT+p3axsmGWIaEEnXUK5mvbPeXwE4z/aGzUYWEb3otizJsC9Vkh7E4HwOuFjSmfX+C4H9mwunfSS9A/hh3V85YiDqUhtbAjMkva/j0LKUQomhlQQxILYPl3QyZU0hgH2zXPWkrQr8TtJFwGGUBQ/TBY5+W4yytPciQOceLvcAQ72nd4aYBqTWnO8KrGf7U5LWBla1fUHDobVKfR9fBuxFudjwWOBQ2//XaGAx9CQ9ZWQPeUkLAUvbvqfhsPoqS20MzreA5wGz6v17gW82F0471R7DbfXrYWAFysJzX2g0sGlM0sclrTjO8W0k7TDImFrqs5KWrZt9XQlcI2m8i+haL0NMg/Mc21tIuhgeW4l0saaDapO6r8EewB2ULTM/aPuh+mnuWuBDTcY3jf0e+Lmkv1OWq55LKbten7I/xC+B/2ouvNbY2PY9knYFfkHZY/5C4IvNhtU/SRCD85CkhanrL9Vd0R5tNqTWWZmy/v6NnY22H80n4LHVzYFOkLQ+ZZOl1Sjj5z8A9rH9QJPxtcii9ULNnShXoz807MvNJ0EMzoHA8cAqkj5Dmdz6eLMhtUPH8MjXRt0HoC46d9XAA2sZ29dSeloxNd+lrKF2KXBO3U9jqOcgMkk9QHVf4G0pV7SekT9qvZH0Rx5f+Xb0Bje2PdRr8sf0JWkR2w83HUe/JEEMQB0jv8z2pk3HEhFTI2k5yg5yI3tonA18yvbdzUXVX6liGgDbjwKX1tLWmAeS1pC0paQXjHw1HVNbjFfJFD05jFJ9+Ib6dQ9weKMR9Vl6EANS9699FnABHbtQ2d6xsaBaRtLngTdSSgwfqc3Oe9gbSddSFoc8HDg5FxlOjqRLbG8+UdswySR1n0l6ku1/AAc0HcsQ2AnYsL6fMXkbAC+h7K1xkKQfAUfY/kOzYbXGA5K2tv1rKAv1AUNdAZYeRJ+NLOYl6SjbuzUdT5vVpUp2tn1f07G0naQXU8pcl6JU5exr+7fNRjW9SdocOBJYjlIscSewp+1LGw2sj9KD6L/FJO0BbDlqX2oAbB/XQEytIukgShXT/cAlks4AHutFZLOl3khaCXgzsBvwZ+CdwImUi+V+TNmtL8ZQdy18hqRl6/2hLnGFJIhB+HfKGkzL88R9qaH80UuCmNjIvr8XUv6gdUoXuHe/BY4CdrJ9S0f77Lo/SYxD0vLA7sA6wCJlWbDh/oCSIaYBkbS37UObjqPNJL3b9tcnaovuJCkT01Mn6VzgPMrSJY+tgmD7yMaC6rMkiAGStCX108dIm+3vNxZQy4yxYUv2VJ6ApJ8zTk8rVWC9GfbNgbrJENOASDoKeCqlzPCxEk0gCWICkmYBuwDr1r2VRywD/KWZqFrlS00HMCSOkvQ24H944hzYnc2F1F9JEIMzk7IaZLpsk3cuMIeyWN+XO9rvBS5rJKIWsX02gKR/tX1h5zFJo+fFYmwPUlZu/RiP98gMDO1SLxliGhBJPwbeZXtO07HEgqnuxLeH7d/X+7OA99h+zvjPDABJ/0dZtv+OpmMZlPQgBmdl4EpJF/DE7mnGf3tUy4Q/D6xCqUMX5UrqZRsNrD1eT9lcaVdga0pFzsuaDalVrqCUWi8w0oMYEEkv7NY+0v2PiUm6DnhVVsGdOkkbAD8DbqaUuw71lcDzk6TjgU2AM1lArsNJgojWkPQb21s1HUfbSPo9T6xiWgW4m/pHzvZmTcTVNvWC13+SMteYMkn30r3EMMMjkyTp68CqlE/AnZ/gcrHhOOrGNmMavUNfxIjMQfSZ7WWajmGILEsZA+4cN8/V6BPoTACStgbWt3143fZ26eYia4cuPbAnGOYeWHoQEQsISftRyq03tL2BpNWBH2fYbnwLcg8sGwZFa0haU9Lxkm6X9GdJP5W0ZtNxtchrgB2p+5HYvpVysWGMw/aN3b6ANYEPNR1fPyVBRJscTlmsb3VgDeDnDPmOXvPZg/VCTQNIWqrheFpH0uaSviDpBuDTwNUNh9RXmYOINplhuzMhHCHpPY1F0z7HSvousHxdMuItwPcajmnaq6XBbwJmUZZ2+RFleP7FjQY2AJmDiNaQ9EvgCODo2jQL2Mv2to0F1TKSXkqZ5Bdwqu3TGw5p2pP0KPC/wN62r6tt19se2iU2RqQHEW3yFuAbwFcpwyTn1rbo3R8o5dW/lLSkpGVs39t0UNPc6yg9iDMlnQIcQ0mwQy89iIgFRB1W2gdY0fZTJa0PfCc9sN7UOZudKD3XbSjbjx5v+7RGA+ujJIiY9iQdON7xYV7qYH6SdAnwbOD8kT00JP3e9r80G1n7SFoR2Bl4o+1tmo6nXzLEFG3w78DlwLHArSwg3fs++IftB0e2ypS0CNmydUrqHhDfrV9DKwki2mA16qc14GFKFclPbf+10aja52xJHwWWqJPV/0EpFY7oKkNM0SqS1qCMAb8P+LDtoxoOqTUkLQTsTUcVE3BINrGKsSRBRGtI2oKSHF4KXAh82faVzUY1/UnaCTjX9u1NxxLtkgQR056kA4AdgKsoJYan2H642ajaQ9JPgOdRFjr8DaU8+De2r2g0sJj2kiBi2qsXKl0PjGxuM/JLO7Jk+tCupjk/SVoH2LJ+PQ9YG/id7e0bDCumsUxSRxus23QAw8D2DZIWB5aoXyO3I7pKDyJiyNXKpecBM4BrgPPq12W2H2kytpjekiAihpykq4H7gP+hzD+cb/vuZqOKNkiCiFgA1Ct/R+YfnkvZSe5SSnVTlkyPrpIgojUkvdv21ydqi7HVq6f/FXgB8G/AurYXbjaqmK6SIKI1JF1ke4tRbRePrCsU3UnakdJz2ArYBLiCMtR0LqUHMbfB8GIaS4KIaU/SLGAXYGvKuvwjlgUetv2SRgJrCUnHUa99AC60/WDDIUVLpMw12uBcYA6wMvDljvZ7gcsaiahFbL+26RiindKDiNao6/E/YPvRug3kRsDJth9qOLSIoZQEEa0h6ULg+cAKlDr+2cD9tndtNLCIIbVQ0wFETIJs3w+8FjjI9muAjRuOqTUkvbuXtogRSRDRJpL0PGBX4KTalnm03u3RpW3PQQcR7ZH/XNEm7wY+QtkH+ApJ6wFnNhzTtNdRBbaupBM7Di0L/KWZqKINMgcRMeQkPYWy4OFngX07Dt1LWY8pS6dHV0kQ0RqSZgAfolzstfhI+zBvGj8/pQosJitzENEmPwSupnwaPgC4AfhdkwG1zDnA4nXb1jOAvYAjGo0oprUkiGiTlWwfCjxk+2zbb6EsPBe9SRVYTEomqaNNRoZC5kh6JXArsGaD8bRNZxXY3rUtfwNiTPnliDb5tKTlgPcDB1GqcN7bbEitkiqwmJRMUkdERFfpQURr1MqbbwNPtr2ppM2AHW1/uuHQWiFVYDFZmaSONvkeZYjkIQDblwFvajSidkkVWExKEkS0yZK2LxjVlou8epcqsJiUDDFFm9wh6amAASS9nrJPRPQmVWAxKZmkjtaoVTcHU7bP/CvwR2BX2zc2GlhLSNqBsiPfWjxeBXaA7RPHfWIssJIgonXqkhEL2b5X0uts/7TpmCKGUeYgonVs/832vfXuVxsNpkUkbSDpDEmX1/ubSfp403HF9JUEEW2npgNokVSBxaQkQUTbZYy0d6kCi0lJFVNMe5J+T/dEIODJAw6nzVIFFpOSSeqY9uqGN2NKFVNvUgUWk5UEEbGASRVY9CoJImIBJukm22s3HUdMT5mkjliwpQosxpQEEa0h6d29tMWkZAghxpQhpmgNSRfZ3mJU28W2n9lUTG0wQRXYBrafNOCQoiVS5hrTnqRZwC7AupI61w1aFvhLM1G1yg5NBxDtlAQRbXAupV5/ZeDLHe33Apc2ElGLpIw1pipDTNFakrYCdrH99qZjiRhG6UFEq0janDLc9AbKhV7HNRtRxPBKFVNMe3UV0k9Kugr4BnAzpff7YtsHNRxea6QKLCYrQ0wx7Ul6lLLRzd62r6tt19ter9nI2iVVYDFZGWKKNngdZVnqMyWdAhxDLvDqWarAYqrSg4jWqGsI7QTMArYBjgSOt31ao4FNc3Wxw3WBzwL7dhy6F7jU9iONBBbTXhJEtJKkFYGdgTfa3qbpeNooVWAxkSSIiAVItyqwTPTHWDIHETHkJG1AmcOZRZlz+BG1CqzRwGLaSw8iYsilCiymKtdBRAy/1wG3UarAvidpW1IFFj1IDyJiAZEqsJisJIiIBVCqwKIXSRAREdFV5iAiIqKrJIiIiOgqCSKmFUmvkWRJG3W0rSPp8im+3g2SVp7E4/eU9I0xjr1C0mxJV0m6WtKXphLToNWfafWO+4dI2rjJmKIdkiBiupkF/JpyYde0IWlTylLjb7b9dGBT4Ppmo+rZnsBjCcL2W21f2Vw40RZJEDFtSFoa2ArYmzEShKSFJX1J0u8lXSbpnbV9W0kX1/bDJD2p42nvlHRRPbZRffyKkn5WX+M8SZtNEN6HgM/YvhrA9sO2v1Vf6ymSzqivdYaktWv7EZIOlHSupOslvb62rybpHEmXSLpc0vNr+30dP+frJR3R8TrflnRmfZ0X1p/xqpHHjDxf0pfrz3qGpBn1nDOBH9bzLSHpLEkz63Nm1fflckmfH/Van5F0aX1/njzB+xNDKAkippOdgFNs/wG4U9IWXR6zD2Vl0mfa3ozyh29x4AhKyea/UJaQ+X8dz7mj7oPwbeADte0A4OL6Gh8Fvj9BbJsCF45x7BvA90fiAQ7sOLYasDWwA/C52rYLcKrtzYFnAJdMcG6AFSjXLrwX+DnwVWAT4F/q+koASwEjez6cDexn+yfAbGBX25vbfmDkBeuw0+fr624OPEvSTh2vdZ7tZwDnAG/rIcYYMkkQMZ3Mouz1QP0+q8tjXgJ8x/bDALbvBDYE/vj/27t71yiCMI7j31+0EFEsYiumEEHESiwE0fwLgiASVNJZiQgBtUorthaBxBeIL4UIii+FhUoKQUKaxIClICIxlSYqMXiPxcx6xzK5NVYh+X2aW253Z2anuGfmuZu5HFggLQA72nFP9bekU0BfPj4CjOcyXgK9knb8Z7sPA/fy8Xguu/IoIlo5pVONwieBQUnDwIGIWPiHOp5E+k36DDAXETMR0QJmO56pRdpnCeBOrR0lh4DXETGf+/Mu7X77BTzNx539ZhuIA4StCZJ6SSPZMUkfgCHgpKT6lhAC6ot3mraNWMqvv2lvUFm6p9uioFngYEM9pXKWOo4FEBETpA/iT8C4pDOF+7bUyqzKadXKbLHypptNi5y69dtytBdJdfabbSAOELZWnCClaXZHRF9E7CJtR10fBb8AzknaDH9XBL8H+iTtydecJqVYupkABnIZ/aQ01Lcu118DruSdUZHUI+liPveG9ncmA6Qv2VeU/8DnS0SMAjeAKpU2J2mfpB7geEP7S3pI/QgpjVW1YwHYXrj+LXBM0k5Jm0gztqZ+sw3EowJbK07RztFXHpI+6K52vDcG7AWmJS0DoxFxXdIg8CAHjklgpKG+YeCWpGngB3C228URMS3pAnBf0lbS6PxZPn0euClpCJgHBhvq7geGcvsXgWoGcYmU1vkIvAO2NZRT9x3YL2kK+AqczO/fBkYk/SSlw6pn+izpMvCKNJt4HhGPV1mnrWPeasNsnZC0GBGrDSpmK3KKyczMijyDMDOzIs8gzMysyAHCzMyKHCDMzKzIAcLMzIocIMzMrOgPiDhrX3iJ8uYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Bar([\"Infrequently (<30 days)\", \"At Least Monthly (30<= days < 52)\", \"At Least Weekly ( 52<= days < 300)\",\"Almost Daily (>300 days)\"], meansList2)\n",
    "thinkplot.Config(xlabel = \"Alcohol Consumption\", ylabel = \"Mean of Heroin Use (days)\", title = \"Alcohol and Heroin Consumption\")\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen from the bar plot above, the group that averaged the highest total days of heroin consumption corresponds to the group that infrequently consume alcohol with an average of 152 days as compared to the group that uses heroin almost daily, which averaged about 86 total days of heroin use. The infrequent group averaged about 1.7 times less than the daily group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GRAPHICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Cdf(alcoholUsedPast12MOCdf, label=\"Alcohol Consumption\")\n",
    "thinkplot.Cdf(heroinUsedPast12MOCdf, label=\"Heroin Consumption\")\n",
    "thinkplot.Config(xlabel= \"Total Days of Consumption\", ylabel = \"CDF\", title=\"CDF of Alcohol and Heroin Consumption\")"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}