FinalProject_Group100.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How High Is the U.S.?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nowadays, medical marijuana and recreational marijuana have been legalized in many states of the United States. With its popularity in the life and culture, we want to explore the relationship between marijuana usage and people's living conditions. The major factors of people's living conditions we consider in our exploration are demographic, education levels, and socioeconomic status. We hope that through our exploration, we will have a better vision of who is prone or susceptible to use and understand further how the usage of marijuana affects to people's overall living experiences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Gregory Thein\n",
    "- Queen Tran\n",
    "- Shiva Upadhye\n",
    "- Diana Thai\n",
    "- Osvaldo Vazquez\n",
    "- Sophie Concordel "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is our research question for this exploration:\n",
    "\n",
    "“What is the relationship between marijuana usage, age, and socioeconomic status throughout the United States?”\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The War on Drugs taught us that poorer demographics are prone to drug exposure and higher usage rates. However, with recent legalization of marijuana across multiple states, we’re curious to see if the validity of poverty and drug use still correlate. Every year the CDC (Center for Disease Control and Prevention) gives out a National Health and Nutrition Examination Survey. From these surveys, we will be able to gather information (our desired dataset) on drug use, age, and socioeconomic status of individuals across the country. What we currently know about the topic of marijuana use in the United States is that popularity has grown over the years. Medical use for marijuana has become more and more popularized, and recreational use as well, especially with the recent legalization. We also know that the media has an influence as well, especially to younger users. As popular artists and actors and influencers display the use of marijuana younger adults are more likely to follow. From the Substance Abuse and Mental Health Services Administration were learned that there were 26 million marijuana users in 2017, two million more than the previous year. \n",
    "\n",
    "For older adults, it was found by the NCBI that marijuana is the most commonly used illicit drug by 50+ adults. The usage has increased by 71.4 percent in three years alone from 2013 to 2016. This large jump was found to be due to the more widespread use of medical marijuana. In younger populations, SAMHSA reported marijuana usage for three young age groups. The results show that 6.5 percent of 12-17 years olds, 22.1 percent of 18-26 years olds, and 7.9 percent of the 26+ group are current marijuana users. The NCBI also found that with the legalization of marijuana in Oregon, the usage among adolescents has raised from 2.9 in 2006 to 18.3 in 2015 in their sample. Interestingly a study by the NCBI found results for marijuana use in relation in neighborhood income that the neighborhood with the highest median income and Gini coefficient (inequality among income) had the highest prevalence of marijuana smoking. These results helped us hypothesize for three different use groups.\n",
    "\n",
    "We were unable to find any previous student projects that asked similar questions, but we looked at the methods and results of a project on if income does indeed hold a significant impact on the spread of communicable diseases, specifically the flu. They specifically looked at the San Diego area and were able to  compress the flu rate, median income and other values into regions with various hospitals using the pandas library. They then used the t-test where discharge/hospitalization rate is dependent on income/poverty and other possible confounds. They learned that there was a negative correlation between hospital discharge rates and household income. We plan to reference these methods as we conduct analysis on our own dataset, seeing there is parallelism in the prompts.\n",
    "\n",
    "References:\n",
    "COGS 108 Previous Project\n",
    "https://github.com/COGS108/FinalProjects-Sp17/blob/master/052-FinalProject.ipynb\n",
    "\n",
    "Key Substance Use and Mental Health Indicators in the United States:Results from the 2017 National Survey on Drug Use and Health\n",
    "https://www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHFFR2017/NSDUHFFR2017.htm\n",
    "\n",
    "Association of State Recreational Marijuana Laws With Adolescent Marijuana Use\n",
    "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5365078/\n",
    "\n",
    "Medical Marijuana Legalization and Marijuana Use Among Youth in Oregon\n",
    "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5474164/\n",
    "\n",
    "Neighborhood Income and Income Distribution and the Use of Cigarettes, Alcohol, and Marijuana\n",
    "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1974881/\n",
    "\n",
    "Marijuana Use Among Adults 50 Years or Older in the 21st Century\n",
    "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6024284/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We believe that there would be three groups that would have increased marijuana usage from the various factors that we decide to study on. \n",
    "\n",
    "First, younger people (anyone younger than 26) will have a higher rate of marijuana usage because of the social popularity and culture behind this drug amongst the youth. \n",
    "\n",
    "Second, we predict that as the income and education level increase (higher socioeconomic status), there should be a negative relationship with drug use, meaning that the higher the income and education level, the lower the marijuana use. \n",
    "\n",
    "Finally, we also believe that there could be a high usage of medical marijuana in elderly and sick people as the popularity of medical marijuana increases throughout the country. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After scouring through all the amazing resources Professor Shannon Ellis offered us, we were extremely fortunate to find the NHANES (National Health and Nutrition Examination Survey), a national survey that included data on all aspects of our research question and hypothesis. This dataset is very large and comprehensive and is broken up into many different files, including a Demographic Dataset and Questionnaires Datasets that have information about drug usage (including marijuana) and living conditions like income and occupation. Every dataset is stored in a sas file, which we plan to use the pandas.read_sas method to help open our files. Each dataset has a comprehensive documentation that describes columns and possible values for the data. For example, the column “RIAGENDR” in the documentation gives us information of the participant’s gender with values 1 for Male, 2 for Female, and . for Missing. Since the 2017-2018 data is incomplete at the time we collect data for this research, we use the 2015-2016 survey data as the latest data for this project.\n",
    "\n",
    "Below are two datasets that we used in our research:\n",
    "\n",
    "Demographic Variables and Sample Weights (DEMO I):\n",
    "This dataset consists more than 9000 rows and 40 columns was released by the National Health and Nutrition Examination Survey 2015 with each row containing demographic information for an individual and each column representing a variable.  For the purpose of this project, we will be considering age, gender, education status, marital status, pregnancy status, household size, property ownership status, and a multivariable income component that encompasses individual, household, family, and social-security income.\n",
    "\n",
    "Links to this dataset:\n",
    "Description of the dataset: https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/DEMO_I.htm\n",
    "Dataset: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Demographics&CycleBeginYear=2015\n",
    "\n",
    "\n",
    "National Health and Nutrition Examination Survey: Drug Use (DUQ I)\n",
    "This dataset consists more than 4000 rows and 40 columns was released by the National Health and Nutrition Examination Survey 2015 with each row representing an individual's responses to The Drug Use Questionnaire and each column corresponding to a variable encoded by the questionnaire. Since the questionnaire focuses on the intravenous and non-intravenous consumption of a variety of drugs such as marijuana, heroin, cocaine, and methamphetamine, we will only consider ten columns that correspond specifically to marijuana usage for the purpose of this project.\n",
    "\n",
    "Links to this dataset:\n",
    "Description of the dataset: https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/DUQ_I.htm\n",
    "Dataset: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Questionnaire&CycleBeginYear=2015     \n",
    "\n",
    "For more convenient in working with data and keeping data consistency, We combine these two datasets, do data cleaning and make one final dataset for our analysis in group. The final data file is \"cleaned_marijuana_data.csv\". This file has 4843 rows x 21 columns where each row represents an individual in the research and each column represents a variable needed for the research."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset filename: cleaned_marijuana_data.csv\n",
    "This file has the following columns:\n",
    "    * ID: Sequence number \n",
    "    * GENDER: Gender \n",
    "    * AGE: Age in years at the time of screening \n",
    "    * EDUCATION_KID: Education Level (6-19) - Highest greade or level of school completed \n",
    "    * EDUCATION_ADULT: Education Level (20+) - Highest greade or level of school completed \n",
    "    * MARRIAGE: Marital Status \n",
    "    * PREGNANCY: Pregnancy status for females between 20 and 44 years of age at the time of MEC exam \n",
    "    * INCOME: Annual household income \n",
    "    * INCOME_RATIO: Ratio of family income to poverty \n",
    "    * HOUSEHOLD_NUM: Total number of people in the Household \n",
    "    * CHILDREN_NUM: Number of children less than 18 years old \n",
    "    * OVER_60_NUM: Number of adults aged 60 years or older in the household \n",
    "    * EVER_USE: Ever used marijuana or hashish \n",
    "    * FIRST_USAGE: Age when first tried marijuana \n",
    "    * FREQUENCY: How often would the user used marijuana \n",
    "    * PER_MOUNTH: How often the user would use marijuana every month \n",
    "    * AGE_REGULAR_USE: Age started using marijuana reguarly\n",
    "    * LAST_ONCE_MONTH: Time since last used marijuana regularly \n",
    "    * AMOUNT: How many joints or pipes smoke in a day. \n",
    "    * LAST_USAGE: Last time used marijuana or hashish \n",
    "    * PAST_MONTH_USAGE: # days used marijuana or hashish/month "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import patsy\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind, chisquare, normaltest\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# set defaults\n",
    "plt.style.use('seaborn-white')   # seaborn custom plot style\n",
    "plt.rc('figure', dpi=100, figsize=(7, 5))   # set default size/resolution\n",
    "plt.rc('font', size=12)   # font size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Demographics Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      SEQN  SDDSRVYR  RIDSTATR  RIAGENDR  RIDAGEYR  RIDAGEMN  RIDRETH1  \\\n",
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       "\n",
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "demographics = pd.read_sas('data/DEMO_I.XPT')\n",
    "demographics.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drug Use Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83733.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83735.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83736.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.397605e-79</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83741.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SEQN  DUQ200  DUQ210  DUQ211  DUQ213       DUQ215Q  DUQ215U  DUQ217  \\\n",
       "0  83732.0     NaN     NaN     NaN     NaN           NaN      NaN     NaN   \n",
       "1  83733.0     2.0     NaN     NaN     NaN           NaN      NaN     NaN   \n",
       "2  83735.0     2.0     NaN     NaN     NaN           NaN      NaN     NaN   \n",
       "3  83736.0     1.0    25.0     1.0    25.0  5.397605e-79      1.0     5.0   \n",
       "4  83741.0     1.0    15.0     1.0    16.0  1.000000e+00      4.0     4.0   \n",
       "\n",
       "   DUQ219  DUQ220Q   ...    DUQ380B  DUQ380C  DUQ380D  DUQ380E  DUQ390  \\\n",
       "0     NaN      NaN   ...        NaN      NaN      NaN      NaN     NaN   \n",
       "1     NaN      NaN   ...        NaN      NaN      NaN      NaN     NaN   \n",
       "2     NaN      NaN   ...        NaN      NaN      NaN      NaN     NaN   \n",
       "3     4.0      1.0   ...        NaN      NaN      NaN      NaN     NaN   \n",
       "4     2.0      1.0   ...        NaN      NaN      NaN      NaN     NaN   \n",
       "\n",
       "   DUQ400Q  DUQ400U  DUQ410  DUQ420  DUQ430  \n",
       "0      NaN      NaN     NaN     NaN     NaN  \n",
       "1      NaN      NaN     NaN     NaN     NaN  \n",
       "2      NaN      NaN     NaN     NaN     NaN  \n",
       "3      NaN      NaN     NaN     NaN     2.0  \n",
       "4      NaN      NaN     NaN     NaN     1.0  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questionaire = pd.read_sas('data/DUQ_I.XPT')\n",
    "questionaire.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We merged the two dataframes based on the sequence number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEQN</th>\n",
       "      <th>SDDSRVYR</th>\n",
       "      <th>RIDSTATR</th>\n",
       "      <th>RIAGENDR</th>\n",
       "      <th>RIDAGEYR</th>\n",
       "      <th>RIDAGEMN</th>\n",
       "      <th>RIDRETH1</th>\n",
       "      <th>RIDRETH3</th>\n",
       "      <th>RIDEXMON</th>\n",
       "      <th>RIDEXAGM</th>\n",
       "      <th>...</th>\n",
       "      <th>DUQ380B</th>\n",
       "      <th>DUQ380C</th>\n",
       "      <th>DUQ380D</th>\n",
       "      <th>DUQ380E</th>\n",
       "      <th>DUQ390</th>\n",
       "      <th>DUQ400Q</th>\n",
       "      <th>DUQ400U</th>\n",
       "      <th>DUQ410</th>\n",
       "      <th>DUQ420</th>\n",
       "      <th>DUQ430</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83732.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83733.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83735.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83736.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83741.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SEQN  SDDSRVYR  RIDSTATR  RIAGENDR  RIDAGEYR  RIDAGEMN  RIDRETH1  \\\n",
       "0  83732.0       9.0       2.0       1.0      62.0       NaN       3.0   \n",
       "1  83733.0       9.0       2.0       1.0      53.0       NaN       3.0   \n",
       "2  83735.0       9.0       2.0       2.0      56.0       NaN       3.0   \n",
       "3  83736.0       9.0       2.0       2.0      42.0       NaN       4.0   \n",
       "4  83741.0       9.0       2.0       1.0      22.0       NaN       4.0   \n",
       "\n",
       "   RIDRETH3  RIDEXMON  RIDEXAGM   ...    DUQ380B  DUQ380C  DUQ380D  DUQ380E  \\\n",
       "0       3.0       1.0       NaN   ...        NaN      NaN      NaN      NaN   \n",
       "1       3.0       1.0       NaN   ...        NaN      NaN      NaN      NaN   \n",
       "2       3.0       2.0       NaN   ...        NaN      NaN      NaN      NaN   \n",
       "3       4.0       2.0       NaN   ...        NaN      NaN      NaN      NaN   \n",
       "4       4.0       1.0       NaN   ...        NaN      NaN      NaN      NaN   \n",
       "\n",
       "   DUQ390  DUQ400Q  DUQ400U  DUQ410  DUQ420  DUQ430  \n",
       "0     NaN      NaN      NaN     NaN     NaN     NaN  \n",
       "1     NaN      NaN      NaN     NaN     NaN     NaN  \n",
       "2     NaN      NaN      NaN     NaN     NaN     NaN  \n",
       "3     NaN      NaN      NaN     NaN     NaN     2.0  \n",
       "4     NaN      NaN      NaN     NaN     NaN     1.0  \n",
       "\n",
       "[5 rows x 88 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = pd.merge(demographics, questionaire, on = 'SEQN')\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then went through the columns and decided on which column we wanted to utilize for our analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['SEQN', 'RIAGENDR', 'RIDAGEYR', 'DMDEDUC3', 'DMDEDUC2', 'DMDMARTL', 'RIDEXPRG', 'INDHHIN2', \n",
    "        'INDFMPIR', 'DMDHHSIZ', 'DMDHHSZA', 'DMDHHSZB', 'DMDHHSZE', 'DUQ200', 'DUQ210', 'DUQ217', 'DUQ211', \n",
    "        'DUQ213', 'DUQ215Q', 'DUQ219', 'DUQ220Q', 'DUQ230']\n",
    "df = merged[cols]\n",
    "df['CHILDREN_NUM'] = df['DMDHHSZA'] + df['DMDHHSZB']\n",
    "df = df.drop(columns = ['DMDHHSZA', 'DMDHHSZB'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then standardized our refused and don't know data (all 7's were refused and 9's were unknown). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst_7 = ['DMDEDUC2', 'DUQ200', 'DUQ217', 'DUQ211', 'DUQ219']\n",
    "lst_77 = ['DMDMARTL', 'DMDEDUC3', 'INDHHIN2']\n",
    "lst_777 = ['DUQ210', 'DUQ213', 'DUQ230']\n",
    "\n",
    "for i in lst_7:\n",
    "    df[i] = df[i].replace({7: 7777, 9: 9999})\n",
    "for i in lst_77:\n",
    "    df[i] = df[i].replace({77: 7777, 99: 9999})\n",
    "for i in lst_777:\n",
    "    df[i] = df[i].replace({777: 7777, 999: 9999})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make our dataframe's columns more readable, we used a translation dictionary to make the column names easier to understand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>EDUCATION_KID</th>\n",
       "      <th>EDUCATION_ADULT</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>PREGNANCY</th>\n",
       "      <th>INCOME</th>\n",
       "      <th>INCOME_RATIO</th>\n",
       "      <th>HOUSEHOLD_NUM</th>\n",
       "      <th>...</th>\n",
       "      <th>EVER_USE</th>\n",
       "      <th>FIRST_USAGE</th>\n",
       "      <th>FREQUENCY</th>\n",
       "      <th>PER_MONTH</th>\n",
       "      <th>AGE_REGULAR_USE</th>\n",
       "      <th>LAST_ONCE_MONTH</th>\n",
       "      <th>AMOUNT</th>\n",
       "      <th>LAST_USAGE</th>\n",
       "      <th>PAST_MONTH_USAGE</th>\n",
       "      <th>CHILDREN_NUM</th>\n",
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       "      <td>2.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1.079521e-78</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>6.0</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.079521e-78</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83736.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>30.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83741.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.08</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.079521e-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>83742.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.03</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>83743.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.079521e-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>83744.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.079521e-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>83747.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.079521e-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>83750.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.36</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  GENDER   AGE  EDUCATION_KID  EDUCATION_ADULT  MARRIAGE  PREGNANCY  \\\n",
       "0  83732.0     1.0  62.0            NaN              5.0       1.0        NaN   \n",
       "1  83733.0     1.0  53.0            NaN              3.0       3.0        NaN   \n",
       "2  83735.0     2.0  56.0            NaN              5.0       6.0        NaN   \n",
       "3  83736.0     2.0  42.0            NaN              4.0       3.0        1.0   \n",
       "4  83741.0     1.0  22.0            NaN              4.0       5.0        NaN   \n",
       "5  83742.0     2.0  32.0            NaN              4.0       1.0        2.0   \n",
       "6  83743.0     1.0  18.0           11.0              NaN       NaN        NaN   \n",
       "7  83744.0     1.0  56.0            NaN              3.0       3.0        NaN   \n",
       "8  83747.0     1.0  46.0            NaN              5.0       6.0        NaN   \n",
       "9  83750.0     1.0  45.0            NaN              2.0       5.0        NaN   \n",
       "\n",
       "   INCOME  INCOME_RATIO  HOUSEHOLD_NUM      ...       EVER_USE  FIRST_USAGE  \\\n",
       "0    10.0          4.39            2.0      ...            NaN          NaN   \n",
       "1     4.0          1.32            1.0      ...            2.0          NaN   \n",
       "2    10.0          5.00            1.0      ...            2.0          NaN   \n",
       "3     7.0          1.23            5.0      ...            1.0         25.0   \n",
       "4     7.0          2.08            3.0      ...            1.0         15.0   \n",
       "5     6.0          1.03            4.0      ...            1.0         18.0   \n",
       "6    15.0          5.00            3.0      ...            2.0          NaN   \n",
       "7     3.0          1.19            1.0      ...            1.0         18.0   \n",
       "8     3.0          0.75            2.0      ...            2.0          NaN   \n",
       "9    10.0          1.36            5.0      ...            1.0         21.0   \n",
       "\n",
       "   FREQUENCY  PER_MONTH  AGE_REGULAR_USE  LAST_ONCE_MONTH  AMOUNT  LAST_USAGE  \\\n",
       "0        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "1        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "2        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "3        5.0        1.0             25.0     5.397605e-79     4.0         1.0   \n",
       "4        4.0        1.0             16.0     1.000000e+00     2.0         1.0   \n",
       "5        NaN        2.0              NaN              NaN     NaN        13.0   \n",
       "6        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "7        NaN        2.0              NaN              NaN     NaN        20.0   \n",
       "8        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "9        NaN        2.0              NaN              NaN     NaN        20.0   \n",
       "\n",
       "   PAST_MONTH_USAGE  CHILDREN_NUM  \n",
       "0               NaN  1.079521e-78  \n",
       "1               NaN  1.079521e-78  \n",
       "2               NaN  1.079521e-78  \n",
       "3              30.0  2.000000e+00  \n",
       "4              25.0  1.079521e-78  \n",
       "5               NaN  2.000000e+00  \n",
       "6               NaN  1.079521e-78  \n",
       "7               NaN  1.079521e-78  \n",
       "8               NaN  1.079521e-78  \n",
       "9               1.0  2.000000e+00  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "translation_dict = {'SEQN': 'ID', 'RIAGENDR': 'GENDER', 'RIDAGEYR': 'AGE', 'DMDMARTL': 'MARRIAGE', \n",
    "                    'RIDEXPRG': 'PREGNANCY', 'INDHHIN2': 'INCOME', 'INDFMPIR': 'INCOME_RATIO', \n",
    "                    'DMDHHSIZ': 'HOUSEHOLD_NUM', 'DMDHHSZE': 'OVER_60_NUM', 'DUQ200': 'EVER_USE', \n",
    "                    'DUQ210': 'FIRST_USAGE', 'DUQ217': 'FREQUENCY', 'DUQ211': 'PER_MONTH', \n",
    "                    'DUQ213': 'AGE_REGULAR_USE', 'DUQ215Q': 'LAST_ONCE_MONTH', 'DUQ219': 'AMOUNT', 'DUQ220Q': 'LAST_USAGE', \n",
    "                    'DUQ230': 'PAST_MONTH_USAGE', 'DMDEDUC2': 'EDUCATION_ADULT', 'DMDEDUC3': 'EDUCATION_KID'}\n",
    "df = df.rename(index = str, columns = translation_dict)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After looking through the data, we saw that there were some numbers that were trailing (we're assuming it is because when we merged the tables together, something happened to it). We decided to then round those numbers and to replace 5.39770e-79 to 0 because we realized that when we combined the datasets together, that number was suppose to be zero. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>EDUCATION_KID</th>\n",
       "      <th>EDUCATION_ADULT</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>PREGNANCY</th>\n",
       "      <th>INCOME</th>\n",
       "      <th>INCOME_RATIO</th>\n",
       "      <th>HOUSEHOLD_NUM</th>\n",
       "      <th>...</th>\n",
       "      <th>EVER_USE</th>\n",
       "      <th>FIRST_USAGE</th>\n",
       "      <th>FREQUENCY</th>\n",
       "      <th>PER_MONTH</th>\n",
       "      <th>AGE_REGULAR_USE</th>\n",
       "      <th>LAST_ONCE_MONTH</th>\n",
       "      <th>AMOUNT</th>\n",
       "      <th>LAST_USAGE</th>\n",
       "      <th>PAST_MONTH_USAGE</th>\n",
       "      <th>CHILDREN_NUM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83732.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.39</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83733.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83735.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83736.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.23</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83741.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.08</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  GENDER   AGE  EDUCATION_KID  EDUCATION_ADULT  MARRIAGE  PREGNANCY  \\\n",
       "0  83732.0     1.0  62.0            NaN              5.0       1.0        NaN   \n",
       "1  83733.0     1.0  53.0            NaN              3.0       3.0        NaN   \n",
       "2  83735.0     2.0  56.0            NaN              5.0       6.0        NaN   \n",
       "3  83736.0     2.0  42.0            NaN              4.0       3.0        1.0   \n",
       "4  83741.0     1.0  22.0            NaN              4.0       5.0        NaN   \n",
       "\n",
       "   INCOME  INCOME_RATIO  HOUSEHOLD_NUM      ...       EVER_USE  FIRST_USAGE  \\\n",
       "0    10.0          4.39            2.0      ...            NaN          NaN   \n",
       "1     4.0          1.32            1.0      ...            2.0          NaN   \n",
       "2    10.0          5.00            1.0      ...            2.0          NaN   \n",
       "3     7.0          1.23            5.0      ...            1.0         25.0   \n",
       "4     7.0          2.08            3.0      ...            1.0         15.0   \n",
       "\n",
       "   FREQUENCY  PER_MONTH  AGE_REGULAR_USE  LAST_ONCE_MONTH  AMOUNT  LAST_USAGE  \\\n",
       "0        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "1        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "2        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "3        5.0        1.0             25.0              0.0     4.0         1.0   \n",
       "4        4.0        1.0             16.0              1.0     2.0         1.0   \n",
       "\n",
       "   PAST_MONTH_USAGE  CHILDREN_NUM  \n",
       "0               NaN           0.0  \n",
       "1               NaN           0.0  \n",
       "2               NaN           0.0  \n",
       "3              30.0           2.0  \n",
       "4              25.0           0.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#OVER_60_NUM\n",
    "#LAST_ONCE_MONTH\n",
    "#CHILDREN_NUM\n",
    "df['OVER_60_NUM'] = df['OVER_60_NUM'].round(0)\n",
    "df['LAST_ONCE_MONTH'] = df['LAST_ONCE_MONTH'].round(0)\n",
    "df['CHILDREN_NUM'] = df['CHILDREN_NUM'].round(0)\n",
    "df['INCOME_RATIO'] = df['INCOME_RATIO'].replace({5.397605e-79: 0}).round(2)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then saved our data then into a csv file, in which we used throughout our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#write dataframe to csv file\n",
    "df.to_csv(\"cleaned_marijuana_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing Values\n",
    "\n",
    "Just from looking at the heads of many of the datasets, we noticed a lot of missing values. WE will define various types of missing values as the following: \n",
    "\n",
    "-MD: In this situation, we would expect the missing values as part of the design. There are instances of this as if someone did not ever use marijuana, the survey skips certain columns, in which those columns would be missing by design. <br>\n",
    "-MCAR: In this situation, the columns missing values are not dependent on anything else. We do not believe that there is any MCAR values within our dataset. <br>\n",
    "-MAR: In this situation, the columnns' missing values are dependent on another column. For example, if education was missing based on gender (this did not happen in our dataset, but just an example) <br>\n",
    "-NMAR: In this situation, the missing values are based on an external factor. For example, people with less incomes are more likely to not report their income.  <br>\n",
    "\n",
    "From our data, we believe that most of our missingness is not missing at random since the participant may not feel comfortable revealing certain aspects of their life. With this consideration, we did not feel like we should remove or impute any of the missing data, as there could have beem many factors that contributed to the missing data. But, we do want to acknowledge that we are aware of this missing data and that we should take that into consideration during our analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For the sake of convenience, we don't separate data exploration and data visualization but include them into the section of data analysis & results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demographics\n",
    "\n",
    "The demographic data was obtain from the Center of Disease Control and Prevention website (https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Demographics&CycleBeginYear=2015) The demographics file provides individual, family, and household-level information on the following topics, which the columns of our dataset:\n",
    "    * Sequence number (ID)\n",
    "    * Gender (GENDER)\n",
    "    * Age in years AGE: Age at the time of screening, two catagories below 80 and above 80\n",
    "    * Education Level (6-19) (EDUCATION_KID): Highest greade or level of school completed \n",
    "    * Education Level (20+) (EDUCATION_ADULT): Highest greade or level of school completed \n",
    "    * Marital Status (MARRIAGE)\n",
    "    * Pregnancy Status (PREGNANCY): Pregnancy status for females between 20 and 44 years of age at the time of MEC exam\n",
    "    * Annual household income (INCOME)\n",
    "    * Ratio of family income to poverty (INCOME_RATIO)\n",
    "    * Total number of people in the Household (HOUSEHOLD_NUM)\n",
    "    * Number of children less than 18 years old (CHILDREN_NUM)\n",
    "    * Number of adults aged 60 years or older in the household (OVER_60_NUM)\n",
    "\n",
    "\n",
    "### Questionnaires:\n",
    "\n",
    "The Questionnaires data was obtain from the Center of Disease Control and Prevention website (https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Questionnaire&CycleBeginYear=2015). Categories of questionnaires data includes:\n",
    "\n",
    "    * Respondent sequence number (ID)   \n",
    "    * EVER_USE: Ever used marijuana or hashish\n",
    "    * FIRST_USAGE: Age when first tried marijuana\n",
    "    * FREQUENCY: How often would the user used marijuana\n",
    "    * PER_MOUNTH: How often the user would use marijuana every month\n",
    "    * AGE_REGULAR_USE: Age started using marijuana regularly\n",
    "    * LAST_ONCE_MONTH: Time since last used marijuana regularly\n",
    "    * AMOUNT: How many joints or pipes smoke in a day.\n",
    "    * LAST_USAGE: Last time used marijuana or hashish\n",
    "    * PAST_MONTH_USAGE: # days used marijuana or hashish/month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <th>RIDEXAGM</th>\n",
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       "      <th>DMDHRMAR</th>\n",
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       "      <th>WTINT2YR</th>\n",
       "      <th>WTMEC2YR</th>\n",
       "      <th>SDMVPSU</th>\n",
       "      <th>SDMVSTRA</th>\n",
       "      <th>INDHHIN2</th>\n",
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       "      SEQN  SDDSRVYR  RIDSTATR  RIAGENDR  RIDAGEYR  RIDAGEMN  RIDRETH1  \\\n",
       "0  83732.0       9.0       2.0       1.0      62.0       NaN       3.0   \n",
       "1  83733.0       9.0       2.0       1.0      53.0       NaN       3.0   \n",
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       "4  83736.0       9.0       2.0       2.0      42.0       NaN       4.0   \n",
       "\n",
       "   RIDRETH3  RIDEXMON  RIDEXAGM    ...     DMDHREDU  DMDHRMAR  DMDHSEDU  \\\n",
       "0       3.0       1.0       NaN    ...          5.0       1.0       3.0   \n",
       "1       3.0       1.0       NaN    ...          3.0       3.0       NaN   \n",
       "2       3.0       2.0       NaN    ...          3.0       1.0       3.0   \n",
       "3       3.0       2.0       NaN    ...          5.0       6.0       NaN   \n",
       "4       4.0       2.0       NaN    ...          4.0       3.0       NaN   \n",
       "\n",
       "        WTINT2YR       WTMEC2YR  SDMVPSU  SDMVSTRA  INDHHIN2  INDFMIN2  \\\n",
       "0  134671.370419  135629.507405      1.0     125.0      10.0      10.0   \n",
       "1   24328.560239   25282.425927      1.0     125.0       4.0       4.0   \n",
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       "4   17627.674984   18234.736219      2.0     126.0       7.0       7.0   \n",
       "\n",
       "   INDFMPIR  \n",
       "0      4.39  \n",
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       "2      1.51  \n",
       "3      5.00  \n",
       "4      1.23  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demographics = pd.read_sas('data/DEMO_I.XPT')\n",
    "demographics.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drug Use Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      SEQN  DUQ200  DUQ210  DUQ211  DUQ213       DUQ215Q  DUQ215U  DUQ217  \\\n",
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       "\n",
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       "0     NaN      NaN   ...        NaN      NaN      NaN      NaN     NaN   \n",
       "1     NaN      NaN   ...        NaN      NaN      NaN      NaN     NaN   \n",
       "2     NaN      NaN   ...        NaN      NaN      NaN      NaN     NaN   \n",
       "3     4.0      1.0   ...        NaN      NaN      NaN      NaN     NaN   \n",
       "4     2.0      1.0   ...        NaN      NaN      NaN      NaN     NaN   \n",
       "\n",
       "   DUQ400Q  DUQ400U  DUQ410  DUQ420  DUQ430  \n",
       "0      NaN      NaN     NaN     NaN     NaN  \n",
       "1      NaN      NaN     NaN     NaN     NaN  \n",
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       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questionaire = pd.read_sas('data/DUQ_I.XPT')\n",
    "questionaire.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We merged the two dataframes based on the sequence number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83733.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83735.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83736.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83741.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SEQN  SDDSRVYR  RIDSTATR  RIAGENDR  RIDAGEYR  RIDAGEMN  RIDRETH1  \\\n",
       "0  83732.0       9.0       2.0       1.0      62.0       NaN       3.0   \n",
       "1  83733.0       9.0       2.0       1.0      53.0       NaN       3.0   \n",
       "2  83735.0       9.0       2.0       2.0      56.0       NaN       3.0   \n",
       "3  83736.0       9.0       2.0       2.0      42.0       NaN       4.0   \n",
       "4  83741.0       9.0       2.0       1.0      22.0       NaN       4.0   \n",
       "\n",
       "   RIDRETH3  RIDEXMON  RIDEXAGM   ...    DUQ380B  DUQ380C  DUQ380D  DUQ380E  \\\n",
       "0       3.0       1.0       NaN   ...        NaN      NaN      NaN      NaN   \n",
       "1       3.0       1.0       NaN   ...        NaN      NaN      NaN      NaN   \n",
       "2       3.0       2.0       NaN   ...        NaN      NaN      NaN      NaN   \n",
       "3       4.0       2.0       NaN   ...        NaN      NaN      NaN      NaN   \n",
       "4       4.0       1.0       NaN   ...        NaN      NaN      NaN      NaN   \n",
       "\n",
       "   DUQ390  DUQ400Q  DUQ400U  DUQ410  DUQ420  DUQ430  \n",
       "0     NaN      NaN      NaN     NaN     NaN     NaN  \n",
       "1     NaN      NaN      NaN     NaN     NaN     NaN  \n",
       "2     NaN      NaN      NaN     NaN     NaN     NaN  \n",
       "3     NaN      NaN      NaN     NaN     NaN     2.0  \n",
       "4     NaN      NaN      NaN     NaN     NaN     1.0  \n",
       "\n",
       "[5 rows x 88 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = pd.merge(demographics, questionaire, on = 'SEQN')\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then went through the columns and decided on which column we wanted to utilize for our analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['SEQN', 'RIAGENDR', 'RIDAGEYR', 'DMDEDUC3', 'DMDEDUC2', 'DMDMARTL', 'RIDEXPRG', 'INDHHIN2', \n",
    "        'INDFMPIR', 'DMDHHSIZ', 'DMDHHSZA', 'DMDHHSZB', 'DMDHHSZE', 'DUQ200', 'DUQ210', 'DUQ217', 'DUQ211', \n",
    "        'DUQ213', 'DUQ215Q', 'DUQ219', 'DUQ220Q', 'DUQ230']\n",
    "df = merged[cols]\n",
    "df['CHILDREN_NUM'] = df['DMDHHSZA'] + df['DMDHHSZB']\n",
    "df = df.drop(columns = ['DMDHHSZA', 'DMDHHSZB'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then standardized our refused and don't know data (all 7's were refused and 9's were unknown). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst_7 = ['DMDEDUC2', 'DUQ200', 'DUQ217', 'DUQ211', 'DUQ219']\n",
    "lst_77 = ['DMDMARTL', 'DMDEDUC3', 'INDHHIN2']\n",
    "lst_777 = ['DUQ210', 'DUQ213', 'DUQ230']\n",
    "\n",
    "for i in lst_7:\n",
    "    df[i] = df[i].replace({7: 7777, 9: 9999})\n",
    "for i in lst_77:\n",
    "    df[i] = df[i].replace({77: 7777, 99: 9999})\n",
    "for i in lst_777:\n",
    "    df[i] = df[i].replace({777: 7777, 999: 9999})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make our dataframe's columns more readable, we used a translation dictionary to make the column names easier to understand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>EDUCATION_KID</th>\n",
       "      <th>EDUCATION_ADULT</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>PREGNANCY</th>\n",
       "      <th>INCOME</th>\n",
       "      <th>INCOME_RATIO</th>\n",
       "      <th>HOUSEHOLD_NUM</th>\n",
       "      <th>...</th>\n",
       "      <th>EVER_USE</th>\n",
       "      <th>FIRST_USAGE</th>\n",
       "      <th>FREQUENCY</th>\n",
       "      <th>PER_MONTH</th>\n",
       "      <th>AGE_REGULAR_USE</th>\n",
       "      <th>LAST_ONCE_MONTH</th>\n",
       "      <th>AMOUNT</th>\n",
       "      <th>LAST_USAGE</th>\n",
       "      <th>PAST_MONTH_USAGE</th>\n",
       "      <th>CHILDREN_NUM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83732.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.39</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.079521e-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83733.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.079521e-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83735.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.079521e-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83736.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.23</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.397605e-79</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83741.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.08</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.079521e-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>83742.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.03</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>83743.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.079521e-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>83744.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.079521e-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>83747.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.079521e-78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>83750.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.36</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  GENDER   AGE  EDUCATION_KID  EDUCATION_ADULT  MARRIAGE  PREGNANCY  \\\n",
       "0  83732.0     1.0  62.0            NaN              5.0       1.0        NaN   \n",
       "1  83733.0     1.0  53.0            NaN              3.0       3.0        NaN   \n",
       "2  83735.0     2.0  56.0            NaN              5.0       6.0        NaN   \n",
       "3  83736.0     2.0  42.0            NaN              4.0       3.0        1.0   \n",
       "4  83741.0     1.0  22.0            NaN              4.0       5.0        NaN   \n",
       "5  83742.0     2.0  32.0            NaN              4.0       1.0        2.0   \n",
       "6  83743.0     1.0  18.0           11.0              NaN       NaN        NaN   \n",
       "7  83744.0     1.0  56.0            NaN              3.0       3.0        NaN   \n",
       "8  83747.0     1.0  46.0            NaN              5.0       6.0        NaN   \n",
       "9  83750.0     1.0  45.0            NaN              2.0       5.0        NaN   \n",
       "\n",
       "   INCOME  INCOME_RATIO  HOUSEHOLD_NUM      ...       EVER_USE  FIRST_USAGE  \\\n",
       "0    10.0          4.39            2.0      ...            NaN          NaN   \n",
       "1     4.0          1.32            1.0      ...            2.0          NaN   \n",
       "2    10.0          5.00            1.0      ...            2.0          NaN   \n",
       "3     7.0          1.23            5.0      ...            1.0         25.0   \n",
       "4     7.0          2.08            3.0      ...            1.0         15.0   \n",
       "5     6.0          1.03            4.0      ...            1.0         18.0   \n",
       "6    15.0          5.00            3.0      ...            2.0          NaN   \n",
       "7     3.0          1.19            1.0      ...            1.0         18.0   \n",
       "8     3.0          0.75            2.0      ...            2.0          NaN   \n",
       "9    10.0          1.36            5.0      ...            1.0         21.0   \n",
       "\n",
       "   FREQUENCY  PER_MONTH  AGE_REGULAR_USE  LAST_ONCE_MONTH  AMOUNT  LAST_USAGE  \\\n",
       "0        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "1        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "2        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "3        5.0        1.0             25.0     5.397605e-79     4.0         1.0   \n",
       "4        4.0        1.0             16.0     1.000000e+00     2.0         1.0   \n",
       "5        NaN        2.0              NaN              NaN     NaN        13.0   \n",
       "6        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "7        NaN        2.0              NaN              NaN     NaN        20.0   \n",
       "8        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "9        NaN        2.0              NaN              NaN     NaN        20.0   \n",
       "\n",
       "   PAST_MONTH_USAGE  CHILDREN_NUM  \n",
       "0               NaN  1.079521e-78  \n",
       "1               NaN  1.079521e-78  \n",
       "2               NaN  1.079521e-78  \n",
       "3              30.0  2.000000e+00  \n",
       "4              25.0  1.079521e-78  \n",
       "5               NaN  2.000000e+00  \n",
       "6               NaN  1.079521e-78  \n",
       "7               NaN  1.079521e-78  \n",
       "8               NaN  1.079521e-78  \n",
       "9               1.0  2.000000e+00  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "translation_dict = {'SEQN': 'ID', 'RIAGENDR': 'GENDER', 'RIDAGEYR': 'AGE', 'DMDMARTL': 'MARRIAGE', \n",
    "                    'RIDEXPRG': 'PREGNANCY', 'INDHHIN2': 'INCOME', 'INDFMPIR': 'INCOME_RATIO', \n",
    "                    'DMDHHSIZ': 'HOUSEHOLD_NUM', 'DMDHHSZE': 'OVER_60_NUM', 'DUQ200': 'EVER_USE', \n",
    "                    'DUQ210': 'FIRST_USAGE', 'DUQ217': 'FREQUENCY', 'DUQ211': 'PER_MONTH', \n",
    "                    'DUQ213': 'AGE_REGULAR_USE', 'DUQ215Q': 'LAST_ONCE_MONTH', 'DUQ219': 'AMOUNT', 'DUQ220Q': 'LAST_USAGE', \n",
    "                    'DUQ230': 'PAST_MONTH_USAGE', 'DMDEDUC2': 'EDUCATION_ADULT', 'DMDEDUC3': 'EDUCATION_KID'}\n",
    "df = df.rename(index = str, columns = translation_dict)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After looking through the data, we saw that there were some numbers that were trailing (we're assuming it is because when we merged the tables together, something happened to it). We decided to then round those numbers and to replace 5.39770e-79 to 0 because we realized that when we combined the datasets together, that number was suppose to be zero. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>EDUCATION_KID</th>\n",
       "      <th>EDUCATION_ADULT</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>PREGNANCY</th>\n",
       "      <th>INCOME</th>\n",
       "      <th>INCOME_RATIO</th>\n",
       "      <th>HOUSEHOLD_NUM</th>\n",
       "      <th>...</th>\n",
       "      <th>EVER_USE</th>\n",
       "      <th>FIRST_USAGE</th>\n",
       "      <th>FREQUENCY</th>\n",
       "      <th>PER_MONTH</th>\n",
       "      <th>AGE_REGULAR_USE</th>\n",
       "      <th>LAST_ONCE_MONTH</th>\n",
       "      <th>AMOUNT</th>\n",
       "      <th>LAST_USAGE</th>\n",
       "      <th>PAST_MONTH_USAGE</th>\n",
       "      <th>CHILDREN_NUM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83732.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.39</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83733.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83735.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83736.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.23</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83741.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.08</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  GENDER   AGE  EDUCATION_KID  EDUCATION_ADULT  MARRIAGE  PREGNANCY  \\\n",
       "0  83732.0     1.0  62.0            NaN              5.0       1.0        NaN   \n",
       "1  83733.0     1.0  53.0            NaN              3.0       3.0        NaN   \n",
       "2  83735.0     2.0  56.0            NaN              5.0       6.0        NaN   \n",
       "3  83736.0     2.0  42.0            NaN              4.0       3.0        1.0   \n",
       "4  83741.0     1.0  22.0            NaN              4.0       5.0        NaN   \n",
       "\n",
       "   INCOME  INCOME_RATIO  HOUSEHOLD_NUM      ...       EVER_USE  FIRST_USAGE  \\\n",
       "0    10.0          4.39            2.0      ...            NaN          NaN   \n",
       "1     4.0          1.32            1.0      ...            2.0          NaN   \n",
       "2    10.0          5.00            1.0      ...            2.0          NaN   \n",
       "3     7.0          1.23            5.0      ...            1.0         25.0   \n",
       "4     7.0          2.08            3.0      ...            1.0         15.0   \n",
       "\n",
       "   FREQUENCY  PER_MONTH  AGE_REGULAR_USE  LAST_ONCE_MONTH  AMOUNT  LAST_USAGE  \\\n",
       "0        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "1        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "2        NaN        NaN              NaN              NaN     NaN         NaN   \n",
       "3        5.0        1.0             25.0              0.0     4.0         1.0   \n",
       "4        4.0        1.0             16.0              1.0     2.0         1.0   \n",
       "\n",
       "   PAST_MONTH_USAGE  CHILDREN_NUM  \n",
       "0               NaN           0.0  \n",
       "1               NaN           0.0  \n",
       "2               NaN           0.0  \n",
       "3              30.0           2.0  \n",
       "4              25.0           0.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#OVER_60_NUM\n",
    "#LAST_ONCE_MONTH\n",
    "#CHILDREN_NUM\n",
    "df['OVER_60_NUM'] = df['OVER_60_NUM'].round(0)\n",
    "df['LAST_ONCE_MONTH'] = df['LAST_ONCE_MONTH'].round(0)\n",
    "df['CHILDREN_NUM'] = df['CHILDREN_NUM'].round(0)\n",
    "df['INCOME_RATIO'] = df['INCOME_RATIO'].replace({5.397605e-79: 0}).round(2)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then saved our data then into a csv file, in which we used throughout our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#write dataframe to csv file\n",
    "df.to_csv(\"cleaned_marijuana_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For more convenient, we don't separate data exploration, data visualization, and data analysis but include them together into the section of data analysis & results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>EDUCATION_KID</th>\n",
       "      <th>EDUCATION_ADULT</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>PREGNANCY</th>\n",
       "      <th>INCOME</th>\n",
       "      <th>INCOME_RATIO</th>\n",
       "      <th>HOUSEHOLD_NUM</th>\n",
       "      <th>...</th>\n",
       "      <th>EVER_USE</th>\n",
       "      <th>FIRST_USAGE</th>\n",
       "      <th>FREQUENCY</th>\n",
       "      <th>PER_MONTH</th>\n",
       "      <th>AGE_REGULAR_USE</th>\n",
       "      <th>LAST_ONCE_MONTH</th>\n",
       "      <th>AMOUNT</th>\n",
       "      <th>LAST_USAGE</th>\n",
       "      <th>PAST_MONTH_USAGE</th>\n",
       "      <th>CHILDREN_NUM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4843.000000</td>\n",
       "      <td>4843.000000</td>\n",
       "      <td>4843.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>4582.000000</td>\n",
       "      <td>4582.000000</td>\n",
       "      <td>1240.000000</td>\n",
       "      <td>4675.000000</td>\n",
       "      <td>4377.000000</td>\n",
       "      <td>4843.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3428.000000</td>\n",
       "      <td>1715.000000</td>\n",
       "      <td>870.000000</td>\n",
       "      <td>1715.000000</td>\n",
       "      <td>870.000000</td>\n",
       "      <td>864.000000</td>\n",
       "      <td>870.000000</td>\n",
       "      <td>1.713000e+03</td>\n",
       "      <td>546.000000</td>\n",
       "      <td>4843.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>88672.875077</td>\n",
       "      <td>1.522816</td>\n",
       "      <td>42.860211</td>\n",
       "      <td>13.639847</td>\n",
       "      <td>3.492580</td>\n",
       "      <td>6.130292</td>\n",
       "      <td>1.979839</td>\n",
       "      <td>300.427166</td>\n",
       "      <td>2.425833</td>\n",
       "      <td>3.517654</td>\n",
       "      <td>...</td>\n",
       "      <td>17.052800</td>\n",
       "      <td>17.289796</td>\n",
       "      <td>33.031034</td>\n",
       "      <td>6.026239</td>\n",
       "      <td>18.013793</td>\n",
       "      <td>26.756944</td>\n",
       "      <td>22.404598</td>\n",
       "      <td>3.572796e+01</td>\n",
       "      <td>49.080586</td>\n",
       "      <td>1.062358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2873.134105</td>\n",
       "      <td>0.499531</td>\n",
       "      <td>15.046097</td>\n",
       "      <td>6.711685</td>\n",
       "      <td>1.277588</td>\n",
       "      <td>162.416967</td>\n",
       "      <td>0.303990</td>\n",
       "      <td>1582.121412</td>\n",
       "      <td>1.616415</td>\n",
       "      <td>1.698411</td>\n",
       "      <td>...</td>\n",
       "      <td>374.406203</td>\n",
       "      <td>4.558000</td>\n",
       "      <td>503.162908</td>\n",
       "      <td>187.757903</td>\n",
       "      <td>5.129602</td>\n",
       "      <td>373.884673</td>\n",
       "      <td>429.130785</td>\n",
       "      <td>4.710994e+02</td>\n",
       "      <td>603.969539</td>\n",
       "      <td>1.332804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>83732.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.397605e-79</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>86174.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>88677.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.040000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>91164.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.790000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>93702.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7777.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>7777.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>7777.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>9.999000e+03</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID       GENDER          AGE  EDUCATION_KID  EDUCATION_ADULT  \\\n",
       "count   4843.000000  4843.000000  4843.000000     261.000000      4582.000000   \n",
       "mean   88672.875077     1.522816    42.860211      13.639847         3.492580   \n",
       "std     2873.134105     0.499531    15.046097       6.711685         1.277588   \n",
       "min    83732.000000     1.000000    18.000000       9.000000         1.000000   \n",
       "25%    86174.000000     1.000000    30.000000      12.000000         3.000000   \n",
       "50%    88677.000000     2.000000    43.000000      13.000000         4.000000   \n",
       "75%    91164.000000     2.000000    56.000000      14.000000         5.000000   \n",
       "max    93702.000000     2.000000    69.000000      66.000000         5.000000   \n",
       "\n",
       "          MARRIAGE    PREGNANCY       INCOME  INCOME_RATIO  HOUSEHOLD_NUM  \\\n",
       "count  4582.000000  1240.000000  4675.000000   4377.000000    4843.000000   \n",
       "mean      6.130292     1.979839   300.427166      2.425833       3.517654   \n",
       "std     162.416967     0.303990  1582.121412      1.616415       1.698411   \n",
       "min       1.000000     1.000000     1.000000      0.000000       1.000000   \n",
       "25%       1.000000     2.000000     6.000000      1.050000       2.000000   \n",
       "50%       1.000000     2.000000     8.000000      2.040000       3.000000   \n",
       "75%       5.000000     2.000000    14.000000      3.790000       5.000000   \n",
       "max    7777.000000     3.000000  9999.000000      5.000000       7.000000   \n",
       "\n",
       "           ...          EVER_USE  FIRST_USAGE    FREQUENCY    PER_MONTH  \\\n",
       "count      ...       3428.000000  1715.000000   870.000000  1715.000000   \n",
       "mean       ...         17.052800    17.289796    33.031034     6.026239   \n",
       "std        ...        374.406203     4.558000   503.162908   187.757903   \n",
       "min        ...          1.000000     6.000000     1.000000     1.000000   \n",
       "25%        ...          1.000000    15.000000     3.000000     1.000000   \n",
       "50%        ...          1.000000    17.000000     4.000000     1.000000   \n",
       "75%        ...          2.000000    19.000000     5.000000     2.000000   \n",
       "max        ...       9999.000000    50.000000  9999.000000  7777.000000   \n",
       "\n",
       "       AGE_REGULAR_USE  LAST_ONCE_MONTH       AMOUNT    LAST_USAGE  \\\n",
       "count       870.000000       864.000000   870.000000  1.713000e+03   \n",
       "mean         18.013793        26.756944    22.404598  3.572796e+01   \n",
       "std           5.129602       373.884673   429.130785  4.710994e+02   \n",
       "min           6.000000         0.000000     1.000000  5.397605e-79   \n",
       "25%          15.000000         1.000000     1.000000  2.000000e+00   \n",
       "50%          17.000000         3.000000     2.000000  5.000000e+00   \n",
       "75%          20.000000        12.000000     3.000000  1.500000e+01   \n",
       "max          50.000000      7777.000000  9999.000000  9.999000e+03   \n",
       "\n",
       "       PAST_MONTH_USAGE  CHILDREN_NUM  \n",
       "count        546.000000   4843.000000  \n",
       "mean          49.080586      1.062358  \n",
       "std          603.969539      1.332804  \n",
       "min            1.000000      0.000000  \n",
       "25%            2.000000      0.000000  \n",
       "50%            6.000000      1.000000  \n",
       "75%           25.000000      2.000000  \n",
       "max         9999.000000      7.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe dataset\n",
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4843, 21)\n",
      "*****************\n",
      "ID                  4843\n",
      "GENDER                 2\n",
      "AGE                   52\n",
      "EDUCATION_KID          8\n",
      "EDUCATION_ADULT        5\n",
      "MARRIAGE               7\n",
      "PREGNANCY              3\n",
      "INCOME                16\n",
      "INCOME_RATIO         427\n",
      "HOUSEHOLD_NUM          7\n",
      "OVER_60_NUM            4\n",
      "EVER_USE               4\n",
      "FIRST_USAGE           38\n",
      "FREQUENCY              7\n",
      "PER_MONTH              3\n",
      "AGE_REGULAR_USE       38\n",
      "LAST_ONCE_MONTH       45\n",
      "AMOUNT                 6\n",
      "LAST_USAGE            51\n",
      "PAST_MONTH_USAGE      30\n",
      "CHILDREN_NUM           8\n",
      "dtype: int64\n",
      "*****************\n",
      "ID                     0\n",
      "GENDER                 0\n",
      "AGE                    0\n",
      "EDUCATION_KID       4582\n",
      "EDUCATION_ADULT      261\n",
      "MARRIAGE             261\n",
      "PREGNANCY           3603\n",
      "INCOME               168\n",
      "INCOME_RATIO         466\n",
      "HOUSEHOLD_NUM          0\n",
      "OVER_60_NUM            0\n",
      "EVER_USE            1415\n",
      "FIRST_USAGE         3128\n",
      "FREQUENCY           3973\n",
      "PER_MONTH           3128\n",
      "AGE_REGULAR_USE     3973\n",
      "LAST_ONCE_MONTH     3979\n",
      "AMOUNT              3973\n",
      "LAST_USAGE          3130\n",
      "PAST_MONTH_USAGE    4297\n",
      "CHILDREN_NUM           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Basic questions about the dataset\n",
    "\n",
    "# 1. The number of entries in the dataset\n",
    "print(df.shape)\n",
    "# We have 4843 rows and 21 columns \n",
    "\n",
    "# 2. Total number of areas that contribute to marijuana usage and their unique values of the columns \n",
    "print(\"*****************\")\n",
    "print(df.nunique())\n",
    "\n",
    "# Confirm the number of missing values in each column.\n",
    "print(\"*****************\")\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first graph, the column is Gender, where the x-axis represents either male(1.0) or female(2.0), and y-axis the total count in the thousands. The x and y axis variables will change for each graphs but they will contain the same format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boxplot graph is used to show the error margine, the outlier data is not necessarly relevent. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following graphs show the difference of the data values. The x-axis represents the different catagories taken for each column and the y-axis represents the number of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x2160 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We will use an outlier to detect any margin of error that the data might have \n",
    "\n",
    "col_names = ['GENDER','INCOME','FIRST_USAGE','EDUCATION_KID','EDUCATION_ADULT','MARRIAGE','PREGNANCY']\n",
    "\n",
    "fig, ax = plt.subplots(len(col_names), figsize=(7,30))\n",
    "\n",
    "for i, col_val in enumerate(col_names):\n",
    "\n",
    "    sns.boxplot(y=df[col_val], ax=ax[i])\n",
    "    ax[i].set_title('Box plot - {}'.format(col_val), fontsize=10)\n",
    "    ax[i].set_xlabel(col_val, fontsize=8)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following graph takes the correlation of each column and comapres it with the other columns, to show the relation that all the columns share. This will help us focus on the specific columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1166f5dd8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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smi6suWiis+7Vc6nkOiumTiIWdywVsCCJSDqO8w1SJ5P37lgcx3FqBV9u7DiO41QVj1jqg5gp/yCwi6SbcuUvAGMl7W1mSwKvAXtJ+kfuukwTrUCQqR5PUAxdEniBsIwYoDvwBfBzSZ+a2QSC/lemI3YhsJ6kudREzeyQWF82sH2/pFPjuZnM0QjL2E3Su/PyfTiO03EUfI6lrhhH0PW6CcDMfkAQu8zYh6BX9ivmiFtCUSKomd0AbAs8A7yc1/Aysz8R9j84K9+wmfUkbMrzPzMbLGl0LD+IIDD5kyis2QRcb2ZDJN1HUA0YjOM49UOdRCwumx94HhgUpaQhJDpeD2BmDYTclLOBhczs+61VYGYLEZI2P23lXANBPeBr54BfAP8hJGUekiv/FWEHt+kAkmYBO0en4jhOPVJoqexTY7hjmcOtwI7RCazNnGGmTYEXo2LylcwtLLmJmY02s5cJw163S/pPPLdKPPcCQbb/NeDqVtodTtgH4f+A1czs27G8f7azm5ntEBNOnzCzLOLpH+vPPtfP+1fgOI4z7/hQ2BxuAC4izJM8kivfD1jOzEYBCwGrxux5iENhZjaAoLb8Ru66lyUNjjL/dwEfFgtemtl3ge8ToiEIczUHEoQvp5hZf0mTJN0O3G5mQwlDduBDYY5Tf/hQWH0haTxhXuXXwHWxeFFgXWAdSUMlbQLcBuxVdO0nhOGzy81siaJz0wgT8L+P4pl5hgMnxLqHApsA+8Zhtb8D55lZNwAzawQ2whWPHad+qROtMHcsc3MzsLSkV+LxxsCtkvKp0ZcRhCrnklCQ9DJhgv+vxZXGvWSOBi7JBC8J0c8usc3M7i3CfM/PJP2VMBx3v5k9CDwNLEzY8RK+PhQ22szWm4d7dxyno2kpVPapMVwrrI7ZcdC2ST9uOW8XDZQnSfSPN0eWtNl50PYlbTLmh6RLOff/eaKkSc8y9Nfmh6TLJ83pykLzQ9JlUhntlyPpMr2M7yqVVEmXLxJ10jL+8ebIsvW7vvzdLyp6IPc69RbXCnMcx3FaoQajj0pwx1LHdCvjXTz1TbicN/FUUlWIIT0KAbjkmTOS7PZd4+jkOr9KXNo5hbQ3e4ByRg26NqT9puVEIam/PUBTYvv9G3sk11lOdJN6XzNIj2w+a0kTYS3n32mleIKkU1f88807k+z2Wnan+dwTx1mA8YjFcRzHqSruWBzHcZyqUoNZ9JVQt46lFZHIHgSZljWA1YFJhCXDA4CzJV1lZicDuwL5bRLvl3Ra3I/+BGArYHo8d72kS+M2xq8C60oaE9s/EBgo6eR43JaQ5Ujg+5LejmV/BsZJGmFmixC0xVYEmoC3gAMI+SynA2tKmhmvOxv4StJx1fj+HMfpADxi6RTMFomMiYYCngOOlTQqlvcHXjKzEfGacyRd3EpdpxFWpq4vqdnMegP3mNnDBEczGbjKzNaS1Nr+qW0JWc6I120uqfhf1Y3AJTHzHjM7Ih4PM7MdCBn6vzOz9QnOZoP0r8ZxnFqjUCeOZUFKkOwDNAPFi9EHAtNbeajPJkYrOwO/yZIlJX0BDJY0Lpq9CowiOKDi69sTsnyAED39quiaQYSI5/Zc8V8JEQvA4cAwM1sjlu8RhSodx+ms1EmCZL1HLJtE8cYWwp4mhxLUhM8wsxOAQYShsp/nrjnSzIbljk8DXiRoc30FsyXtdwb6mNm1wB3R9nfAU2a2YVE/ZgtZmlkmZHlQ7vxB8bpRubIlmVt7jOjUPo9/TzGz/QjKyMdLUuJ34jhOrVIny43rPWJ5QNJgSZtI2kLSvbH8WEkbEQQfvw28nrvmnHhN9rkf+AQYEPW6kHRRFIC8jCCzQiyfQRjyupy593PJC1nuCvwiJ9GfaY0dTlA/zn6Tt4Cl8jdjZk1mtlvuutHAZwTJfcdxOjt1ErHUu2Npl+ho7gAuLWE3iyCr/8dM68vMuhMEKgtFtmMJSsnHRbtUIcu7CHNAe8fjd4GJZrZdzuwwIH/sOE49USeOpd6HwlI4FXjWzLaOx8VDYZJ0AHBs/DxsZl8RtiL+N3Au0L+oztOBbeLfe9K6kOU1zD0cBiFq2TR3vAfwdzM7miBa+Toh+nEcpw6pF+3GunUscZhodCvlexcdzwS+Fw/vAU5uo76vCA7j9FZOTyZEJXnbtdrp21PAyvHwwVz5ZMK8T3Y8kTCX0yaSlm3vvOM4nYgajD4qYYEeCnMcx3GqT91GLA5MTZT57t7QNVm6fqEy3kW+KMxih2W2KWmXKsUO0K+xKdk2VVzyyjFnlTaK7LD6oUl2Axq6J9dZzk4ETYnGX5Yh8d48H8QVu5chVtrYmP5vaurXsgXmnd1m9U2yu2OhdLHMiqmTiMUdi+M4To1QLwmS7lgcx3FqBXcsjuM4TlWpj/xIdyyVEEUnbwLGAX0l7Zg794GkgfHvjYDfEwQkewFXSbownluOIDA5IJ5/HjguZtSfTMjiX1rSe9F+ceBdwnLj0cALwNiirm1atKzZcZxOhA+FORkbmtkekq7NF5rZ8gQNr6GSPjSzHsCDZjYeeAi4Exgu6clovxdBdPKnsYpXCPIz58XjnQnZ+Bkvx+x/x3HqhTpxLL7ceN75DXCKmS1VVL4HcI2kDwEkTQO2AO4HtgYeypxKPH81sGiMZABuJjiWjG2Au+bPLTiOUxO0VPipMTximXfeJQxbXUFwHBlLEiT6ZyPpc5gdzeT1yTLeYE6C5AfAl9G2C/A2c/aBAVglCmxmjJF0VOW34ThOR+NDYc5sJF1vZjtE1eOMN4Gl83ZmtirBSbwLrN1KVSsy93DXjcAwwhzM9cCQ3DkfCnOceqMGo49K8KGw6nEQcDRh3xcIQpTDzWwxgLgx2CXAEoRdIzc3s9nOxcyGAxMljc/VeStBdHIjWpGncRynvii0FCr61BoesVSJuNfKkcS9WSRNMLNjgdvMrJngcC7PpPvNbBvgXDMbQPgdXgB2KarzczN7B3hdUouZ5U8XD4UB7CPpDRzH6ZzUScTijqUCJE0gJzqZKx9JTqBD0n3AfW3U8TqwbRvnTs79vVPu7+NzZmk6FI7jdBoK7lgcx3GcquKOxal1eiYKAU5qmZFc50qN6YHSxJY00b7LFksXFjz6k3TByq8SX/9ShSUBbh97QZLdEssPTa5z3YVXSrZtSBShXKpLz+Q6X2+enGy7UENjkl33RDuAyS3pv2ljQ9q08KwyXv1/1/xqkt2Ejz5MrhPgurKsAx6xOI7jONWlSo4l7nR7IbAqMIOQjP1a7vz5wIbAlFi0HWH16Q1AD+A9wpzt1Era91VhjuM4NUKhpbJPK2wPdJe0HnA8cHbR+TWALSQNjp/PCfJTN0jaCHgWOKDS+3DH4jiOU39sCIwCkPQEsGZ2IkYzKwGXmtljZrZv8TXAv4DNKm3cHYvjOE6NUMWIpS/wee642cyyqY9ewAXA7sBQ4GAz+2HRNVOAfpXeR6eYY+mMasJmNoGQRd8CNAK9gf0kPRPPdwcmAGdLOjOWHUPQEVuYIAnzctYOQQJmZUnTYwb/nwljoQsBDwKnSJqZ8HU6jlOjVHHyfjJzkrUBukjKVslMBc7P5k/M7AHCXEx2zbT4/59V2nincCxFdCY14SGSpsf2tgBOzrW3E8FZ7m1mZ0tqiQ7mTDMbDBwoaVju/rL//1bs9/aSXjGzBoJTPBf4VZn9cxynliiUsU91+zxGEK69xczWBV7MnfsOcLOZrUYYtdoQuDpesxUwAtgSeKTSxjvjUFhnVRMeBHyaOx4OXEWInLYqo549gCslvQIgqQCcCmwVnanjOJ2UKg6F3Q5MN7P/El46jzCzI81sW0n/D7gWeILw0n2NpJeAPwLDzOwxYD3gb5XeR2eMWDqTmvB9cchrScKk2NGxPysBvSQ9b2ZXAkcBd5eoK2N5grOcjaSCmX0AfIswvOY4Tiek0FKdiEVSC3BgUfG43PkzgTOLrvmQMOcyz3RGx9KZ1ISHxDmR04HlgI9i+XCgl5mNIkjArG9mK+bXmbfDu8Cy+QIzayQ4r/IyuBzHqSnqJUGyMw6FZXQmNeETCQ/+g82sieC8NpI0VNIWhIn4gxPruhrYP0Y9xDmWk4B74/Cf4zidlEKhoaJPrdEpIxboXGrCsa7hwMOEeZYxkiblTK4CnjezE0tlukp6x8z2AC40s56E6Go0cHipfjiOU9vUS8TSUCjUnpa/Ux12GbR90o87v7TCUjWoLl4sXSvq6E96JdumaoXNKkNHw7XC0jTA+jYslFznpy3TSxtF5odW2FszPkmymzClvJHmadPeLDuUeHutTSt6IC/99H9qKmzptBFLLWBm2wJHtnLqfEm3f9P9KWZqIU3csX+Xbsl1Xvzeo8m2py7xkyS7tV9/JrnOoYt8L9l2CmkOa0BD9+Q6Ux3G++NHlTaKbLrqfsm2vRvTfqvxzVNKG1VAc+ID+6WZ6Q/h73RbLNk21Qn1KcOxLdbUp7QRMHbE1sl1Vkq9vOe7Y5kHJN1JyI9xHMeZZ6q1KqyjccfiOI5TI7hjcRzHcaqKD4U5juM4VcUjljJoQ/vqz4RM0HsI4pCDCGKNbwNHSvrAzPYmCC8en7vuJuBiSaPN7HiCtHMTQezxaEljzGwEsDqQX9J7raQr8qKVsb6hwDBJe8cck98AmwPNwCzgRElPZkKYkuba676ora7AROCI9pYhR4HKcyWdH49Xjvc0ONZ3k6RROfsPJA0sJZYpaURbbTqOU/vUYk5KJXR0xNIA3AacJWkkgJltBtxtZuu0d6GZrQJsC2wQJU1+REgeXDWaHJt/OCfyB4Jz+3HMPRkE3BNzYNoLUme3FRWWbwHWKtHWEWY2SpLK7GMpsUzHcTop9ZLH0tGZ9wOAzzOnAiDp/wi6XhuXuPZzYBlgXzP7tqTnaF22pRx2B34bdXaQ9Cbwd2Dv1AokPQLMMrMVS5geCYyIcizlML/FMh3HceaJbzJi2aQoc315gnpma+KQ45kjDtkaBUnvxjySQ4CTzGwqcAJBlgXgjDhUlnGopBeB/kX96A+MjUNKk3J7FuT70m701AofAosC7Wl/3UuQpj6OELW1Rz5aKiWW6ThOJ6XFh8LK5oFW5liaKBJUjKxEUPBdDCjOCOsNTIsRwWRJ+8b61gT+ZWYPRru2hsIm5cUkszkWwqY2/c2sa5FzWYnyh5oGAe8k2B0JPMPcznUaX7/n4t+pPbFMx3E6KfUyx9LRQ2HvAQPjHAYw+0G/ImGfgOcI4pG947n+wPcJOyv+EPibmWUptq8QnEOrOzqWIu6+eAtwWtwTOpPbP5iw8U0SZrY5MFVSScciaQpwAHB+rngskN8hcyPm7CSZMb/EMh3H6UAKLQ0VfWqNjp68LxDmCM4zs9/GsreBreOWvzKzvwOPmtkUwhv6ryV9QRCb/C7wtJl9QXCSx0QhSfj6UNhDkk4q0Z/jCLs8PmFmM4EZhF0nx8dVYd83s7z+SLYfS9ZWM2Gv6J1Tv4C4uu1GYLVYNAL4kZk9F+uaCexfdE17YpmO43RS6iWPxUUo65jtlvlp0o/bsyH9/eIf7z+dbJuqFXbeZ/NJK6yQphXWp6Epuc57Jr1Y2oiO1wprLuO/60K7Cx7npjFRBPO9WZ8n19nRWmGfNLcrKD6bf59Taj3R3PTY+aSyQ4mXV9i6ogfyKq/fU1NhizuW+UQtCFTusMw2ST9u10TFWIBLfvRZsu3uz6YpEX/enL72YJmu6erK0wtpo6J9u5TxEEp8sE0u457+8/xlybbD1zwmye7jlvSteRrLGBHvmuhYUgVQATbo0j/Z9o2GtO91auJvD9Ar8cXqrk/TXioyPv5cZT/s/7d82stgMd8ff3dNOZaOHgqrW1yg0nGccqmXyXt3LI7jODVCvQwguWNxHMepETyPxXEcx6kqPhTWQZjZcgTRygGE5cfPE5YJvwasEJciZ7bPEuRP7ickOeaVeI4C+hByV14mLH3uS8i03y3mtbTWfi/gImA5YCFCRv9TMRfn98BXwJWS2pyRjZn/z0o6Ih53B8ZJWjYKTX4g6eKc/ROEhMjBwFXAepKeiOeagPeBv0k6uZ2vznGcGqdehsI6OkGyLMysB2FC/AxJgyVtADxJyES/C/hZznYN4FNJr8aiIfGa7DNAppydAAAgAElEQVQmlj8Qj38iaQ2CovG27XTjGOB/kjYC9gtNWRNwLiED/sfA/mb2rRK3s4uZ/bic+4+MIziZjKEE3TTHcTo5LYWGij61RqdyLMDWhETHJ7MCSVcTdLkuA/bM2e4LXFpO5TGLfwng03bMtgBmmtm/CRL2/wa+C7wm6dMY6TxKaRHNw4BLM1WBMvgXQY0g++12IThWx3E6OYVCQ0WfWqOzDYUtT+uilW8APQhaX0sDHxH2aTkiZ3OfmWVDYc2SNo1/Z+KYixOGyi6V9J92+rAosIikLcxsT8Kw3KXMHTVMAfqVuJfngWuAc4Bfl7CFOUKUM4HHgR9HFYC+BF2ygW1d6DhO56AWo49K6GyO5V1al8ZfkTCHcgVB+v4N4M6ieZIhklrLrnpA0jAzG0CYi2lzg67IJ8zJT7kLOJ7gXPrkbPoQdMtK8WfgMYLKcUZrIpS9Y3nGDYRIZRmCMnJ6hp/jODVLnUyxdLqhsJGEYaDZzsXMhgMTJY0HrgN2AHalzGEwSZ8QnNLlZrZEO6aPAlvFvzcGXgL+H7CSmfWPw2kbE6KKUm02A3sR5mcyxgLbmllXADNbAegm6aOczWhgXeDnwD9L353jOJ0Bn2PpAOKKr22AE83sMTN7krBXyi7x/KeEye3uuUn7jPvMbHTus0Mr9b8M/DV+2uJ0YDUze5ywsuwYSbMI8i3/JjiUKyW9m3hPIudYJN1PcF5jzOwRwq6YexZd00KIrqZKmpzSjuM4zjeFa4XVMa4V5lphKbhWWO1ohT028GcVPZA3+OCfNRW2dLY5lm+EOJx1XyunJOmAxDrWBs5o5dTNki6al/6l0iPxP5iZZWxhc9hzA5Jtf9AlTYn3hTJebmaRvil4qsNsSnxYAjQk2qaqEEO6swC4/Jkzk223W/2QZFunNGv1W2G+t1EnW967Y2mNOOk/eB7reGpe63CceeHet+7t6C7UFVsts1Vpo3mkUMZLTi3jjsVxHKdGaKmTmQl3LI7jODVCi0csjuM4TjXxobA6wcwGM0eIMuNjYE1geUmFaNcEvAqsGs//t6iq3YDNgT8QhCwBFgYek/SrdtofAdwkaVSu7ANJA6M22kXAkkBP4APgAEmfmNkEWhHWzGmgOY7TyfDJ+/riAUl5YUfM7P8IgpKjY9G20e5zM5skaXBxJWYGcIOk4+NxF+ARM1tTUvrG7nPYh6B0vHes73CCgvJh8XxbagKO43RCPGKpfzJRy9HxeF/g1DLr6EOIWipVH/4QGG5mjwEPARdAnfzLcxzna3jEUl9kQpQZ9wDnA6fH4aiFgYHZHigEscu8/buSdot/72pm6xFUkicDp7WiAlCKAoCkW82sAPwSGAG8CBwa/x/aFtZ0HKcT4o6lvvjaUBiAmd0BbA8MAq7MnWp1KCxyg6Tj44Zko4BXSrTdmuhkphO2HvAfSbeZWSOwB8HBrBHtfCjMceqIehkK61RaYR3A5QQdsu0JApfJSHoD+BXwDzPr2Y7pWGDH7MDMNmLOQoJdiPMpUbDyBWBGOf1wHKfz0NJQ2afW8IglUDwUBrClpP8XN+J6WVJ+nqR4KAzgN8WVSvq/uAjgFMLOk60xAviRmT1H2MdlJrB/PHcC8Ld47sv4+WXu2vxQGMD5km5vox3HcWocz2OpEySNJmzy1db5TVopa0u18GtS+ZL2K9H+LEJk09q5KQRZ/dbOLdtevY7jdD7qJPHeHcs3gZktQ9gtspiHJJ00v9ptShRhXIruyXUuVMYbVep/JOuUoW77UPNHpY0iSzSm7fr8ZRlKvEt1aW9Ucw7jm6ck11mOEvF2qx/CyLF/S7KdNXF8aSNgrzWOSm5/WuJ3de1u6YrRP79uarJt30Rh054Njcl1vvFV2qLNfl3S/zupFJ+8d5KR9BYuSOlUiaZFly9pk+pUnNqipcGHwhzHcZwaJCZnX0hQCpkBDJf0Wu78EUC2EvZeSaeYWQPwDkFhBOBxSV+bO07BHYvjOE6NUMU5lu0JO+muZ2brAmcD2wGY2fIECap1CKNvj5rZ7cBUYKykbea1cV9u7DiOUyO0VPhphQ0JeXTExO41c+feBoZKao5aiE3AdEJ+3LfN7EEzu9eiRlUldPqIxcyWJeR3jM0VPwAcHIUcTwZ2Bd6L5wYQRB9Pi+HiWcAPCEmKXxJWaPUiyKcArAs8Rfj9zpR0Txv9+EDSwNzxUGCYpL3jbpJ/JDjyPsAtks7O2R4LHAEsl094NLNhzFkx1gw8BxwraaaLUDpO/VHFnJS+zC0l1WxmXSV9FVeiToxDX2cCz0p6xcwGAn+S9A8z25CQu7dWJY13escSebk4E97MDs4dniPp4ljeDXjZzC4jePElJW0ez20PnCtpO+Jke3yAz2uG+9+APSWNiyrJ/zWzByQ9G8/vDtxEGPMcEdvdCtgP2EbSZ/EfwTmE5cfZJumeee84dUQV81gmE15iM7pImr2kz8y6E9REpgDZs/IZ4CsASY+a2ZJm1pApvJfDgjgUNoAQ+k0jyuOb2c5mtigwEvj5fGjzQ+AQM1uDEGFskDmVKNv/OnAxc+ezHAocI+kzgPjjHinpMhzHqUsKFX5a4TFgK4A4x5LpCxJfUkcCz0s6IKp6AJwEHB5tVgXersSpQP1ELKsUZcLvVnT+SDPbBVgaeJewQmIK8LSZ7UfIdP8rYUXEkQQl4WqQ/Si7EaRZLgJWAG4ws6MlzQCGA5dLkpnNMLN1JD0JLAe8BrM1w/4ENJnZ2zldMxehdJw6oopDYbcDm5vZfwmK6PuY2ZGEZ0ojYUuQbma2ZbT/DfBn4Doz25oQuexdaeP14lhaGwrLH54j6eIYMdxEFIY0sx8CkrRL9OKbA7eY2cAKPHXxHFpvYFoMOVeXdCpwqpn1B64C9jez6whvFYub2aFAP+AQ4EnCBNtyhLeKx4HBZrYyIbLJ8KEwx6kjqpUgKakFOLCoeFzu77ayPbeuRvsL1FBYnNj+M3BTnLjfDPiDmXWJjuQl4MsKw783zCwv/zIUeJrwb+U6M/tO7MMk4E3C2vLdgSskDZE0lLD8b4iZLUZYPHCmmfXL1TmY+lF9cByniCoOhXUo9RKxJCPpCjPbGTiIMPx1FvCcmU0mOIE9Kqx6P+BCMzud4LCfAK6V9JWZ/QK4Mk7cFwgO50pgTL49SVPN7FZgP0mnR/s7YvTVl+D49s+16SKUjlNH1KJScSU0FAq16O+carDXsjsl/bjfIl3XaX5ohTUV0uucH1phhTLe+RZuSPuuytEKayxTxuPet+4taVOOpItrhc0frbCRb91dtpu4bKndK3og7/fOdTXlkha4iGVeMLP9CTkxxfwmzoPUFI2JTuBDZibXWc7Y6XPTP0iyG9jUN7nOVGcBMCtxxLq5DMfyevPkZNtUGkl/CKZSjrO4eszZpY1y/GL1w0ra7HnDrOT6Tivjxeaz6Wm2M8v4l3pp9zRh0S9a0v87qRQXoVwAkXQpcGlH98NxOpLb37qrpM2Og7b9BnpSf5QRvNc07lgcx3FqBI9YHMdxnKrijqXGaUdD7OhcWXfgC+Dnkj41s5nAf4uq2k3Su2Y2BPgtIbmoEfgnQf6lEJMzD5Q0LrbdHRgnaVkzGwGsDkzK1XltXJ1WALaXNDJel9cX6wIcD2xJ0AkrAL8GPiXkuWws6fV43TbRduNcFq3jOJ2MellKVbeOJTJX4mR0NlsVlf2JsI/8WcCk4kTLaLM6If9la0nvm1lXQhb90QQRt1IcK2lUK+VTgXPM7DFJE4uvARYFfiypxczWIsgwGMGJXBnlYBaOfdjSnYrjdG7qZbnxApUgWUzMtl+aEAW0x4HAaZLeB4hibkcBB8xjF6YQ9km4qJVz+wOnxgxaJD0NrCVplqRrgYmx/bNi396Yx744jtPBVFE2v0Op94ilWEPshFxZf6AHcD1wdTzfv8j+XUm7AYOAuRIDJE02s15xyKo18lHtGWZ2fO74UEmZKNxFwPZmtitzD5f1lDSXw5P0Se7wQOBx4OnoaBzHcWqCencsrQ2FvSxpsJn1AO4CPszJSbc6FEaQYFkeyGTuiVIrLXGYahphP5eM3gT15Iy2hsKIczT7Ag8T9mzJ+NTM+kqanThhZjsA/5E0WdLHZvYoQfvMcZw6oBajj0pYYIfCJE0jqA7/PkpEt8clwAlmtoQF7iJIsvw9nh8L7JSz35Ig25Lal3eAkwnzOBlXAyfF4TrMbH3CfiwuOuk4dYprhdUBkj40s6OBS+KDu3goDGJWvZkdB9xI+M56AZ8AA+IKsL/EOsYSIpVJBO2wjOKhsIcknVTUl2tiRJJxJnAq8LiZzQJmAdtKmv/pv47jdAj1Mnlft45F0gTCtsKlyq4nzLMAbWtLSLofuD9fFvdJmRml63dp47q926lzYNHxDrm/mwnLm9ukvbodx+l81MtQWN06lm+CWtQHcxyn81KLw1qV4I6ljklV7Z3UMiO5zsXKUHjduPvSSXYvfPVJaaPIl4V0ccOmhupPIS6UqJrbXEh/9+xavX3OZ5OqQgxBVPKWsecn26coJ9885jx2XuPw5DqrzeXd0xWTUxWrZzXM/3iipU5cizsWx3FoWnT5JLty5Phve/POSrszz1zYSUUwfSjMcRzHqSr1Ea+4Y3Ecx6kZPGJxHMdxqoovN65xokDjLcDLhAgzk29Zg7bVhvPqxk0EFeNd2tLhim08GG1uypW/AIyNKsVNwG+AzQkqxbOAEyU9GZUAXgXWlTQmXnsgMBB4jCBBA7B+rl9HEfTFWlVTLu9bchynlvDJ+87BA5KGAZhZN0DAc7QtsTKXpIuZHUB4kB/SThvjgGFEaRUz+wEhgTLjDwQHlakUDwLuiVL3BWAycJWZrSVp9vKsfN6MmX1Q1K+0u3ccp1NRH25lwZJ06UOIGNLXYQbxyVLKx88Dg6J2GMDuzEm4zI5/m1MpfpMgBbN3PP8qMAo4rYx+OY5Th7i6cedgkyjR0kIYgjoU+AVtqw1nki59CerHtwG/T2jnVmDHuKnX2gSJl2XMbHFCFFTszMYD6+SOfwc8ZWYblnFv15hZtlh/QXpBcJy6xYfCOgezh8IyzOwXlBgKM7NGYARBruWLhHZuIMjfjwceyZV/RnBWXYucy0rAW9mBpBlmtk+s57KE9gD2LJ5jSbzOcZwapT7cir/ptkrU6dof2MHMtk6wH0+YV/k1cF2ufCZhAcFp2b4tZrY8cDDBceXrGEtwLMdV5y4cx+ls1MtQ2ILqWM4ws9G5zynFBlFWfzhwgZn1+noVX+NmYGlJrxSVH0cYhnsi7p9yGTA8OqNiTifs/eI4jtNpaSgU6iX4corZZ9mdkn7cifNJK6xP4khrOVphfbt0K20USdUKK+eNb2ahOcmuHK2w7g3ljUjf/tZdJW12WGabqtcJ5Um6pMrEzA9+VoakS99ErbCPW8rbCumut+4uOyvlyGWHVfRAPmfCTTWVAVPvcyxVwcwuBFZp5dSWMbKpSRoTxQ3Xb1g4uc5+ac9VAHokPlu7NS2aXOfjsz5Ktu3f2CPJrpwHe/dEEcqXZn6YXOfApn6ljcrk2t3SHpYAe94wix3LeBDfPOa8JLtUJ/TT1X6V3HavLk1JdgMb0l+APi2kbXE0sfnL5DorpV5e892xJCDp4I7ug+PUCimRSDmRjTOHWpwvqQR3LI7jODVC6lYXtY47FsdxnBrBIxbHcRynqniCZBFmdixwBLBc3AMeMxsGZDNzzczR6ZppZhMISYJ5J31UJsbYSv0jmCMe2QAMAM6WdJWZnQzsCryXu+R+SaeZWVeCmONWQLas43pJl0YRyJskrZtr50BgoKSTo0bXXPvS5+zuBLpI+mmuLH9PjUBvYD9Jz7RRR6n2twSOjvfbE7hA0vVmtjdBgyw/kP2ipENba8dxnM5BfbiV6kYsuxOEGIcBI8xsK2A/YBtJn5lZA3AOsBdzssuHZE4okdkZ82bWH3gpOhyAcyRd3Mo1pxHyddaX1GxmvQkikA8zx9GUhZktQ3AaTWa2fFFOypCcY90COBn46ddrSeIS4Ifx++sDPG9m98dzN0g6vp1rHcfpZNRLxFKVBMkoH/86cDFzIpRDgWMkfQYgqQAcKSlVsqQUA4Hpsd62+tUV2Bn4TcymJ0q0DM7kUCpkX2AkcC0hi74tUkQs2+Mz4DAz+x7wBfBdSenrbR3H6VTUS+Z9tSKW4cDlkmRmM8xsHWA54DUAM1sP+BPhDf/tnH7XfWaWfS/NkjYt0c4ZZnYC4YH9MvDz3Lkj49BbxmnAi+REIM3sIIKj6WNm1wJ3tNFOe86qC2HYbV2CUvJLZva7XD7LfVG7a0mCavHRJe6pvfaHEIYXbwQWBy7OqQTsambr5q65QtK1FbTlOE6N4KvCIma2CGH+YnEzOxToR9i/5G2Cc3le0uPAYDNbmRDVZFQ0FBaH2f5CiJIyvjYUFjfZGmBmjZKaJV0EXJTbTGsaUJzK3TuWt8UWBAn+G+Jx5miuyN+TmZ1OuP/2Iow224/f6yBJxwHHmdm3CSrK2RyUD4U5Tp1Ri9FHJVRjKGx3wtvyEElDCXLwQwhijGfm9ikBGEwV5qck3UuINi4tYTeL8DD+Y04Esjsh2igQHvp9zGyVeK6RsNPj0+1UO5yg9TU03u8vmDP8l+dEQtTS3lBZe+13A242s29F2/eBD4B0/RXHcToVhQr/V2tUYyhsOLBHdiBpqpndCnybMPl8R9zxsC/wEkE1OCM/FAZwvqTbE9s9FXg2pz5cPBQmSQcAx8bPw2b2VezHv4FzJRXiCqsrYz+agJGSHox1DDCz/IquCwiOc+dcI4+ZWXczWz/fubhb5PDY7u2S8ivWMpt22zezXwN3x343AndLui9eUzwU9rmk7dK+OsdxapF6iVhchLKO2WLpLav+467U2DfZ9s2WNG2lVP2tcpnUnCbj1q8xXVdqWsusZNveXdL0un5A7+Q6AX7/5vUlbbZcesvk+k4jXVcMYPW3R5a02aKM9u9+9u/Jtm//5MAku5kz0t+ZT5mRdv+flSHWCvCvt/9VtjDkHoN2rOi/2WvfvM1FKNsiLuO9ppVTD0k66ZvuT7Uws98Dm7Ryah9Jb3zT/XHmP6lOxXHy1Mtrfk05FklvEeZh6gpJfyAkNDqO47SJ57E4juM4TivUVMTiOI6zIFOtFV5xFeyFwKqElaTDJb2WO78fcAAhF++Pku42s0UJaRQ9CPJY+0iaWkn7HrE4juPUCFXMvN8e6C5pPeB44OzshJkNBH4NbEDIy/uTmXUDfk/Ij9sIeJbgeCpiniKWKOVyYC6TPivvDkwgiESemSvfi6AV1gAsBJwSl8/+h7CcdmVCbsckoohkG+02AAcREhO/isV/kfSveL4AbC9pZDweCgyTtHf05McDWxKEMQvAryW9WCR0mXGtpCtohbjs9ypgPUlPxLImQs7J36KQZG+CCsBqsa3JBLHNV+L3NxL4vqS34/V/BsbFvv0S6E7YvXJsbHY34DFg5Zwm2crAxZIGt9ZPx3E6B1WcY9mQoPyBpCfMbM3cubWBxyTNAGaY2WvAD+M1p0ebf8W/z62k8fk1FLYTQZBybzM7O+Z09AN+B6wS1Y2XBJ4ys2UyKZf4YL8pE5psh/0J3nazmOU+ALjXzD6ND/ipwDlm9pikiUXXHgssCvw49mstYKTFZBtyQpeJjCMIbz4Rj4cCn+fOXwb8V9Jh8R5XJeT2rBfPzwCuMrPN87pnUZ7l2pwC8uDs3JyuOo5TT1Qx2bEvcz+Hms2sa5S3Kj43haCYki/Pyipifg2FDSe8yT9PkHuB8ABdCDjIzFaICYMrSKokJ+hQ4LDsjV3SJwQV4YPi+SmE0O+iVq7dHzg1a1fS08BaMUu/Ev4FbJ5l9gO7ELS9iGOWP5B0QWYs6XngLmDHWPQAIUJK3/jbcZy6pIpDYZMJ0lMZXTLNxFbO9SEI3ubLs7KKqLpjMbOVgF7xAXol8YEZncAmwErAKDN7k6ASXAmLthKJjCeIU2ZcBPQzs12L7HpKmktxODqmjDPMbHTu84MSfZkJPA78OErb9wXeieeWZ249s7b6ehBwhJmtWKKtPPdlfaT13B/HcToZhUKhok8rPEZ8qY8KHS/mzj0FbBQVQ/oB3wX+l7+GMFXwSKX3MT+GwoYDvcxsFGEuZf34wJwK9JB0CICZfYfgYB6V9GLb1bXKZDPrLyk/F7ISYZMtYLZcyr7Aw8Afc3afmllfSZOzAjPbAfhPPCx3KAzCSopdgGWA22B2KvN7zO1A8n19OdfXT8zscOBqwo+bQn7fl2JxT8dxOiFVnGO5nTCS8l/Cc3gfMzsSeE3SnWb2V4Lj6AKcEKcU/ghcHVeMTSTMYVdEVR1LnLgeBqyWPfSjzP3BBFHKq8xsQ0lTgDcJnZ9ZQVMXAH81s19KmmFmiwMnAYfljSS9Y2F3ybOAe2Px1cBJZnZ0dD7rEzYgm5eJi9HAeQTRyV3jJ2v/dTP7laS/A5jZ6sA2BGf3o1xf74oObm/CPJDjOAsY1dIKi0P9xfo343LnL2POhotZ2YeEOeJ5phqOZUhOqLE/MKYoksjmWk4kOISHzWwaYRXY5ZJUboOSLohKwA+b2SzCaqtTJf23Fdtr4gM740yCgOXj8dpZwLZxQQGEobC8HH1JOZm4COB+YGlJk4sm1/ckqDw/SVjp9SlhxdpnrUzCHw6U2pPGcZw6pRaViivBRSjrmN0TBe0+bkkTawSwMkQoPy6kifZ90pK+JU+3hvR3oX4NaXpdU2evWC/NzEJzsu30Qlq9SzeWJ0J51YRbS9rsPGj75PoOmF6ertkmH95S0mbHQdsm13dW3/Tff+kH00Z8x/4wfX+9vzSl/U7lCJBCZSKUWy2zVUUP5HvfutdFKFMws7WBM1o5dXPcsOub7MtthGgsj8vUO22S6lQcJ0+9vOjXrGOR9BQ1IkgpacfSVo7jOPNGvezHUrOOxXEcZ0GjXuZY3LE4juPUCPUim++OpUzM7FjgCGC5uPZ7BEHw7VtReydbUjwG+Imk0Wa2HGHJ8wDC9sPPA8dJmhKXQ38g6eJcG08Qlm0PJigK/CAu0cbMbiLkrKwFbA0sTFjmnOXFbCopfYbZcZyaoV7mWFzduHx2J+ig5YU33ydkqmbsRsiux8x6AHcCZ0gaLGkD4Emi7EsCPQk5MnMh6cyoH3Y48ECse7A7FcfpvLRQqOhTa7hjKYOoRvw6IWLIa3vdSMi8z/ZBWB14Op7bmpAL82RmLOlqYNEYyZTiauC7ZvbTeb4Bx3GcbwB3LOUxnDlJnTPMbJ1Y/hSwspn1IuihPZi7pi29sDdoXe4lI3sNaSZsNXBeVHF2HKdOKVT4v1rDHUsiZrYIQaDtsKiD1g84JGcyEtiOIOdyXa78XWDZVqpckaBtNg3oVnSudywHQNKrwPmEHeEcx6lTWgqFij61hjuWdHYHrpA0RNJQYB1gCLBYPH8DQb5lCUnjc9eNJIjBrZ0VmNlwYGK0Gwtsa2Zd47kVgG6SPipq/2+EfWQ2qf6tOY5TCxQq/NQa7ljSGQ5cmx3EvaBvBTaPx+MITuau/EWSviCITp5oZo9FzbB1iHMyku4HHgXGmNkjhDmVPYsbj5uA7cPXoxvHceqEepm89+XGiUhatZWygwnKzdnxGrm/h+X+fh1oUzwpilx+TehS0oii47co2tVN0miCurLjOJ2cWnQSleCOpY75spAmmterS9N8aX+Fhp5Jdq81TyptFBnYNV0EcwbVX3k9q5AmutFIF3omCmZOLUPYMpWeDY3JtjPLGLi4vPtULkwQmBzY0D29/Rnpumqp4pKrv3BWcp3d1zgqya6py/wf4KmXPBZ3LI4zH0h1Ko6TxyMWx3Ecp6rU4tLhSnDH4jiOUyP4UJjjOI5TVXwozHEcx6kqHrHUIFHL68D8Ut9Y3h2YAJwt6cxc+V4EuZQGYCHgFEn3mdl/gEZgZeAjYBJwv6TT2mh3dGx3XK69cZKWNbPFCNpifQgZ9S8Dh0qaFm3XJuSxbCDp6VydqwGnEdSLpwOfAr+W9G5UVF499ivjWklXlPWFOY5TU3jE0rnYiaBIvLeZnS2pxcz6Ab8DVpE008yWBJ4ys2UkbQoQH+A3SRo1D20fQ3BKF8c6zwMOBM6N5/cDziaIWu4dbZYArgd2zDmrHQhbNe8Wrzt2HvvlOE6NUS+T9wtK5v1w4CrCPihbxbIZhCjlIDNbQdJ7wAqSqr076IfAz8xssyihfzTwVwAz602QaDkF2MDMFo3X7EkQuxyXVSLpdoKsjOM4dYprhXUSzGwloJek54EriXL3kqYTHuorAaPM7E1g3yo2nf3a5xJ0xI4B3gNuJ2zMBWFPl9tiX24GfhnLlwNei/3vYWaj43Dba7n6z8jK4+cHVey74zgdQL2oGy8IQ2HDgV5RkbgBWN/MVgSmAj0kHQJgZt8hOJhHJb1YZhvFCsV5deJNgGskXWlm3YBjCRt37RT79lXsW09gKTM7E3ib4FyIczGDYx8/yLXhQ2GOU2fUYvRRCXUdsZhZEyEq2EjSUElbAH8m6HsNBK4zsz7R/E1gIjCzgqbGEhxFxpbM2ejr1wQpfeLWxS8R9nL5AdAoacPYt40J+7b8FLgG2C86u+xe1iA4LMdxnJqmHiOWIWb2TPy7PzBGUn71VDbXciJwAfCwmU0jrALLNvEql78Al5jZWEKkMokwKQ9hov5CMzsinvsYOAg4npxacuQy4BBJQ8xsN+Ds6Pi6A5MJ+71knGFmx+eOH4pilo7jdFJqcVirEhrqZd2083U2XWpI0o/73a6LJNfZvYwg9/FZHybZ9WtMFyz8VpceybaftaQFn7vNShe2/F3zq0l2izX1KW0UWbEp/fsHuHLCP0vaDF5qs+T6+jemiYUCLNywUJLdtEK6sGQ5zCBtbU130kU4rx5zdpLd1qsdXNoox31vj2oo6wLgO4utWdED+ZWPnym7rflJPYPNZ/YAACAASURBVEYs84WYb3JGK6dulnTRN90fx3Hqj3qJWNyxJCLpKeIkuuM4zvygXibv3bE4juPUCB6xOI7jOFWlkLiRXK1T0rFE/a1bCBpXBaAHcL2kC+L554DHJP0qd82WhAzzBkJ+xgWSrjez64FvA8sSlvW+B7wo6dA22h4BbA98Ky7VxcxWB8YAP5E02syWA84CBgBNhBVfx0maYmYnEzLt15f0Vbz+CcIS5EOBNQjLjnsC4wkrto4hyLism+vHgcBASSe30c+9gZUlHZ8ruwm4OPbxeGCz2L8W4GhJY3K2rX2HvQhaYesRVpMVgL9Kur3oN8n4WNLPW+uf4zidgwVNK+yBTNgxJvnJzK4Fvge8CGxiZn0kTYn2lwA/lPRZXC77vJndL2m3WMfJwAeZflYJ3ifkhdwRj3cjOAGiRMqdwHBJT8ayvYAbCfkgEJzYb4BT85VKOira703OKZjZsonfSRJmtgphv/sNJBXM7EfA1cCq8fwGtP4dXklwNodHu8WAf5vZQ/H87N/EcZz6oF5W6VaSINkHaAa+IuRq/JMgU7JXzuYz4DAz+x7wBfBdSR9V2McbgV0AzKwLQdU3Sz7cmpC/8WRmLOlqYNEYyUAUboxqwR3B58AywL5m9m1JzwFr585/7Ts0s4GASfprZiTpY2CNopwcx3HqiBYKFX1qjdSIZZOoVdUCzCIMI3UBNiTIkrxMiCj+Fu2HAEcQnMLiwMVmdoqkSr6Bp4Cd4tDQesCDwCrx3PKEbPVi3gAGxb+/APYHRsQlwymsEu83Y0mC3le5FKLM/bbAIcBJZjYVOAG41cz60vp3uCwxKgMws1OAHwOLmNmpBIWATYr6eE9+SwDHcTof9RKxlD0UlmFmBxGcy92xaAkz25QgbzJI0nHAcWb2beBWwrzIXRX2cyQh63wz4I/A6bH8XeZ++89YEXgrO5D0sJn9H/CHxPZeljQ4O8jmWNqxL9YKg6gXFnXJJkvaN9a1JvAvM3sQ2JnWv0MRnEvW/5PitX+O9U7Eh8Icp+6ol+XG86IVNhzYJupcDSVEMb8iPGBvNrNvRbv3gQ8IMvWVcgNBSn4JSeNz5SOBzfORiJkNByYW2UGIErYiOJ1q81zsR+/Yh/7A9wlRyA+Bv5lZlrL8CmGosJk2vkNJ7wBvmNnsVN+4f8xqUINxr+M4VWGBVjeOK7MaJL2UK76VIBHfRBBevNvMviJocN0t6b5KOylpXJy8vqKo/Asz2wY418wGEO7nBeKcTJHtdDPbB3i80n600z+Z2d+BR81sCvE7kPQFcJuZfRd42sy+IDjzY4AVaOM7NLOlCY70ZDN7lOCEegH/IGxYth5fHwoD2DLbmdJxnM5HvQyFuVZYHeNaYa4VloJrhdWOVthi/ayiB/LHn8u1wvKY2TIEmfhiak6t18xuIygm5/lc0nat2Xc0qQ/sWYn/sQJc9dGTpY0iRy2+QZLdjV+OK20U6dVzqWTb1NUydyyUHuRN+CjNWY4dsXVyncvsn74uZK1+K7DVMluVtOvXJd1Zf5HogAFmNaT9W5nY/GVynQuX0ddUmrqkvwClOox7nr2w0u4kUy8v+h3uWCS9RSfR4JK0Y0f3wXGc+qVeJu873LE4juM4gXqJWOp6B0nHcRznm8cjFsdxnBqhFrPoK6FmHUuUgzmDIBDZG7gXGAHc2JZApJlNAFYmiEzOJQoZbScQEidbCNv9jgGOikuRR8e2phIiuUWAYyX9K4phrk7Ycjjj/7d33mGWFFX//+wSXaIkCS8usugRUMlBEXTJQQyAkhZZsij8UFkUUIKgguCCgIqEBQkSRMkviyQJAgqCr4q+fgVhVUSC5BeWvL8/TvVM3brd93bPzsxOqM/z7LN9+1ZX170z06eqzjnfc4GkaWY2C/ikpKvCPbYEdpI0ueJzrQA8CKxfCFGWfQZJr4T33ouLWX40jPEdklaO+tsOD1N+l6QZnb/VTCYzlBnIrbCgrXghrobyIrB7kIqK25yIq4HMDZwp6ayQl/dX4IHQ7ApJp3S615A0LGa2KJ6vsZ2kB81sLjyHY4t+6H7z6KH9NVxB+ODw3mcl/SW8Z/gDe3p47yuSri/p72XgJDO7U9J/ao7hBeBcM1unUG1ugpmtHjTHwI3o35v2kclkhh4D7LzfH1eTP9rMdgK+DhxUvGlmE4GVJH0wiA3/ycx+hk+qL65SoS9jqPpYPoFLljwIIOlNPGHwln6+z0nA9hXvjQeerdHHi8BUoEl54geB63Gj1pRYlHNRfOX1eB/6yWQyQ4wBzrz/MP7cAZ8wp8lOdwN79gzFk9tfx8uLrGVmt5nZZWa2TLcbDckVCy762CLJErLsX6P/BCKRNNPM4iD684NawDvxL3mP6L0TQl2VggMl/TEcnw580sx2oXW7rBNHAPeY2YdrtI1/c64J4zwU2AFXRm6WuZXJZIYk/bViMbO9cCHgmCdwtXXwCfEi8ZthJ+cVM5sHL+1xZnju/gW4T9JNZrYrcBr+7KlkqBqWv+PLrx6CDP7yNBeIrCSoC78YnfpskI/ZD9iFSMiS6q0wQp2VPYHbcZHMrkh6NUjMXAScFb1VCFq+El4vGM7F7/8Ol3X5JL4Vlg1LJjMC6C8fi6RpJBJYIcG7kIRYCNcsJGnzdnyyequk48LpW/Atf/DyHl3FfIfqVti1wJZmNgEgWNCTcGHH/uQrwKXpSUln4Eal9lZVEI48Gji+wTX344blq9Hp+2ndntuK3vozBRcBXwaeDXpkmUxmBDDAW2F34kK84M+VO+I3g3P/ZuAcSXFhxLPpfSZtggc9dWRIrlgkvRAqQZ4VinsthG8BTcd9LXXY3cziPcSPhv9vMLM38f3D/8FLKJdxEPAHM7swvE63wtokZySdb2afqjm+gm8D20avvwKcGcoSvIHXm/lccs1N+FJ1DzKZzIhhgBMkTwfOC8K2r+G7MpjZCfgqZQO8xtU+ZrZPuGYP4FDgnKC2/hKuyt6RLEI5gtlu/Mdr/XCXHJOWkqnmwifTxVM1A6EVtnoDrbDXZr1Zq91CNYUVAX7+ZNfJGgDP/HjP7o0CTbXC6jBPg82IJoKR842pNxed01phC4+t/zv97FuvdG9Ec62weZZYsbEw5DzzLtenB/Lrr/0ri1COBszsSGDjkrf2kPTIYI8nk8kMfUbKND+vWDKZTCbTrwxV530mk8lkhinZsGQymUymX8mGJZPJZDL9SjYsmUxm2GBmQyr6KVNONiyZjpjZ0snrNSrazXZ55qq+M4NDfz+0zeyU6HjX6PiK2ej25tkaVGZQyOHGowQzWxFYAnhU0mMl768JfAPXOpsi6amQEPU1YLmo6VRCGLWZ3Shps3D+IOCqDvc/ANgRWBx4FLhE0jlJs56+u3yWZST9u+T8epJ+k5w7Dvh6EDLt1OcYSW0hkmY2XlKlenRQgX0H8ESZUrWZVSb0Sjo/aXtkh7bHJG3XkPS7kvt9oijh0KRd4HYz2zWUC+8P3h8d7wX8JBwv2k/9d8XM9pB0bjheVdKfwvFRkr5Rs4/54p+tmb2zqm0/fnfDmmxYRjih/stP8UzbJ4HxZvYSsGPycD4LOAxXdf6WmS2AG5SPJF3Gs9q5K86nYzga13PbExfCGw9MMbNlJdXSVkv4Cb3G7QJJu4Xzx9FumOYF7jSzSZIe6tDnzVGfJ0o6JJw/t6TPQmboZFwi43FgGTO7DviipNejpmfj2nfX4PpvnVYFB+L6TRfjxrdT27oGvslE4ATgF2Z2rKSOWZtm9gjtaRdjgFmSVoxeU3JcmuNgZr+sek9S8TNY1cxKxyZpl5LTu+E/Q3DxxKKf9PcaM7tU0o7h+GBJU8Nb02n9HZgR/hWq4sVnmwV8qGxso41sWEY+JwFflvSr4oSZbQb8ANguaveSpBvC+0fikjGTSmbxs2ocp2wh6YPR6z8GAc5f0irauYGZpaup4mG1bHKu4L8qzgMg6WAz+xBwsZmdiX+u4r3XKq5dq1OfgSPxVcqKAKFm0JHAsbgERsFyuFDox3D9uZ9IurWiz2WALfGyCGvg9YB+VqEHV9fA154ISLomyH2caGbb0Ppd3ZA0f2/Uzy+AzUvGWPf3o6CQLhoT7l222nsMOKNGXwVVxq3s57pUdLwNbpTL2u6Ar77nx+tEXS7pZTI9ZMMy8lkyNioAkm40s68m7WJdj8ckfb2iv7Fhtj42Pe4whjbNDElvhRIFMXdJmtihn25UzXbvCorVN+OrsjGh7Ypl7akxuwYmSuopeRC22o4ys18n934KnymfFkRVdzWzw3EZ8sOStm/gAqzXmtmCuOG/yMxelrRTh8/a6QHeaCIg6VkzuxfX0HuV3u/qhqRdvDX0ZkXBuglm9u3QR3xc+r1LUtTnS/HriOck3VZ2fQV9mQhBh98BSZcDl5vZIriRucTMngUukvSLBmMbsWTDMvJ5veJ8aghiIzEzHI+Btpn9eKD4gx+DlyyFzn+oVe/11Vlc+wFhZnPjK4kd8RLSVQ+lprPr1yrOV33fAG+G9xcGVurS/1q4KOB44MaS9+sa+NoTgVCa4lzgKeCDDSqiVnFkxfFRacMG/HfD9ouHFfpYYDEz2xz/vVuspG2j3wFJzwPTzOzPuNr4j/FV56gnG5aRz+Lhjymm7A9rBVoNhiiZ2Ut6Vx/G8OGwxRXvRY8B3p60Ky1TYGbjkq2GuL/FouO0P3CJ73uAtbqUGFjLzO4K/awSHa9c0f4VM5sg6W/ROCfQWjuniKr7TPj3Eu4/2VzSCyWfc11822wzvNDcRcDnyoIK6P15Fd9plYFvMhG4AzhU0oUl76VjfU/0cpyZvZveichfw//nmdkShYEK22uvSrqpos9YDXRsxeTmHjPbqOx6SbeXnL6foOKL1zHaOTpOKfw3Y5LjVUrG+oHQ71ahr7Pxn12GbFhGA/dT/gt/f/xC0gp1OgsrgN0lTQt/eEvjD6l944ds0nct+eCqBw5wK7Bu0/4Ch0uqM8v9QIM+wbfUrjazaXhpg3cB++DO4phH8Qf7pXjgwrzATmaGpDOTtr8G/hcvH/sa7rfYPLQ9PG5Y9+fVcCKwHlGZ67Ad90rYokuJ/RwzgeKzzKI3WGAX4BgzWxn/vrYCHjez9SuCNkSvwaua3OyfXFPcbz5KJhaSmpSW+Ex0/KOKY8zsT+HwYtwPVEwmJtBruEc12bCMfN5bcb5lxmpmhxUV48xsE0k3h+PTJcV/zCcCb4Xjd+JhpBPxUsuTy25kZvtWDa7k4VpG25aZmW0aSqWegIdRzwIOk/Rk0vSOYAD3k/RieNh9HNhHUlw9tC1KKOL89ISk35vZRNyQbA78C1+J/Ctp+k16V2jdKp3WfgiaV/o7EjgYX1X9GPeJ7FmsGEK7JhOBtwO3mdk6kp7Fa6JPNbNtJf05aXuwvFBdJw4AVpP0unml17Vw43oX5ZVWNygLhY+RVKw4MLPFgB8CD1Dx3ZnZssB3JO1mZg8B4/CqrB9Pgygk3WZmi0p6zsy2C+1mAZck3T4Vzm8S/hX0GNXRTjYsI5+6y/PN8HBd8NyVIhEtNUyrRaGfrwcHq8xrbFdRte9cV1o7NYJfx6uJ3gRshO/Zb4gbtwOTa3+Eb4UV22A/BZbFix5Nito1CQsutm2ewx3zLedjn5Sko7t9uIhfRsezgJkd/BxnAEVQxmnA94E/AqfikWUFTSYCpwA7BaOCpCvN7MnQ56ZJ2+/S/SE6U9JLZrYK8FQR3m5eaK+MC2v0Sehja+B7wCmSftCh6SmhX4B/SppoZmvh2663Jn1+Evg6sDZutKfj0XlL498jAIpKo2fKyYZlhKMOyX0JdXMO5oqO46imNp9BxDR56eYWzOwjyevjSu43htYETfCHXDFTnCnpF2Z2E/Ab2hkf5zeEbZ3vmtndSbsmYcHQum0Tj7XFJ2Vm/47azcL/5v6MrxgeTK6/lN7VDcCCwYDtJumepO0ykk41s4XwbbzzJc0K+UcxTSYCYyX9tuVDekRdk63HmFlmtjBe1nY6gJktBczTx/4In+97+Cptq6rt14jFlCSCSrovRHSlHAhsEY6flXRYaHcjkWFJ8m1m4Vth9wIn5rBjJxuWTEHdiJgxZraQpBcl/RogPDw6zfCvM7ON49l3WHXsi8+iC6pKSR6enlBvJv0pxWsze77k2qoorZaoriZhwYEJkt4qOZ+Os221ZmYb4ls4myVtP1jSdgIeqZU6rIsSjR8B7ogc/KlhaTIRmKvkHJQbgjo5R1PxVdSzuK9oXdx4pqvKgiKAoqzPIvHwAeBt+PbkXmbW0zD1QwXiUpJbRccz04a4YX06HN8W+nzezFJjkZYKXzD0/SPql04f0WTDkilYLvhCxiTHyybtfojH8E/BndYr4hnbp1HNMcB0M9sEf0j9BPcHrJm0q7sVNG+x5STpSujZmir7fX7I2mVOPg60ScJE1AkLvok+7qdLuqPuKkDS38yszND/O+SFbA58M6xcvgj8IWnXZCIw3cy+CxwbHqgLAkcDt5S07ZpzJGk6HpVGuO+rwPqSnqi45M/0Rm1VcXSX91OeN7N3S3pQ0ithHCvRuzUa87biINnCbDG4Ffk195nZnQ3HNmLJhiVTcBG9vpD4+OK4kaRLzOwF3B+zAvBPfA/bqEDSz0Lo6I24g7hqX7zuVtBPgHPM7EB5Qt+i+PZImdTHFDzr/ijgEWB53PnaMrNsEhYcmF3BxoXqNDLP6C/bttkfd1h/O/hC1seDGL6QtEsnAu/Ct3W+X9Ln8cBXgfvNbByuG3c+0TZQE6xV+yzeMqoyLK/U2Lpt4ocC95lcaWZnAQ/hE6G9gV1L2t5tZgdI6vluQtBBum1aRX6eBnJp4kwL1kWsMmm7Lh75szkuPXJAl/aT8JDczdSadNnpmgnAuZI2Ss7vD3wez8d5AfihpMpVk7lw4LLAP8o+l7kKgHDn/uNE24FlkWtm9gQVSruxT8fac4jmwzPqX0q/r5LoufnwCLY7GwYBpGPdGv85vQv3H31f0jV97S/0uWmH8PCizX7JqQXx7b/bJX27pP0kdcmhCb6xlskHHsJd5ocqrlkan0i8C58InV/h8xsHnAO8B3g4tH8Y+KykmVG7dLU5H56Bv72kj3Ua/2ghG5YMUC5Wic/cW8Qqwx/VzvhD/TV8u2j9+A+vpO+L6X0YbIjrPT0ElcKB6fW3SeoUDlyE1e4g6ZLkfC11YXOhzCqZkzYVXDP7Hb71VNb+tqjducnbM/Ecoh8ryQ8Jq6q2tmUP8IZBAem1i+Ph1scn58+l+jvYs2SsVW2PKTsfrpsL30Zbr+S92krQyXWlk48O7T8GHCBpy4r334Gvxh+V9C8zWy4OI7deAc7CuM3EE3G/2m0yNlrIS7dMQV2xyhn4NtEkSQ+a2fRORiVQmWwW7tMiS568V7UVVLy/DO5M3Qv4H9pzDtLM+TH4FtLLRPkpxYrAzMbjRvUfkmZU3ZeamlVqkKBXZsA6tK0dFBC9vw69K8yflzRJv7tl8e2xX5W0fTx5vQC+jTYD96lVjftNM6sKqKj1syrps8oP1YN5zstewH74luDZHfp7AnjCzCaa2am4tM7S0ft9UZ8YVWTDkimoK1b5PXx/egUzO5savoYaD+DpwMYdtoKuTC8IocoH4HkGb+HaVv8sufdh0TUTcNXca0lWG8FRfTFeL2YGsJKZPQXsXOFnKc3mN7P5CydxeF1HCr5oWyZFX7StEsyM27QFBUQrzC/gARMLAyuWTQYUCSia2c64f+Lgsu0pSWdEbTfAH9TfB9q2uJLxrExF9Fndn1VJn5WTD/OclQNwOfuf4quQLcrahvYL4Pk9++PG5EB6JWGKNrXr5oxWsmHJFNQSq5R0AnBCeLDvDaxjZt8BLpD0QB/vXRindBY+E8+abtkKMrP7cOmTM/CIpf8uMyrJNV/AH1BfknRtSZPjgcuS7bG9ccd16itA0neT/lfEH96T8MJfBWlo6mp4iHRZoEGajLoVXvPl5JK2VaRBATNwg7lrnRVmmNn/CDdAG6ldSSBuOw9uSDYFdlFSUCzyhxTMj/tEJnf6AJ1+Vk0mH4G78GTO90t6zbxmTtV9i3otVwCfAk6VdHFJ0zT4oNZqbTSRDUumoK5YJdCzCrktRGTtBlyArx76wqzQ5zegVgDBb/Dtia1wKZXKbRAzWw7PA3kGWFchq7yE1VJnuqSzrbOiQOwY3wA3TqsnfSi0G4PXafksnt3etoortgODE/lkvALjFookWqL7VgUFpHkgtVeYZrYtviU6VVLblmXSdg38e70e/17LJiap6sNMRZI7llTnrPmzqjX5iNgQnwD9ycwuxw1bFR/GfSW/wbfLqlaPfVqtjSay8z4DlDqZe2jiJ+jjvW+RtHHdAIJwzdvw0OC9cXmXw4FLJT2TtHsO3wK6hfa6GnH01i3p1lQ4f4ekDUvOH4zPvH+PPwwP6eAMfje+rfNHfGupUmXZXLm3KEj2HVUkYTYJCgjtixXm1viDsG2FaWZv4f6MF2kVg0wLrWFmr+HReA+WtK1VRTH9zuv+rELbur6wov0CuKHbG1+FXxCHFUftPoRHLn44fJ6PSWpL3E1Wa3umq7XRTl6xZICBNx5dKGbRdQMICNs55wHnmdl78YfBH2itKAnwiZpjeMbM1lYkaWJma+Oz5zKm4FtM50r6YzA0bZjZgcCX8Hod14Vz84bP8FrS9iT84XcQbrBWspBZnq5auv28LKnpnqwwJ1GywpTUqVhbyrsbtK0iXT11/Vn1wRcGgKSXgGl4/ZT34b8vZe3uAu4yTzidBFxori69djSGOqu1UU02LBmgo5N5lqRNSs73J4Vybq0AgpI8godxuZKj047TLafwwJiMO2fjOhtTcBn8W+lNJNwU2LZizCvgGlinhK2rBcxsEXnxp5gvh/9Pxg0nVFewXAPPpfl8cr4vqrk94dlmtppcjXkefFvsFVxosQUz20PSueF4VUl/CsctRgpcgy6sGiYTVg74amlGgzGmq5Lbws9nu6jPyxOD0cgXZmbzh/On4npwJ+OroimdBiZXwr4YT8adkLz9G3y19hHcWEPD1dpIJxuWTEETJ3MjukXRSCqyxetWuywEINPCYWUP62IMq+C+kE8DlwO7J+OYYZ7wuU3o4x7ga2GmWzbuV/Hv5iJziZC9gd+b2W8l7RC16xiaapHcjLpIpJjZfvH+fhfGhGu+DOwYfAHfxR/Yf8eN3EHJNbvhM3FwiZ7CmLXlEIXvahruW7gbX8Fca2Z7SSoTA+1K2DK8ErgaV0lYFfhq+I6KFVtTX9ipuHzLWHzley/wJ1zd+lPJ/dcMn2ldfELxI1znbAqthcE6rtZS39FoJBuWDNDMydwH6kbR1AogqHpYh7Gn57bHo7XmxR+aJqltZhvYCJ8hzzKz1XHH7/UVbePxPAQcai6suUO39gkHAVd1beXsSGuBrU4Uq4FP46G2s/Cw2XfL642kTn6oVrguc/gfC2wj6R/h9Q1mNj2MrzSPpsv9wA3fzpJ69M7CqmEqvSvHqslHWTEygFUlbRBWLhviSbSvm0vcpJyI16553cy+iZcfeAgPh+9RKqhhNM5llNdlabKnmhnhhBnjnfg2zzr9ZFSQdEbxD1en3Qef6abbBvfjORfxv51Iql2a2SJm9mUz28PMxoZz7w9jTzkfr8z4CUnT8G2QNswlYo6mN2roLeCokvDWov2aZnaNmZ1nZkuG0/viD8EmNNEc64s+2YtyJejVgYclPdehryqF67It0nkjowKApIdpVRMuxVxiBdrFLReJjUro835aK0M+E3xfcX+dfGFFMbcNgHsif8j8JW3nkvQH8+JgC0i6P2zDdVWxTphdHblhT16xZIBmTuY+9t8x5yHcp80hbWZL4NtMMZcBv8V9Esub63YdRfm++Up49vYdZvZHPIy5jMnARxSSG8MDZjNc9LCsyuVZuF9nPPCtEHW0HJ0rUZbRJCyzSduebULz+vST8S2mYgJRNsNfPHzmscBiYfVYFXLeluQYVozzRa/XBL6BP/SnSHrKzD6PF5JbTtKxFWNOiZ9ThwBXNfCF/V+YHOyAb1uOxf1M/yhpWxidLXH16uL3tpZgaMSoD7XNK5ZMwZfxP+yT8boof8F9GVU1UmoTomjuxeXo160Tmmlm65hZEaKbRnotJK+98Vk8cmd7YHVJbf4gSf+W9G1Jq+AG4mEze8RcHj7mZUUZ8+Ha/6N3xpvykqQbJJ2F59M8AkwM22KDQlg1TYhez2+erAq96s1H4BFg4/FAg4/gK4VDSrr8Hb5dtlM43jk6TrnRzI6PVoxj8YnDDVGbs3Bfza9w4/sTPES8yvj+zjw5Mv6M++O5JQBIegT3gdyGb2/eA6wXzpfxOdz5fj0eRTgRNzL7l7S9yVz6/mh66/JcjatuZxqQVywZYMD1j2pF0Vh9+ZFiVTHLzGbi9ctfoQuSbgZuNhdhTAUPXzezJdRajGwJqv9G4hn/Y5K+3u3+FfRpK8w8S3wNYJHgD3gQ+BlemgAFJQK54u960XV3499pm69C0uQGYzkO97PMMLOn8VXNT3EZmIKXJN0Q7nsk/mCfpN6iZClfA84yV0X+G74l+xBRoIV5ng946YOnwvFaIST49pLP9B/cn1dwM9Wq1N8xs6uB5yU9FgzLmZKuqBhvFXkrbE4PIDM0MLPDJB0XjjcJD2HM7HRJZbO7JtTNeZhBPfmR+MH0dCej0ikiLeFY3AF9Hh6+XNSH/0pF+7Fhm2QsMDMcj4HyrUOrVhM4KWm3JB5EsAjwHHC3epND47FsIGlN89K5N+G+oX2Kn1vUX6xY3FMTxcwuUJJ8aa1aZXH9lK8qypgPn/ENfCvwsDDm50qMVSPjGyLwdgmGf8VwTSopcytudO4Nr+PIwDbD0uQzRUZryWBUZgK/oDllhdFGFdmwZAo2w2eh4DPH4gGV6lc1pkHOQ135kaKE7Rhglei4LI+gVkSaXMBxB3xrRy/wsQAAHRNJREFUbRs8JPdTHSKAxuNbhYR7i5KQZytREzCzHjUBRXVRQj7GvvjW0YsERQEzO1vSjyQVD1OA58O4nzevCLlFRQ5Jqli8IO5DWIN2ccf0Z70gnql/Fknyopmdk97IepM5C4n9psa3bRIQ9Vn8rNbGt+vWxB/gF3bJnan9mWjfHlsQWNW8oFyP6Kh1KS9Q4jsadWTDkimoCi+dbUdk3ZwH1Re4/EDde6uBrlOIaqolItggP6W2mgAeZLBBPPMP24N30l5uIP65PFb1cFWkWBzxczP7dUnbNGLuVeCCisi4tYFxePXQwrCnpMa3yEWpyjc6EF+lXQw8WtZniBK7PwQKbAwcEaLMri7L8WnymSS1lUU2F+W8llY16yblBUYl2bBkCrqFl84OjXIeVCFwab11W/6FK9o+K+mXAObFmU7Fcz1aqBORZq2FswoWAsZJKpV570KRn1K3HAHAPHjd9XhLaVzJuAAmmNe8HwOsGI6L/g+vMb4mIbRvS09I+oC5NMokPO/pdnz18FDUpirf6IMV91kGX03tjK+ofo5XJm3TVgv+tbuApXB/2d7Uz/Ep/UxlSHrGkvoxalBeYLSSDUumYLkwixuTHC/b+bJalOY8mFnHnIeQc3Fa+Aehbgsus/EGsIyZrYpHZE3DlQJasJq6TkoKZ5nXOp9CryRLU4rZdl01AXADfJ+ZPYhvdS2Mh0uXjeHIiuOumNlESvJ5QlhyTFFyt01dGSCsIg8N124EHGdmy0tav6Tv+fAtrANCv+8r6e8NfHVwrbkm2HZ4iPDLknYK/cyDR+HtAhhuvL8oSWl/fflMJdcvgP8c0vO1ywuMRrJhyRRchBc2GoPPPtfHZ8pl9Sia0jXnoSbFw3qCpLXDNtF9+ENyoqT/Lbmmka6TeXLcNNzHsX4cJdaQYpVRuxyBpGvCSm5l/IH1PPAXlagVAzcoUXwO418veZ2uxN7CDXHZ9lY64y8Uk6uUCgrtte3wVcYC+NZY/P4KeJTfjvjn3lEu9NiNtfCkxvGESLfAk3hp64vxCcMs3G81vohAq/GZ7iv7TGZ2HK3f1Xz4ivoHSbva5QVGK9mwZAouA74vl6//C66R9F90qd5XkxvN7HjgcElvhZyHb9Ga81CH4o/+BXAHcOhrcyVy+RG1VXjNbBKew3CEygs89YVCTSClbDtubXwW/gt8tbQWXkfkkHTFh6/aNg7XXSBpt3D+OCI5kXQlVnLP9Qo/lyq0yqxd9BMz+wye4zIe37L6XOrnCaG7i+Bbme/DyxpUGpXgi9sJf5jfjU92PpeEJ1+F/x5MoNdPs2S4pi2bvuozRfeM9dfSnK2ZwBlqr4dzFV5e4Kgo4KC0vMBoJRuWTMF36A1n/bekiebiimdTXh+9CXVyHvrKEx2MShGR9h5JfzWz/wcsij+YWqoymtnP8RnyYcDT8SqjYibcjSL6qVTe3rw+fcpp+EriB/iW0EH4Sut84KNl/Qf+q+J8HVoMkbn8/3746uOtMJbFzexnahWXvAR/EP8eL0j27SiCK66d8gbuzxhLd9/dr/HKoNfjUXSbA5uHHJXDQ9+To7Gui2+trYOvMvtCj/6apPM6NTSzKyR9Ss3KC4xKsmHJFIxTby2SIpT1ITOb7d+RmjkPdSgemqua2UXhdXFc3CstCLUPvh8/EXfynoVv8x2Gh1UXvID7cMoe+G2GxczGqCTRz3qVbU9K30uYimeQx7wmr+2yiKQLwrmrKhz9VTQNvIiTLr+Er5jWksvGL4I/sBeWlGbqd1sJzCfp42a2PLAnviW5oJltiW/jlQUPdK0JZL1JtJ/Hjc/CwLvUodxyF5oY4kXDGGqXFxitZMOSKeiJkpH0yej8bBcxKtm7jvMT6kQwFRR1Wz4Tneu2x70LnpcCLsh4hnkS5K9oNSxV+TpVD+qb6d2KOjF68J4LbBznp1RQ9kCbYa66e52ZHYXLiWwNtPlS6L8ovvja7XFH9FvQkyMzL15UqwV1Fyidjn8P/wS+YWbHAFvg0Vtn4gmoaZ9dVwy4isDFeAZ/pyTauvRFq612eYHRSjYsmYJ/mdm6cgkQoGer4fF+6LuW3lhZglyBorot6UPNqot3Fde+HA4vDa9fMbO02mBan70bsWFYq+J8J8oeaPvjGl7b4Vn6WwJ30C7CCfBhM3ss3G+x6PjtJW3r8mbJSuIQfJXXlJbvIazurgeuN7NUTqcui1I/iXYgaVJeYFSSDUum4Ct4BcWbcX2mFYFNqFaNbcLYYuugC3XrtgDdi3cF3lZsWxURPCEibZ64kWavMFNlQqm5NldqRMZQskIKBvAb4V8b1iqvs4Q6lOJtQDz2t8xsKbVKnTyPi4c2pdNK4ADcb9S4T9VPoq1LX4xBk/ICo5JsWDKAq8aGFcq2uBT5b/HoqNIKig2Jtw46jaFWlrw1K951HXC8uRbaW8GofDOcnx3qPlCaroQ6YdHx78Mk4Ay1Sr20XmC2laTpHfqMFaGPw7fhvoXrpU0ADqdVxLE/mO2ZfVUSbdrOIg28Cqq04Mp4NvzfpLzAqCQblkwPYa/6pwPQ9TjzGiBlEh0toZxWI0sen+2egucRPB0MTRXfCv/+FiLSFsej3BolFZZQpVe2ctxI/VMbvox34+oDR5mrDpyDZ76nMv+H4P6OUuSy/8XxDWb2FC41Px5fKe4rl1FpSifj0W8z+5Ik2pRYA6/s+h6jXGMrtvg9ux/33UFveYHiOEM2LJnBwfCQzvRhM4vWUNdaWfL4Q3Uy3Yt3Ia+eeKiZfS20e7oi4bAptfTKbABqw0NPpN3lwOXmWlm7A78wsz9J2idqWghBlhn1FiFIM1smGPL9kvPr1R2rmb0tTFD+HCVnxgrEszOzf7Z7kzbKElSB0jDyeCv2SyQh6dF1VSHk7+/D+EYk2bBkBoP/kVSnBnitLHm57Py38dyJTYB9zOXRf5aGxRZbIZLeNLP3qf/KAXSKAIr9B/1RG74bz+JqzE/h25gx69GqvEx0nApB1kq6DO+PBw4O9/6OpJfNbGtcr22lEGjRUrSrG93CeKMVQxOWwrcjyyY1LYYl2YrdSSWiluG9LYDv4pUxJwP/xH8fd6Ik2m00kg1LZihRK0ve2iXbZwIPAPvQXhmxqhyAMXucjT/Mr8ELj1Vt/fRJJ62ClnuY63Ptjhu5K/AaI2kE3q+7ZZ9X9N8t6fJi4Mf4ltkxZvYaHs02ORpfvLVU1EK5RyUFuQIDEcb7F/XK+Deh03bdCXho9gq4qvFSuHFZrQ/3GZFkw5IZDD6dnjAv5rSPpOOLcw38EVWS7d8vufdAhYMuh89QPxbG+RNJt5a0q62TFpJRS1WbJe2IZ6IXbR/Go/fOxmVPXg/nCwXo/qTsIfuWpDPDPR/B9eVWV2vRtTTKb0G8vsyHJbWVLWBgwnj7EtHWjeeCb/CvZjYNOCb2VWWyYckMAmot97sOHm66BV5Kl+i9unVbukq2RwxIOKikpwhOY/Nqg7ua2eHAfZIOi5o20UnrqNqc+Jx+K+kz0CPDMjWcLxSgCw5s8LGafFfxWJ4BJqdKBGVbSWb2PXwyUGZY+j2MV9Kmyf1XxRUOHiwZWxEa3q2AXGys/pGNSjvZsGQGHCuvZV8mw1HbH6H6ku0DWQ6g4E38QVvI3Mc00UlrotocByxsQ69hSWf3L4WM9U8DHwr3/j9gN0l3J22bJF3GD/vnU6NSRfB1VQVl9HsYb+hvGh46vRe+VfqUeVXOs5PmdUPDFwhRjmOBueKIxzTKcbSSDUtmMJhBvVr2jfwR1kWyPXARXkAqPZ4t9eIQifWZ8O+l0N/madKimumkNVFtrlvx8/t4rssbZjYV92P8GV8dfTQZa5uKcfisZVtRVUaoo8Kvma1MyfZgYCDCeI8kRBiaa65thvtDbsW3EWNOCFuO3XgZl6UB9xsVxy1RjqOZbFgyg0FdGY5a/girIdleoApRQDPbqu7gK3gUj7b6KS57My+wk7kSb/GgKa2Pbu214cvoqNpM/a2iBSVdHXxay0u6MYyhTaHXXHRyLzzS67ywdfd+fMXYUrumyggl/aWqA/PjfpbJZe0l7WFmc4UQ8WLiMHM2w8Nfl/S4ma0Yjh8KfZf5Xpas02GDYIhRSzYsmQFH9WU46voj6kq2Y2a749tRM/HKgQ/j2lcr0yFxsAbfpPehuXSHdnF99BPonOldW7W5ou0Y2rXSCmf6JsAt0GOsFym5/2W44sIawPJm9gRwFF4bpoVuocGhWbq1NFOtcjFpn+8DrjSzdSQ9G8Y81cy2lfTnquu6MCsERWyD17nBvDrluJK2RbnnNhSJpYatym/hkWHz40XhLgGO7accqWFPNiyZQUOtMhyTaJfhqOuPaDJjPBhYFd8Cm4r7Vq7CV1B9RtLR0JPPMR534s4oaRfXRz80fl1CE9XmqrbpdQ+Y2cW4UOY+ZrYMrrt2S0mfC0k6PBge4VuYq1cYg66hwSqphWNms4CTVVLHHg9S2CkYFSRdaWZP4rkxm5a0r8N5eI2XeYCNg/G6kPJM/Zfxz92Nqbji9CpyQdOFcd/Nd+mfwnjDnmxYMoNCcMTeGJy844GHJLVoO9X1R6i7ZHvMM+FB9ay5aOXn1Fk7qxZh1nsxLhEzA1jJXA5l59TPEtHNwf2CSiRszOwT6bkG38EUXCV5qqTfhq2tPxMizRJeCX3PMrOZwMeT8OGYrqHBVr8WTsFY9dYEIozlLiupYFkXSeeb2ZXAq5JeDYZ1D9zYpDyuLtL9gbXUmrD7AnCEmd3a13GONLJhyQw4ZrY/PsO9G982mIVrXL0z8Uf0V92WmFgG/u/9YVQCxwOXSerJsjezvYET6VAjvgtT6c18v1FSEQl3EL7KakwwEvfiW4HIC4k9gOuBnZ40j7/7pzsYlbRt1XHdWjgFVU79eSrO1yIx9G/DV8uTgHckTe+r2WVVnlBZ8bJRSTYsmcFgMvCR4kEl6Q8hDPSX9EbUQM26LQ0ptKLGAAvb7JccLlhN0gHxCUlnm9le8Tlr1cvqFj0Vz/znrjjfiJBbsycwdxjbg3hdmudpNyxVwpppHge0fq/xcUtosOrVwimYbmbfxX0Vz4dV4dGUb9s1wlxu5gC8/PTxwOolzU41s1JJliRacYyV66/lksWBbFgyg8HL6exX0v+ZWarEW7duSxPux2fnb9AawtqmFdWQqpDhFuetpGUq2pUxEHU+dsQd+kvgDuZ34NpeqSwOuI+gWIEtB/yrQ7/x93o/5aHBtWrhRByPS/Tfb2bj8MTL8/FVYJ8wTx6djAd6TMV/x6rUji+lXTRzJTzQIY5MHE+7LybWYhv1ZMOSGQxeN7Ml1JqBvwTtv3+16rY05D7cgf8mcICk6/up32fMbO3YJ2Bma+MPQ6JzTeqjF0rEY9Pj2RmnXMX4MTNbDvi0qmXwJ0o6NozvXHUWDq3zvTaqhRP8b8eHf/3FFNwXdm7YBjy4qqGkDxbHwa9zDB4evVXSLhX6LK5ZoF9GPALIhiUzGByLZ9Gfh4f7vhPPl0hDb2vXbWnALsB78FnnBbgkf38wBa+4eSvwN1xVeFPaK242EVYsZsLF509ViftCfO0/OhgVqHbIl1Hne21UC6cs56egS85PJ1bAw4JPCaugBcxsEUnPV11gZqvhAps3AeuovvbaL4F1+zjOEUU2LJkBR9Id5sW4dsOduX8HPqX2csC16rY05JUQWfaf2YkuSpE0w1zbbBtcfv4e4Gtqr7jZRFhxU0l/668xBlpkbMIxQEsiZ6DJ9lvX71XNa+FckrxeFl+9/KrLWCoJRuEi4CIzWwnPo/q9md0rqUUcNeRNHY4bzX0lNb1vrnkfyIYlM+AEh+ib+CxwFp4o95+SpnXrtvSV/v7D3wi4PERerQ5sSPvMvYnf5DIzewY3rlf0U7JdlaRNGU2c9zGl36s1rIWT5PzsjOcvHSypTKqnMSHr/lAzO4LycOO78VXjCfjnXyW6NjXCZWQfSyAblsxgUDhFCxYKs9zPajaqKNakSTZ7bUpCqN+iJISaBsKKktY0szXxKK5jzOxq4MzZWcWU+HE6UasqZqDO99q4Fo6ZLYYneS4MbCSpUwBBn5DrhpVVoyx8PwuFf1VjbAuLJ4ic9s8Ihz/ZsGQGnNgpWmAuNX8uPusvqFW3pSFNstmbMJl6IdSNhBWDD+T+YHg/hUuazC9py74MMgp3jikNdy7ZmuxEne+1UU0VM9sWOAlP5uzPn1UZbauLwgh3U1OgOiy+r7lWI45sWDJzBEl/C/Ie8bladVsa3qdJln4TaoVQK6mPHuRs3pSUhlqnLIkHBCzD7OX3/DCK9FpWXtZ5tqn5vTYNmb4Kl1U5yrz6ZE/Yb0nOTy2CnE2ZYU3LMjdRU5gtZezRQDYsmTmCmc1FIoRo9eu2DAVqhVCHra1peLTQtvjs/lkzmyLpmqTtOHzV9lm8Bso0YAtJz83GOCfiUXngGlmDKeveqBaOpIFIMKxa+ZSdr6umINrzXYrovTaDNRrJhiUz4MSRSIH58BK8VybnZ1CvbstQoG4I9YnA7mFf/5t4TsSDuLLyNUnbh4Gr8QJm43AJmtkxKjBwpZnr0LgWToi025/e0tSnz44fruGKtZaaQlUeS6aXbFgyg0EaiTQTz/6+KTlft27LHCeEUO+Aa051CqGeK/hflgUWkHQfgJmV6UqthD90T6G+sGU3BqQ0cx2qAgesohaOmW2JS7gcjRvZ9+Cln4+RdO0ADTOmlppC2KYrRdIx/TqiYUo2LJkBJ3KKrojnMzxattev+nVbhgSSHsazsztRPKy2xBPuCBn1ZVFHAyFs2dcQ4tnGmtfC+QqwtXoLnP3VzO7B68QMhmGppaYAPBEdfwk4eRDGNqzIhiUz4JjZCnhdldeAJ4HxZvYSsKOkf6ft1b1uyxynItpqIWCcpFil9yYzuxNYHvh4iIb7PkGUMaHWVkxDmoQQ9zeNa+EoqZop6UkrL408EBwCXNVNTUHSGcWxme0Uv8442bBkBoOTgC/HmcwhNPcHeM16ovNd67YMBZSIS5rZ53CZly8n7b4T8lGel/RYMCxnSrqipNtaWzENx9kkhLi/aVoLZx4zm1eubQb0BHT0m2JCJyQ9UlNNISYnRZaQZZ4zg8GSqTyGvPb6wvG5kHR4NC78B711W1Ln/5DBzJY1s+l4tNX6kq5O3t8IDx1eycw2xKO9qqpIPhO2XuLry7ZihgtNa+FcBEwzs7dDT2j22QxSeG/4Wa0LPAX8BngU30rcqOOFmTbyiiUzGFTNxNOJzWTqJR0OCcxsEm4Ij5BU9fBLpUsWxDPVD5T038l7dYUthwux6kDXWjiSTg9BDbeHxNgXgNMk/WCQxnsr/r3fG17H4cS3F43M7G56Q4wLvxUAA+23Gi5kw5IZDBaPHyqBMlmTunVb5jhm9nO8aNRhwNNVD01JO5dcuxjujG4xLKovbDlcqFIdqKyFE/wVpT4LM9tvgP0Za+PjXRMvLnZhReb9znji7i243/CdwPsoD0gYlWTDkhkM4kJQcWJZKgRYt27LUOAF/EGyYcl7HQuISXrGzEpXccGw/nz2hzfnSVUHUqy8Jk0ndqTC6PQHkZzOGHxr8wgzWxq4OjFouwPvx6MVXw7tv4RHPB6b9jsaGYp/sJmRxzhJO4JX9JM0NRynJWfrJh0OBd5bcb6rM9e8INTC3dqNAspq0nRiUKLD5GrVdwFL4SoIe9Nq0LbG/WmzQvsZZrYjcBfZsADZsGQGhyWj423w0NM2VL9uy1BgpzqNSpRw58f9JoPlNxjKNDUUAxqBFfKLtsK3wwwPjf6ipLQM8UuFUSkIygpDbst2TpENS2YwqCUrYvXrtsxxGhi7VEByJvAj9b0i5khiqIXqPgk8RihljI9vvJmNT4INXjazFUOCLNCT/DvUPs8cIxuWzGBQV1ZkTtZtGRAknRf24DfC83KexLXCMs0Z6K2wq/DfvwnhX0EabPBV4Eozu5neLdstcN9LBhgza1Y2spmBxcyewIs8FU7R4niipKW7XDsBOFfSsMwlMLN34BFgDwGPAO/GH1rblKkOjCbM7JeSJkavD5N0XIf260i6t+r9gSYONjCzRYBP4GoCfweurVEKYdSQVyyZwaCqKFTXYk5ldVuGGVOBQxXK8kKP2OLJ1PTTDFfMbA1JbQXNzOwTkq7CHeMxccXJNuakUQn0BBtIeh44v0PbUU02LJkBp6F0eQtldVuGGcvHRgVA0vXmdddHOlMJ9V/M7EZJm4XzBwFXSfpn0r4s3wkoT6icAwxZte2hRjYsmSFDg7otw4k35/QA5iDxg3juivMxS+GruPT9yoTKQWY4r5wHlWxYMkOJunVbhhP/MLNt42qRZrYNXm9lpFMVtFH1gP6LpD0HcDyZQSIblsyQoW7dlmHGFOByM9sH16FaAVia4av/1YSxITdkbHpc0X6or+7yVlhNsmHJDBma1m0ZJmwNnAMsjj+YnsJzJbZm5Dt/x+P14cE/e5G7U7pikbRp/NrMVgVekzQo4dlmdoqkgzo0SYMNMhVkw5IZStSu2zKMWDk63hmXhjdGwX5909rw4Wc9DQ/H3gsvvPWUmZ0t6ewBGGLK+zu9WRJskKkg57FkhgxmdoekNlFHM7spnc0OR9K8jZGOmc0N7C5pmpldhG8BzgL2lfS3kvZ3AJ+W9LiZPYKHH/8TuFXSBwdhvKJabmjIlW0YyuQVS2YoUbduy3BltM3iTqS32FchKDoROAKvvZPyejAqK4bjhwDMbLB8L/Pixq8sKi3TgGxYMkOJunVbMsOD1SRtHI5fD2KOMrO9KtrPCqucbQhVNs1sQWDcwA8VgBmSjhmke41osmHJDCXq1m0ZNpjZxfR+llXDlhAAknapvHBkMFd0fFh0/EJF+/Pwn/U8wMZm9j7gQuC0gRleG/8apPuMeLJhyQwl6tZtGU40krAZYYwxs4UkvSjp1wBmtjAVYbuSzjezK4FXJb1qZssAezBIEwtJk+JCcyHf6NVhnkc1R8iGJTOUqFW3ZTgxO3I2I4Af4jk8U/AcnhWBE+iwApEUr2beBkwK/94xgOMEwMx2AY4xs5WBw4EtgcfNbH1J3xzo+48kRopTNDMyqFW3JTM8kHQJLrZ5HHAP7sz/CR5uXYmZbW1m1wG/A/4DrD7AQy04APcLvQ7sh4e4bw98bJDuP2LIK5bMUKJu3ZbMMEHSdcB1ZrYu/uA+EfhZWVszOxiPFvs9vlod20lGfwCYKeklM1sFeKpIyh3EqLQRQzYsmaFE4dwekxyvMmeHlekLoUjbzsDncTWFhYF3SZpZcckUQvVGSX8MhmYwmRV8QNsD0wHMbCk8mCDTgGxYMkOJPtdtyQxJZuCGYpKkB81segejAq6jtj1wipmNAxYws0VC7ZPBYCrwR+BZYPOwyroUOHCQ7j9iyJn3mUxmQDCzrwC74tUzzwYOkrRlzWtXAvbGZfTvlfTpARto9RgWxUs3zC0phyI3IBuWTCYzoJjZR3AjsTVuYC6Q9EDNa+cB/lfSSgM4xKp7T8T9Qht0K6GdaSVHhWUymQFF0m2SdsPFJR8FLmhw7ev41tSgYGYLmNkXzOwB4DI80GD8YN1/pJBXLJlMZkhjZvdIWncQ7nMaXkr5ClwF4FRJWw30fUci2XmfyWSGBJH8TcwYPLFyMPgwcB/wGzyhM8+6+0hesWQymSFB8MWUMlgKBmb2IWAf3MiMAT4m6S+Dce+RRDYsmUwmEwh5LG/gApqTcKl/JK09J8c13MjO+0wmkwHM7AA86//3wIcknR4Myj5zdmTDj2xYMplMxtkF1zH7IPDF4qSk382xEQ1TsmHJZDIZ5xVJrwXZ/Hnn9GCGM9mwZDKZTDtZXXs2yM77TCaTAczsCeBm3KhsHI6BUVHts1/JeSyZTCbjVImgZhqSVyyZTCaT6VeyjyWTyWQy/Uo2LJlMJpPpV7JhyWQymUy/kg1LJpPJZPqV/w8uzQDqxnrsKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check if values have a correlation which each other , and if they do we can compare those values. \n",
    "f, ax = plt.subplots(figsize=(5, 5))\n",
    "corr = df.corr()\n",
    "sns.heatmap(corr,\n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the Demographics (DEMO) data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our research attempts to explore the relationship between socioeconomic status and marijuana consumption, we focus on the following variables as measures of an individual's socioeconomic status: <br>\n",
    "1. Annual Household Income <br>\n",
    "2. Ratio of Family Income to Poverty <br>\n",
    "3. Education level - Adults 20+ <br>\n",
    "4. Education level - Children/Youth 6-19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the coding scheme in the dataset uses numerical values for categorizing response, we convert them into verbal labels for better interpretation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating labels for interpreting visualizations\n",
    "income_labels = ['0-4.9', '5-9.9', '10-14.9','15-19.9', '20-24.9', '25-34.9', '35-44.9', '45-54.9','55-64.9','65-74.9','75-99', '100+']\n",
    "education_kid_labels = ['9th grade','10th grade','11th grade','12th grade, no diploma','High school graduate','GED or equivalent','More than high school','Less than 9th grade']\n",
    "education_adult_labels = ['less than 9th grade', '9-11th grade', 'high school graduate / GED', 'AA degree', 'college graduate']\n",
    "amount_labels = ['1/day', '2/day', '3-5/day', '>6/day']\n",
    "frequency_labels = ['once', '2-3 times', '4-8 times', '9-24 times', '25-30 times']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INCOME: Annual Household Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove unknown, missing, and cummulative values (12 for < $20,000 and 13 for > $20,000)\n",
    "df.drop(df[(df['INCOME']==7777) | (df['INCOME']==9999) | (df['INCOME']==12) | (df['INCOME']==13)].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of household income')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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npPWAfwJusn1ts8WKiIimjGgAl6QPAScAWwLfkPTRRksVERGNGenI3bcDk23vB7waeFtzRYqIiCaNNPH3dW6JaPtx4PHmihQREU0a6cXdiyWdTrld4laUWybOl6SVgV8D2wFzgVMog8CuB6bbnifpUGDn+vp+tq9Y6D2IiIiFssAav6S9KXfbOhlYDrjI9scWsM6SwNeBv9dFRwMH255MGQQ2TdLGwBTK3P67AceNdiciImLk5pv4Jc0AtgeWtH028G1gG0mHLGC7RwLHA3+qzzcBLqqPZwLbUs4czrM9YPsOYAlJK41qLyIiYsQW1NSzE7CF7QEA27dLehtwKfBvQ60gaQ/gHtvnSurcl7evsw1gDuXMYVngvq5VO8vvISIW2owZM3q6Xiy+FtTU82BXwgaevLg7Zz7r7AVsJ+kXwIaUs4SVu16fBNwPPFAfD14eERENWlDi/7uktboX1OcDw7wf21vbnmJ7KnAt8G5gpqSp9S07US4SXwLsIGmCpNWBCbbvHd1uRETESC2oqecTwI8lnQ/cBqwO7AC8ZyHjHACcIGkp4AbgdNtPSJoFXEY5AE1fyG1GRMQozDfx2/6dpMnANMpUzFcDn7E9v6ae7vWndj2dMsTrM4AZIyxrRESMgQX247c9m9JOHxER48BIR+5GRMQ4kcQfEdEySfwRES2TxB8R0TJJ/BERLZPEHxHRMkn8EREtk8QfEdEySfwRES2TxB8R0TJJ/BERLZPEHxHRMkn8EREtk8QfEdEySfwRES2TxB8R0TILvBHLaEiaCJwAiHJ/3n2AR4BT6vPrgem250k6FNgZmAvsZ/uKJsoUERFFUzX+1wPYfjVwMHA4cDRwsO3JQB8wTdLGlFsybg7sBhzXUHkiIqJqpMZv+8eSzqpP1wDuB7YFLqrLZgLbAwbOsz0A3CFpCUkr2b6niXJFxNg5/4KXLvQ6r93m1gZKEgursTZ+23MlfQv4MnAq0FcTPMAcYDlgWWB212qd5RER0ZBGL+7afg/QT2nvf27XS5MoZwEP1MeDl0dEREMaSfyS3iXpk/Xpw8A84CpJU+uynYBZwCXADpImSFodmGD73ibKFBERRSNt/MAPgZMl/RJYEtgPuAE4QdJS9fHptp+QNAu4jHIQmt5QeSIiomrq4u5DwFuHeGnKEO+dAcxoohzx7HbcPheMar3px28zxiWJaJcM4IqIaJmmmnpiMXTDy9cZ1Xrr3HjDGJckIpqUGn9ERMsk8UdEtEwSf0REyyTxR0S0TBJ/RETLpFdPRDzrrXLhtaNa7+7XbDjGJRkfUuOPiGiZ1Pifxdb/1vqjWu+691w3xiWJiPEkNf6IiJZJ4o+IaJkk/oiIlknij4homST+iIiWSeKPiGiZJP6IiJYZ8378kpYETgLWBJ4DfBb4PXAKMABcD0y3PU/SocDOwFxgP9tXjHV5IiLi6Zqo8b8TuM/2ZGBH4CvA0cDBdVkfME3SxpRbMW4O7AYc10BZIiJikCYS/38Bh9THfZTa/CbARXXZTGBbYCvgPNsDtu8AlpC0UgPliYiILmOe+G0/aHuOpEnA6cDBQJ/tgfqWOcBywLLA7K5VO8sjIqJBjVzclbQacCHwHdvfA+Z1vTwJuB94oD4evDwiIho05olf0guB84BP2D6pLr5G0tT6eCdgFnAJsIOkCZJWBybYvnesyxMREU/XxOycBwHPBw6R1Gnr/whwrKSlgBuA020/IWkWcBnlADS9gbJERMQgY574bX+EkugHmzLEe2cAM8a6DBERMbwM4IqIaJkk/oiIlknij4homST+iIiWSeKPiGiZ3Gx9YcwY5cDiGbMX/J6IiB5JjT8iomWS+CMiWiaJPyKiZZL4IyJaJok/IqJlkvgjIlomiT8iomWS+CMiWiaJPyKiZRbrkbtrHnj2qNa7/Yidx7gkERGLj8YSv6TNgS/YnirpZcApwABwPTDd9jxJhwI7A3OB/Wxf0VR5IiKiaOpm6x8HvgksXRcdDRxsezLQB0yTtDHlrlybA7sBxzVRloiIeLqm2vhvBXbter4JcFF9PBPYFtgKOM/2gO07gCUkrdRQeSIiomok8ds+A3i8a1Gf7YH6eA6wHLAs0D1tZWd5REQ0qFe9euZ1PZ4E3A88UB8PXh4REQ3qVeK/RtLU+ngnYBZwCbCDpAmSVgcm2L63R+WJiGitXnXnPAA4QdJSwA3A6bafkDQLuIxyAJreo7JERLRaY4nf9u3AFvXxTZQePIPfMwOY0VQZIiLimTJyNyKiZZL4IyJaJok/IqJlkvgjIlomiT8iomWS+CMiWiaJPyKiZZL4IyJaJok/IqJlkvgjIlomiT8iomWS+CMiWiaJPyKiZZL4IyJaJok/IqJlkvgjIlomiT8iomV6devFYUmaAHwV2AB4FHif7VsWbakiIsavZ0ON/43A0rZfBRwIHLWIyxMRMa4t8ho/sBVwDoDtyyVt2vXaRIC777576DUf+uuoAt51112jWo8HJ45uvVHGG7h/YJThRhfvfwdGF2/SKOM98Pf7RrXeaPdvzqOP9zTe3Q/dO6r1Rvv38uCDD44y3Oji3TeKr2+0sfru/cuo1hv1b73XuWWM43XlzCGTVt/AKH/sY0XSN4EzbM+sz+8A1rI9V9JWwKxFWsCIiMXXZNsXD174bKjxPwBM6no+wfbc+vhKYDLwZ+CJXhcsImIxNRFYlZJDn+HZkPgvAV4P/EDSFsB1nRdsPwo842gVERELdOtwLzwbEv+PgO0kXQr0AXsu4vJERIxri7yNPyIieuvZ0J0zIiJ66NnQ1DMmJC0NvAJYBrgXuN52o6czklau8e6z/UCTscY7SVtSuvZ2vr+f274h8UYVa3Vgi65Yl9geXX/BxBuXxkVTj6Sdgc8ANwNbApcDqwEfG6or0xjEeyVwHKWn0T8BvwbmAfuOl+RR4/XkBybpIGAdyoX+1wE3Ai+j7N9XE2+hYu0FvIPSm+O1lL/NlwPH2v7hWMZqSbwVgc9TfnfPBe6kfI+ftT26gRPPAuOlqedjwJa2dwM2Ah4HdqB8YU04AtjR9hbAhoCB91AOBo2oyeMDlO6vG1EObIdL+mBD8fYCTgY2Bj4I7Az8UNKuDYTb0fa7bB8PvAlYz/aulB94E8ZzvPcA29o+EJgCvBDYHjiggVhtiHcCcBrlN/cJ4EzgCuDEhuI9jaSfNbHd8ZL4l6PUuAEeAVavTS/PaSjeJNudcYt3AOvavotSI2hKr5NVL39g/yBpjfr4pcDSkpYAntdArPEeb3lg2fp4GWAF24/R3N/meI+3gu3zbT9i+/vAzvXM4sUNxXuSpKWAJSUtWR+PmfHSxn8acIWkXwBbA8dJ+ghwdUPxLpH0U+BcYEdgpqR3Af/TUDyoycP2H+lNsur8wGbT9QOT1MQP7EDgl5L+StmfdwMH0dwZ1HiOdyRwraRrgXWBf5X0aeDHDcRqQ7w5kg4EZgJvAG6r4416wZQK103AALDWWG14XLTxA0haj9KOep3tGyWtaHuUk6WMKN7OlPb9a23/TNLawB110FkT8bannHZ2J48dgD/Z/mYD8d5FuW7y5A+M0uyD7c80EK8PWNH2PWO97bbFk7QCJUncbPt+SRNtNzbyfTzHk/R8ykF6Hcpv4QhK5dK2hx0gNYbxL7T9mjHf8MDAwLj919/fv0uP423a8Pb7+vv7V+rh/qzQ39+/WX9///L1+cQef54HJ96YxXpfj/dt3MTr7+9fo7+//239/f179vf3v76/v/8FPdyvk5rY7nhp4x/O2j2O95YmN257YHCNUdLBDca7z/aVtu+vz5+Q9L6m4g3hkh7GGu/xHuphrHETr3ZyOJFycbfpTg6DY68AfFbSC8Z62+OmqaetJL3G9oU9jLe77f9sOMYrbP+2yRhtiSdpZdujm9M48ZB0ETDV9oCk5wGnAm8DLrT96oZibka5/jMRmEO51tYHTLd96VjEGBeJf4gr3ucB2wF99Yr/WMdb5H17e5U8ejFIrV6/6PbvwMcBbJ+XeAsVq3/Qom9Trgdh+6axjNWSeL8BtrY9W9JKlCnkt5Z0te2NxzpejXkxsLvtO7uWrQ78l+3NxyLGeOnV8xdKN86HKUfGVWjgSniXE4CvAB8CpgGrU2bCO5FSGxhzQyUPSU0mq2cMUpPU1CC1L1C64/6G8v29ENid8v2N+b6N83g/p/wO/lRjCfh6jbXNGMdqQ7xe9yICWLI76Vd3UvZxTIyXxL8F5Qv6pO3rGrsS/pQVbJ9fH39f0i9sT5XU1CAS6H2y6gxSu0/SWpQa6mcpNayx/oG9mnIgvcT2ifX7a3KW1vEcb1PgeOBrtbdZ07+FcR3P9ndq1+21KDMDzAZmNtlrCThb0s8pv+vZlKae7YGfjlWAcdHUAyBpEvAN4CzgvbabOPp3Yp1NuTNYp2/vS2rsoxps93sez0wejf3BS7rS9mb18RKUNs3Jki5zuT9yEzEPoExlsJ7tyU3EaEO8+n0dSTkT3q7hRDzu4w2KfUGTuaUrzkaUpuRJlNH6l9oes3FJ46ZXj+05tnen/LCaHlX3TmAF4HOU0cEfBl5AbWtsgu2Hbe8FLC/pazR/tnaJpJ/WgXA/oQeD1GwfBZxOGQ3duK54f+xxvEb3z/Zc2/tRvqu+JmMt4nh/ovc5rPH9q1anNGOtA/QDL61jQcbEuKnxd5O0l+2TehhvT9sn9zDea4G9bDc1XUMnTk8GqanMrPo+ynWab3cuyEt6v+2vj2WsrpgbAA8CdwGfpFzLOMr2w03EGxT747b/vaFtT7Y9S9IEytxOGwFXASc00TxRO1asYftmSVOBzSgz484c61g13uconSga/56Gif9vtg9pOMZxlAPaTEqvnknATpS2/zHpTj1eE39PTscWVbzxRtIPgFsoZzFTgR1s/62pz1XS54HNKXM8/Rm4hvID29D22xuI9588/cLcNsAFAGMdr/OZSTqS0hvrTMosls+1PX0sY9V4PwDOoZzxblsfT6ZUEPZvIN6fKRc6P9GLbsyDDqT7UEavN3YgrTEvsj1liOWXjFVT8ni5uPuk+gX1Mt5EYDVJfW5w/v8hevU8qUddEJuMt7Ltt9a4uwJnStqW5k6rp9jeUtI/UKb42KXGbiqRXEeZjvnTlAv061B6ojTplba3ro9nNrhvq9g+SWWerG1tzwW+JOlXDcW7EdirxjiE0sPuHNt/ayjeYZQD9b8D/wCcQTmQHguM+YG0mtA54HQWSNqaMuvwmBgXib/2OjmacsV/LjCxXoDdv6G+vSfafq+kzSkDOp4P/K42MV0+1vGqf6Hs34U8PSE21aunl/GWUp1byfYPa5/lU2ludtUJkla3fYek3QAkLQ8s3UQw25+TdA2wL/B+4G+2L2oiFrC6pDcBsyWtaft2SS+iucn8Or+/6yk9X26qz5syYPsPwDRJ61Outx0g6YW2V2swbq8OpAB7AEfXM8U+SmXhaspvckyMi8QPfJPSlfPJWobKDHonU7rSjbWX1P8PB3aq7ZsvAv6TMoVxE3YDLgK+YNsNxVhU8Q4BZkmaavt/bX9J0jLA6xuK9zHgDEmbd/3NnEm5WN8I2zMl3QR8h1JRaMpHgU0ooz7fKOlk4DLgvQ3FO4BSC74PuEbSLZSacVPxnqyE2L6OMkd+k3p+IHWZ/G1aU9uHcdLGL+lS21sOsXzM2sQGbbfTjnqu7R26ls9quJveWsAy9Q++cb2O1xV3Vdt/Vo+G/3fiNR2nxloGmGb7ez2K15N9UxlRuyJwP2XWzDFrllhA3Eb3T9IbKQfSTShnuicDv6V0Gf95QzEvZJiz3aHy3GiMlxr/bySdRLmwNJtyFfx1lC+oCctJ+jWwjKT3UpoljqLhboG2b+s87sUPutfxupwKbNOLpN8drxeBbD+kMtFdTxI/Pdq32qR60yLo6ND0/n0P+PCgnjxrDPfmMXIg5drFmyhN12NuvCT+DwJvpAx4WJYy4OEs4EdNBLO9iaTnABtQho/Po1zA68nt2KqeJatFEK9XfaXbEG8871sv4v0G2FDSBcAM279sOB62fyXpO8ArbDeSw8ZF4q+9aX5EQ4l+mJiPUu692XF8r2JX4+0H1u30HsYC+K9xHK/X+3Zxj+M1vX9/t72vpE2BT9aSmbDyAAAJ40lEQVQ+9ucDt9k+tqmgtr/Y1LZhHI3cbaFxlRwlTZP0ZUnfLk/1lrEcqTgo1lvq/8tI+iKwq6QjavfOxTreIti3yfX/CZI+CKwqaZ/azbmJeD3dP2qFx/ZVtt9MaVU4HxjTe+D2WhL/YmS8Jsdai9oR+Bnl4tnPKc1KJ4x1rOoD9f9jgL9RZlm9izLf0uIer9f7dlj9/9+BV1B6+LyM0s+9Cb3ev1O6n9iebfsnto9sKF5PjIumnjbQ8MO4d6BMdzDWPkCp5R8D3Eb5gb2W8gMb69Gt6w0xUvFMSU3foWrtriHwN6j5uyr1Ml6v962X/dyhR/tn+1tNbHdRS41/8bGe7Q/YPtP2hfX/D1BGgTZpbdufs32D7a9Q7nUw1iZ0mgw6xnqk4iD9kvYH5qrMgkhtw23q9H2oeJs1FK8T6/EexIJB/dxrvCb7ufd6/8alJP7Fx6JKjo/3IDnuAXxU0p2S7pJ0B2Vg0IcaiAWwC6Xb743AKyQtR5ny+mMNxnsAcI23KqUpZN8GY91UY60MfLmhWFAGjG1MGTD2Zkn/SOn08KmG4g3evxUp311T+zcupaln8bEHDQ/jHmQXyqCVzg/sNsoP7APzXWt0/gnYEHgM+JTt06AMlKOZLqSrAYdSDpqzbM8Gtmgw3mOUUch/o0xdfBnl+1sNuHaMYz1MuVfELMrfyZk0OF048PpB05e8lTLraVO3IN0I+EdK1+lTgf0pk9Gt2FC8cSmJfzHRi2Hcg+JdS0lK3WMTtmgo3KcoYyImAv8l6Tm1bbWpLqSfohxoJvQo3vGUaSnWpPTG6qdMQT2Tcq+DsTTUrQk7XY2bOKj1evqSD1JmcD0TeIPtm2q8/6bse4xAEv9iohfDuBdhvMds31/jTgMuqM09Tc0n8lhnNscexZtQJ2W7SNJrOiOSJTUxKnOoWxP2YuDdE7ZvBrD9JzU3S+7jdfTzHEqng068xX/umR5K4l98ND6MexHGu13S0cAhtufUHhrnAsuPk3iW9E1gb9t7AEg6ELh7zAPZf5H0VuDIetGzab2evuRMSf9NmQ30LEnnUroCX9BQvHEpiX8x0Yth3Isw3l6U6XUHauw7Jb2Gcmes8RDvXyht4fO6lt1FQ33d65z4+0nag4Y7cPR6+hLbR0iaQunGfAewMnCs7bObiDdejYvZOSMiYuTSnTMiomWS+CMiWiaJfzEmaaqk0wYtO6K27TYVcw9JR4zwvc8oX11+mqSpo91uL0jqk3SKpH+Q9CWV20GOZL29JS053L4vSpIu74yuHeX6+0q6QdLbhnl9aUm3z2f9NSVdXh/fLmnUt7qUtL6kQ0e7ftsl8UcM7a3Ar20/aHs/23eMcL2DKOMRxqNdgbfa/v6iLki9K9zLJL10UZdlcZRePeOYpKMo08gCfM/2MZJOAU6zfY6kHYHdbO+hcm/WlwHPBY6x/Z3ae+Jw4AngVsqNwqGMcj0PWInSX/wbkrYDPksZmHQfpedMd1mmUyaT+zOlJ8b8yn0zcAll8NH/Am+mTBVxMuXuR0tRhuhfVZetRUm2R9v+vqRfUG6gsR5lBOksSi+Q5YHt67LjgbUplZ+Dbf9iUDE+ROnKSt3ePpT7EL+kln8NYH/b53aV+72UuYxOA74ErC1pZn3/T2zPqNNffLl+po9QevxMqN/JFnU7l9dY/0jpGvk4pcfMP1MGZX2z7suLgONsf62W8dq6z8sCb7H9R0mHU7o73kkd3Srp1YO3a3tO136sCZxEyQ8DwIeBzSlTM5wo6W0uNzxHZbbWUyn3Eb6laxtD7eczSFoPOJry/a0IfMD2pZL+SJlS4/eU7+8Ttbx/ovzNzgN+AEwH/nWobcfwUuNf/G0j6Redf9SZMyXtQklSW1CS/9slrT/UBiRNAram1Oh2BJ5Qme75BGDXOnPm/1CmjYDyA9yBkhj3q+/9Rtd7LwIO7tr+C4GP1LJMY8Hz/axF6WP/KsrBZTNK4r29LtuNkojeD9xTB5RtC3y2zt0CcIXt11IGoT1seztKEplCOQDdW2eTnAYcN+jzeC6wuu17hijbo7Z3qvuzf/cLtk+k9M3frS5amnJnuMk8NZfMCcC+9XP6KiXpDeeNlOQ2BfgaJbm+jHKQ2J5yEOtOelfY3pYyvfXudW6lrevn927KjK7DbbfbkZSD/9Z1P0+0/Q3KgeXdnaRf7QNcX9/79a7lI93PdYED6nf1BWDPunw14O229wd2B75oeyvKnfWWre/5LWUUbyykJP7F3wW2p3b+8dS9XNehzEMz4HLj68spc+J069xkYg6wHyV5f5+SLFcCVgV+UA8o2/PUvUavrnc9u5syC+OKwAO2/6e+/kvKD7rjpcDvbD9ay9J957Kh3Gv7zvr4TkoCFWWOG2zfbPtLdR9/2bUPv6+xoMxjBOXm37+vj/9Wt7U+8Lq6X2cAS3QdMKAkwnuHKds1g8o1P9fXfX6YpwbBvahOhwHP/Jw6OlNHfI5Sqz+fUtt/nHIG9EZJ36UcXJecT9n6gatsz7P9AKV//XDb7db9uV5LScLD6ad+n7Z/1bWtkewnlArFIZK+VcvS2Z97bd9XH/8rpYJzEbAlZawAlLPHFeZTthhGEv/4dQO1mUfSkpQfzM2U0+5V63s2rq+vCmxi+03AzpSbatxPGWQ0rR5QDuep0ZGDB3/cCyxbtwOlJnlT1+s3A+tKeq7KnZk2WkDZhxpccgOl5oqktSR9ry7r3AFqEiWh/2E+2+i4EfjPul87Ue478Neu1+/jqdrxSMrWbR5P/a6Geu+fJL2iPu58To8AK0uaKGl5npr/5p3AKbZfA/wO2Jsya+lltt9Zy909v9DgeL8HXqlyd6xleOrAP9R2u3V/rhsy/xHGvwdeVd+7EU8l7qH2cyjHAofafg/lwNTZn+7BbntT7nc7pb7+prr8+cBf5lO2GEYS/zhl+yzgD5Iuo9T2T7d9NaV9eH9JP6e0IUP5Ya8i6VJKM8GRth+jnOafXZd/kDJMfqhYA5Q23B+q3DxlW+Dful6/BzgCuJQyMdlDo9ilrwNr1VrftylNB98AVpB0MfAL4LDOPDgj2NbL67YuBf7YParW5X7Kd6tMabywZgE/ZfgJ3/4F+IqkWdTmItt3Uz73KylNJJ228iuAb0o6nzLB2rcpk7pNr2XfjzLH/3BzKl1L+byvpFx36Hw2Q22320eBD0n6JaUp6L3z2d/jKd/LxZT29keH289h1v8uZaK8WZSzhxcN8Z4rKNMznE+5hnJWXb455awlFlJG7kYMQdLuwCq2/2NRlyWGJulUyoX5PyzwzfE0qfFHDO00YGM1dxPv+H+ozUi3JumPTmr8EREtkxp/RETLJPFHRLRMEn9ERMsk8UdEtEwSf0REyyTxR0S0zP8BEYJY8bxt5UsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['INCOME'].value_counts().sort_index().plot(kind='bar')\n",
    "plt.xticks(np.arange(12),income_labels)\n",
    "plt.xlabel(\"Household Income (in thousands of dollars)\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"Distribution of household income\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above visualization provides an important insight into the distribution of income in this dataset: a majority of the individuals surveyed belong to the 100k+ income bracket."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INCOME_RATIO: Ratio of family income to poverty\n",
    "\n",
    "The poverty threshold is calculated according to the Department of Health and Human Services and adjusted to account for differences that arise from geographical location and family / household sizes. \n",
    "\n",
    "The ratio of family income to poverty is thus defined as: <br>\n",
    "\n",
    "$$\\text{Ratio of family income to poverty} = \\frac{\\text{family income}}{\\text{poverty threshold}}$$\n",
    "\n",
    "The values in INCOME_RATIO range from $0$ to $4.99$ with all values $\\ge 5$ denoted by $5$. For instance, an income to poverty ratio value of $0.5$ indicates that the income is $0.5$ times or $50$% below the poverty threshold, an income to poverty ratio of $1$ indicates that income is equal to poverty threshold, and an income to poverty ratio of $5$ indicates that the income is $5$ times the poverty threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 25,
     "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": [
    "df['INCOME_RATIO'].hist()\n",
    "plt.title(\"Distribution of Income Ratio\")\n",
    "plt.xlabel(\"Ratio of family income to poverty\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCOME</th>\n",
       "      <th>INCOME_RATIO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>INCOME</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.876722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INCOME_RATIO</th>\n",
       "      <td>0.876722</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                INCOME  INCOME_RATIO\n",
       "INCOME        1.000000      0.876722\n",
       "INCOME_RATIO  0.876722      1.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['INCOME','INCOME_RATIO']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that INCOME and INCOME_RATIO are strongly correlated with an $r^{2}$ value of 0.877, making INCOME_RATIO, a continous variable, a good proxy for INCOME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDUCATION_ADULT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4235, 21)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_adult = df[df.AGE >= 20]\n",
    "df_adult.shape"
   ]
  },
  {
   "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": [
    "sns.countplot(x='EDUCATION_ADULT', data=df_adult)\n",
    "ax = plt.gca()\n",
    "\n",
    "# add title and axis labels (modify x-axis label)\n",
    "ax.set_title('Distribution of Education level - Adults 20+')\n",
    "ax.set_ylabel('Total kids')\n",
    "ax.set_xlabel('Education level')\n",
    "ax.set_xticklabels(education_adult_labels,rotation='vertical');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     424\n",
       "2.0     496\n",
       "3.0     915\n",
       "4.0    1294\n",
       "5.0    1106\n",
       "Name: EDUCATION_ADULT, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_adult['EDUCATION_ADULT'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that about $56$% of the individuals in the dataset had some college degree. This breaks down into $30.5$% of AA degree holders and $25.5$% of college graduates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDUCATION_KID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(244, 21)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kid = df[df.AGE < 20]\n",
    "df_kid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.0    124\n",
       "19.0    120\n",
       "Name: AGE, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kid['AGE'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see only two unique ages - 18 and 19 - appear in this subset of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": [
    "sns.countplot(x='EDUCATION_KID', data=df_kid)\n",
    "ax = plt.gca()\n",
    "\n",
    "# add title and axis labels (modify x-axis label)\n",
    "ax.set_title('Education level - Children/Youth 6-19')\n",
    "ax.set_ylabel('Total kids')\n",
    "ax.set_xlabel('Education level')\n",
    "ax.set_xticklabels(education_kid_labels,rotation='vertical');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.0       2\n",
       "10.0      9\n",
       "11.0     49\n",
       "12.0     18\n",
       "13.0    104\n",
       "14.0      4\n",
       "15.0     54\n",
       "66.0      4\n",
       "Name: EDUCATION_KID, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kid['EDUCATION_KID'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that 42% of all children (aged 18 and 19) were high school graduates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ## Visualizing the Drug-Use (DUQ) data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To better understand marijuana usage, we will explore the following components that provide insight into consumption patterns: <br>\n",
    "\n",
    "1. AGE_REGULAR_USE: Age started regularly using marijuana\n",
    "2. AMOUNT: Number of joints/pipes per day\n",
    "3. FREQUENCY: Number of times / month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AGE_REGULAR_USE: Age started regularly using marijuana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='AGE_REGULAR_USE', data=df)\n",
    "ax = plt.gca()\n",
    "\n",
    "# add title and axis labels (modify x-axis label)\n",
    "ax.set_title('Distribution of ages when started regularly using marijuana')\n",
    "ax.set_ylabel('Adult Count')\n",
    "ax.set_xlabel('Age')\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(15,5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows how the age participants begin to used regularly sits around 15-19 years old. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking all the columns we need and then also replacing the values of gender to Male and Female and ever use with True and False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": [
    "# remove unknown, missing, and cummulative values\n",
    "df.drop(df[(df['AMOUNT']==7777) | (df['AMOUNT']==9999)].index, inplace=True)\n",
    "df['AMOUNT'].value_counts().sort_index().plot(kind='bar')\n",
    "plt.xticks(np.arange(4),amount_labels, rotation='horizontal')\n",
    "plt.xlabel(\"Amount (# of joints / pipes per day)\")\n",
    "plt.ylabel(\"Number of individuals\")\n",
    "plt.title(\"Distribution of Marijuana consumption amount\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FREQUENCY:  Number of times marijuana  is consumption / month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": [
    "df.drop(df[(df['FREQUENCY']==7777) | (df['FREQUENCY']==9999)].index, inplace=True)\n",
    "df['FREQUENCY'].value_counts().sort_index().plot(kind='bar')\n",
    "plt.xticks(np.arange(5),frequency_labels, rotation='vertical')\n",
    "plt.xlabel(\"frequency (Number of times marijuana is consumed / month)\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"Distribution of frequency of marjiuana consumption\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1198ab7f0>"
      ]
     },
     "execution_count": 37,
     "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": [
    "df_adult = df[df.AGE > 19]\n",
    "sns.countplot(x='EDUCATION_ADULT', hue = 'AMOUNT', \n",
    "              data=df_adult)\n",
    "ax = plt.gca()\n",
    "\n",
    "# add title and axis labels (modify x-axis label)\n",
    "ax.set_title('Adult Education vs. Number of joints/pipes used')\n",
    "ax.set_ylabel('Total Adults')\n",
    "ax.set_xlabel('Education Level')\n",
    "ax.legend(amount_labels, title='Number of joints/pipes used', loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x119587400>"
      ]
     },
     "execution_count": 38,
     "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": [
    "sns.countplot(x='EDUCATION_ADULT', hue = 'FREQUENCY', \n",
    "              data=df_adult)\n",
    "ax = plt.gca()\n",
    "\n",
    "# add title and axis labels (modify x-axis label)\n",
    "ax.set_title('Adult Education vs. Frequency')\n",
    "ax.set_ylabel('Total Adults')\n",
    "ax.set_xlabel('Education Level')\n",
    "ax.legend(amount_labels, title='Frequency', loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Digging Deeper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subsetting data\n",
    "\n",
    "Since we hypothesize that younger people (defined here as below the age of 26) would exhibit higher consumption of marijuana, we will now analyze this subset of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18.0, 69.0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_age = df['AGE'].min()\n",
    "max_age = df['AGE'].max()\n",
    "min_age, max_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>EDUCATION_KID</th>\n",
       "      <th>EDUCATION_ADULT</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>PREGNANCY</th>\n",
       "      <th>INCOME</th>\n",
       "      <th>INCOME_RATIO</th>\n",
       "      <th>HOUSEHOLD_NUM</th>\n",
       "      <th>...</th>\n",
       "      <th>EVER_USE</th>\n",
       "      <th>FIRST_USAGE</th>\n",
       "      <th>FREQUENCY</th>\n",
       "      <th>PER_MONTH</th>\n",
       "      <th>AGE_REGULAR_USE</th>\n",
       "      <th>LAST_ONCE_MONTH</th>\n",
       "      <th>AMOUNT</th>\n",
       "      <th>LAST_USAGE</th>\n",
       "      <th>PAST_MONTH_USAGE</th>\n",
       "      <th>CHILDREN_NUM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83741.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.08</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>83743.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>83759.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.74</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>83761.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>83784.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  GENDER   AGE  EDUCATION_KID  EDUCATION_ADULT  MARRIAGE  \\\n",
       "4   83741.0     1.0  22.0            NaN              4.0       5.0   \n",
       "6   83743.0     1.0  18.0           11.0              NaN       NaN   \n",
       "14  83759.0     2.0  19.0           15.0              NaN       NaN   \n",
       "15  83761.0     2.0  24.0            NaN              5.0       5.0   \n",
       "23  83784.0     1.0  22.0            NaN              4.0       5.0   \n",
       "\n",
       "    PREGNANCY  INCOME  INCOME_RATIO  HOUSEHOLD_NUM      ...       EVER_USE  \\\n",
       "4         NaN     7.0          2.08            3.0      ...            1.0   \n",
       "6         NaN    15.0          5.00            3.0      ...            2.0   \n",
       "14        NaN     7.0          1.74            3.0      ...            2.0   \n",
       "15        2.0     1.0          0.00            1.0      ...            2.0   \n",
       "23        NaN     NaN           NaN            4.0      ...            1.0   \n",
       "\n",
       "    FIRST_USAGE  FREQUENCY  PER_MONTH  AGE_REGULAR_USE  LAST_ONCE_MONTH  \\\n",
       "4          15.0        4.0        1.0             16.0              1.0   \n",
       "6           NaN        NaN        NaN              NaN              NaN   \n",
       "14          NaN        NaN        NaN              NaN              NaN   \n",
       "15          NaN        NaN        NaN              NaN              NaN   \n",
       "23         12.0        5.0        1.0             13.0              0.0   \n",
       "\n",
       "    AMOUNT  LAST_USAGE  PAST_MONTH_USAGE  CHILDREN_NUM  \n",
       "4      2.0         1.0              25.0           0.0  \n",
       "6      NaN         NaN               NaN           0.0  \n",
       "14     NaN         NaN               NaN           0.0  \n",
       "15     NaN         NaN               NaN           0.0  \n",
       "23     4.0         1.0              27.0           0.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "under_26 = df[df['AGE'] <= 26]\n",
    "under_26.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also standardize the gender labels to help tease out differences in gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>EDUCATION_KID</th>\n",
       "      <th>EDUCATION_ADULT</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>PREGNANCY</th>\n",
       "      <th>INCOME</th>\n",
       "      <th>INCOME_RATIO</th>\n",
       "      <th>HOUSEHOLD_NUM</th>\n",
       "      <th>...</th>\n",
       "      <th>EVER_USE</th>\n",
       "      <th>FIRST_USAGE</th>\n",
       "      <th>FREQUENCY</th>\n",
       "      <th>PER_MONTH</th>\n",
       "      <th>AGE_REGULAR_USE</th>\n",
       "      <th>LAST_ONCE_MONTH</th>\n",
       "      <th>AMOUNT</th>\n",
       "      <th>LAST_USAGE</th>\n",
       "      <th>PAST_MONTH_USAGE</th>\n",
       "      <th>CHILDREN_NUM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83741.0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.08</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>83743.0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>83759.0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.74</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>83761.0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>83784.0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  GENDER   AGE  EDUCATION_KID  EDUCATION_ADULT  MARRIAGE  \\\n",
       "4   83741.0    MALE  22.0            NaN              4.0       5.0   \n",
       "6   83743.0    MALE  18.0           11.0              NaN       NaN   \n",
       "14  83759.0  FEMALE  19.0           15.0              NaN       NaN   \n",
       "15  83761.0  FEMALE  24.0            NaN              5.0       5.0   \n",
       "23  83784.0    MALE  22.0            NaN              4.0       5.0   \n",
       "\n",
       "    PREGNANCY  INCOME  INCOME_RATIO  HOUSEHOLD_NUM      ...       EVER_USE  \\\n",
       "4         NaN     7.0          2.08            3.0      ...            1.0   \n",
       "6         NaN    15.0          5.00            3.0      ...            2.0   \n",
       "14        NaN     7.0          1.74            3.0      ...            2.0   \n",
       "15        2.0     1.0          0.00            1.0      ...            2.0   \n",
       "23        NaN     NaN           NaN            4.0      ...            1.0   \n",
       "\n",
       "    FIRST_USAGE  FREQUENCY  PER_MONTH  AGE_REGULAR_USE  LAST_ONCE_MONTH  \\\n",
       "4          15.0        4.0        1.0             16.0              1.0   \n",
       "6           NaN        NaN        NaN              NaN              NaN   \n",
       "14          NaN        NaN        NaN              NaN              NaN   \n",
       "15          NaN        NaN        NaN              NaN              NaN   \n",
       "23         12.0        5.0        1.0             13.0              0.0   \n",
       "\n",
       "    AMOUNT  LAST_USAGE  PAST_MONTH_USAGE  CHILDREN_NUM  \n",
       "4      2.0         1.0              25.0           0.0  \n",
       "6      NaN         NaN               NaN           0.0  \n",
       "14     NaN         NaN               NaN           0.0  \n",
       "15     NaN         NaN               NaN           0.0  \n",
       "23     4.0         1.0              27.0           0.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#functions for converting from numerical to categorical data\n",
    "def num_to_category(num_val, data_dict):\n",
    "    if num_val in data_dict.keys():\n",
    "        return data_dict[num_val]\n",
    "    else:\n",
    "        return np.nan\n",
    "    \n",
    "# GENDER\n",
    "df['GENDER']\n",
    "gender_dict = {1:'MALE',2:'FEMALE'}\n",
    "df['GENDER']=df['GENDER'].apply(num_to_category, data_dict=gender_dict)\n",
    "under_26['GENDER']=under_26['GENDER'].apply(num_to_category, data_dict=gender_dict)\n",
    "under_26.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Exploring relationships between indicators of socioeconomic status and marijuana consumption patterns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adult eduation and Age of regular use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": [
    "sns.stripplot(under_26['EDUCATION_ADULT'], under_26['AGE_REGULAR_USE'])\n",
    "plt.xlabel('Education Levels')\n",
    "plt.ylabel('Age Regular Use')\n",
    "plt.xticks(np.arange(5), education_adult_labels, rotation='vertical')\n",
    "plt.title('Adult Education and Age Regular Use')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The stripplot shows that different ages that participants began using marijuana regularly grouped with their education. This helps us visualize whether education plays a role in when they begin using regularly. The graph shows that the range of ages for college graduates and above seem to be a some years old later than the other groups. Running significance tests can confirm or reject this observation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "nonull_edu = under_26.dropna(subset=['AGE_REGULAR_USE'], how='all')\n",
    "lower = nonull_edu[nonull_edu['EDUCATION_ADULT'] == 1]['AGE_REGULAR_USE'].values\n",
    "nodip = nonull_edu[nonull_edu['EDUCATION_ADULT'] == 2]['AGE_REGULAR_USE'].values\n",
    "dip = nonull_edu[nonull_edu['EDUCATION_ADULT'] == 3]['AGE_REGULAR_USE'].values\n",
    "aa = nonull_edu[nonull_edu['EDUCATION_ADULT'] == 4]['AGE_REGULAR_USE'].values\n",
    "grad = nonull_edu[nonull_edu['EDUCATION_ADULT'] == 5]['AGE_REGULAR_USE'].values\n",
    "#print(lower, nodip, dip, aa, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're extracting ages of regular use from each education level in order to run the ttests."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adult eduation and Amount consumed / day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.swarmplot(under_26['EDUCATION_ADULT'], under_26['AMOUNT'], data=under_26, hue='GENDER')\n",
    "plt.xlabel(\"Education levels (adults 20+)\")\n",
    "plt.ylabel(\"Number of pipes/joints consumed in a day\")\n",
    "plt.xticks(np.arange(5), education_adult_labels,rotation='vertical');\n",
    "plt.yticks(np.arange(1, 5, step=1), amount_labels)\n",
    "plt.title(\"Adult Education and Amount consumed/day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adult eduation and Frequency of consumption "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": [
    "sns.swarmplot(x='EDUCATION_ADULT', y='FREQUENCY', data=under_26, hue='GENDER')\n",
    "plt.xticks(np.arange(5), education_adult_labels,rotation='vertical');\n",
    "plt.yticks(np.arange(1,6,step=1),frequency_labels, rotation='horizontal')\n",
    "plt.xlabel(\"Household income (in thousands of dollars)\")\n",
    "plt.ylabel(\"Number of times / month\")\n",
    "plt.title(\"Adult Education and Frequency of consumption / month\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Income and Age of regular use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\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": [
    "sns.stripplot(x='INCOME', y='AGE_REGULAR_USE', data=under_26)\n",
    "plt.xlabel(\"Household income (in thousands of dollars)\")\n",
    "plt.ylabel(\"Age started using marijuana regularly\")\n",
    "plt.legend().set_visible(False)\n",
    "plt.title(\"Income and Age started using marijuana regularly (aged 26 or younger)\")\n",
    "plt.xticks(np.arange(12),income_labels, rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        AGE_REGULAR_USE   R-squared:                       0.004\n",
      "Model:                            OLS   Adj. R-squared:                 -0.001\n",
      "Method:                 Least Squares   F-statistic:                    0.8696\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):              0.352\n",
      "Time:                        22:08:06   Log-Likelihood:                -506.83\n",
      "No. Observations:                 212   AIC:                             1018.\n",
      "Df Residuals:                     210   BIC:                             1024.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     16.4066      0.384     42.734      0.000      15.650      17.163\n",
      "INCOME         0.0421      0.045      0.933      0.352      -0.047       0.131\n",
      "==============================================================================\n",
      "Omnibus:                        7.648   Durbin-Watson:                   1.709\n",
      "Prob(Omnibus):                  0.022   Jarque-Bera (JB):                9.110\n",
      "Skew:                           0.292   Prob(JB):                       0.0105\n",
      "Kurtosis:                       3.830   Cond. No.                         18.1\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('AGE_REGULAR_USE~INCOME', under_26)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit() \n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-value 0.352 shows so far that AGE_REGULAR_USE and INCOME have no significance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Income and Amount consumed / day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bv2mFEEJcGHwmD6XUbqDgWxNzgTAgS2vdJNiBCSGEKL38NVs1ApoA84CbtNYK6AcsPheBCSGEKL18Jg+tdbbWOguor7XO75hpDUbfHEIIIS5ggTwkeEIp9QpGz34dgQPBDUkIIURpF8jdVrcCJ4DewEGMjpuEEEJcwAK52+o08O45iEUIIUQZEciZhxBCCOFGkocQQohiC6QP8wbAAIxnPFxv1Q12YEIIIUqvQM48vjd/y1t1hRBCAPJWXSGEEP+AvFVXCCFEsQX6Vt3rkbfqCiGEMMlbdYUQQhSb3KorhBCi2CR5CCGEKLZAXoyIUqoGEJk/rLXeG7SIhBBClHqBPCT4KXAtsB/jIUEnxtt1hRBCXKACOfNoB9TTWjuCHYwQQoiyIZBrHjso0GQlhBBCBHLmcRGQpJTaYQ47tdbSbCWEEBewQJLHzUGPQgghRJniM3kope7WWn8F3Idxkbyg54IalRBCiFLN35lHsvl767kIRAghRNnhM3lorWeZv789d+EIIYQoC+QJcyGEEMUmyUMIIUSx+btg/pKvcVrrUcEJRwghRFng78zjkPnTAYgFdgJVgZbnIC4hhBClmL8L5l8AKKX6aa3vN4vHK6Vmn5PIhBBClFqBXPOoqpSqD6CUUkCl4IYkhBCitAvkCfNHgV/M17Lvw3hoUAghxAWsyDMPrfUioDPQE7hca70y6FEJIYQo1YpMHkqpfsB8YBzwmFLqhWAHJYQQonQL5JrHCOBS4CjwKnBDUCMSQghR6gWSPOxa62yMV7E7gdNBjkkIIUQpF0jyWKSU+gGIU0p9Dsg1DyGEuMAVebeV1vo5pdTVwN/AFq31tOCHJYQQojQL5IJ5ImADLEATpdRTQY9KCCFEqRZIs9UUjNeSZBf4EUIIcQEL5CHBZK31yGAHIoQQouwIJHlMVUq9AWzOL9Bafxe8kIQQQpR2gSSPm4AtQGNzuHB/5kIIIS4wgSSPbK318KBHIoQQoswIJHkkKaWexbhV1wmgtf4jqFEJIYQo1QJJHmEYt+razGEn4Dd5KKVCgNGAMqe/T2u9sdA0fwPp5uBurfVQs/wD4AOt9a4C0y4DbtJa7wkg3mJzOJxYLGCxWFxlTqcThxNCrBY/cxaxXLsdi8OOJSzcrdzucHou1+k0fqzWoqe154HFAtaQgOLwtgxv2+xzfT7k5DkIsVo8pve2DLvDibXw+nxtc56dEIsTQrxUT4fdc7u9lHmLwek0WlwLb7NP9lywWP/x+nzVq1y7k/BQzxsdnU6nuTuK3p95dgehIYWW4W3f+NjHvuIrzvqKU1dc8VmsRt0tIj5fdchjfd6W6Y9ZBzym97bvisvLMopzbAl4m00BfybF2Mde65UPgTwkODSgJbnrY87bSSl1BfAacF3+SKVUJGDRWl/hZd56BRNHMDkcTl75fTPfL99LufAQRlxlY3CHBCauSubNmVtJz8yjf5s4RvVtGvAOBSNprPjkTloe+51MItgW3pT2d79PdrVGPDd5I1PW7qNa+XBe6NWEPi1qw5KPYMHbxsGq/TC4ciQLth3hhV83knI8gysb1+SdgS2omLkfxveHo9uMFdmuhgHfQlik1zjSs3J5YsI6/txyiLgq5XjthmZ0blidN2du5ZvFewgLsfBQt4bc06UeU9ft59XfN3PsVA7XtazD6zc2IyLU+z9T8vEM+ny4iBOZuQAkxkTz8/CO5NodPPbTWpbsPIatZnne7t+CprUr8tJvm5i0KoWKUaE8fXUjBrSJhxWjYd7rkJsBrYdCz9dZkXSCkHHXcUneepwWyI3vRPhd042Vbp0OM56C9P3Q5Dq47mNIPwC/3Av7VkOd1nDDl2RUTOCpSeuZsfEgsRUjGdm3KVc1qcmHc7bz5QKjWt3bpR4Pd2/o+wM8ss3YzyeSAAs0vQFuHA0H18GvD8CRLZDYBW74ktSQaoyYsJa/th0hsVo0b/S7mDZ1q3itVz8sT2Lk1M1k5zmICgvh9RuaccMlcQCMXrCLj+ZuJ9fuZEinBJ6+upHXOpCWkcuICWtZuec4TWpV5L1BLWgUEwFTH4UNEyC6OvR8DZr181qv8gVaB3YfPc2In9ax+UA6bROq8N7AllQqF+a1Xvlkz4PpT8Da8RBZCa58GVrd6rMOPPPzenYfO83ltuq8N7Cl13rVonZ578v056+3YPGHxt+dHobLn/Jer8Kj/S+nsNPH4JdhsONPqFYf+n6EI75DwMeWTfvTeXLSOrYdOkXH+tV4f1BLwkKsHvWqXWJVAJJTMzzqQGJMdImOLd7qVfkiNtuS/23MF6XUAYyzBwvG8x67tNaN/c5kzBeqtc5TSt0BdNNa31FgXHvgOyAJI4E9p7VeppRqAgzRWj+llHoNuBpIBpoBVwJ5wGdAJFALeAHjLrBxWut25rJ/At7VWq8whxOA3XPmzCEuLs4txp9Xp/D4xHVuZd8ObcvQb1biKLBbRl3XlNs7JBS1yS4rfvmQdutedCtLqtSG31t9wVsztassPMTK4turUv2HHm7TZvUbR/vJEaSZB2eAIR0TGHnsaUha6L6ybi9Clye8xjHyt018s2SPa7hSVBivXNeMh39c4zbdmCFtuG/s3+TYHa6yp65W3H9FA6/L7fH+X2w7dMqt7LoWtcl1OJi+4aCrrG61ctzZKYH//Oa6UQ+rBZbcWYvY8V3d5s/t+zmfTFvCo45CN/J1ewHa3gPvNTYOMvk6Pw57FkHy8jNl8e15O+5DPpm301VULjyE9wa25L5xq90WO/audr4PeJ93hoPr3cuueRuWfQrHd58pa3IdT1oeZ+LqFFdRjQoRPNFT8dQk9/m/HdqWIf9b6Xa3SYjFwtJnu5FyIpMbP13iNv1HN7fihV83etSBXUdPs2DbEVdZo9gKzGy9Cua8XGDB4TBoHHw/0H0bBo2Dxn2YufGgx/4YM6Qt941d7VEHflu7n60HT7rKutiqUy8m2qNeLX+uO5FhPr65rxhtHOjzWaxwy0QY389tsry+n9FxRg0OnzzzKNmA1nGczsnzqFfzu2zHMuNJ92U+vAaqJHiPYedcGFvona4DxxlfPgrXq+4veV+GL78+AGvHnRkuH8vky2cy4ufNbpN5O7a83LcJYxbvIenYmRiubR5LdHioR71a/Ew3wkKs3D5mhUcd6NuydomOLd7q1Vf969G9e3eARG+tPoH051FLa11ba10Lo+lqWVHzmPPlKaW+BT4CxhcanQG8g9FHyH0Y3duGAr2BaUqpNkAXoC1wO1Ahf5swEsNVwL3AA1rrbUCmUqqJUqqquaErAolxbfIJj7LZmw+7fbi+pvPHmbLKo+zw8XQ27jnsVpZjd7Bp62aPaXdt3+T24bpi2L/aY1qSfW9q4bjTMnNZuP2Ix3Rzthx2O2gArPOzzXsLVPR8y3cfY+1e93mSjmWwYleqW5nDCYe2uB8oAQ7s2kir3DUe5ez6Cw5vcf8HB+Nso/B+3rfaY5szcuzM1+77HfxvHwc3eJbtXeKeOAD2/e2xvsMns1m685jH7LO3HPK4TdHudLJpf7rHfgNYsO2I1zqwdu9xt7KtB09iTy60H+w5oGd4boO5v7zV5zlbDnnUgTVJJ9wSBxj7zVu92n3Uz/tSC39OTgfo3z0my9y93C1x5MfqrV7lJBWq904H7PdSf1wxePnf2THbe70qrn2Ftu/UQXbv2u4xmbdjy4rdqW6JA2BdcprXenUwLQvAax1Yvce9rLjHFm/LzM5zrw+FBd4WA2itkzAO4IFOfwdGwvlBKbVSKTVfKfU8sA3jbMFpHvyPYZxJdAQWm/Os0lo7tNbpQP5/8wFgmFJqLEbSCTPLRwNDgFsw+h0JSP5pYD6rBfq2rEVooXbA9oWmK0pI4mVuw3anhdoxVWndoJZbeWSYlZYXt/SYv36TS4gp736dpH29qlD3Mo9pSeziM47CcceUD6dHk5oe0/VqXovIMPeq0C6xms/lNqjpeUJ7ha067eu5z9OwRnm6KPdv96FWC7Uv7mZ8Uyygjq01yyI6ea6sUS+IbQ4RhXo/rtsJEgpNX7cj7QvFXSEylGubu+938L99xLXxLKvfDWKUx/oK16E6laPoWmibrRbo26I2hZuiQ60WWsRX9lgGwFVNa3qtA4X3cYv4yh71jdAoo9mqsARjOm/1ubeXOnBp/Wq0iHPf7+0Sq3qtV/Wr+2nkKPw5WUOh+UCPOhBtu5w6laPcyrxtc8Ma5YloUKjeW0Mhvn3gMYDRTOWtXhVX3Y7uw5UuwqaauIfn49jSxVadBjXc9127xKpe61X+vvFWBzo2iHErK+6xxdsyI7xclyuoyGse5ht18/NlbeBQAPMMBuK01v+HcZaRDnTRWmea44cDzYH7lVK1gYpAFpCutbYrpTYDDyilrEAUkP9JvAKM1lrPUEoNxUgYAJOAJzCS0ICi4svXp0Vtdh45xbhlSZQLD2XEVTbaJVbj41ta8dZMzYnMXAa2iWdA6/hAFwlA62vvZum+Ddj2/0om4Rws35Q2t77PHZXrsu94JpPXpFC9fATPXduYyok1oM+HsOAd4xvjpcOJaNSDLwanMvK3zew5dpqrm8byaHcbZH8MP94C+/82/vGaD4D2vnsFfvRKG6mnc5i56SAJ1aIZ2bcJretW5ZlrGjFm0W7CQqw81K0BHRvE8NmtrXl9+haOnMrmxlZx3NGhrs/lfjO0LX0+XMyBdOObUIu4Sjzfuwl5didZuXYWbj9K41oVeO2G5jSoXp7dRzOYsCqZylFhPHW1onpCLbjhC5j3GuSchrZ3E3LxjfSq1p1F326iQ/ZiLBawN7yGsEvNu8QHjYVZz0FasnFg7PQItLgZpj5iNF3Ft4c+HzCsfG0OpWcxdd1+aleO4qXeTejYIIaRfZrwhXnNY1iXel4P2C79x8APN8OhTcZB6ZLboeVtENcWpj0GBzdC/a5w9Rs8Y63Aqew85m45TP0a5Xn1+mY0q1OJXUdPe9arm1vx7OQNpGflUblcGG/3b0HV6HCqRofzxo3N+WjuDnLtDu68LJEeTWKpFh3uUQdOZufidDpZuvMYF8dV5v9ubA5V2hv7Zd0PUL4mXPUKJHb2qFc0vAqAro1qeNSBDj7qQPdGNXh28gbWp5ygQ/1qvHZDMypEhHnUK283ALi0vA2O7YQ1YyGqCnT/D9Tt4FEHrM1v5Iuqabzw60Z2Hj5Ft8Y1ePrqRl7rFdU7ey6zYm3fMdTtCNe8BYs/MIY7PQINunuvV8V15cuQfRK2zYIYG/R6lz6149h5NCOgY0uri6rw/C8b2HLgJJ0bxvBS7yaEhlg86lX+jRSv3dDMow7EVYkq0bHFa73KOu53swO55nF5gcEsjDMCexHzRAP/A2Ixzg7e0FpPKTA+HPgGuAgjMT0NJABorb83p3kBo+Op/UA8cD3QAeM6xzEgBWihtW5qTv8hUF1rfXOhWBLwcc1DCCGEdykpKX6veQRyq+4OoBJgB57CSCDr/M2gtT4NDPQzPgejiamgJYWmeRWj58KC9gA/+Fhs/u3BQgghgiyQax7fAzUxbredDfw3qBH9A0qpP4AqWuu55zsWIYS4EASSPBzAAqCy1vpHc7hU0Vr30FoXPpMRQggRJIEkjzDgLWCBUqorEF7E9EIIIf7lAkkeQ4GdwJtAdeAO/5MLIYT4twv0gnkVoD1wEIgDzsnrQ4QQQpROgSSPnzHOOPKflXdiXAMRQghxgQokecRqrTsWPZkQQogLRSDXPLaaT4ELIYQQQGBnHpcBe5VSRzGarJxaa0kmQghxAQukPw9bUdMIIcS/VXJyMm+//TYHDx4kMjKSyMhInnzySWbOnMm0adOoUaOGa9qOHTsyfPhwunXrxh133MEddxg3p+7cuZORI0cyduxYnnnmGTZt2kTlypXJy8ujSpUqPPvss8THx/PRRx/5XWatWrWwWq3Y7XYyMjJ45ZVXaN68+TnfJxDYixGbA2Mw7rI6CNyptfbz7mMhhPh3yMzMZPjw4bzyyiu0atUKgPXr1zNq1CjatWvHkCFDuPnmm73O++2339K5c2fq1avnMe7JJ5+kSxfjzcCrVq3i0Ucf5eeffwbwu8wxY8YQEREBwMKFC/n444/54osvSryd/0Qg1zw+BO42+/MYCnxGQmZcAAAgAElEQVQc3JCEEKJ0mDdvHpdeeqkrcQBcfPHFfPfdd37mMjzzzDM8++yz2O1+3yNLmzZtCAsLIykpqVix7d+/n4oVKxZrnrMpkGseFq31OgCt9VqlVF6QYxJCiFIhJSWFiy66yDU8fPhwTp06xeHDh2nTpg3Tpk1j+vTprvH33XcfnToZfYJcfvnlLFiwgNGjR3PVVVf5XU+1atU4ftx4Bfo333zjc5l33nkn2dnZHD58mM6dO/P000+ftW0trkCSh10p1RtYiNG7X3YR0wshxL9CbGwsGzdudA1/9tlnAAwcOBC73e63iQmMs49+/fq5JSBv9u/fT2xsLBBYs9V7771HSkoK1ar56dQsyAJptroT45Uki4HBwD1BjUgIIUqJ7t27s3TpUtauXesqS0pK4uDBg1gsFj9zGsqXL8+oUaN47bXXfE6zePFiIiMjXckjEI8++iiHDx/m+++/D3iesy2QM49TwJda69lKqQeBtCDHJIQQpUJ0dDSfffYZ7777Lu+88w55eXmEhITw7LPPsmPHDo8mpsTEREaNGuW2jPbt29OrVy+2bNniKnv77bcZPXo0VquV6Oho/vvfMz1dBLJMq9XKq6++ym233caVV15JzZqe3UsHWyA9Cc4GPtBaT1NK3QLcorXufU6iOwukJ0EhhCi+onoSDKTZKlprPQ1cXcSWO6sRCiGEKHMCabbKUUpdBSwD2lEKO4MSQghxbgVy5nE38ACwArgfGBbUiIQQQpR6gbyeZAdw/TmIRQghRBkRyJmHEEII4UaShxBCiGIrMnkopaKVUnFKqZpKqReVUnXPRWBCCHGuOBxOfl2zjz4fLaLNq7Pp89Eifl2zD4fD/6MMRVm+fDlKKX7//Xe38j59+vDMM88AcOjQIVq0aMGMGTPc5nvsscc8ljd48GD69+/P4MGDXT/z588vUYz/VCB3W00CPgf6AZuBL4GewQxKCCHOFYfDyX3jVrNox1EycoyXGB49lcOzkzcwfcMBPr+tNVZr0U+T+1KvXj1+//13evXqBYDWmszMTNf4yZMnM3jwYL7//nuuueaaIpf35ptvUr9+/X8cz9kSSLNVOeA3IE5r/QYQEtyQhBDi3Plt3X63xJEvM9fOwu1Hmbp+f4mW36hRI/bv38/JkyeN9f32G3369AHA6XQyZcoU7rzzTnJzc9m2bVuJ1nUuBZI8woFHgNVKqSZAdHBDEkKIc+frRbs9Eke+zFw7Xy3cXeJ19OjRgz/++AOn08n69etdr3hfunQpNpuNqlWr0q9fP8aPH1/ksp5++mm3ZqvU1NQSx/dPBNJs9TjGrbqvAbcBDwc1IiGEOIcOpGWWaHwg+vTpw8iRI4mPj6dNmzau8gkTJpCSksJdd91Fbm4uWmueeOIJv8sqLc1WgSSPblrrp8y/P1ZK/R+wMogxCSHEOVOrUhRHT+X4HV9S8fHxZGRkMHbsWEaMGEFycjLHjx9n27Zt/Pnnn4SEGFcDXnjhBX755ReUUiVeZ7D5TB5Kqbswni5vrJS61iy2YjRjPXsOYhNCiKC767JEnp28gcxcz6arqLAQ7u6ceFbWc+211zJlyhQSExNJTk5m5cqVDBgwwJU4wOgn5KmnnmLkyJEsXryYG2+80TXu3XffBYxmq6ioMwntmmuu4ZZbbjkrMRaHz7fqKqUigFrAcxhNVmC81+qw1rrMdAglb9UVQviTf7fVwu1H3RJIVFgInRvGlPhuq7KqqLfq+jzzMBPEHqXUfUAbINIclQgsOPuhCiHEuWe1Wvj8ttZMXb+frxbu5kBaJrUqRXF350T6XFz7gkwcgQj0OY8aQLI57ESShxDiX8RqtXBdyzpc17LO+Q6lzAgkecRqrTsGPRIhhBBlRiDPeWxVStUOeiRCCCHKjEDOPDoDe5VSR8xhp9ZakokQQlzAAunPo+G5CEQIIUTZ4e85jxe01q8qpX7AuEjuorU+9zcVCyFEsDgcsHESLP0E0vdBxTrQ4QFo1h+s/7znipSUFPr27UvTpk1dZe3bt2fMmDFuZQDffPMNn376KZ9++inz58+nZs2aABw7dowuXbrwyiuvuJ77mD59Os899xyzZs1yTffRRx8RExPDzTff7LbcZs2auV6Hku+dd95xzfdP+TvzmGr+/rxEaxBCiNLM4YCfboNd8yA3wyg7fQSmPgKbp8DAsSVKIA0aNGDs2LGu4ZSUFBYsWOBWVlBCQgIzZsxgyJAhgJEoatWq5TbNxIkTGTx4MBMmTOChhx7yu/5KlSr5XFdJ+NwjWut15p9rgN7AUxjvuNpw1qMQQojzZeMk98SRLzcDds6FjT+f03CuvfZaZs6c6RqeN28eXbt2dQ0nJyeTlpbGPffcw5QpU8jNzT2n8eUL5IL5GOAvYDxwOfAN0DeIMQkhxLmz9BPPxJEvNwOWfgwXD/jHi9+xYweDBw92DT/66KMeZU2bNnV1DhUTE0NUVBTJyck4HA5iY2OJiIhwTTtp0iT69etHxYoVadmyJbNnz+baa6/Fl7S0NLd11ahRw/Wqk5IIJHlU01p/ZP69VinVv8RrFUKI0iJ9X8nGF8Fbs1XhssJ69erF77//Tl5eHn369GHx4sUA2O12pk6dSp06dZg7dy5paWmMGzfOb/I4581WBUQppWIBlFI1kc6ghBD/JhWLeKq8qPFB0LNnT+bMmcOqVato3769q/yvv/6iWbNmjB07lq+//ppJkyZx7Ngxtm7des5jDOTM40VgiVIqHagA3BvckIQQ4hzq8IBxcdxb01VYOejw4FlfZeFmK4DXX3/d9XeFChWIjY0lPj4ea4GL9RMmTGDAAPcmtP79+zN+/Hhq1KjBl19+ycSJEwGIjo5m7NixHs1WACNGjPC4A6u4fL5VtzClVIzW+miJ1nYeyFt1hRB+ebvbCozEUb9bie+2Kqv+8Vt1lVIfa60fVEotxXzOo0AHJTnAL1rrD856xEIIcS5ZrTBonHFX1dKPCzzn8SA063dBJo5A+Gu2yu9M9yYv48LN8ZI8hBBln9Vq3FFVgruqLjT+kscgpdQzwBZgGrBYa+1q41JK3RDs4IQQQpRO/jqDehRAKdUY4yHBx5VSacBMrfWPWuuS3b8mhBCizArkxYhbMM4+UErFAFcGOyghhBClW5HJQyl1K2AHIoC3zR8hhBAXsEBuI3gEmA3cBsRjNGEJIYS4gAWSPDLN3ye11tkE9mChEEKIf7FAkscuYBkwRin1H2B9cEMSQghR2hWZPLTWQ4FWWutpwBda6+HBD0sIIURpVmTyUEo1BWYopTYCQ5RScs1DCCEucIE0W30IDAWOAF8DI4MZkBBCiNIvoIvfWusdSimn1vqIUupkSVeqlArD6GQqAeMW4Fe11r8ppUYAa7XWcwtM+yPwudZ6fknX683UdfsZtyyJ6IhQHuhan9Z1q7IhJY0P524nLSOXgW3j6d86jiMns3lvtmbzgZN0aRjDg90aEBFa/LfTj1+exC9/76N6hQgeubIhjWIrsmTnUb74axe5dgdDOibQo2ls8TckIxXmvQb7/oaEy+CKZ3GERvHlwl3M2nSQulXL8XgPRXzVcrD1d1j+OYSEQ6dHILELHNoE89+AU4ehxSBoc6fPVTlyc1n+0WCqntjISUs0tZt0onb/t8h2Wvh47g4WbD9Kk1oVeLyHIqZ8BKz9Hv4eC1FV4PInoXYr2LscFr4LOaeh7Z3GO4TS9rF+zENYju8CiwVHswG0GPAcADuPnOL92dvYfyKTPi1qM6RjApacUzD3NUheDvHtodvzEF4eln9hvKeoUhx0fQ5iGjJfH+brRbsBuOuyRK5QNXxu34G9uxj51Y/szq1KvPUoXbv24LYr2xn7Zu6rcGij8cK8Lk+CJQQWvQfbZkL1RtD1eahUx2e9euHrX9mWWYHokDxG9azLtV3aM2P9AR78YQ12pxOrBd64oSkD2yWQnJrBu39oklIz6Nk0lns718Oalwnz/w/2LII6l0C3F4z9umoMrPsJyteAK56Bmk3J2zGXn78fjcrTHA6pSfXrXqdVy1bsPZbBExPXsflAOjUqRDDq+qZc1qC69zpQnHq1aQrMfgEyT8BF7eG6T414wGsdWJ2UyifzdnI6O4/bLq1Lnxa1IW2fsb4jW8F2NVw2wne9Ogu81iuLpVjLWDtnArmLPybX7mRBjZu447Y7qVUpqkTHlhCLhV+mTKLzxheo4kzHWfdSIgdPghA/h2wvdYDdC2DxB2DPgfb3QaNeJdxjZwSSPFKVUsOAaKXUTcCJs7De24BjWuvBSqmqwFrgN+Ay4CO/c55Fi3cc5aEf1riGl+48xrSHLuOWr5ZxMisPgBV7UqkUFcboBbtYsScVgHXJJ8jIsfNi7ybFWt+va/bx/C8bXcMrdqfywz2XMmTMSnLsDgCW7TrGz8M70uqiKsXbmF+GwfY/jL/3/w1ZJxhd6VHemGG853/N3hOsT0njz0HRWH+8FfNdl0bluvcv+LYvZJgvTU5eZhyELx7odVXLPx1Kh/QZrvPWpE2pOKtV5a2sG10H6HXJJ9h55DQTLj8Ovxa4TLZnEdw9G8Zef+YNpkmLoFwMSZP/Q+OT6wmz2gHI2/g2u2o1JL5DPwZ/tZz9aVkA/L33BGEhVm5LHgmbJp/Z5lOHjAPczKeNspQVkLwCfdNC7vp2FXaHsc1Ldx7j94c7o2IreN2+EV9MZqm9BQDb7PFkzJlPzdp1uWrZENi7xJho32rIyYDwaFjw1pmyQxtZ3H2yR72a+lAnHvtsIjvsRl/UmfYIHph+mJXNjjP8+79d0zqc8NTkTTSoWYknJq5j19HTrs/PaoF7T/wX/v7uzDafSIbm/WHaY2c2IGkJ3DGNn7/9gEEh843PybmTPb8MIqfxJgZ9uZQD5r48lZ3HHV+vZN4j7bnoOy91YMPEwOrVwGisE28/E8P22TB+AAz7C7ZO96gDR+5awW1frSUz1/isl+9OpVp0OB3/vAkOrj+zPx15vHX6es96NayD18+uOHLtDu/16tK6AS9j18blNFswjFCLAyzQ9vAmhn1ZkbtuuKZEx5boUAsPrhtOuMWYlj3z4IdBcJuPLnHXT/BSB6bCuH5G4gDjf++u2RDXJuDt8yeQZqu7gETgKNDGHC6piRj9hABYgDylVCUgQ2udq5R6QCm1Rik1HWgAoJSqqJSaoJT6Qym1USk1XClVSSm1UykVYk7zplLK+xHPiz82HXQbzsy18+3SPa4PN99v6/a5Ptx8swrNG4jC8xw7ncO4ZUmuxAHGwePPLYeKt+C8bOOftaAt0zzWt+voabavnosrcYBRsVZ+feag4Zp/qs/VVU1d5zZc13qYvX//4bG+FbtTOb5hlvvM2Wmw8ivPvhPW/ciBtEzCLHZXUajFwaGVk1mfkub6B883a9NB2DrNfRlbp3mWpafw54r1rsQBkOdw+t3HK+0N3YbXOBswa13SmcThWt9Uz/UdWMcff+9wK8rMtfPd0iSO2su7lTux8srEQss0TViV7Eoc+WZtOgRbCq1v+x+weYp7WcZRWPU1DSzubxBKsBxiwp8LXIkjn93pZN7yVZ51YPOUgOvV8TW/em7EgbVw+qjnPspOY/uKGa7Eke+PNTvOJA4/61uxO5Xjp3M811dMPutVMRxaOdlIHKYwi52GJxYz+e8Ut+mKe2w5sX7amcRhciR5ryuA5/9rxlHjTMReYD85HaCnF71RAQrkbqt0YDmwEJivtU4tYpYiaa1Paa1PKqUqAJOAF4CewB9mb4WPAJcC12G8wReMJPKj1roH0AMYobVOAxYBPc0Ecg3gpRZ7lxAT7VHWIq6yR5mtRgViyoe7lSV6mbcodau5z2OxQLO4ikVOV6SQcKgU715Wrb7H9kWGWYmt5aVPkzqtMHK4+/y+nAyt6jac4QynSo1YEgrFHVM+nArVvayvlpdOaGo0JsJLK2B4tbrEV4ki1OoeX2JMNFSt5z5x1fqeZdZQEmrX9Fiuv8+vtuWYx3BijcoQXb3o9UVUIiHWff8AtIivRDmyCpU6ubJZLa8xNK5VkYhQ93/PutXKea6vUjxUa1BobgvUaslhp3tdznKG0apRfY99CZAQVwvPOtAw4HoVFWvz3IiIihBZCaomeoyqVNtz+ro1qxrzFFQ10Xu9iiz542Y+61UxhNdo6FG2z1oLVdPzrLY4x5bsmCZ4dLVUrlD9K6hwvTDrQNHT/XOB3G31FTAI42HB25VS75+NFSul4oF5wFit9fcYB/7pQH1gk9Y6W2udC6wwZzkEXK+UGoeRbMLM8tHAEHP+P7XWAX8luantRXSxGR+I1QJDOibQr3Ucw6+oT4hZqdolVmVIpwRevb45FSKMClunchTPXdu42Ns8rEs9WsQbFSgsxMLjV9kY0DqeQW3iyW9m7dGkJte3LGa3lxYL9HkfIs3KWT4Wrn6TEVfZaFDD+LYbGWblpd5NqdR6ADTKv2HOAq0GQ8tbjesFVnOX1mkNHR/2ubra141kr9Noy850hrPR2oiKvV/n+V6NqVM5CoAKEaG8en1zQi8dBnU7masLgcseg1a3QLthYDGrX/3u0PZuLu77CKscZw4qq63NaTHwJWpUjOSZaxoRbh5MG9eqyINdG0Cv96BcjDFxuRjo9a5xHaJmc6MsJAKuGsXVbRrRt0Vt13L7tqhNTz/XlYY3zaMyxqW9KqTTovxxbu+UCL3fP3NwqxQPPV6FK1+GKubBMbw89HqHmy6t71mvLomnZ+uGRJJtrsVJ+yqn6NPpEprXcT9g1q0Wxc3tLuI/fZoSGWZsc4Ma5RlxlQ2uecv4fMH4vPu8b1y3qtPaKLOGGZ9lq1upU68J2x21XZ/TlJoP0LR+Ai/2bkLBY2bv5rXocklzzzrQ6eGA61W51jcb187yWcOg70cQEmZ81oXqQNNLOjGkY4Irji626tx0aX3jMwwzD+BVEuHKl73Xq5CS97Hhs14VQ4set7O83BWu4V/tneh23RAGd0go0bFlWJ/LmRXa1ZVA7JYwrDd+7jsQH3WAVoNxfSlo1BuaB9wwUzSn0+n3x2azLS80vKyoeQJYZk2bzbbFZrN1N4etNpttivl3dZvNts1ms0XZbLYQm8222mazXWGz2d6z2WzDzWm62my2pALLW22z2X6x2WxNvawrwWazOZOTk52+JB097TyUlulWduRklnPXkVNuZaezc51bD6Q7c/PsPpcViB2HTzqPncp2KztwItO599jpEi3XmZPhdB7c5HTm5biKHA6Hc9vBdOeJjBz3aVP3OJ1p+9zLTh11Oo9sC2hVjrw8555FPznTNs1xOu1n9kdunt259UC683R2rvsMR3c4nScPu5elH3A6j+1yL8tKdx5Y8pPzwMYFHus8fjrbuf3QSffC3Cxjm3Oz3MsPa6fz9DG3opTjGc59xzMC2r4Txw47p06Z4Nywa6/7iOxT5j4usH12u9N5aLPTmZXuNqmvejVx7jLnjj3u9XHTvhPOF39d79y8L809jowc57aD6U6Hw3GmMC/HiCGn0LYc2WZ8hgXYU/c6l/w5xZl8YL/HchdsO+zcX3h/eKsDxalXR3c6nTvnO51ZhT4np9NrHTiUlulMOlqo3melG/szkHp1FnitV8W0b492Ll+71nkyyz2+khxb7HaHc+f2Lc7TG35z2xd+eakDzrR9xv97MSUnJzttNpvTZrMlOL0cx4vshlYpNR54QWu9WylVA/hYa12i9KWU+gDjbCa/1/aOwMta69fM8XcCD2HcHhwJvISRPj8CjmFctG8GNNFaZ5t3aQ3QWntcRZNuaIUQovj+cTe0BXQAtiql9gJ1gGyl1AHAqbWu7X9W77TWj2Bc1/A1fgzGrbyFNfMxSwhG85UQQohzIJD+PM7eFZYgUEp9A9QG+pznUIQQ4oLhM3kopV7QWr+qlPoBt3s7QWt9S9AjC5DWesj5jkEIIS40/s488m8c9nOJXwghxIXI5/1uWuv8J8G2ADcCz2A0Dckr2YUQ4gIXyM3SP2EkkGcw+vYYG9SIhBBClHqBvhgxv+lqXXFe/yGEEOLfKZDksVUpdSvG0+CtgWNKKRuA1npbMIMTQghROgWSPBqZP3cXKPsC4w6sbsEISgghROkWyHMeXc9FIEIIIcqOkr9dTAghxAXHZ/Iw+9cQQgghPPg78/gdQCn12TmKRQghRBnh75pHrlJqJdBQKdXCLLNgvBCxY/BDE0IIUVr5Sx5XYrxF9zNgOB7djAkhhLhQ+UweWms7sFcpdR1wL9AU2IaRTIQQQlzAArnb6guM/sNnAwnAV8EMSAghROkXyEOCDbXW+Z0T/6qUWhLMgIQQQpR+gZx5RCqlygEopaIweu0TQghxAQvkzOMDjBcibgSaAP8JbkhCCCFKu0BeTzJeKTUDqAfs1lofC35YQgghSrNAX8meCqQGORYhhBBlhLzbSgghRLEVmTyUUk+ci0CEEEKUHYGceVyrlJI7rIQQQrgEcs0jBtivlNqN0QGUvNtKCCEucIEkjz5Bj0IIIUSZEkjyyAPeBGoAE4H1QFIwgxJCCFG6BXLN40tgDBAGLMB4aFAIIcQFLJDkEaW1notxrUMDWUGOSQghRCkXSPLIUkr1BEKUUpciyUMIIS54gSSPe4GhGHddPYHRMZQQQogLWCDvtkpRSr0O2ICNWuvdwQ9LCCFEaRbIE+YvAJ8CnYCvlVKPBj0qIYQQpVogzVa9gC5a68eAy4GbghuSEEKI0i6Q5HEIKGf+HQ4cCV44QgghygKf1zyUUksxXkdSA9iulFqH0RmU9OchhBAXOH8XzKV5SgghhFc+k4fWOglAKdUOI5FEFhh9f5DjEkIIUYoF8m6rbzHebXU8yLEIIYQoIwJJHtu11t8EOxAhhBBlRyDJ42el1I/A5vwCrfWo4IUkhBCitAskeTwA/AycCHIsQgghyohAkscxrfWbQY9ECCFEmRFI8jiqlPoC+BvjuQ+01l8GNSohhBClWiDJY4f5OzaYgQghhCg7Akke/wt6FEIIIcqUQJLHTxjNVVYgEdgOXBbMoIQQQpRugfTn0SH/b6VUZYw+zYUQQlzAAnmrbkFpQL1gBCKEEKLsKPLMo8DbdS1AdeDPYAclhBCidAvkmkfBt+tmaa0PBSsYIYQQZYO//jxu91GO1vq74IUkhBCitPN35tG40LAFGApkAJI8hBDiAuavP49n8/9WStXHeDX7NODRcxCXEEKIUiyQC+YPYCSMx7TW04IfkhBCiNLO3zWPOhhPl6cC7bTW0hmUEEIIwP+ZxyYgG5gLfKKUco3QWt9yNlaulGoPvKm1vsIcHgGs1VrPLTDNj8DnWuv5Z2OdHo5sg3U/QHg5uGQIlK/OiYwcfliRzInMHG5oVYdGsRWDsmqX9APw93dgz4FWt0LVs/goTdIS2DINqiZCq9sgLApSd8Ga8RASDpfcDhVrkZ6Vy08rkjlyKpu+LWrTrE4lv4tdtv0gsxcsJIH9DOh8MZG2rgDM23qYBduP0LhWRW5sVYfQECtbD6bzy5p9VI4K5+Z28VQuF87RU9n8uGIvGTl2+rWOo3718pCXA2vHw9FtYOsJ9a7wv21OJ2z6BZJXQHw7aHoDWCys2Xuc6RsOUKtSFIPaxhMdEcq+E5n8tDIZgEFt46lTOcr3Yu12do8dTlTKIjLKX0T9m9+Fmk3JszuYvGYfWw6k06Vhdbo2qmHMsGs+bJsFMTZoeSuEhrPj8Ckm/51CufAQbmp3ETHlIziRkcPsCZ/QIuV7iI6h5s2fUCm2HnmnUkn+uBeRWYfIjKpNvXu+M+pAbiasGQepu6Fxb6jb0VjflmmwZxHUbgXNB4DVysZ9afy2bj/Vy0cwqF08FSPDIP0AN38+nxWp5SkXHsqk4R1RtSoZy130X9gxG6o3gW7P+64DDgdsmAj710DCZUYc/urVnFfhxF5oej20vw9CArmh84ycPAeTVqew4/ApujeuQacGMcWa368TybBmrPF3q8FQOR6yTxllaSlG/Ylr47Ne+TJjwwFe+HUjWbl2rm9Zh//0bUp4qNXrsYWMVPj7W8g8DhcP8luv/th4kP+bsZXsPDtDOyVwT5f6AOxYMYvxfyzC4sijWYeeXN/9cqxWi9c6cCg9ix9XJJNrdzCgTRx1q0X7rFczlqwmd/47VHSepHaHQZQz/6d9sTidTq8jlFKX+5pJa/2X36UGQCn1FDAYOK21vtQsmwwM0lrnFpiuRMlDKZUA7J4zZw5xcXHuI49o+PIKyM0whitfRM69S7j6s9XsOnIagPBQK5OHdyzyYPqPZaTCpx3g1EFjOKIS3LcQqtQt+bI3T4EJd2C+DBnqd4fe78HnXSA7zSgrH4vj/qX0Hr2JzQfSAQi1Wvjh3ktpm1DV62J/X3+AB77/2zXc1bqG/w2sx3eZHXlpyiZX+YDWcdzRMYEbP1tCTp4DgHrVo5k8vCO9PlzEvhOZAESFhTD1oU40mP+AEXO+6z+Dln6+p8wZBQvfPTPc+XHmx93Hnd+sxGFucpu6Vfj01kvo+d8FHM8wqlWVcmHMerQLNSpGel3svrc7Uuf0me1IccYQN+xnnlhsYdLqFFf5qOuacnvUEvh1+JmZm1zHjis+oc9Hi8nMtQMQVyWKaQ9dxpfvPc+TuV+6jkPZhBH63D6Ov96QGNJci9jtqEniY7Nh2mOwc45ZaoGB38GxHTDn5TPra38fKxo9zS2jl5FnbnSTWhWZdndTur/2K7sdNYx5TXve6AVf94TkZWeWEVkZx0Nr6f3VRs86sPkNWPHFmWm7/weq1fderz5pD3lZZ6ZtdiP0L96r8e4fv5rpGw66ht8Z0IL+reP8zBGgkweN/7PMVGM4qircv9TYjvx9YbHCrRONxFioXtH9Ja+L/Usf5o7/rXQra5dQhQn9qnkcW7h3AXx9FRzbbpSFRMBdf3itV+XCQ3li4jq35d51WSKDqu2m75RssogAIJ7D9GzdkNfltTwAABmvSURBVB5tmnjUgXF3t/v/9s49zsqq3OPfuQ93lDsGKsJ+RBFFJQnkpiiaKEppmmUElgc1vJWX0jrnlB4r7WhpWXi/oqUWqVghhAISKGql+AMlrqIgclPkNjPnj7Vm9p6ZPcDQXu8cZtb385nP7L32u9/fu/a71nretdaznsUpt73Ems3bAGhVWshzEwfT7dmv1CpXd67uxUkzv0AqfxUAOyoKWND7ai74wX0AB0taWjPvu5ow/7cNxG54FxgDPARgZm2ALZJ2+HmWC4HVQEf/eWvgbqAt0BW4E3gUFyo+JanMzH4MvCrpiT26gtceTt9cgA3LWTzrdyxZ27EqafvOch6bt5wbzzri38ttXSz8Y9pwgGvU//44DL363z/3/LupquDgCszcu9KGA+Dj93nlxam8tbptVdLO8goembusTuPx4KxF1d7PKO/HitmTeGBr12rpT722ijyoMhwAS9Z+wl0z360yHACf7ijjuTlvMDHTcADMm7Rr4zFvUq33D6/8fJXhAHhl2Xp+8+KSKsMBsH7LDqa88R4XDs7ew+v68ZuZ7S1d+ZDt8+/j6ddGVjvugTlLuaBVjWt4awq/LZ5YZTgAVq7/lLtmvst5239PXkZMhxJ2oMnXkKrYWE3vgLwPYe6vMio4QAXMnwQfvkM1XrmPRzd8qarRAHhr9SaWzZrMv8q7U+3EwB1PTOXSTMMBsHUDy2Y9xlur0w8sO8sreOTlpfR/5/7qx86bBO17krVcZRoOgH8+DaNug9I9e/Bas3lrNcMB8NDLS3NjPP75ZNpwgHs95xfVjWhFOcy/x/XqMpk3qU7j8cNnFtZKm7d0PWvmTqNjjbaF2belDQdA2TY2z3uYp187odr3H5izlGzP9I/PX05eyXy20r8qbQUdWf/6H3l4Z/NaZeDXM5dUGQ6AzVt3Mn3OXL6WpVyt/tcRVYYDoCivjPxFz2bNcyX1DU+SMyQ9CezISBoJ/NnMOgGXAQOA0UCx/7wnMFnSycDJwJWSNgKzgJFmVgCcCvx+jy+iqHmtpPySFrXSmhcX7PEp602Wa8ialpNz50FJq1qHNSut/QTerLju4Yaan+VTTklJMc1q/E5FBXlZf7tWpUW1L7WkBPJraBbXvhfVv1Rj6Kmoea1rcHq189J8F/mrqDFEUUEe+cUtKCqont68uLD2b5xfSGlJ7fy1Ki1kG7XTC1rWHpYpIx9KW1Oz4aeoRZY8l2bNc2Fptt+ugratW9U+L1BQ2rJWWrPiAiiqUTaKm+9xuSK/sPY93QXFBfkU5le/tmx52ytq/m4AJVmGo4uaZy1XddGsOHsTWlKSpVebRa+ouFnWcpWt3hQV5NOssLZVqcgv3ONyX1TSnGzlakdh7fJSlldcKy2TBjMeWTgVeA44BHhT0jY/fDXPf/4BcKaZPQxcD1U1cRIw1n9/mqTte6x4zFholfG0/JnPcujxZzGoZ7uqpPYti7ngcwftVYb2iN6joHPf9Pv9Dtr103Z9OP4KKMyoCMd+HQZMcBqVdO7LEYNOY0TvTlVJbZoVMf74jGNqcMmI3pTkp3sT5xfOoOPwCUw8oVe1yj9haE/GD+5B+5bpQjioZzu+Ofhgju6e7ul0bl3KmEFHuGurpKDYDRfsiuHfrfH+Oi4a0oOWJelKc+ZRXRl3/MH07JhuHHt2bMkZR1XvJWWyrHO6h1FRAcvyDqBwwDeZMLRnVXphfh4TT+wFQ65y11rJgAl8eZDROWNI7Ojubfnm4B48tt9F1XpFG/Pb0nPM9azMT19LRQWszuvk5guOGZs+uLCZu5/DrqNa92XoNYwf3IM2zdKGaUTvTnQbeA5DSt4ls4eQRx5fOeV4N9aeyf6H0H3gObXLwOAeMPSa9HF5+TD02rrLVYuOVGPQZbt/AMigbfNixh1/cNX74oJ8Lh3ea4+/v0v6fBHap+dtaW/wuYvdnFGVYCsYNDFruaqLW84+slbauf0/Q5sBX63VtjBoIhycMRvQogOlnxuftVxdc+qhtc57xUkpzh99Op1I96D65S3msBO/wjeylIELB/fg8K5pg9V9/+acOvDorOVq4KixvFyWXtq3qaI5pf2zrhOvos45jyTw8xGTgYHA05JGm1kHYDZwJLAdZzyuAs4AFkv6lZkNB+6XdKA/z6vAcuB6SW9m0cg+5wGwdRNoqivkqZFQUERZeQV/1Ro2bNnBiN6daNO89hNjTtm5DRY9D2U7wE6tV4XbLZveg3emuQnYg3wk/e2fuDwXFEHqFCgsoby8ghcXr2Xt5m2c2LsT+7fY9VPH6o2fMvPlv3EQqxgwYLAb0wWWrfuEOe+uo3eX1hzVzRmIjVt2MG3hB7RtXsQw60hBfh47ysqZ/vYatmzfyYjendK9keVz3VzUIcOrzrlLPngLVvwNuh0HnQ4DYO3mbcx4ew1d2zZjUM925OXlsXVHGdMWusg6I3p3orRo10+0K2c/xvZXH6aka18OOO1qaLYfAK+v2MDC1ZsYeEg7N/kIbkji3RnQwaD7AAA2b3V5bl5cyAmHdqSoIJ+y8gqmz36ZNgvuoE2nHvT64g/IL3T5XvbgBMqXz6NZj+Po/MWfpsvA0lluIrrnCGjtG6O1i2DZbDjgaOjiGq+PPtnOCws/oEOrEob06kB+fh7s3MYDf3ie2/+ez8Ed2/LkJYPTGVz8FzfhfUA/OPK8XZeB1W/AqgVw4CDokHJpdZWrVx+EDwV9z0lP8NeTV5Z+xDtrPub4Xu35zH456oWDmyjWVPfaTnU9jIoK5/CwcaWr/y29AcxSruri/Y2fcvnk19jw6U6uHJHi5D5+37wsbQvlZe63/3Q92Cm7LFcr12/h5ucWsnnbTq48KcWR3dyxm9csZ8qTD/NpeR7Hnvo1jurhykW2MrBtZxnTF65he1k5Jx3WKd3jzlKuFq1ez1tT/pe2ZR9x5KiL+SS/NSeeeCLUMefx/8V4XAkMk3STTx8HfAtYC5QC38f1tX4BrAM2AH2AwyRt815aZ2eGj6+hUbfxiEQikUgtVq5cuUvjUT8/uhzjL2iAfzsnI/1e4N4sX+lTx6kKcMNXkUgkEkmABjUeucDM7sd5X53ewJcSiUQiTYZ93nhIGtvQ1xCJRCJNjf9P3laRSCQS2UeIxiMSiUQi9SYaj0gkEonUm2g8IpFIJFJv9vkJ8z2gAOD999/f3XGRSCQS8WS0mVlX1DYF49EF4Pzzz2/o64hEIpF9kS64QLbVaArGYz4wGBeht2w3x0YikUjEUYAzHPOzfdig4UkikUgksm8SJ8wjkUgkUm+i8YhEIpFIvYnGIxKJRCL1pilMmO8xZlYK9AVaAB8C/5QUdFLIzDp6vXWSNoXUauyY2UDgeNL3b5qk2vuE7mNaDaTXHRfxulJvtqSPdv2tqNeUiBPmHjM7DfhvYDFuc6q5QDfgO5Jm7eq7e6n3Wdw+7GXAYcCrQDlwaWNphJKsoGb2XaA3biOxzwNv47Yunibpl/uqVgPpjQPOx3nZnIgrm4cCP5f0VNSrt1574H9w9a4ZsAJ3L38k6eNc6yVFHLZK8x1goKRzgX64/dVH4m56CG4GTpE0ADgKEPA1nEEJgm+EJgCbcHnsBtxoZhcH0BoH3AccDVwMnAY8ZWZjcq3lOUXSVyXdBZwF9JE0BtdI7MtaDaH3NWCEpGuBoUAn4GTcjp5Rr/5Mwm161w+4BpiC2yH1nkB61TCzv4Q4bzQeadrgnvwBtgLd/TBSSSC9VpLW+dfLgcMlrcQ9mYQiyUYo6Qra0swO9K8PAUrNrBDI4T6mDaLVEHptgcrNr1sA7SRtJ1zZbOx67SS9IGmrpMeB03wPJ/jWpmZWDBSZWZF/nTPinEeaycA8M/srMAS408wuAxYE0pttZs8BfwJOAaaa2VeBVYH0wDdCkpYRvhGqrKAbyaigZhaqgl4LvGhmH+HycwHwXcL05JLUagi9W4DXzex14HDgSjP7PvD7qLdXbDaza4GpwBnAEjMbsJvv5ArhHtwWARVAj1ydOM55ZGBmfXBjy/+Q9LaZtZf0YUC903DzHa9L+ouZ9QKWS9oWSO9kXBc6sxEaCbwn6e4ca30VN4dUVUFxQ1hI+u9camVo5gHtJa0Ncf6G0mogvXa4hmaxpA1mViApWISGxqxnZvvhjH1vXH24GfeAKkm1wn4E0J8haXjOT1xRURH/dvGXSqVGJax3bODz56VSqQ4J5aVdKpXqn0ql2vr3BQ1w/65vjFoNpHdh1Nvrcx+YSqW+lEqlvp5KpU5PpVL7J5ive0OcN8557J5eCeudHfLkkipqPr2a2fWBtNZJmi9pg39fZmYXhtDaBbMbqVZD6H0S9eqPdx65BzdhnoTzSKZ2O+BHZrZ/rs8dh60imNlwSTMS0jpP0mMJ6PSV9PfQOklrNZBeR0lrot5en38mMExShZk1Bx4BvgTMkDQokGZ/3JxYAbAZN/+YB1wiaU4uNKLx8GTxRPgzcBKQ5z0xcq3X4L7fSTRCSS2C9PM5mfwEuBpA0p/3Va0G0kvVSHoQNz+GpEVRr956bwBDJG00sw7Ak5KGmNkCSUfnWs9rzgLOk7QiI6078FtJx+VCI3pbpVmDc9HdgrPQnQngoZDBJOAO4FvAaKA7Lmb+PbinkpyTrREys1ANbK1FkGYWchHkj3Gu1m/g7l8n4Dzc/ct1A5ukVkPoTcPVg/e8ngG/9nonRL16k7R3F0BRpuHwrMDlMSdE45FmAO4mXyfpH8E8FNK0k/SCf/24mf1V0jAzC7UOApJthCoXQa4zsx64J+Uf4Z7yQlTQQThjPFvSPf7+fT2ATtJaDaF3LHAX8CvvBRi6LjRqPUkPebf8HrgIFhuBqSG9yYBnzWwarl5vxA1bnQw8lyuBOGyVgZm1An4DPAOMlxSikavUehZ4ibTv98Fe+9aA46DNqd0IBak0ZjZfUn//uhA3vjvYzF6W9LkQml7rKlzojj6SBofSSVoraT1/z27B9chPCtyYN3q9GtrTQ7YtGTr9cMPirXBRJeZIytm6tehtlYGkzZLOw1XQ0Ks/vwK0A27CrWKfCOyPH3sNgaQtksYBbc3sV4Ttec42s+f8Qss/kswiSCTdCvwOt2o/KBlay0Jr1dBLIm87JV2Ou195jVjvPZJvB4Pnz9MdNyTXG0gBh/j1Qjkh9jzqwMzGSbo3Qb2vS7ovQb0TgXGSgm3unuQiSHMRkS/EzVs9WOnkYGYXSfp1AL0jgY+BlcB1uLmdWyVtybVWFu2rJf0k4PkHS3rJzPJxsdD6Aa8Ak0IMtXhnlQMlLTazYUB/XETrqbnW8no34RxTgt+rOvR/KOmGwBp34oziVJy3VSvgVNxcSE7c5aPxqIOkupYNpdfYMLMngHdwvalhwEhJ60P8rmb2P8BxuHhoq4HXcBX0KElfzqWW13uM6hOdJwDTAQLpTZd0gpndgvOUm4KLPttM0iUB9J4Ansf1vEf414NxDxpXBNBbjZs8viYJF/Uaxvg/cJEWghljrzlT0tAs6bNzNSweJ8yz4G9yknoFQDczy1PA/UOyeFtVkYA7azAtT0dJ53jtMcAUMxtBmCGCoZIGmllLXCibUV43VEP0D1wo9u/jHB5647yDQvNZSUP866kB89dZ0r3m4sqNkLQTuM3M/hZI721gnNe4Aef5+Lyk9YH0/gtn8H8CtASexBnjnwM5N8ae/EqjVZlgZkNw0cJzQjQeHu8R9DOcJ8ZOoMBPal8RyPf7Hknjzew43KKh/YA3/XDZ3Fzreb6By98MqjeqIbytktQCKDYfi0zSU96n/RHCREXON7Pukpab2bkAZtYWKA2ghaSbzOw14FLgImC9pJkhtDzdzewsYKOZHSRpqZl1JVwU38r690+cR9Ii/z4UFZL+BYw2syNw849XmVknSd0C6iZljAHGAj/zvdY83EPHAly9zAnReKS5G+emW/W0Yy7y5X04V8lcc7D/fyNwqh/v7Qo8hgthHoJzgZnAjyUpkEZDaAHcALxkZsMkfSDpNjNrAZweQOs7wJNmdlxGeZmCc34IgqSpZrYIeAj3oBGSbwPH4FYnn2lm9wEvA+MD6V2FexpfB7xmZu/gntBD6VU9zEj6B26PjZAkbozlAi6ODnV+iHMeVZjZHEkDs6TnbIywxnkrx5X/JGlkRvpLId0w/RNdC19pgpKkVhbtLpJWWwKhLiq1QmpkaLUARkt6NAk9r5lI/syt/G4PbMBFu83ZEMtudIPmz8zOxBnjY3C97vuAv+OWA0wLpDmDOnrd2dq5vSH2PNK8YWb34ibrNuK8Ez6Pu8khaGNmrwItzGw8bojlVgK7fUpaUvk6dKVJUisLjwAnhDYcmVoJ6CDpE3PBJRMzHiSUPz88vKgBnEdC5+9RYGIND6sD6zo4R1yLm8s5CzcMn3Oi8UhzMXAmblFNa9yimmeAp0OISTrGzEqAI3GhEspxE6OJbE3pSazRS1gLkvOlT1or6u17em8AR5nZdOA/Jb0YWA9JfzOzh4C+koK0YdF4eLyX09MEMhZ1aG7D7WVcyV1JaXsacwP7uwS1fpugVlPQm5WwXuj8fSrpUjM7FrjOr8F4AVgi6eehRCX9NNS5Ia4wb+o0qgbWzEab2S/M7EH31s7O5YraDJ2z/f8WZvZTYIyZ3exdd3NOE9Ab7P/nm9nFQBcz+w/vwh5CL9H84R+cJL0i6Qu40Y0XgJzuKZ400Xg0MRpxA3snbi/4v+AmJKfhhskmBZCb4P/fDqzHRUZeiYtNFoLGrvdf/v9PgL44z6ueuHUQIUg6f/dnvpG0UdIfJd0SSC8R4rBVE8LqDlkwEhfaI5dMwPU2bgeW4CroibgKmvNV0bhggTVdnKeYWcjd9nplhHpYaOF3hmvsekmug4CE8ifpgRDnbWhiz6Np0UfSBElTJM3w/yfgViyHopekmyQtlHQHbp+UEORXDn9UkusVtRmkzOwKYKe5yKX48exQwxDZ9PonoLcjIb1q6yC8Xsh1EEnnr1ESjUfToiEa2B0JNbBjgW+b2QozW2lmy3GLz74VQGsUzp37baCvmbXBhbr/TgCtSr1NgLxeF9yQzqWB9RZ5vY7ALwLqfRsX76kA+IKZHYBzJPleIL2a+WuPu3+h8tcoicNWTYuxBA5ZkMEo3KKoygq6BFdBJ+zyW3vPYcBRwHbge5Img1uMSe5dhLsBP8AZ3ZckbQQGBNICl6fTcePzq3Crvcv9dbweQG8Lbq+Zl3DlZAoBtwoATq8RquccXMTiUNsx9wMOwLnFPwJcgQsA2T6QXqMkGo8mRBIhCzK0Xsc1bJnrVgYElPwebs1MAfBbMyvxY80hXIS/hzNU+QlogXPhvgE4COchl8KFnp+K2ysl12TbprXSjTyEcUw6VM/FuMjLU4AzJC3yen/A5T2yB0Tj0YRIImRBQ2h5tkva4LVHA9P90FWI+DvbKyOwJqAFkO8DIc40s+GVq+bNLMjKYbJv05rEAs8ySYsBJL1n4aJb7/Ar9TfjnDkq9WKspnoQjUfTInjIggbSAlhqZj8DbpC02XvO/Alou49rAcjM7ga+KWksgJldC7wfRExaY2bnALf4ieTQJB2qZ4qZ/QEXxfcZM/sTzs17eiC9Rkk0Hk2IJEIWNISWZxwutHaF119hZsNxu/zty1rg5qROl1SekbaScOsg8HtqXG5mYwnsWJN0qB5JN5vZUJyL+nKgI/BzSc+G0GusxKi6kUgkEqk30VU3EolEIvUmGo9IJBKJ1JtoPCKNFjMbZmaTa6Td7MfxQ2mONbOb9/DYWtfn0yeb2bAaadea2WdzdJmRyL9NnDCPRPYBJO2RQYpEkiIaj0iTxcxuxYXHBnhU0u1mdj8wWdLzZnYKcK6kseb28e4JNANul/SQ99i5ESgD3gUu8ucaYGZ/Bjrg1kr8xsxOAn6EW9y3DuexlXktl+CCU67Gef/UvNb7gcm42GCfx8V9OgS3R/z9fnX2bbjRhFXA+cChuLAiZV73G/7zx4EVuEWHk4E+uFXXz0r6rpkdgfPkyqu8Vr+KPhKpIg5bRRo7J5jZXyv/8BF9zWwUbmXzAJwB+bJvNGthZq2AIcAY3HqAMh/GfhIwxkfzXYUL/wIubMlI3BqXy/2xv8k4diZwfcb5OwGX+WsZze7jf7WRNAo4A7eeBuDXuEb+OOBZXLDLScClXvOXwM/8sT2A8bgQMj8ErgSO82n4710iaRjwHHD1bq4n0gSJxiPS2JkuaVjlH+m9v3vj4lJVSNoBzMXFx8qkchOfzcDlOAPwOG7lfAegC/CEN0onk96XeoHfmfJ9XA+hPbBJ0ir/+YvA4Rk6hwBvStrmryVzd8lsVMazWgGU+tedJS3013uPpAVAVx8mpqbmEt+T2AB8IOkjSVtJr5DvDfzS52scLg5UJFKNaDwiTZWF+CErMysCBgKLccM7XfwxR/vPuwDHSDoLOA23adEG3EK90d4o3Uh6hXLNxVMfAq39ecDFa1qU8fli4HAza2Zu97x+u7n2bIuz3jOzXv56r/Ehzt8zs75ZNHe3uEvABT5fVwPP7Ob4SBMkznlEmiSSnvHeTi/jhomekLTAhwG518zOJ93Yvg90NrM5uPmDWyRtN7PLgGd9DKZNuMiz3bNoVZjZN4CnzKwcFx13LG6uAUlrvYfWHGAt8MleZOkif93luHmT24ClwB1+2Gwn6WGp3TEBeNDMCnGGZk+/F2lCxBXmkUgkEqk3cdgqEolEIvUmGo9IJBKJ1JtoPCKRSCRSb6LxiEQikUi9icYjEolEIvUmGo9IJBKJ1JtoPCKRSCRSb6LxiEQikUi9+T+XxxOqnMbiqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(under_26['INCOME'], under_26['AMOUNT'], data=under_26, hue='GENDER')\n",
    "plt.xlabel(\"Household income\")\n",
    "plt.ylabel(\"Number of pipes/joints consumed in a day\")\n",
    "plt.xticks(np.arange(12), income_labels, rotation='vertical')\n",
    "plt.yticks(np.arange(1, 5, step=1), amount_labels)\n",
    "plt.title(\"Income and Amount consumed / day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 AMOUNT   R-squared:                       0.025\n",
      "Model:                            OLS   Adj. R-squared:                  0.021\n",
      "Method:                 Least Squares   F-statistic:                     5.422\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):             0.0208\n",
      "Time:                        22:08:07   Log-Likelihood:                -273.41\n",
      "No. Observations:                 212   AIC:                             550.8\n",
      "Df Residuals:                     210   BIC:                             557.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.2805      0.128     17.863      0.000       2.029       2.532\n",
      "INCOME        -0.0349      0.015     -2.328      0.021      -0.065      -0.005\n",
      "==============================================================================\n",
      "Omnibus:                       22.796   Durbin-Watson:                   2.002\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               10.843\n",
      "Skew:                           0.357   Prob(JB):                      0.00442\n",
      "Kurtosis:                       2.153   Cond. No.                         18.1\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('AMOUNT~INCOME', under_26)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit() \n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we take a look at AMOUNT and INCOME, the p-value is smaller, sitting at 0.2 but still shows no significance. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Income and Frequency of consumption / month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x='INCOME', y='FREQUENCY', data=under_26, hue='GENDER')\n",
    "plt.xticks(np.arange(12),income_labels, rotation='vertical')\n",
    "plt.yticks(np.arange(1,6,step=1),frequency_labels, rotation='horizontal')\n",
    "plt.xlabel(\"Household income (in thousands of dollars)\")\n",
    "plt.ylabel(\"Number of times / month\")\n",
    "plt.title(\"Income and Frequency of consumption/month\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              FREQUENCY   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                 -0.005\n",
      "Method:                 Least Squares   F-statistic:                   0.02552\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):              0.873\n",
      "Time:                        22:08:07   Log-Likelihood:                -351.97\n",
      "No. Observations:                 212   AIC:                             707.9\n",
      "Df Residuals:                     210   BIC:                             714.7\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      3.6864      0.185     19.933      0.000       3.322       4.051\n",
      "INCOME        -0.0035      0.022     -0.160      0.873      -0.046       0.039\n",
      "==============================================================================\n",
      "Omnibus:                       25.972   Durbin-Watson:                   1.955\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               16.764\n",
      "Skew:                          -0.556   Prob(JB):                     0.000229\n",
      "Kurtosis:                       2.187   Cond. No.                         18.1\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('FREQUENCY~INCOME', under_26)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit() \n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now perform a multivariate OLS regression to explore how marijuana consumption trends correlate with income and education levels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How do income and education correlate with Marijuana consumption patterns?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multivariate Regression: Household income and adult education vs. age when started using regularly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        AGE_REGULAR_USE   R-squared:                       0.199\n",
      "Model:                            OLS   Adj. R-squared:                  0.189\n",
      "Method:                 Least Squares   F-statistic:                     19.37\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):           3.07e-08\n",
      "Time:                        22:08:07   Log-Likelihood:                -364.00\n",
      "No. Observations:                 159   AIC:                             734.0\n",
      "Df Residuals:                     156   BIC:                             743.2\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept          13.3346      0.753     17.697      0.000      11.846      14.823\n",
      "INCOME             -0.0937      0.052     -1.812      0.072      -0.196       0.008\n",
      "EDUCATION_ADULT     1.2921      0.208      6.223      0.000       0.882       1.702\n",
      "==============================================================================\n",
      "Omnibus:                        3.762   Durbin-Watson:                   1.851\n",
      "Prob(Omnibus):                  0.152   Jarque-Bera (JB):                3.811\n",
      "Skew:                           0.367   Prob(JB):                        0.149\n",
      "Kurtosis:                       2.810   Cond. No.                         37.6\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('AGE_REGULAR_USE~INCOME+EDUCATION_ADULT', under_26)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit() \n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We decide to look into multiple variables that could affect usage, and found that AGE_REGULAR_USE, INCOME, and EDUCATION_ADULT reveals a p-value of 0.00, which shows significance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We decide to look into multiple variables that could affect usage, and found that AGE_REGULAR_USE, INCOME, and EDUCATION_ADULT reveals a p-value of 0.00, which shows significance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multivariate Regression: Household income and adult education vs. frequency of consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              FREQUENCY   R-squared:                       0.004\n",
      "Model:                            OLS   Adj. R-squared:                 -0.009\n",
      "Method:                 Least Squares   F-statistic:                    0.2935\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):              0.746\n",
      "Time:                        22:08:07   Log-Likelihood:                -258.50\n",
      "No. Observations:                 159   AIC:                             523.0\n",
      "Df Residuals:                     156   BIC:                             532.2\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept           3.9230      0.388     10.109      0.000       3.156       4.690\n",
      "INCOME             -0.0183      0.027     -0.687      0.493      -0.071       0.034\n",
      "EDUCATION_ADULT    -0.0148      0.107     -0.139      0.890      -0.226       0.196\n",
      "==============================================================================\n",
      "Omnibus:                       14.518   Durbin-Watson:                   1.812\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               14.556\n",
      "Skew:                          -0.688   Prob(JB):                     0.000691\n",
      "Kurtosis:                       2.449   Cond. No.                         37.6\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('FREQUENCY~INCOME+EDUCATION_ADULT', under_26)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit() \n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Digging Deeper: How do age and gender correlate with marijuana consumption?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multivariate Regression:  age and gender vs. age when started using regularly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        AGE_REGULAR_USE   R-squared:                       0.066\n",
      "Model:                            OLS   Adj. R-squared:                  0.064\n",
      "Method:                 Least Squares   F-statistic:                     29.26\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):           5.32e-13\n",
      "Time:                        22:08:07   Log-Likelihood:                -2494.9\n",
      "No. Observations:                 825   AIC:                             4996.\n",
      "Df Residuals:                     822   BIC:                             5010.\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==================================================================================\n",
      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Intercept         16.0272      0.565     28.364      0.000      14.918      17.136\n",
      "GENDER[T.MALE]    -1.7402      0.355     -4.897      0.000      -2.438      -1.043\n",
      "AGE                0.0825      0.014      6.071      0.000       0.056       0.109\n",
      "==============================================================================\n",
      "Omnibus:                      353.257   Durbin-Watson:                   1.970\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1915.522\n",
      "Skew:                           1.894   Prob(JB):                         0.00\n",
      "Kurtosis:                       9.432   Cond. No.                         132.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('AGE_REGULAR_USE~AGE+GENDER', df)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit() \n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AGE_REGULAR_USE, AGE, and GENDER show a p-value of 0.00 which indicates significance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multivariate Regression:  age and gender vs. amount consumed in a day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              FREQUENCY   R-squared:                       0.015\n",
      "Model:                            OLS   Adj. R-squared:                  0.013\n",
      "Method:                 Least Squares   F-statistic:                     6.444\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):            0.00167\n",
      "Time:                        22:08:07   Log-Likelihood:                -1342.0\n",
      "No. Observations:                 825   AIC:                             2690.\n",
      "Df Residuals:                     822   BIC:                             2704.\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==================================================================================\n",
      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Intercept          3.8950      0.140     27.882      0.000       3.621       4.169\n",
      "GENDER[T.MALE]     0.1989      0.088      2.264      0.024       0.026       0.371\n",
      "AGE               -0.0097      0.003     -2.876      0.004      -0.016      -0.003\n",
      "==============================================================================\n",
      "Omnibus:                       77.154   Durbin-Watson:                   2.088\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               59.526\n",
      "Skew:                          -0.562   Prob(JB):                     1.19e-13\n",
      "Kurtosis:                       2.315   Cond. No.                         132.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('FREQUENCY~AGE+GENDER', df)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit() \n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FREQUENCY, AGE, and GENDER have a p-value of 0.004, which is significant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multivariate Regression:  age and gender vs. frequency of consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              FREQUENCY   R-squared:                       0.015\n",
      "Model:                            OLS   Adj. R-squared:                  0.013\n",
      "Method:                 Least Squares   F-statistic:                     6.444\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):            0.00167\n",
      "Time:                        22:08:07   Log-Likelihood:                -1342.0\n",
      "No. Observations:                 825   AIC:                             2690.\n",
      "Df Residuals:                     822   BIC:                             2704.\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==================================================================================\n",
      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Intercept          3.8950      0.140     27.882      0.000       3.621       4.169\n",
      "GENDER[T.MALE]     0.1989      0.088      2.264      0.024       0.026       0.371\n",
      "AGE               -0.0097      0.003     -2.876      0.004      -0.016      -0.003\n",
      "==============================================================================\n",
      "Omnibus:                       77.154   Durbin-Watson:                   2.088\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               59.526\n",
      "Skew:                          -0.562   Prob(JB):                     1.19e-13\n",
      "Kurtosis:                       2.315   Cond. No.                         132.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('FREQUENCY~AGE+GENDER', df)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit() \n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We decided to do two permutation tests: 1. Income and if they ever used marijuana for younger people and 2. Is there a significant difference between genders of adults that ever used marijuana"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Null and Alternative Hypothesis for Permutation Test 1 \n",
    "\n",
    "$H_0$: There is no correlation between income and marijuana usage for younger people <br>\n",
    "$H_1$: There is a correlation between income and marijuana usage for younger people <br> \n",
    "Significance Level: 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking just to columns we need and then dropping the null values (just because it is makes it simpler for our hypothesis testing) and we decided to drop the refused and unknown because of the test statistic we decided to use. We used a difference of means, so if we included these values, the numbers would be skewed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCOME</th>\n",
       "      <th>EVER_USE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>14.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    INCOME  EVER_USE\n",
       "4      7.0      True\n",
       "6     15.0     False\n",
       "14     7.0     False\n",
       "15     1.0     False\n",
       "33    14.0      True"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('cleaned_marijuana_data.csv', index_col = 0)\n",
    "under_26 = df[df['AGE'] <= 26]\n",
    "income_ever_use = under_26[['INCOME', 'EVER_USE']]\n",
    "income_ever_use = income_ever_use[income_ever_use['INCOME'].isnull() == False]\n",
    "income_ever_use = income_ever_use[income_ever_use['INCOME'] != 7777.0]\n",
    "income_ever_use = income_ever_use[income_ever_use['INCOME'] != 9999.0]\n",
    "income_ever_use = income_ever_use[income_ever_use['EVER_USE'].isnull() == False] #NA means that they have never used marijuana\n",
    "income_ever_use['EVER_USE'] = income_ever_use['EVER_USE'].replace({1: True, 2: False})\n",
    "income_ever_use.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observed Difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.519763940573247"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = income_ever_use.groupby('EVER_USE').mean()\n",
    "observed_difference = df1.diff().iloc[-1, 0]\n",
    "observed_difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running 1000 simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_repetitions = 1000\n",
    "\n",
    "differences = []\n",
    "for _ in range(n_repetitions):\n",
    "    \n",
    "    # shuffle the income\n",
    "    shuffled_income = (\n",
    "        income_ever_use['INCOME']\n",
    "        .sample(replace=False, frac=1)\n",
    "        .reset_index(drop=True)\n",
    "    )\n",
    "    \n",
    "    # put them in a table\n",
    "    shuffled = (\n",
    "        income_ever_use\n",
    "        .assign(**{'Shuffled Income': shuffled_income})\n",
    "    )\n",
    "    \n",
    "    # compute the group differences (test statistic!)\n",
    "    group_means = (\n",
    "        shuffled\n",
    "        .groupby('EVER_USE')\n",
    "        .mean()\n",
    "        .loc[:, 'Shuffled Income']\n",
    "    )\n",
    "    difference = group_means.diff().iloc[-1]\n",
    "    \n",
    "    # add it to the list of results\n",
    "    differences.append(difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graph displaying the average differences in income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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": [
    "title = 'Average differences in Income'\n",
    "pd.Series(differences).plot(kind='hist', density=True, title=title)\n",
    "plt.scatter(observed_difference, 0, color='red', s=40);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.246"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_val = np.count_nonzero(differences <= observed_difference) / n_repetitions\n",
    "p_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this p-value, we fail to reject the null hypothesis and cannot accept the alternate hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Null and Alternative Hypothesis for Permutation Test 2\n",
    "\n",
    "$H_0$: There is no correlation between gender, education, and marijuana usage for younger people <br> \n",
    "$H_1$: There is a correlation between gender, education, and marijuana usage for younger people <br> \n",
    "Significance Level: 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking all the columns we need and then also replacing the values of gender to Male and Female and ever use with True and False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GENDER</th>\n",
       "      <th>EDUCATION_ADULT</th>\n",
       "      <th>EVER_USE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GENDER  EDUCATION_ADULT EVER_USE\n",
       "1    Male              3.0    False\n",
       "2  Female              5.0    False\n",
       "3  Female              4.0     True\n",
       "4    Male              4.0     True\n",
       "5  Female              4.0     True"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('cleaned_marijuana_data.csv', index_col = 0)\n",
    "adults = df[df['EDUCATION_ADULT'].isnull() == False]\n",
    "adult_gender_edu_usage = adults[['GENDER', 'EDUCATION_ADULT', 'EVER_USE']]\n",
    "adult_gender_edu_usage['GENDER'] = adult_gender_edu_usage['GENDER'].replace({1: 'Male', 2: 'Female'})\n",
    "adult_gender_edu_usage['EVER_USE'] = adult_gender_edu_usage['EVER_USE'].replace({1: True, 2: False})\n",
    "adult_gender_edu_usage = adult_gender_edu_usage[adult_gender_edu_usage['EVER_USE'].isnull() == False]\n",
    "adult_gender_edu_usage.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Education Counts Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>GENDER</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUCATION_ADULT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>128</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>159</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>327</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>563</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>474</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "GENDER           Female  Male\n",
       "EDUCATION_ADULT              \n",
       "1.0                 128   124\n",
       "2.0                 159   210\n",
       "3.0                 327   363\n",
       "4.0                 563   440\n",
       "5.0                 474   394"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_cnts = adult_gender_edu_usage.pivot_table(index='EDUCATION_ADULT', columns='GENDER', values = 'EVER_USE', \n",
    "                                               aggfunc='size')\n",
    "edu_cnts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conditional Distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>GENDER</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUCATION_ADULT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.077529</td>\n",
       "      <td>0.080993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.096305</td>\n",
       "      <td>0.137165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.198062</td>\n",
       "      <td>0.237100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.341005</td>\n",
       "      <td>0.287394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.287099</td>\n",
       "      <td>0.257348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "GENDER             Female      Male\n",
       "EDUCATION_ADULT                    \n",
       "1.0              0.077529  0.080993\n",
       "2.0              0.096305  0.137165\n",
       "3.0              0.198062  0.237100\n",
       "4.0              0.341005  0.287394\n",
       "5.0              0.287099  0.257348"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond_distr = edu_cnts.apply(lambda x: x / x.sum())\n",
    "cond_distr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graph displaying the education level and gender based on ever use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": [
    "title='distribution of adult education on gender'\n",
    "cond_distr.plot(kind='barh', title=title);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We decided to use a TVD test statistic for our hypothesis test. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test stat\n",
    "def tvd_of_groups(df):\n",
    "    cnts = df.pivot_table(index='EDUCATION_ADULT', columns='GENDER', aggfunc='size')\n",
    "    distr = cnts.apply(lambda x: x / x.sum())\n",
    "    return distr.diff(axis=1).iloc[-1].abs().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observed Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0297505895720227"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs = tvd_of_groups(adult_gender_edu_usage)\n",
    "obs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running 1000 Simulations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 1000\n",
    "tvds = []\n",
    "for _ in range(N):\n",
    "    \n",
    "    s = adult_gender_edu_usage['GENDER'].sample(frac=1, replace=False).reset_index(drop=True)\n",
    "    shuffled = adult_gender_edu_usage.loc[:, ['EDUCATION_ADULT', 'EVER_USE']].assign(GENDER=s)\n",
    "    tvds.append(tvd_of_groups(shuffled))\n",
    "\n",
    "tvds = pd.Series(tvds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.121"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pval = (tvds >= obs).sum() / N\n",
    "pval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this, we fail again fail to reject the null hypothesis and cannot accept the alternate hypothesis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our research question explores the trends in marijuana usage -- a sensitive topic that is deeply intertwined with the socioeconomic fabric and political discourse of the United States, we have decided to restrict our sources to academic, peer-reviewed, and government-funded research in order to ensure that our questions and hypotheses are rooted in empirical research and motivated by accurate facts. \n",
    "\n",
    "For the purpose of this project, we will only use datasets released by the National Center for Health Statistics (NHANES) to ensure that we have reliable and comprehensive documentation about the components, sampling and measurement methodologies, and processing and review procedures. Given the data is government-funded research, and made accessible to the public, we believe to have permission to use this data in order to find correlations as long as there is no breach in privacy. \n",
    "\n",
    "Our project also involves analysis of demographic information, and therefore it is imperative to ensure the privacy of the individuals involved. The publically accessible NHANES datasets that we will be using for the purpose of this project use a sequence number for every participant and omit critical demographic information such as names, addresses, and other geographical location details that could be used to trace or identify the participants. \n",
    "\n",
    "We are aware of the privacy issues concerning data, and we will definitely take that into consideration while working with this dataset, but we do want to thank the CDC for taking many preliminary actions so that we have to work less on the aspect of privacy within our data. \n",
    "\n",
    "In regards to our data having potential biases, the data was collected to represent a varied demographic shown by the codebook of our data source given by NHANES. Interviews were held in-home as to not exclude those that may not have computer access. The only issue may be that responses from participants younger than 16 had a proxy who provided information for them and for participants who could not answer the questions themselves. This could lead to misrepresentation of information if the proxy did not fully know the truth and/or younger children withholding information as to not \"get in trouble\", especially in regards to marijuana usage. Our topic specifically did hold an issue of having missing data or omitted responses towards marijuana use, most likely due to the not-yet legalization in most states of the drug. We handled this issue by cleaning the data and dropping the responses missing data. Luckily we were still left with a significant amount of responses to work with. We also did find that a majority of our income data did were in the $100,00> income bracket. But we did not know if they survey considered income inflation, depending on where in the United States a survey participant lives, the cost of living is much different (e.g. Silicon Valley cost of living is much higher than smaller town in Texas)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our project asked and investigated if there is a relationship between marijuana usage and people's living conditions. The major factors we specifically looked at included of people's demographic, education levels, and socioeconomic status.  We predicted that younger people (anyone younger than 26) will have a higher rate of marijuana usage due to social popularity, that as income and education level increases there will be a negative relationship with drug use, and that there will be a high usage of medical marijuana in elderly and sick people with the increasing popularity of medical marijuana. To obtain this information we used the 2015-2016 NHANES (National Health and Nutrition Examination Survey) data, a national survey that included data on all aspects of our research question and hypothesis. Every dataset is stored in a sas file, which we then used the pandas.read_sas method to help open our files. We then combined both the demographic and drug use datasets, and did data cleaning and made one final dataset for our analysis in group.\n",
    "In order to find a relationship for the questions we were looking for we conducted multiple hypothesis t-tests and found the p-values with an alpha of 0.05. We found that there was a statistically significant relationship for regular marijuana use and the income and education level for that same individual, the amount of use of marijuana per person in relation to their income and education level, and the frequency of use by their age and gender as well. Unfortunately, we did not find a statistically significant relationship between regular users and their income, the amount consumed per individual and their income, if income affected younger people ever using marijuana, nor a relationship between gender and usage of an individual. The results we found in our analysis allowed us to conclude that there was a negative correlation between income and education level with marijuana usage and income and education level and the amount of marijuana usage even with the recent legalizations of marijuana. We also were able to conclude that marijuana usage was more popular with younger adults. However, for our other hypotheses and questions we did not find enough of relation or correlation between regular users and their income, income of younger individuals and their usage, nor gender affecting usage as well.\n",
    "Some limitations we had for this project is that our dataset did have a lot of missing values for marijuana usage. We also learned from the CDC websites and the NHANES survey that people younger than 16 had people fill out the survey for them if they were unable to answer questions leading to possible false reporting of marijuana usage. If we were to expand on this project it would be interesting to obtain our own data using a survey, or finding a dataset with the information specifically in the San Diego area. Having a smaller population would allow for less variability across the country with income value, states that may have more relaxed laws on marijuana, and overall hopefully less missing data.The impact this work could have on society is that with this information, more education of the impacts, dangers, but also benefits of marijuana could be given to younger users especially. This would allow a better understanding of how legalization has made an impact on the use of marijuana in the U.S. along with adding to and furthering research on previous findings."
   ]
  }
 ],
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