FinalProject_group101.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we used the National Health and Nutritional Examination Survey data to explore how one's income ratio to the poverty level affected ones health, especially comparing those of people with incomes below and above the poverty guideline. Overall, we found no significant difference between high income individuals and low income individual when looking at health factors related to nutrition. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names & IDS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Nicole Chang XXXXXXXXX\n",
    "- Alex Morrow XXXXXXXXX\n",
    "- Delaram Mojtabavi Naeini XXXXXXXXX\n",
    "- Ranjani Shankar XXXXXXXXX\n",
    "- Derek So XXXXXXXXX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QUESTION: HOW DOES AN INDIVIDUAL'S INCOME AFFECT THEIR NUTRITIONAL HEALTH?\n",
    "\n",
    "For our research we wanted to see if there is a relationship between income and health. For income we looked at the ratio of income to the poverty level (a dollar amount set by the government). This value ranges from 0 to 5, which means an income of 0 times the poverty level to an income of 5 times the poverty level. Values above 5 are topcoded as 5. We decided to divide the range into two categories of 0-1 (below the poverty line) and 1-5 (above the poverty line). To measure health, we looked examination dataset of Body Mass Index(BMI) and blood pressure, and we looked at lab measurements of Cholesterol, Triglycerides, Insulin. We also looked at a questionnaire with Yes or No questions about the individuals’ history of health problems such as diabetes, heart problems, and thyroid problems. We then compared every health variable with the two-category variable of income to see if the health level between people below and above the poverty line is significantly different or not. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our background research we were interested in seeing previous work on the relationship between health and wealth. According to National Bureau of Economic Research, causal relationship of wealth to health is not very significant. However, we found that the variable they used for health was based on a 1-5 scale answer to the question “Would you say your health in general is excellent, very good, good, fair, or poor?” Another article similarly explores income inequality and its relation to race and mortality, however it does not specify any specific definition of health. Thus, we intend to find a more empirical answer through looking at different measures for health as indicated by examinations and lab results. \n",
    "\n",
    "For the sake of our project, we defined nutritional health as health conditions significantly influenced by diet. Thus, we explored the dataset we chose and isolated specific datum which we believed most relevant, and researched those variables. For example, among the variables we chose were insulin, cholesterol, BMI, and blood pressure, of which we researched in order to understand what level was healthy or not.\n",
    "\n",
    "References:\n",
    "- 1) https://www.nber.org/papers/w9554\n",
    "- 2) https://www.nber.org/reporter/spring03/health.html\n",
    "- 3) https://www.niddk.nih.gov/health-information/diabetes/overview/what-is-diabetes/prediabetes-insulin-resistance\n",
    "- 4) https://www.nhlbi.nih.gov/health/educational/lose_wt/risk.htm\n",
    "- 5) https://www.health.harvard.edu/topics/cholesterol\n",
    "- 6) https://www.nia.nih.gov/health/high-blood-pressure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logically, a lower income leads more processed foods high in sugar and fat, as these foods are generally more affordable than nutritious foods. Both excessive fat and sugar intake and nutritional deficiency can lead to health problems and diseases. Therefore, we hypothesized that people with lower incomes are more susceptible to nutrition-related health problems. Specifically, we predicted that people with lower incomes would have higher cholesterol, higher blood pressure, unhealthy BMI, higher insulin levels, and more diagnoses of nutrition-related health conditions such as diabetes, congestive heart failure, coronary heart disease, heart attacks, and thyroid problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall\n",
    "\n",
    "Our dataset was obtained from Kaggle.com, but originally came from the Centers for Health Statistics, part of the Centers for Disease Control and Prevention. This dataset is called The National Health and Nutrition Examination Survey (NHANES) 2013-2014. We attempted to download later datasets, but were unable to obtain them from the CDC website. The dataset we obtained has six subdatasets (demographics, diet, examinations, labs, medications, and questionnaire). For the purposes of our project, we chose to use the demographics, examinations, labs, and questionnaire datasets. \n",
    "\n",
    "We looked at all the sub-datasets separately in order to obtain as much participant information as possible. All sub-datasets have the participants labeled by a sequence number, we merged the demographics dataset with each of the sub-datasets by matching the sequence numbers. After merging the datasets together, the demographics-examinations dataset had 6,417 participants, the demographics-questionnaire dataset had 5,314 participants, and the demographics-labs dataset had 2,329 participants.\n",
    "\n",
    "All 4 datasets can be found here: https://www.kaggle.com/cdc/national-health-and-nutrition-examination-survey/\n",
    "\n",
    "Demographics Dataset\n",
    "- INDFMPIR - Income to Poverty Guideline Ratio\n",
    "  - Observations: 9390\n",
    "- RIDAGEYR - Age in years at screening\n",
    "  - Observations: 10175\n",
    "\n",
    "For the demographic data, we decided to only keep the age and income ratios of participants. Income ratio tells us the family income to poverty guidelines ratio. The income ratio was on a scale from 0 to 5, meaning at a measure of 0 to 1, the participants were below or at the national poverty level. Participants with an income ratio ranging from >1 through 5, had high enough family incomes to be above the poverty line. Age is relevant because it could play a factor in income and the level of risk for diseases.\n",
    "\n",
    "Examination Dataset\n",
    "- BMXBMI - Body mass Index (18-25 is ideal number) (kg/m**2)\n",
    "  - Observations: 9055\n",
    "- BPXDI1, BPXSY1 - Blood Pressure 1st reading diastolic and systolic (mm Hg)\n",
    "  - Observations: 7172\n",
    "- BPXDI2, BPXSY2 - Blood Pressure 2nd reading diastolic and systolic (mm Hg)\n",
    "  - Observations: 7409\n",
    "- BPXDI3, BPXSY3 - Blood Pressure 3rd reading diastolic and systolic (mm Hg)\n",
    "  - Observations: 7408\n",
    "\n",
    "We cleaned the examination dataset to only include diastolic and systolic blood pressure readings and body mass index. High blood pressure is often connected to cardiovascular disease, diabetes, chronic kidney disease, and high cholesterol levels. These causes can be linked to poor diet and nutrition, which is why we decided to keep these readings. Body mass index shows us a height to weight ratio, which can show us whether someone has a healthy or unhealthy amount of body weight. \n",
    "Questionnaire\n",
    "\n",
    "Questionaire Dataset\n",
    "- BPQ020 - High blood pressure\n",
    "  - Observations: 6459\n",
    "- BPQ080 - High cholesterol\n",
    "  - Observations: 6428\n",
    "- DIQ010 - Diabetes\n",
    "  - Observations: 9578\n",
    "- MCQ080 - Overweight \n",
    "  - Observations: 6462\n",
    "- MCQ160B - Congestive heart failure\n",
    "  - Observations: 5761\n",
    "- MCQ160C - Coronary heart disease\n",
    "  - Observations: 5751\n",
    "- MCQ160E - Heart attack\n",
    "  - Observations: 5766\n",
    "- MCQ160M - Thyroid problem \n",
    "  - Observations: 5758\n",
    "  \n",
    "From the questionnaire dataset, we selected the column variables that represented questions asked about whether the participant has ever been diagnosed with any medical issue that corresponded with nutritional deficits. This included medical problems like high blood pressure, high cholesterol, diabetes, overweightness, congestive heart failure, coronary heart disease, a heart attack, and any thyroid problem. We selected these variables because the causes of these conditions are associated with unhealthy diets and lack of proper nutrition.\n",
    "\n",
    "Labs Dataset\n",
    "- LBDHDDSI - HDL (good cholesterol) (mmol/L)\n",
    "  - Observations: 7624\n",
    "- LBDLDLSI - LDL (bad cholesterol) (mmol/L)\n",
    "  - Observations: 3105\n",
    "- LBDTRSI - Triglycerides (mmol/L)\n",
    "  - Observations: 3146\n",
    "- LBDINSI - Insulin (pmol/L)\n",
    "  - Observations: 3093\n",
    "  \n",
    "From the labs dataset, we selected the variables that had an effect on health based on nutrition. HDL, LDL, and triglycerides are all factors of total cholesterol and having a high cholesterol level leads to higher risks of heart disease. Triglycerides, insulin are associated with metabolic syndrome, which may increase the risk of heart disease as well. High levels of triglycerides are also associated with abnormal thyroid levels and type 2 diabetes. Labs are the empirical evidence that the participants have a higher risk of said diseases, that they already have the diseases, and/or determine whether or not they are at risk or unhealthy. Without proper nutrition, elevated levels of LDL, triglycerides, and insulin along with low levels of HDL could lead to life threatening diseases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import seaborn as sns; sns.set(color_codes=True)\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind, chisquare, normaltest\n",
    "\n",
    "#load Datasets\n",
    "demo = pd.read_csv('demographic.csv')\n",
    "exam = pd.read_csv('examination.csv')\n",
    "quest = pd.read_csv('questionnaire.csv')\n",
    "labs = pd.read_csv('labs.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we isolated the columns we chose to analyze (as listed in Dataset) and dropped all nan values in those columns. Also, in accordance with Safe Harbour, we drop the data for the ages below 18."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Demographic: drop unneccessary columns and NaNs\n",
    "demo = demo.filter(['SEQN','INDFMPIR','RIDAGEYR'])\n",
    "demo = demo[demo['RIDAGEYR']>=18]\n",
    "demo = demo.dropna()\n",
    "\n",
    "#Examination: drop unneccessary columns and NaNs\n",
    "exam = exam.filter(['SEQN','BPXDI1','BPXDI2','BPXDI3','BPXSY1', \n",
    "                    'BPXSY2','BPXSY3','BMXBMI','BMXWT','BMXHT'])\n",
    "exam = exam.dropna()\n",
    "\n",
    "#Questionaire: drop unneccessary columns and NaNs\n",
    "quest = quest.filter(['SEQN','BPQ020','BPQ080','DIQ010','MCQ080',\n",
    "                      'MCQ160B','MCQ160C','MCQ160E','MCQ160M'])\n",
    "quest = quest.dropna()\n",
    "\n",
    "#Labs: drop unneccessary columns and NaNs\n",
    "labs = labs.filter([\"SEQN\",\"LBDHDDSI\",\"LBDLDLSI\",\"LBDINSI\",\"LBDTRSI\"])\n",
    "labs = labs.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the column names were changed from the original code/number combinations to more reflective names of the type of data in the columns. This gave us a better understanding of what was in the dataframe, without having to refer to the code dictionary that was provided with the original datasets. Finally, we also created three different datasets to analyze, merging each of the other datasets to the demographics dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#demographic and examination merge, column rename for clarity\n",
    "df = demo.merge(exam, on='SEQN')\n",
    "df.rename(index=str, columns={\"RIDAGEYR\":\"age\",\n",
    "                              \"INDFMPIR\":\"income_ratio\",\n",
    "                              \"BPXDI1\":\"diastolic_1\",\n",
    "                              \"BPXDI2\":\"diastolic_2\",\n",
    "                              \"BPXDI3\":\"diastolic_3\",\n",
    "                              \"BPXSY1\":\"systolic_1\",\n",
    "                              \"BPXSY2\":\"systolic_2\",\n",
    "                              \"BPXSY3\":\"systolic_3\",                                   \n",
    "                              \"BMXBMI\":\"bmi\",\n",
    "                             },\n",
    "         inplace = True)\n",
    "\n",
    "#demographic and questionaire merge, column rename for clarity\n",
    "joint = demo.merge(quest, on='SEQN')\n",
    "joint.rename(columns = {'INDFMPIR': 'income_ratio',\n",
    "                        'BPQ020': 'high_blood_pressure',\n",
    "                        'BPQ080': 'high_cholesterol',\n",
    "                        'DIQ010': 'diabetes',\n",
    "                        'MCQ080': 'overweight',\n",
    "                        'MCQ160B': 'congestive_heart_failure',\n",
    "                        'MCQ160C': 'coronary_heart_disease',\n",
    "                        'MCQ160E': 'heart_attack',\n",
    "                        'MCQ160M': 'thyroid_problem'},\n",
    "            inplace = True)\n",
    "\n",
    "#demographic and labs merge, column rename for clarity\n",
    "combined = demo.merge(labs, on=\"SEQN\")\n",
    "combined.rename(index=str, columns={ \n",
    "                            \"INDFMPIR\":\"income_ratio\", \n",
    "                            \"RIDAGEYR\" :\"age\",\n",
    "                            \"LBDHDDSI\":\"HDL\",\n",
    "                            \"LBDLDLSI\":\"LDL\",                                            \n",
    "                            \"LBDINSI\":\"insulin\",                                      \n",
    "                            \"LBDTRSI\":\"triglyceride\"},\n",
    "               inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results of Examination Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We print one row of the examination data to review all the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEQN</th>\n",
       "      <th>income_ratio</th>\n",
       "      <th>age</th>\n",
       "      <th>diastolic_1</th>\n",
       "      <th>diastolic_2</th>\n",
       "      <th>diastolic_3</th>\n",
       "      <th>systolic_1</th>\n",
       "      <th>systolic_2</th>\n",
       "      <th>systolic_3</th>\n",
       "      <th>bmi</th>\n",
       "      <th>BMXWT</th>\n",
       "      <th>BMXHT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73557</td>\n",
       "      <td>0.84</td>\n",
       "      <td>69</td>\n",
       "      <td>72.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>26.7</td>\n",
       "      <td>78.3</td>\n",
       "      <td>171.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SEQN  income_ratio  age  diastolic_1  diastolic_2  diastolic_3  \\\n",
       "0  73557          0.84   69         72.0         76.0         74.0   \n",
       "\n",
       "   systolic_1  systolic_2  systolic_3   bmi  BMXWT  BMXHT  \n",
       "0       122.0       114.0       102.0  26.7   78.3  171.3  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fb89425c320>]], dtype=object)"
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     "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.hist('income_ratio', bins=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following plot shows the frequency of income ratios in our dataset. As you can see, there are a large number of individuals that are closer to 5, meaning their incomes are almost 5 or more than 5 times the poverty line (as income is topcoded as 5). There is also a significant increase of individuals immediately before and after 1, meaning those individuals' family incomes are below and slightly above the national poverty guidelines. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fb89463a668>]], dtype=object)"
      ]
     },
     "execution_count": 6,
     "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.hist('age', bins=31)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows the frequency of ages in our dataset. It seems like most age ranges have about the same number of individuals, when looking at the bars from age 20-70. There is a large increase of members in the 80 years old range, but since the data is limited to 80 all members older than 80 are plotted at 80. Additionally, there is a significant amount of individuals who are between 18 and 20."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following three plots show the frequency of diastolic blood pressures. The three different plots represent three different readings on the same individuals. The highest diastolic blood pressure reading is about 70mmHg for all three plots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fb89447f940>]], dtype=object)"
      ]
     },
     "execution_count": 7,
     "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WNmrT62Mt/TW+vWRm3hwRvwc8ApwaEROZeSAiJoA1wG5grEdbbTMze5mdnWvaRaamVjE9vafxcsOqTfUsZS1tWiEMku+14VO3lvHxscZfnvuGQESsBE7KzN3V40uAp4AngHuB9cD26v97MnO6mq9rm6Th9INnDzQKy337n2PP954ZYI80aHW2BI4DPhMRxwEH6ATAJZk5FxEbgW0R8VvA08CGecv1apM0hI59wQSXvOe22vPffsOltOO7drn6hkBmPg5c2KXtfuCCpm2SpOHgGcOSVDBDQJIKZghIUsEMAUkqmCEgSQUzBCSpYIaAJBXMEJCkghkCklQwQ0CSCmYISFLBDAFJKpghIEkFMwQkqWCGgCQVzBCQpIIZApJUsDr3GJ4EbgbOBvYD3wLekZnTEXEhsBVYATwEXJ6ZT1TLdW2TJA2HOlsCc8D1mRmZ+XLg28CmiBijcxP5KzNzLXAnsAmgV5skaXj0DYHMfCozvzRv0p8DZwLnAfsy865q+hbgsurnXm2SpCHRdzhovogYB94JfB44A9h1sC0zn4yI8Yg4uVdbZj5V9/kmJ1c26d4hpqZWLXjZYdSmetpUi4b79RzmvjU1qFoahQDwMWAvcCPwusXvzqFmZvYyOzvXeLmpqVVMT+8ZQI+WRpvqWcpa2rRCGCbD+t4s8XMzPj7W+Mtz7aODIuLDwEuAN2bmLPAwnWGhg+2nAHPVN/1ebZKkIVFrSyAiPgi8EviFzNxfTf4asCIiLqrG/jcCt9Rok9QSP3j2QKMtrH37n2PP954ZYI/UVJ1DRF8G/AbwAPDliAB4MDNfFxFXAFsjYjnVYaAAmTnbrU1Sexz7ggkuec9ttee//YZLaccATXv0DYHM/AYw1qXty8A5TdskScPBM4YlqWCGgCQVzBCQpIIZApJUMENAkgpmCEhSwQwBSSqYISBJBTMEJKlghoAkFcwQkKSCGQKSVLCmN5WRhsaq41ewfJlvYen58BOkkbV82TGNL2Ms6VAOB0lSwdwSkHTUeCey4VPnzmIfBt4AnAWck5n3VdPXAtuASWAG2JCZO/u1SSqXdyIbPnWGg24FXgXsOmz6FmBzZq4FNgNba7ZJkoZE3xDIzLsyc/f8aRGxGlgH7Kgm7QDWRcRUr7bF67YkaTEsdMfw6cCjmXkAoPr/sWp6rzZJ0hAZ6h3Dk5MrF7xsk51Po6BN9bSpFg3e83m/tOm9NqhaFhoCu4FTI2IiMw9ExASwppo+1qOtkZmZvczOzjXu3NTUKqan27M7qU31LGYtbfqAq7uFvl9K/NyMj481/vK8oOGgzHwCuBdYX01aD9yTmdO92hbyXJKkwekbAhHx0Yh4BDgN+J8R8Y2qaSNwVUQ8AFxVPaZGmyRpSPQdDsrMdwPvPsL0+4ELuizTtU2SNDy8bIQkFcwQkKSCGQKSVLChPk9A5fDeANLS8FOnodD03gDg/QGkxeBwkCQVzBCQpIK1cjjIG1csvV5j/F7uQRoerQwBb1yx9Lz/rzQaHA6SpIIZApJUMENAkgrWyn0Cktqh6UEe4IEeTRkCkoZW04M8wAM9mnI4SJIK5paApFaZP4RUZyip9OEjQ0C1eIE3jYqmQ0if3fTaok8uHeinOiLWAtuASWAG2JCZOwf5nBoMT/5SW5V+cumgv9ptATZn5vaIuBzYCrxmwM+pGvxmLwkGGAIRsRpYB/xcNWkHcGNETGXmdJ/FJwDGx8cW/PyrT1rRaP7n81xHQ7/+rVy5nGUNV+pv+zdfqD3vH7z/Hzf+mw56/qPxHMM2/9F4jlGf/2g8x1KsL+o857x5Jur+3rG5ubkFdqm3iHgl8EeZ+bJ5074JXJ6ZX++z+EXAnw2kY5LUfhcDd9WZcVjHA/6SThHfAQ4scV8kaVRMAC+ksw6tZZAhsBs4NSImMvNAREwAa6rp/eynZopJkg7x7SYzD+xkscx8ArgXWF9NWg/cU2N/gCTpKBnYPgGAiHgpnUNETwKepnOIaA7sCSVJjQw0BCRJw81rB0lSwQwBSSqYISBJBTMEJKlgw3qy2IKM8gXrImISuBk4m855Et8C3pGZ0xFxIZ3rLq0AHqJz1vUTS9XXpiLiWuA64JzMvG8U64mI5cBHgJ8F9gFfycxfG9X3XES8FvgAMEbny+B1mfm5UaknIj4MvAE4i+p9VU3v2v9hre1ItfRaH1TLLNpnqG1bAgcvWLcW2EznjzQq5oDrMzMy8+V0TvjYFBFjwHbgyqquO4FNS9jPRiJiHXAh8HD1eFTruZ7Oyn9tZp4DXFNNH7n3XPUa3AxckZnnApcD2yJinNGp51bgVcCuw6b36v+w1nakWo64PoDF/wy1JgTmXbBuRzVpB7AuIqaWrlf1ZeZTmfmleZP+HDgTOA/Yl5kHz6DeAlx2lLu3IBGxjM6H7V103tQwgvVExEpgA3BNZs4BZObjI/6emwVOqH4+kc4lWk5hROrJzLsy85CrD/R6PYb5tTpSLT3WB7DIn6HWhABwOvBoZh4AqP5/rJo+UqpvZO8EPg+cwbxvCJn5JDAeEScvUfea+B1ge2Y+OG/aKNZzNp3hg2sj4n9HxJci4iJG9D1XBdllwG0RsYvON9E3M6L1zNOr/yNb22HrA1jkz1CbQqBNPgbsBW5c6o4sVET8FHA+8PGl7ssiOAZ4EZ3LnpwHvBf4HLBySXu1QBFxDPA+4NLMPBO4BPg0I1pPAQa6PmhTCPzwgnUADS9YNzSqnUQvAd6YmbN0xtLPnNd+CjCXmU8tURfrejXwUuDBiHgIOA24A3gxo1fPLuA5qqGEzPwq8CTwDKP5njsXWJOZdwNU/3+fzj6PUaznoF7rgJFcPxxhfQCLvE5oTQi04YJ1EfFB4JXAL2Xm/mry14AV1fADwEbglqXoXxOZuSkz12TmWZl5FvAI8PPAhxixeqrN7S9S3SCpOspkNfAAo/meewQ4LSICICJ+HPgxYCejWQ/Qex0wiuuHLusDWOR1QquuHTTKF6yLiJcB99FZsRy8i/WDmfm6iPhpOkcyLOdHh4M9viQdXaBqa+C11eFvI1dPRLwIuInO4YXPAr+Zmf9tVN9zEfEm4Go6O4gBrs3MW0elnoj4KPB6OuH1JDCTmS/r1f9hre1ItdDZZ3PE9UG1zKJ9hloVApKkZlozHCRJas4QkKSCGQKSVDBDQJIKZghIUsEMAUkqmCEgSQUzBCSpYP8fGO5KydTgrg4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist('diastolic_1', bins=25)\n",
    "df.hist('diastolic_2', bins=25)\n",
    "df.hist('diastolic_3', bins=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The three scatter plots shown give a visualization of income ratio compared to diastolic blood pressure readings. Each of the plots looks very similar to one another and the orange line shows the linear regression between each of the variables. From a visual standpoint, income ratio does not predict diastolic blood pressure. Picking out a specific plot (the second diastolic reading), at an income ratio near zero the diastolic reading is a little above 60mmHg (approx 65mmHg). Near an income ratio of 5, the diastolic reading is about 65-70, meaning there isn’t a significant difference. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fb89436bcf8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter of income ratio vs diastolic 1\n",
    "sns.lmplot(x='income_ratio',y='diastolic_1',data=df, line_kws={'color': 'orange'})\n",
    "\n",
    "#Scatter of income ratio vs diastolic 2\n",
    "sns.lmplot(x='income_ratio',y='diastolic_2',data=df, line_kws={'color': 'orange'})\n",
    "\n",
    "#Scatter of income ratio vs diastolic 3\n",
    "sns.lmplot(x='income_ratio',y='diastolic_3',data=df, line_kws={'color': 'orange'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following three plots show the frequency of systolic blood pressures. The three different plots represent three different readings on the same individuals at different times. The highest systolic blood pressure reading is about 110mmHg to 120mmHg for all three plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fb8940aa128>]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fiYjfBx4CToiIscyciYgxYD2wGxjp0FbZ9PQ+Zmfnei2x7yYnx5maGr5dhvPrHnTHGxaL/TuvhD4yTKy7MDo60vOX567DQRGxNiJOmvf8fOAJ4DHgbmBT2bQJuCszpzKzbVtP1UmS+qrKlsCRwP+IiCOBGYoAOD8z5yJiK7AzIj4I7AG2zFuuU5skqQa6hkBmfgc4p03bPcDZvbZJkurBy0ZIUoMZApLUYIaAJDWYISBJDWYISFKDGQKS1GDeT0Bqw/sPqAkMAakN7z+gJjAEVojxo9awelX3P6fXDJI0nyGwQqxedVjP31olyR3DktRghoAkNZghIEkNZghIUoMZApLUYIaAJDWYISBJDdb1PIGImAA+A7wIOAB8E3hbZk5FxDnADmANsAu4qLy/MJ3aJEn1UGVLYA74aGZGZv4r4FvAFRExAlwDXJqZG4AvAlcAdGqTJNVH1xDIzCcy89Z5k74MnAKcCezPzNvK6duBC8vHndokSTXR0z6BiBgF3g58DjgZeKDVlpmPA6MRcVyXNklSTfR67aBPAvuAK4HXL305zzcxsbbfL7FoXohNC5nfL4a1j1j38hp03ZVDICI+DrwEOD8zZyPiQYphoVb78cBcZj7Rqa2X4qan9zE7O9fLIsticnKcqal6XTR40B1JhVa/qGMfqcK6l9dS1z06OtLzl+dKw0ER8RHg5cDrMvNAOfkOYE1EnFs+3wrcWKFNklQTVQ4RPQ14P3AvcHtEANyfma+PiM3AjohYTXkYKEC5pbBgmySpPrqGQGb+IzDSpu124PRe2yRJ9eAZw5LUYIaAJDWYISBJDWYISFKDGQKS1GCGgCQ1mCEgSQ1mCEhSgxkCktRghoAkNZghIEkNZghIUoMZApLUYIaAJDWYISBJDWYISFKDGQKS1GBVbi/5ceBngVOB0zPz6+X0DcBOYAKYBrZk5n3d2iRJ9VFlS+Am4FXAAwdN3w5sy8wNwDZgR8U2aUV6+pkZJifHmZwcB3jucbt/40etGXDFUrV7DN8GUN5gnvLxOmAjcF456XrgyoiYpLgf8YJtmTm1dKVL9XLE4WOc/66bK89/yycuYG8f65GqWOw+gZOAhzNzBqD8+Ug5vVObJKlGum4JDNLExNpBl9BWa5NfOhR17Ed1rKkK616cxYbAbuCEiBjLzJmIGAPWl9NHOrT1ZHp6H7Ozc4sssX8mJ8eZmqrXhvygO5IWp479qG41VWHdhdHRkZ6/PC8qBDLzsYi4G9gEXFP+vKs15t+pTdWMH7WG1atqvaEmaQWocojo7wJvAF4A/EVETGfmacBWYGdEfBDYA2yZt1inNlWwetVhPe9klKReVTk66DLgsgWm3wOc3WaZtm2SpPrwjGFJajAHnaUBaZ1c1ov9B55l75NP9akiNZEhIA1IryeXAXz2ip/pKTgMDXVjCEhDxLOStdTcJyBJDWYISFKDGQKS1GCGgCQ1mCEgSQ1mCEhSgxkCktRghoAkNZgni0krWK+Xpnj6mZk+VqM6MgSkFWwxZxirWRwOkqQGc0tA0nN6HT7yAnXDzxCQ9Jxeh4+8qunw62sIRMQGYCcwAUwDWzLzvn6+Zh15v2CtVF7VdPj1+5NpO7AtM6+JiIuAHcBP9vk1a6fX+wWDO+gkLY++hUBErAM2AueVk64HroyIycyc6rL4GMDo6Ei/yjtkvda27tg1Pb9Gr8s0bf7leI26zb8cr9HP+Xvd53DgwLPs27e/0ryt/5Nr165mVQ9b3r28Rj8s5efcvN81VnWZkbm5uSUrYL6IeDnw6cw8bd60bwAXZeadXRY/F/jbvhQmSSvfK4HbqsxY14Hqv6d4E48Cnr0iSdWMAT9E8RlaST9DYDdwQkSMZeZMRIwB68vp3RygYopJkp7nW73M3LeTxTLzMeBuYFM5aRNwV4X9AZKkZdK3fQIAEfFSikNEjwX2UBwimn17QUlST/oaApKkevPaQZLUYIaAJDWYISBJDWYISFKD1fVksVqIiFOBm+ZNOgY4KjOPi4hdwP7yH8B7MvMLy1pgKSI+DvwscCpwemZ+vZze9gJ+dbi430J1R8QE8BngRRTni3wTeFvr0OKImAO+BsyWv2ZzZn5t0HWX03fRpk9ExDkU185aA+yiOHP+sUHX3amPl8vsYoD9vFN/6LROB72+29VNcaTkDooTup6lOKnrHZn5VPm3+Cbw9Xm/6qcyc7qftRoCHWTmLuCM1vOI+G2ev87e2PoAGLCbgN/hBy+10ekCfnW4uN9Cdc8BH83MWwEi4mPAFcAl8+Z5RWbuW64iF9BufcMCfSIiRoBrgIsz87aI+ADFe3pL3yt9vh+ou0Ifh8H28wX7Q0T8Mm3WaU3Wd7t+/GvAf87MuyJilOKaav+lnA7w3cw8Y4Hf1zeGQEURcQTwZuA1g67lYJl5G0BEPDet0wX8gJF2bct5Mt9CdWfmE8Ct82b7MvD25aqpioXq7uJMYH9rOYoA3sUyh0C3uuvYxzv0h07rdODru13dZejuKueZjYivAj+yXHUtxH0C1b0WePigi99dGxH/EBFXRcQxgyqsjZMo6p0BKH8+Uk7v1FYb5TeltwOfO6jp1oi4OyJ+IyJWDaC0ThbqEycDD7RmyMzHgdGIOG4gFba3UB+HmvTzg/pDp3Vaq/Xdrh9HxBqKYJo//aiI+D8RcUdEvLvcqukrQ6C6twBXz3v+ysx8GXAWxTfrKwdS1cr2SWAfz1+3J2fmmcCrgB8FLh9EYW0Me584uI9Dvd7TQv1hGPxA3RFxGHAD8FeZ2QqBR4ETy/790xT7by6hzwyBCiJiPfBq4NrWtMzcXf48AFwF/MRgqmvruQv4ARx0Ab9ObbVQ7sR8CfDzmdnaCTx/vT8JfIoarfcOfeJB4JTWfBFxPDBXDhnUwkJ9HOrTzxfoD53WaW3W90L9uPz/di3FpXQua82bmQdaO6/Ln9eyDOvbEKjmYuBPW3vpI+LIiDi6fDwCvIniYnm10ekCfnW/uF9EfAR4OfC68sOnNf3YchO69U3qjdRkvXfpE3cAayLi3PL5VuDG5a+yo4uZ18ehPv28TX/otE5rsb4XqrscGvojikvkX5KZc/PmXxcRh5eP/wXF8Fzf17fXDqogIu4FLsvMz5fPXwh8luLa3WPAN8r2RwdU3+8CbwBeADwOTGfmaZ0u4FeHi/stVDdwIcUhcvcCrTuS35+Zr4+IH6c4imkOOBy4HfiV5T5SqE3d59OhT0TEK8raV/PPhyx+Z9B1t276dHAfL6cNvJ9HxGm07w9t1+mg13e7uim2Xv+kbGvdK+VLmXlpRLwB+HA5/fByvve19t31iyEgSQ3mcJAkNZghIEkNZghIUoMZApLUYIaAJDWYISBJDWYISFKDGQKS1GD/H4cZq+20nonzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist('systolic_1', bins=25)\n",
    "df.hist('systolic_2', bins=25)\n",
    "df.hist('systolic_3', bins=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The three scatter plots shown give a visualization of income ratio compared to systolic blood pressure readings. Each of the plots looks very similar to one another and the orange line shows the linear regression between each of the variables. Visually analyzing the plots, income ratio does not predict systolic blood pressure. The regression lines are virtually flat at about 120 mmHg on each of the three plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fb894322a20>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3htnunUBvnDzzBs5dXAYhBCmZwPWAcxeXsXfuDTz2wD1DvfbIKAhN035N07Q3NU2jmqa90992WNO0r4X+XdE0bS10zBVN0y6GPv/7o2jri+eWoChkJII6/bRpp7mJ/bZ5u65g+HrTU2kQAoAClbrd87nibZ4ppAFQUDDaxvXY79mUvGW95knDMNu9E57bU69cAwFh9FJAMxGceuXa0K89yhnw5wD8RwB/yTfoun4FwHfyvzVN+42ENn1Y1/Xzo2ggR6lsYHoqBcdthWUPS1CnGxYWS3ju9CJurzVguxSKRDBbTPftUg8Cm7mRJ8+8gVOvXINhOcikFHzHkTksXq+wDDxFQjGnghCCcs3E8loDTz37Wts5tptFGB73bFrBXDGDcs2E7fROtcTvHT/PRtVELqMGFNC+udzA2z9I9OP296ozvZV+7ARaxrAcyBKJbJMI2z5sjMwA67p+BgA0TUv8XNO0FIAnAIxkltsN89MZ1Aw7iJ0F+hfU2a5Lt7BYwtNfuIh60wJ8PV/HY3Wq5tG7Sz0IbOZGnjzzBk6+fCWYRTRNB195fRksyILAdT2sVgyAAoQQqIrc0RXdDlUSH/dsWoEkEczkU/j4j3zXls4BsJjpIwenezrHJIRubcXt70VnutvxnbATaJlMSoFpu5BDNtijbPuwMUkc8GMAruu6/lps+zOaphEAZwD8vK7rfZUq3bVrqu+G/NDDR/Hp5xcAxUNalWHaLkDZ9t27Cz0f73pbOx4AvvjcAkzLhSRJkAiBJFE/kw0sgSIl93XOXq/bqS3plBw8kClVhmE5+OLf3MBD7/lmvPTX10EIgcJfWB7rr0fhu3WA53sTigTMFdPIZ9XIOfpBp74MYtwHcY5eMejzcWx2v4Zx/DDvybDxoQeP4I9eugSXUsig4I7vhx48MvQ2TpIBfhLA07Ftf0/X9auapqUB/AaA3wLwkX5Ourpag+f1l+F993wOH3v8eFsUxN3zOaysVHs6/ocfekebC9jr8QBwc6UGx3UhSxIopSAAZAlwPcCyXUxl1J7PuXt3oefrdmpLLqMwfWEfEiG4uVLDykoVTcOGLBFQysbZ/wFKmbGt1C1wBeLZQhoplWkVh8/RK7r1ZRDjPohzbLcf28Vm92vQxw/7ngwb3/+ug2jUTUah2W5Qkuj733Ww7zb2a7AnwgBrmnYAwPcC+NHwdl3Xr/o/TU3TfgfAyVG16b5je3H3fG7Lx2/FFQ3zdtWGBdcDXI9pNrCZJIEiAymFqZn1E4q22fXiPF/4s4bhwPUoivlUcGzYjYy7cIQw4ysRRgNk0wpulurwKEWlYWOtYkKSCDyPGeEkPnirGAQFMOzMumFju27/oGmDSaBlJhWTkojxYwBe0HV9lW/QNC2vadq0/zsB8MMAvjae5g0fYUEQgInlcFAKOC4T0XFcinRK3rZIdjeRn/hnKVVCuW6iUrcSV8kffvddoKAsQoDSQFIql1GC/RVZguNSOI4HgNEprscM9E4QBNpJAuWj1KS+E8DXMEzbhSIBpu3i5MtXcPLMG0O/9ijD0D6pado1AHcB+HNN074R+vjH0E4/7AVwWtO0BQDnAdwL4KdG0dZxIByuU22ySsoS4ctvDBTMhS/mU0PNFIt/Nj2VRjGXgmm5icIsjz1wDx5732GkVRmuR5FNK3jvt+7BXbungv3nCinM+lU6PFYpCbIEmI43kaFJceyEcCqO7QrpvJ2EeIC3SRiarus/A+BnOnx2b8K2NwC8a9jtioO7mbfWmrBst6eS8lu9RtiVDYfrOI4HiTCtBo9SHNw9BUopri7X0DBsVBuWL66eCsTV+3WP4+FBTdMJQsRkScL0lBpxQYv+l/Gpn3xf4vkO7y/i0N4plMoGMikZV5drMGwvaMtnT11CIaeimE/h+kqNVeigfEY8OaFJncZxK+FU46QshNvfOwzLgUdZ5Zj49mFjIjjgSUFYeKXa8IXNOwivbPcanUrJdxLMqdQtwKciZInAcSnWKgYKuRTyGaXvsKEwz9c0HSbUTgFVkUEp9WvLkSDmuBsHGO4TIcDN1ToAgtlCKrl/Ph0BsIyyzc4/KnQLv+qXF90JGWCdsJPbvhV0WqPvc+1+S5gUDngiEBZekSTSVXhlu9foVEq+k2BO3XDYDIyw8kIEFJQCtYYNUNq3exzm+co1k0UuEKCYV4Psr3ItmfPt1qdqwwYhzBBXm05y/3IqKGUvmEJWmRiOsRvN0C8vupMoizh2ctt3GsQMOIRS2QAhgGm5oGCr+RJJFl7ZzjWSXNmG4eAjD7cL5pTrFlzXCcRg8mkFpuPBcTwosoSUKsGwvb7d43D20/Jao024Ziqroly3sXS7BgIWTsYRd61vlOq+0UbQTkoRoRd4/547vYjVDRPwOTfL9jA/nZ2IiIJuNEO/WW78WbpdNeG47F4VcmriPYlnET787ruGrkEQR1SAyGQz3x7annT8JEeITBqEAQ4hk5J995ktElEKuBRQFTIwF7mbK5tUC467gm7Fg+N6cDwPcwWWjmzaLMUWwJbChvj1eC05fnzTdFBpsMhdRWbGdKNm4ukvXMT733UAZ8/firinTcuF3LBRzKc2pRdM28WumSxSvpgNn0VOwpd1M5qhH16VP0uEsEQaThnt35WP7BfPIuQr8ABGZoTbKIeaiXKdxXaH6a542zlevXD7bUVZDBKCggjD98MlKVqcz/O8gbnI/biyvYrLDDrsaKNqBnG8EmmtDhumg1OvXGtzT/MZBbWGvSm9MOmu7UDDrzinAz80D/7fNEosRlfgyUhX4Dni94VH3gRhhR3azvH86csTfV83Q0wGYtPtg4SYAYdg2B5mCylUmw7gePD8mFaJSAMLw+nHle1XXIafM5OSoRDJF1JZwr75PF5euAnLpwQkAhzYlcOHv+8dwazuys0KTr1yDU3LiWSyuZ4HWZIgEfa75TBFMMf1oCgS0ooE0/Zgux4qNQuKTNhMiVIYthdp52dPXUIuo6Bh2Kg07IBGqTftoA+9uuO9uLz9usXbEdOJX6vcsINnyXHYWBWyCoxQfDcwXiEYjjj1EjaztktBCEAocGM1WUTp9loDGZWtnVTqFhzXgywRNEL3ddC4UygPYYBD4C7ovrkUVEWC7XiBmz/Im9urK9uPuAw/Z9ydvLZSw8WlqHyGR4HrpQaefuECnnz0GADg7PlbSKdkmLYT5MJTsNRnwAMhzFki4Dwv4DguTMsFAZBWJRSnUnBdDx9+8EhHvd9baw1UmzYIfH7d9eB5FAuLJVy5WenJHe9llX6rK/lbzWCMX8swXcgSU03jCFNGHOMUguGIP2fh1wFPf6cAlFAVbaA1jnvncrh2u+pHDpGAcvGoi4XF0sAN48CjNDpFO4goiNFi0jKAttKeuDvZMJJnUhSAYbmRxAtedkeRozMy12MRGQBBPquwVGOQSJjO9FS6J73fuuH4XgUBBRPqmcqpePHcUs/ueC9UxijpjqRrTeVU1A1n03sXzyJkesMUD7/7roG3sxPizxm/rbIUvcdEkhLH8fEH34FawwalAAFlniMB8hllZOO9nXs7RvsrZsBhhF3Q9ZqF2anhluvppz1bTbDoFsvo+vKWAEsbbpXdIZAl6s9+GfbvyqNStzBTSMOwXN/VZJ8RiSCb9pWzNqnYnE0xRSzXo34ySTpIJunVHe8lKWKUOrRJ1yrkVLguo2C63Ts+sx9nFET8OSMApvMqTNuDYblBNBAXtYqP433H9iKTlmHZXkBNFXMqsmllZOO9nXsrDPAEgbugvahVbYWHih8zO5XC1xfXOn75wi7xyTNv4HdPvh7Z9/D+YuR8GVUKEh6AljBOEmSJBCv8t9Ya8DwKx1de40tIqiJhz2wWv/jRE0G0BBfYub3WgO3zmxzVhg3TcvHxT72cOCYH5vNtkQam7WJ+OoOGP2MMT8Adl4IQEpzvhx4+2uYyNww7iFnmHOUodWg7XevAfL4nDeHHHrhn5GFncYSfM36fZwpycI9dj8We315rIJtWsHc2Gzn+YJf7Ogj0Iw7VLyRC/Nh68HVTUH/7sCEoiC1iK+Is8WOuLtfwldeXg5lfNxGQsGAI3/fzZ6/g0ye/EWlDpemg0WQRCQ3D7mh8CVio1CMnDuHooRlUeOYfWm9+xgFTVOoWFhZLba5qNq2AgiKbYtlzlbqFct1ESpU6jkk3WiXujtuOBwogl2mJD336+QUcPTQTnKPetLBWMeF6HmYK6eCa4X2GTSdNGnW1XYT7k1alIEWXEMB2PJTrJo4emul4zKDHoF9xqH5x4thuAPDLTrWef759mJB/4Rd+YegXGRNmAPyzZtPqaIS6IZ9PoxEySnH85xcvBjNNJhMpwaPAteUa7v/2/T0ds1o22M0m8CMNWMztW7dq+OB7744c+5t/+rdwPRrwoxIhcD1mrHYVM0EbJJ8OKOZSuFmqQ5ElZFQJthsdhLvmc3jiYQ3Hj8zj82fehOPSQHWNgxBg13QG6ZSCa8s1PP69R7B3NotryzWU6xb2zGRx/zv3wbBclP2Fp1xG8UPmksdk71wuco65QhqPf889OH5kHtqhWYBSvHWrBtvxQAjjnHdNZ4PzEcLC8B7/nntwbbmGm6tNSBLBXDGDXEYNrllrtvaJX2fQ6NanTtjs+Ronwv1ZXmfjK0mMmlIUCVMZFYblBvc0n0+j4M+KhzHe8e9NJqUAFIFi4Hav9d3aHiyv1XGj1Ahmvu/51j34Xx57Z9/nyufT/76f/QUFsUVsxkNtJrgDtPjZ8AsiznkuLJbw3Jcvo2GybXHBkPjLhWed/eJHT+Djn3rZnzHIKNfMIPRLIiQIQeN9KeRUqIoE02q2ZsAUqDRsFLIKrpfqeOrZ14L+fOThkBqavz+/Xqcx4egWafDYA/cEtMqlqxswbQ83VmpsFV6WMFNI40apHowtQDE9lQ446PA1e4lo6FUTuZe6av2Eq63VLMyNeY2hG3h/+D0lIXecUprItw5LAOh6qd7GL28mDrVTIAzwFtGNY+wouBPjZ3mpc9IhBGlhsYSnX7iAaqN7PGXTdAIDFObCeBtth5Vn55chBJGwnfnpDG6vN1FtWG0LD47jYq3CVtvidAs/vpcx6RXhsZMkElRlkCU2O19ea4AQErSlUrewVjFACEkcg16vFe8XgIFnd4WvV8jujIyxcdd0W1gswTBdeJSyWHSXPcsFx4uE+G0Hv3fyPL7y+nLwt0ep//f5Lc2C+4HggLeIbpxXR8EdQto4VICnPbeHIL14bgmG5W7aFpa51s6F8TauVQzf+LKwr5lCOhK288iJQ0EYURyex14KkkQ2DfsZBA8YH7twOwDqew00pFWcAkA6jkE/1+qmiTyIMLZJzwRMwrj57RfPLWEqpwahjxL7BXXDGVgbzl1YAcC+hySkwc23DxNiBrxFdCvlfenqBlRFirjGKUXCRtVEMZ/C7bUGAIK9sxkcv2euLQri8P4innr2tbYEik6wHQ/XlmvB8bxt/OdvPv+3cF22is0jFuLUgOe/AOLgWxyX4kapzvbxY0VX1pv4xGfO+YJBFIpM2Mo0pWgYzpbKm4dpGkrZOR2XXY+HQblea9afy6iglKJcs4Nr9urWb0Yj9RPq1Atd0U2Dud+yTL3SI9vNGNtOduAgEKbHeJadIrOX16Da4PFahsF/0e3DhDDA20CnUt6qIsPxZ55zxQyyaQXVho2m5SKXpdg/n4fleDAdDye+bV/EzeHnCRdE3AyqImHfrhwsx8PZ87dweH8x8nDKhABSK7Ce6wjvnc222u3H33IjHNAjaD2T8Ta5lOL6Sh1EAggITJvJYioywUceZhr729Ep5sI+skTheYAkSfD84OTw2CqKjCMHsz2Xnk+6FkfYve7V9e41Myt8vboR1WDuh47o9XqDyhgbp7g7HzMe+ggkZxTuVPREQWia9iFN0/6VpmkH/Fpt/5emaf+vpmk/PewG7hSE3ctiXg20S8o1E6btotawkc8om7qfQVZaD9QDx0wh3TUjrOC3J64jHKZL2DkQGGI+GZZ6eEI8D4FgT9N0tuXGh11eLuzTSgjhUSDRsd2qS9zNvd6qaFKvFM26b3y5BnM/dESv19uJlEcc46ZAho1NZ8Capv0cgCcAeAB+AsAfAFgGsAbg32rRXdk+AAAgAElEQVSaNqXr+q8MtZUThF5K1nBN3Urdhu24UCQC1/NQbVgwLBeZFJsB2Y6HW6sNfOIz5wL9BH4ep8cZMK88zJGUETYzlYZESEQAJ6Uyy7p4vcwWOCQWvuXFaAhu/MIz4XByR3hvibBZ4nrF8EsbEaYTHJpF9pKxlFYk3F5nEQ6zhUywyCbLEuYKadSbNqpNG6ZNsV4xMJVRAuEh/sWMU0MXlzba7lkv7nW/oknd+hnRYF5vQpWliAZzr9lcvV5vlNmAYQxSKGfcFMiw0QsF8b8CeB/YbPkqgM/puv51ANA07SUA/wXA28IA91OyJpdRmTvuJ03w+m6247UtrN1crePpL1zEkx9sZXkpigTPF4aPI52SsW8uh5ulettncTd5fjqDmmEjl2l90U3bheJHQhDfmnaiPMJiLByqLAVJEmHD7HoU1KNw0Cpt1E+EQnh898/nAr3gA7tycCiQVmVYtou66UAiBERiYj4bdTsof/T0CxcAQpDLKMhlFNxaa+DStQ1M59Mo5NQ2N7ybe71V0aRu/eTn/PXnFrCy3thSdEGv1xtHBMMwyhndyfXteqEg5nRdv6nr+nUAdW58AUDX9b8CcGBorZswbKVkDS8XND2VCjLLwmDBEUxrN3yebEqOyFLxXyXS0tnNpBVkfG2FTu7ZIycOwXFoe7v8GeX0VCqi+RCH5yEizsNkKVs8McBoCp4U4ktJoJhX+45Q6CV6ZL1qAn7oHiGkrfyRYbkwTCc4R9NyQUB8oaHhuOFbcZMff/AdW3ate73eONz3O4H2GCV6McBlTdN44vf/Ef5A07QZAJOZzjMElMoGUkp0yMJB/0mlvA2blTPKZVTMFdJtUn9hrd3wefbN5TCVVZFSWJlsRWa6DQd25QAwSconP3gUTz56rGv58ONH5vGxx4+3t8tyg3b1IjytyEydLJ9lfGUhp+LArhwO7s4jn1H9Lxyb+fKKHbmMitlCCpSip/LmncbXsNxgbC2HrYLPFTOgfmwoQav8EXsRtN4ovMK0E3rLDNoN73Tvu83a7ju2d8ul33u93lbatV10+44ItKMXCuIPAdwF4O8SuN4fBPDKwFs1ocikZNxabcCjNCgLL4UEbZJcpfnppcANzGVUpFN2QEHI/goXnxQ3DKdNxCbMp2VUKZK1ceVmJZHbjOO+Y3tx93wuONdnT11Cw3BgWE6QgpwEAkZ37J3LBSvPfOGuVDYwlYtmcsVLGwEIIhSOHprBqVeu4ZPPLUTC7Z778uWA75UkErSJ1yLjwi98bP/9f34FN1fqLI2bUnj+cYrCFgA9yvpz9XY1IKtdCqSG4IYPIsQrfI/DPPZm5+nVLe+2X1L7gXa+lW+7UaqzqBSZ4OB8PrGd/dAed4qo+nawqQHWdf3fdPn49/x/AABN0+7Xdf3sIBo2aVhYLKFStwI323Y8rJabyGdT+OH3v6PjcY+cOIRnXroEE2wmkE0rMCzXD/NixsLz473SKTnCm125WQnqrwEUN9eaACjmiixz7dK1DRRzKRTzqU25tjg31zBs1JqsHZ0U0ygQVGV2XQ9HD8105ffifeUc7uxUqk1o/fNnr0CRAI+2ohps24Npsxmr7GfBWY6J7/2O/a17ULP8GS47zvUA6lLk0hJWy82gHx5XVvHBatvRSB267WBQXOe4SsAnXffpL1wEKEUuq7a2+Zy6LBHUDRsAARymnsfb+dDuQnDeTs9AfLzH1e9Jw7Yy4XRdp7quh7+6/22b7ZlYvHhuCbmsirli2jeIbAZbzCqbVlgIu4F7Z7P40AOHcWBXDgQEEgFSqoSZqTSK+VSENwvXX6s2HZ/3lFhMsekwbtNye+La4tycS1u8cpJmMK9yQSkC1/Xi0kZXfq+Ty/v1xbU2oXVKAdvllae5+DoD9dvE8v5TQULKi+eWAnEeVZH8yAiClF8OR5YkqIoUvFQAxlPn0jI8rzcapFcMiuscF2eadF3DdGBYbnSbz6lzsX5+n5qWm9jOXmkPwRUzDDoRYwRl7EYL7iZdurrBDAcA17de1KW4vW5sWnYlyQ0M679ywZOm6WC9akYiEmzXw8xUyuc4WbUBx22lTZqWi+srta6lw1+9cBuL1yt+uBkLieM0KZ8khmfBu2dYgsNG1cT8dAalsoEXzy3heqmOtCqhtNEMVNNUmURqusXd6s+8cCEQEmL5/CSSYURpSwwjLAIEXwC81rBxqb6BT/z+V30PgCWeFPMpJofpZ90BLHPt+ko9MPT8mvmsio2qiUE+7p1CvOKiRXG3mte8a1rsBepRirTayppsGDYqdRu31xr43379L7q6+4NuP/csAGCjaqDasIOXMyEs+gVoce7dxKeOHprBq/oKFq+X8dvPn8fe2UybANSwQ+R2AsUxaC2IUYjIjwxhHVJJIrBdGqTFUspmaR7opjrAm2F+OoNqw0apbLSFg9mOh9UKeyj9oAo/Lz6ULBEqe55JyZHjFxZL+PTzC76BZeFmXkLUQ5iCWKuaWC0baFpuRICn3rRRKpsRyUrbZQYw3H8+brfWGoHx5ddgWr+ta7lep5RP1lbHYwKtN9eaQfQF72vTdAJ+cX46wxboFCl4CJnQEQlC4XrVbe4F/HphVHxJzk4a0VzT2fALn/J+mzbLmtyoGlirsqKrlLJwwYbpBO7+dtu8Wftlic1wN6oGynU74hkxqVK2PwXzTvjY87L0vN+315v4/NkruL5SB7t9jD57+oULQR+Srj/IELmt6HWPA0KMpws6CcOEIfnhXNtxnbgYTjwJgsPzouFr8aiFbqXDXzy3BEVhCRG9pLYrMrPuDcNpy9yTQheONIEgsR5b0y/YuZlb5LhRHYp4LTJKOFXBx4EZ8Y2q2Za5lk3JQbgfpX5lazDRnkG6ukkhXnXDwVRO7ehW85p3PH46PC5c+hNMagOyRDZ19wfdfh7WWOmgvufxFyiAbEoOxj5elr5ptipryxIT0CGkVYOw0/UHGSK3UygOoQURQ1ivdb3cDDK5wsIwAIK0XUppT64TP+/S7SoMixmGXLpVgiiTlmE3ojOCcJIDACgSK4zoeTT4hKDFlyaVPS+VDUxPpZjaWQ/9d/1VbhbLy/LtgzLyoZkvNyKyv7iVVI+Nh4DxCtPh6xdzKjJpBWvlJhzPf6lIBFNZJVKLjPfP4eXRAciyBNt2YTs0+FI/cuIQ7n/nPpx65RqjiIIxpJjKKUESCtDu6nZyVbu5sMePzOPKzUqklpsqExRy7Dq8RLvtuFjdaDJZRb/yiR+GHbm5iiyxBBmFwPY/4jNEx3XRaNo9Z0f2gqQMM76Y/Bt/ssDGLvSMe34yDjNkrNozH4//+79dhGG6gV6vbbMon/D9JmDG+0aIouFRPf2KKPWCcWUB9gvBAYfQptdaabmvXBiGieIxPsyjbMa4mevEz9s0HTTMVhZc03SCsusH5/N440alLcOMgBkwRWZl33l4z621BhzHg6pI2OvroiaJlMxPZ3BjtY5KvbumML8OEzvzIivYa1UTtINSGputSW3Zdzybz3W9wGh6HqtyoSoSZou+ZvFsDjP5FD7+I9/VsRZZcD1/4TCbkuE4LLtw1i9DxFfr0ykZpu2wHjGdTzQMF5mUHRjh8P3qtBofjkBJWqVfWCzh7PlbKE6lMK8wd3q1zHhTVZGwVjHAIjXYv2deugRVkYIXSdg6EQLMFtOo1CwUp1JYWW+2JexUGjZ2FQfrsHYKUcullaD0VdBAXwL0t/7590T2XVgsoWE48LyWXm/AG4f2o/5pwrSW5XhwHTci7j8ojFvHuFf0fEc1TXtY07R7Y9s0TdM+wP/Wdb3QfuTOQdxtCWdycWEYvzJL4OJm00rPpeL5YlHwYBIEZdcfOXEImZCmAw3tk/FnyhEt4ZTM6rH5C1HdsqGqmxhfgGWzhamMqQzrV7lmcTuWCNejyKSVtuy7JDogqCHXoc1xIZ4keUwiAeWahTit0Gm1XpJYCB8v2hm/ZidXNRyBkuTCJh2XzyioNWw/84/dRQpgeioFWZYwlWH95/aX94678/weJ/UbYGWWRoF4bb64TnUYL55b8qN3Wnq9YYrM9Wgk3LIXQapBYKeI+PQzA/5tAN8T21b1t9/bvnsUmqb9GoD/CcBhAN+u6/p5f/sVAIb/DwD+ja7r/5//2XsAfBpAFsAVAB/RdX0ZQ0KpbIAQ4HaVLTQpMkE+o6BpskWR/bvyAKUoN+xA/3bvbLZnjdu4vCOlTNKRL1Q9+cGjeO70Im6U6sFim6pIoJTi4tIG7n/nPlxc2sBbtyowbSZOvl41sVE1kU0r+I4jc3juy5fx28+fB0Cxdy6H+7TdPVEPnN/jbv5qtbcER1kiePKDRwGwRIzrpTpMi7mjvA+KLCGTUlipcltBuWai1rBRzCnIZtRIAsITH7g3cIu5EeWzJ0KYO+z4RmytwsosFQNjTeFRElSzpZQGM07P8bBeNXFwPh/RKC7XLExPqZGZEsu+czCvRGdLYRc2ycUt5lNwPQrD93L4ouPKBnuu0qqEE0f3+ELfoYVMhyKtSDi8v4jD+4sBBRCHaXubRtx0S664UarDtF3YjgdZktoiEzh4hE6YXolX6+ZgYk8pyBIJ9HrVUCacaTMPaK6YhuV4kUrG8TEdJHaKiE8/BniPrus3Y9tuAtjX4/GfA/AfAfxlwmcf5gaZQ9M0AuCzAH5M1/Uzmqb9OwC/DODJPtrcFzIpGTdX6/4MivGOtmNj/648fvGjJ7Z8Xu4OcY3dJDz9wgU8+eix4Dph1zilSNioWzh7/hbecaCISzEukM0yPXz1wjJAOS8LXF+p43qCYE8SuLGz/SiPXjAzlQrKwnAN41qzVVmDx+Nm0gre/64DOHv+FhRFwv75PKoNG+t1Ey5FJJHkiQ/cG+j6JmXW1ZoW1sqmXzyxVaKGLdKxMkacKnI9nzP2F4IyKRlHD81EqIVK3cJ61fKjJFoURSalRMpH8e3xck/xzw/O5wEAS7erEbqJUsCwPHx9cQ175lhm/8o6E+afnlLhUAT95/RPEvpJtoknUlRD9waeF0QmPPnosUQjnGRw4+BiT2G93krdQq1pY9d0JkJlTfup5KOiBXaCiE8/pNIbmqa9P7btQQBv9nKwrutndF2/2sf17gNg6Lp+xv/7P4GlPg8PPAAVNHAhkyIL+gV3h+IzJg6C6Aox0Nk1PndhJVhJ58cCbKbRMnwk4O8ojYrpxMG1FDhfGW9Xt+N4WZiIhnE4egEtoaG4S99LIkmSG1mt28hnlbYSNR5lVAijN7zIYmmYBoi3Y6aQRhJFEad8utElSVrCXM85PoZNXyioXLPahIR4/+OzRA5F7h5xk5hcEaJm2u9N+3PXL5LEnjppX4PSHUELjBL9zIB/AcDzmqZ9BsAigCMA/on/b7t4xp/xngHw87qubwA4BOAtvoOu6yVN0yRN0+Z0XV/r9cS7dk313AjLY0kIZb+QpapImM6nYHvA7t1bp7cf2l3A9HQOz5++jPOXS20aulyq8uZaA7/+3ALeuL6BetPxPyfIZRW4LoVhOm1lU+KvBs65hnlEx2UJGNSLzm4zKRkSAYpTaZRrVlsMcrfXjiwT5DMqHnrPN+PZL16GZdkwYzKbzAX34FD47qcauKfcQFqWi+srdb+EUwrrNSsY6/C4La81GN9OKZqmyyI1wKI2WqncEjaqVkTdzaMAXIpa08Z0PgXDdrE/VOVXVZiex2rZgGl72DOXw+MPsmiA01+7gZWNlnv84HcdxEPv+ea2ti1e3YDhG5Xf+7MLePexPRG9ZE7D8AXW6yt1OP7iJAXgui4s20U+o2C9ZkGSJcwWUliP0UCOS2FaDtZp8vN4c60R0D+qwrIrA/6VRu8np0c80MiY94uHdhewXDHx+b94IygTpSoEu6YzaBgONmpm8FxQAHfvK4KC0TR8rO87tndL1x4FtvO97wU9G2Bd1z+vadrDYBTAo2DawH9f1/XtivH8PV3Xr2qalgbwGwB+C8BHtnnOAKurtY7xtXHMTTFXeM9sLgidMm0Xs/kUVlaq22rH3fM5/PMPH8dTz76GN25UWKFLf3rEviQUjaaDN0PGl39W26QqchxJizheiN9V5VYJo9UNA4bpYM8sc4vfutW9n2w2TeB5HqayClZWqnBtBxu15DaGM+1WNgy4HkUuw4osOi7jciXCEk5W1hvYvysfGWs+bty9ZuL21C8Wyk4uSWzhZ3XD6EjxmJaLFdtgmXuGE3GDAeDIgWJAfSwslvC7J1+PJJEAwOnXrmMmpwau+d3zORzZN4VvvFEKUq2bho3Tr11vG4PoM0hD/zMsbzRRyKoBpbNRBxQ5GvoHAOtVC/PTUtvzuLBYQqPpBNWDbcfD8kazRcFIBE6syAr1jbIqYcvP98JiCV965SoKORW7fJ53dcPAzVIdpuOBejRyT8o1E4pMIunJ2/1uDRP9tq1fg91XXIuu63+l6/pP6Lr+qP9z20ponJbQdd0E8DsA7vc/WgJwN99P07R5ALSf2W+/GMXKKY92YKV2WivEAMFUTg0iJTqhF+nITqB+kH84emAqp6JuOEGfNwPnV8PUTG2zNkss7jfs6nPjyTP0NqN7uHs9V8wE5svzWrNfis78Ou87QDGVVTe9xy+eW2IuO9oTJk69ci1yXp5cwdOfOzWBvxT5iyc+PqAtSoc/h/H9OCr19gXSTtWDw9RMEph+89YfKp7oE6YbpnJqQEeFeyBLJFKySmCTGbCmaf9W1/Vf8n//xU776br+ia1cXNO0PABF1/WyT0H8MICv+R//NYCspmkP+DzwTwD4461cp1eEV07XaxZmp1IDXzk9fmQ+iHbg1ZH3zWVQadgo5FSsV82IKE0YXD/Y4YYnhE6KZnEQIJKUUMgxgzSTT226Gs2vweOkedIHW1X33dxYG5grnEIuoyKlytiommgYDiQQ5HIyLIeyMkkdEkk4eNRBSpUx53p+Ygib0s0V0lirmF3bTgFfQxiRSIuk1fFS2QhisOMwLKftbzn0Vgz3v5hTIwtfhZyCTEoJaI1WhAcz4PFKv/FoCO7BhLWNw21Orh6s4MkPHsWL55awUTMDo0jAkndmplJtFVr6AU/0Cb8sCjkVG/5M17TZdkUmfsq8N5EJEePCZhREOPDvm7ZzIU3TPgngcbCoiT/XNG0VwP8I4E81TZMByABeB/BTAKDruqdp2o8C+LSmaRn4YWjbaUOvqDUsrFUMrJWbeO70IoD2arO91BxLAj/WsFwcOTgd7MtX/MPViONgugleEGvJw7IkX9Sdl5YPG4G4MacAbq81AjEby/FwwBd74X3qBFliFY/D+sAAM7JmyHCGQ+1kiWC9aqLaYCvlRw5OR5Iu5lQ5yLRbq1pQZYJ//dtnsF6zghltyjfOluPBdjxsxPjqME/bDfzl9tzpxa5vKy5AlLSHRxGp4ZdJ+UkLoZwFHn43W8xgtpiBabuo1CzksyyRJp2y4blMOJ7HWFPKvQSG40fm2xMiwIwvBfCxXz0NHmr44QePRKoHGybzaFgUD8WVm5XImHP6pWk6QczyU8++tulkIynEjUdByKHqrZbjQSaIRHJQyopKKjJLUmkYLn78V76ETIqFT67XrI7fn4XFUkQ3mve5n7b28x0dJUgvbucOxWEAb/bDAS8slvD0CxdQN5xQyBhFPsuqT/AMqHB4WKVuodKwgppjPOQmSYIvfmx4XwBBtlyt2d2lB9gCmuW4yKYUf1bGpkdpRULDdNvSmDkkv4Q8QFHMp6HIBPe/cx/Onr8VGLdOkCU2i5RlKdLu335+AXaHSZTs87NclOWx9x3GYw/cE4yF7XioNu3AXe72OKoyfwltOjwdkUuzEk4AwWwhBUWR2+7XyTNv4HNnrnQ8BwFQ8CuSXLlZCbSOuVaFR9l15meywT3mYyzLEhzHxaofSscExtj9CD9nvB1J5wa4vjGC43iYX71pR8LfeOLQY+87jMP7i8Hz57pekK3XaRzC6PTs3v/Offjq68sAaen/lqsGmlayJ5NJscgM4veh03jxdoS/ky0vr32surV1s+/ok7/8pY73+umfjQd+dcfu3YW++JyuHLCmaff08q+vFk4w+MyUEJZBxUut83ptfJ9IOFUfNce6CYRwHdW7dk8hl5bbjg2Da+g+9r7D+KY9U0FJoFxawaG9BXzogcM4OJ+LaOzKMsGe2Sx2FTO+YhiB6Zf64Tq/TcvtGLJ213wO+3flI/rAx4/M48VzSyAk+TGSJebmJmn78v5aNpN44+nW3Z5ex9teRKAsEf/+Jod/cVxc2vANY2fwZ+KxB+7BY+87jLQqs6zAlIL3fuseHNpbiOjhPvbAPYFOLkCQSjFNBZ6avWs6i1xGibQjfm74hpi78+Hn8+LShl9qihk+4u/HxpRlW4a1ejeqJmRJwlwxHczM+w1xk2UJF5c22kpeeRRBGa04vWz5iRlcz5nfzk7hiJHvZKzPvbZ1FHUBt4rNKIjLQEc6jIOC0Qc7HmE5SMflNx2wPVa+/eOferktc4rr9JqWy8rgwHfzVxv4p5/8y4iWazh7KhC48VeNuUxerWlH3HmO8IyWUqBhOvjzV68ik2aLaDzKYWW9gVrThmF7+Ja7pnH00AxOvvwWXJdieZ3p6RICyIRpR3z21CWs+1oP3aRe7ju6BxeXNlCpW3jjRgW/+ad/25rZUgpVZl+mMBfoesCUKsEEc52bJhNj4e7h9VKdKWcB8Gx3U+NKQ5EjvRriMDcuSwSWQ+G5rXu8UTUwPZWOUC83SvWuhUpZqJ8XHHN4fxGH9k4F7u2Jb9uXODMLrzGsVc22svT1poVLVzfw47/ypQj98sH3HMJjD9yDf/rJv2QavS4FIdSvHCJF2pIk7SkRxlWH3XKPAoR6AT1UzKeQScmJIkU3SnVU/UicVEpGMcfa7DguFq9X8KnnFzA3lQo0HX78V74ULEpKMuN9eX88SttU4PjPa8u1YC0gnHHo+joT4f6E+xxHPEtxFHUBt4quBljX9beVXGVGlSJuO0sVZr9zPdl45pQkEdgODfYPI6zlCrSyp4LsLfBzA09/4SJsx4VhJRuiJHtTM1zUjGi21WrVAqlZmJ/O4NZaI5hxRs5FAcd35WzH7ag9EMbnzlxBPqOgYTitFwEJ0QGxcCOOct0OYp1ZrTcHT3/hIiTCxif8UtkMBP3TD+HzJi1eless1Oub9rB48YXFUpBE0a0dXICon9I6cdfYdthzwMFpiTAsx8PnzlzB7bVGkOLM++VSgFJWOy+jSnjmpUvBC4dS+On0PBlHCq5NSCsqQ5ZooK9cyKWw1w9H5G11XIq60VLSs20Xa1UPpsXkN2WJMOGqUL/DvHjY+AZt9++FEnMzeGbjetXCfj9bcH46g0rdihhuNsOWOmbQxbMUFUUKhKvC4zoJwjx9G1hN0w5pmvZeTdO2tSg3kSAtlyk+5eehW/HMKS9J3Ryt8KWwlisPL+ICNyz7DJgppINyMD3nASdcLwAFK1u0iSGRJRLMbHpBMxYXSxGOZe58HPX5XdZIllDSq1ZwGNwj2So6tbHhh38BftmjDhmLYXABon50Z8P7zhYzgSpapW6z56nL9c5dWMFUTo1oMvM+ZdIKSzeWpchCHgC/gABFMZ9qlbdq2MHCnucxyU5KgVrDbhMp4uJGnJrit7HaZKKZM4V0W7/DYj7hMecax7zd4fUnvqgc+Hr+F/GRE4eQScnBM8SFneICUGHEw0l7Fa4aB/pRQ9uvadp/B6MlngewqGnaX2iadmBorRsxDMtFMae2LWBJpBW6lU0rQUn0huFAIqTtoQdavA0v3fLWrQp+9+TrKJUNWI4XiP3MFVn5H9vxgsD4rYDGfjctty0zLY648PlmSMqm6lRPLmwneKjVXCENgJVEchyvbZy7IZviUpm97d8tvDW+mQL47KlLgZhQMZ/q+mKYK6ZRzDERocXrZbihmXXDsLFeMXHp6gaeeva1SAWGeMl2QtgilGm7HbUfODxKUW1YKOZUSLFv7fvfdQCG5SKlSJgpZDCdV4PxJwBOHN2Dcs3E7bUGrt6uwrDcIGGDwteTliVk0q0wuBulOkobTRh+Zh2nlihY9iGlCJ7duuHg9loDK+sN/N21Ml7VV5BWpLZ7xePeSehvArYAl1Jlv9o4+07w0LjjR+bx5KPHsH8u24r7RvJ3jiNel27fXA4nju6Babm4tlxDpWbh/ncm00SjRj+pyJ8C8HUAH9R1ve7H8P6fYBoNjw2jcaNGJiVjvWpAlpkYj+sxVyk+65BlqS2cyvRXVnkYGNcgUBTJL+/jQZYoFL+0EcDUsXgdsEHHovRyPo9GOdLtgMCPsPCtXpgLZtQBd3nZLIdIhGkFx64f5WzZwgsFglX1fpqa1C++AAS/TVwtjdMHhumiIlkgEkATbOJUVmFZZR4NKCmuGU0pZZQCZdeI0xHcNfY8GsQt8+esFzguxUbNgiS1oiBkieDs+VvIqFIgdDNTyGCmwMLfFIng8o0KKBB5wQdZcIqEA/P5SFjhwmIJdcPpSE1J/kyWz5DDMdiuR3FztY65YgbTsoRbq43I2PN7klYlfOpfPpgouBTXteaGMh6F0U2YKCzEw+mUsHbz2fO3cHh/cexGuB8K4gEA/1LX9ToA+D8/DuB9w2jYWJAgxiMRlka6mSgL174NeF2wBzybkv2ZMoKFCe7OVRo2KKW+vu3oQanXFwXQDfmsAoAE3GMYPIGgXLOQScksM8sfr7hFDb/rPC/slvaHuOngdCNbQKOBVCXA4m/jmYEdmCXGe3bQjK7U7cB7np5Kt9ER/FlhBUIpAKbYJvVJrbC2tegrWWaVJZIy/EBpUMU7cZw8L1EjufurjlEanfrCK3ezMQof1cKUT/P0mn26nRJDk1yeqJ8Z8DqAbwWbBXNoANpXeXYoDNtDLiOj1nTgUDYzmvINC88USypZwwRQWCSApMhwPQ+2Q+G6FJbNAueVhG8YpcDS7drI+8khEQm21+znUBgAACAASURBVB8V0AndYpflYNZPYbteW4pqGIoiww2Jt1iOF3G5VVli5+gTXHvDo/BVwhwQANk0E8qv1KvBjFCRgDgzzvln12OJLDwio5BTMVtIoVxjmXlMVIhVOG6aDso1E8trjSDR4YkP3OvrNfOXC40k3oTLXnHwRcxwWSfH9x4qdQuFnIpGk9Fnt9cNuJ4XzHgJmEhRUlqzIrGqzDP5aMYnjw7oJJ/qUabp8Oh778YLX2FGjMUWu/4ko7XgKZHWQjaHKgPEv6m96vb2W2IoHPFRrlnIpCWsllkiD1tEBRojErjvhn4M8FNgGWyfAVMpuxtMCe1/H0bDxoGMKmG96gb6sR5l5Wz2z2UDoRaO8Ir2TCGdGHDPXaWmydw5RSaB2ztOcIPrUS9wZXmoz3aSHDrBDvW3G90hSYBM2nfoNBvtB9zlTilSUFbnE585h+sr9cjLwHY8eBJBWmVGK5yFxkMUeR88yqIo8hkFRw4WASBwp5lrboBSQFXkiN7xkYNFrJSbkaQX3oZZf0ZbqVl+BAGFREhgVON9sh0WjsVU81Rk0hKqfm1BTm8YCUkRLPY4E5SDCmN+OoP1mtkxgYkvmJ09fwt7ZzOARCBLEm6tNeD6Qvw8wiHpFLbLZt4cvej29lNiKB6Zsl4zUW2EqnODxZTXDHvsVZJ7piB0Xf89AD8EYB4shXgewD/Wdf13h9S20YMvS4NHQ0RXZMPop5QNr4bheu3Gd1AUQD+gYF9OlpBAgtndMIxvGN10iQFmaBtme2HS4HeCLc1+w4ho7dKWPGekZQSYyqpoL8sTBT+mERPRMW0X5ZoZMFrFvBpxe4MyUTQ6JrJfbonrEUdX/5P700o9ZzXbahElvc7Pl0dpx0iAR04c6uoSuV5LXxmEBHrAhazih8B5KORUn1ZoHy9gcwGnpDb1KpQV/25Koe9vuA0SIWOnIXqeAWua9o90Xf8TAF+Kbf+wruvPDbxlY4BhuZgrZlD1KwArMkEhl0kUK+nkEiWVstk1nYHnNUCIhIYZTqlkGAQF0Cv4tXipcI9SzBUzKNdMthK/SSSGKpPIjLYfSKT3nkp+NV4+ToQAP3D/YXz+7JWOi3abYTofXTk3bC+UJu0nqPjXJX6mYbgsT8N0goSTiOYGiS4EvXhuCctrDaiKHEm04C7z8SPzyGVUNE0brkdbi4KeB0ppkGV4eH8xooHAPCgWNhaOCAkblfC2+LhEkhlAOqYds/YpHUMUCVhUEI8E+ul/9J34o1MXUSobLH6XsIzDmXwKt9caoYSdFpUT157eDP2UGIp/N8Phbvw+S/5zM+5kjH4oiM8A+JOE7b8L4I4wwPPTGdz2s8UoZfztyoYLiRD8s0/+ZSBaE17RjrtEnUrZ3L2vGIma4KvDcdsh+TPvpIymTgjzhi0Dmzyj5Zs4JwsgyJDbDHwRkaD3skVhbBZqlVIYv0tpXD+XNfVVfQWyFOVIe03g4KnHGZXi5Jk3cHFpA+WaGRkjfl1KgdWyEehByBIrVPmFry4FfYh4DTQqZhMWV+J0BC9Rn1YVLCyWMF1IsegXytLEM6qEpsna8NyXL+O504swLBfz06xu24vnlrBRt7BRNWHFZpYUzHtohl7uScMS7qvnevjU584jrcqR55qDV+nmz2FkzMGqcmdTMvbN5XDfsb0olxsR4/hNu/P4+uJaJJkpPM6ZVMv0hDMjea3FpDbFIxt4Xb+4MW5LxJAlltkKhAoCeKCgKNe6q+gNG5tSECG9B0nTtG+O6UB8P1rFNHc8jh6aQbnOHvBwELlHWTbQ7fUmnnnpEhYWSx1dol5K2VS6RD3EA9R7QfzLwc8zaLgeheV425GP7YpuiRaqQvxKEv13rLUgRSFJwMmXr+DWWgOq0n4xPhsOX8X1WCZgZCZFW2NcyKkBx8s5Rf58VOoWVstNNuMjBClVwtMvXEBpgy2WAYDjuCjXbdiOh0xaws21pl+bEMF5jx6aCeKNO71YV8tNqH2IApg2E0IKP9ccPAHCS6DNAFbJpNKwcPTQDF69cBvPvHQpKDd/baWGr7y+3Ja4Ex637zgyB6DF195aa6Dhq7jFv2tx8GP49TqNfZCIkVaCZ5bRgK3sPJZYNT70wgFfBvB3AHJgpYguh/79F7BSRXcELi5toJhLtXN9fmhNWEw6HuydJLoS3s7fzsePzCemxEYvuLX2cxd6mCAAVFXuq4m7ZzI9tYvFtSYL8mx34ZIQYNd01q/CwQRakhanusFxKWamUpGXhCwRzBYzbaFN/PkwLRcUBIoiYa6QxvRUmiU3OB52TWehKlKwyKgoEiyHBs8bD+XiojeBeFFS/8DGzna6J6G0gSJRJJ0nQMTThYPrEWA6n8bFpQ08f/pyhHMNigqQ9nYQwhIv1v1JSLieIAH3sqSuwu2bhZXFv5t7Z7P4gfsP4+DufEA9yDLB7plsRBt7HNiUguB6EJqm/Xdd1793+E0aH0plA8V8iqVqyiRwN3nIUTcx6VrTxnNfvhy4RGH90fBD9NyXL2/qim81MYK5e8NlkymYHkA/V+Hxzpvxx5Yv7J7E6257Rk8IsmkFaxWD3UunNQvitE23asTw9zEsN8j+AtiMqlwzMT2VTnw2eNiT51GsV02slltlkyp1C8V8CiXHAPVowIv6kehw3FaJqFuryboe4bYV8ypKZbO1oQdQJD/Xv3fyPM5dWOlIhXkU2KiZMEwbhakMMioz1E3TCfqXdKgiS7BsN8gUvFGqw3FdmMHLkPqJOwTrFSMSwhcOkwtzvA3DxkaN0Xof+9XT2DubwX1H90Sue3h/MSgl9fFPvYxcqC7gONFPTbiI8fVpCVfX9bcG3qoxgXNHiiL5ilOthQOerslDX8KhLoQAN1fr4Nqqt9YauHRtI9Af3ahbTGzHdmA5o1puGx76NYZBUcYeDtxmkENHeB5F03SCeFpFkUBtN7IA1wvvHl/ZBxCEk2XSShAWxZ8PQuAb1/Zz8xAyznfzF8FWn5DV8tb4zPBzDTDj+5XXlzc9jlKWoVigjJpiGX7dGUn+klEkgo26hUrCQh8FGzMH0RA+AG3rLw3DxmrFCLwICorrpQaun72Cmal0hB5KOn7c6EcL4g81TXuf//s/AfANAK9rmvbRYTVu1IhmtbVWMtiKsseq8vp8btgNqjbsiMZskv6oYTow7OHxp5MOKWFWO2qUayYTZAETaJnKttxP5uFs3sBOfeDhY3Exm5lCuuMxbqy01Hafja0Ob/i5BpjwD9AbEyYRoOwr/PHqGr2A+CWYuu/UHsIHRDneSt1uUThyK+SMUnTU/41zxONEP6nIDwF41f/9XwD4fgD/A4CfHXSjxgXOHe2byyGfVZFNK8imJGRSCvIZFXtnswGfGxZWcVwWzkTAFidMiy3iGZaLt25VcaNUh+WL7Yw7CWNcGMSi4HbfXZbtYe9sFieO7oFle6g1bSghXQUCIJfemgIrBSJcP38+smkFRIoKkysySRR8H3YcdieEn2vWDtaQzZrD+kFgO6y6RFJYXBz8M9vxNq3ALYGVkbrtJ3hwiuT4kXnc/859qNQsv7oJr4xNIu02LRfXV2q4vdZA07ADTe8Xzy3h/nfuCzjicaKfMLSUruuWpmkHAczpun4WADRN2zucpo0HPNRl9+5C15LUYTeGa916CcH6QP8xjwLJ2K59ogBmp1K4fKMSEWbh5WmeO72Iayv1LZ07pUiRkKnw85FSuBay5yvFMTGfQWErgkq8wkRalSOZcP1khkm+mls2reD4kXkcOVgMQuUclwbxvhSM5nDd9my+SJv8nzT4SQLPZK1iYP+ufNDGs+dvoTiVCtTkPI/CI4gkXfA22o4Hw6/2wqMmzp6/1VNJomGjn9f91zRN+zmw1OMXAMA3xpVhNGzSEXZjCjkVlLYLTwtMFgiYe91xBX0b7mgkww7JzwePnogXTt0utuIZ8IW+h999V2R7r5lhXNSdguIHvoctbgUUXloBpUwZEEHSg9cmoxlHO/9NfYqAshb7gxam/4r5ljyn49IIjy9JCLIYeZ93shjPRwH8BzCdkn/tb3svgGcG3ahx4qln/hoXr5YTP5Mlgv1zWXz4+97RlpkzW8iMPatGoDtYPDDFesWELNFI2vPyWgP57NZDkkplA089+1ok+iWj8lRdsNkbpbi93thyJmEnBNEcrUz6TQ28qkj4BycOBZEBAJtZLl7vPp/iXD5P9jhxbA/+8cNH8cWvvhnUb3Nd6vO7BLJMMJ1TAUJwo9S7d1HIKbAcCsdfMC1kFRh+CF44CoKHkW1UTdh+iKFEKNKqBMP2IsY3PCQ86mPcWhD9GOAruq7/SHiDn4J8R2TBAd2NL8De+DdWG3j6hQt48tFjAV3BV7xHoacgsH1YCWF0HgXq21THurpcw6VrLJa8mE8xesNxg1ppvMqxLJGeykDFQcC4Tl6u5+ZaM5D6dHyRdK7ve3utkZhCz5FOKTi8vxj8HY7a6IZd05mI0NTlGxX84amLeOncW5BlCbMhYap4qvMnfv+ruFZqdDx3S0mNotZwMD+TCQxsWCM4HsWQy6hswdMXFvrE738VN9eaQVFaVhUEkbAzlrUqB9ER40I/FMRNTdN+Q9O07x5aa8aMbsaXw6MsFjTsvnCXaNyr/AKbg1eBSMJ2Xp6y5K+6+0keSa7uqVeugYAEhqZfsGMozwxCSziqdT6P0iD7qxviVYXDURudkE1JifTN5//ijd70dgnpSJdwvWxW+Zj1jJf96qTB3VGYZ5Ox6VUreRTo5+r/AIAH4M80TbugadrPa5o2/qJKY4Dr0QjdwFe8hf2dbLTqjrWDb92KYeThT1xz2AktuoYTHAzL6buqMxdrlwiQUuWgXA8XjuISpymVVSuWQILsr25diVcVDkdt7PELc4Zx9Jumkc+mIiWVeP+appO4PU7JGZaL+ZlMoipeOONOIsSnOmjHbNJu2aabjQ3f37C9tnaPGqTfODhN0yQADwP4CJgs5WsA/gDAH/FqGROCwwDeXF2tddQ1jaOf1VCJEDx2/9147IF78NSzr+H2ehPr1fEKe+wExFe6h3INwssg+TMr3zvxQkLlnZBLKzAdxmP2Ap5iHaYUuLHkVAcBAjU1gBls1+1P0CjOYWZSLIXZ9SgURUJakfyK2hRHDk7j6KEZnDz7VsfkkpQi4Z4DxSAC4hO//1UsbxjB+Yq5llt/9NBMRIiIo5hTkcuqqDZsqDKb+XPOFpQnnzC1t6msymJ2wfSNKaUsKcZlCRyyJIUWKdmCtiJJTG/YV1frpIAWFl+fn86g1rTheDQSZ2zaLhQCTOVahRVqDQsORaRsUhxP/+z7e75HALB7d6GvV3jf5l/XdQ/ARf/fCoCDAJ4AcFXTtB/t93yThF2F1OY7+fAoxcmXr+DkmTcCER+B3jBsTyGiFEbhF5X0AoPcDXOFVM/GF2AGMM7ncpqKSzCyv1vxpk6fxhdoHzPD8gKDaNsuKg0btuthppDGrbUGTr58JbEKC0e4qvDCYgmVphOIA7muh7WKiUbTxuxUCp87cyUxRbvSsFGuGnj3sT2oNCw4Dov0sCwXps3E5Alhoj+rFROEeHBd6hegZT89j0KVpSBqwvU8lg1J0VGYKLxwliTMU6lbaBhOhKJoNG1Umk50v6Yz9qoY/egBzwL4QQA/CuAYgD8G8D/ruv6y//m7AZwCmw3vSDT7FGchIDj1yjUc2juFYi6FZkIlYlUGXK8/eck7GRTwBWi8bacdM9efSWvGh1dVZaQVCabj+XHY9P9v792jLLnKw97frsc5p8/px/Rj3tJo0MgqCYwABe4YkI1sgqyYi4xl5QLGwVmKY2wvYuN1F/YKSXy5znKCMX5AkGNyfUlYRmAvFBm0YiNEsBUjuIyFZVnISCU0kpjRvLt7+nmeVbXvH7uqTp33o093nR7t31oz3V2v/aiqr/b+9vfAMCIdZOd7cXap1DYtUD90s8eVYX2DRJS9Xva7zaPedtiWET9zlmmQz9mcXy4iEHhB0HZhOGsb3P1jNzRY8uRzFrmMydqmysRhGoLpQoa/P7nctfxAwtJahZlCllLFC8M81tuXLLvq1a0o/EClSprImCo/nBBcWC7i+UqfPjedY61YC1VCKjDR/rk8lbC+ybpH+mfVtjAEZdNo1woTqbYcZ4i27tA7xSBWEC8CfwV8HPii67oNQz7XdR91HOeLo6zcTlOuqoDbIszE64WxaTvhB5JixePM4iazU1lmJgXFco21Yi1+KZSjjha+STyv1SDfEGqFv1/BtzCTY3WjynTBpjChZi6XLm/GpmWV8GOYDV/wtWKtr5FtICVBZ+OBrjQ/K1FKIEEUDrN1dN7MdN6OBUI/PZG0dOjHuwyUR+Anv/gkQhiYpqBc8cjnLCo1NVOwTIOpvB3qmrt7ilW9gAvLxbZ63XbtSwrkStXH9wLKVY+jB6YpT2ZZ3agyM2nH+8Mr4fkqF99U3o7Nxx48caolQJFhwPx0Dk/CXcePcN/DJzl5ZpWaL7EMmJuZaMjXl7aTVN86YMdxfgr4puu6zzmOcwD4LcAHPui67vltrOOwHGVAHfD7fu+vqdT82BSmH4RQuqrChIVtGSyvVwaawmqGY+8eJYD9QDI3nWWzVG1JZzQOxOvxCb30uCAAI3TDVnpYZaGhVCcBB+cLnFsqdjWZE8De2QmWVksIofS4cdB6+h96zE6pwDnnl4oqgpzRmgvQCI0/ZqdyCKHMBouV9l/L+eksNV+yWaoCdbM/QyinmY1SLc5dF7kzt2OcdMD/Dog+h78L2Kj+vWJywt32uqvCMID9vyVTE3acynx1o5pKjreXGkLAerEWmkxJVjfGU/hCXQAlFwXHhuijEAqnKP9d0pujl71yPmexuqmEXOS5FlmSDPKtiQLn7JnKxhk+mlFjRcHaZjWOIdyJjVKNcsVTo3xDxCN0ldG52pCvL00GUUEcdl33lOM4FvCjqKzIVeDsttQsBSKvoCgVTS+m8zaz0zmklJQrNcrV7r7umtEgw0WuclWlUvJHkTZ5G4hk7URWeYWVKt5Y2YonUz9FuuJAqsDwAtnVaSJiImuyvF5lbirDesnD8wIytolADhTwPlKlTGStrum0okXPy2vdI6/VwswtZuinrOyLVXJTiQp3KQVcWknXe3WQEfBaGHjnTcB3XNfdCLen+wkZMUcPTvc1SslmTGanVfxUtXBRzzelGT1jNXLsA9sy2D+fxzmyh0/8ypv4uTtejmUZGGPWkPqCoCCbMblq32Ro0taf8FxarWCbAstS+eGu2jfJgbk8uYyFPaCTw8q6EoZW6GmXy5gdM6lUu6gNQPW/aRgNglwIQcYyMA3l0DAOH8NBRsD/CXgUyADvD7e9EWWO1hPHcT4K/CRKN/tK13WfdBxnHmU1cQyooNIcvdd13UvhORL4Nqq/AP6Z67rfHqDOA/PgiVMYhiDoooYQoGIGh4GoN8sek3kb2zL6TnCpGYxx0p32QxAELfGBCzmL9ZTNntphGCKOkSylHKiOEpVXzfcDKtSzikTvxHqx2rdKb61YYyJnk8uYIJQ+ulxtNe/s5xM2mbOoBbBZquJLEVuc5LImpQr4DK6n3g4GyYjxW47j/BkqC8bJcPMZ4Gf7vMQXgI8BX0tsk8BHXNd9GMBxnN8GPowK/BPxhsRoe9t57uxq1wcmnzV51bF5HvvuYpwJAdBOGJoGggCylsHH7/t2gwmibQqGNLLYNkwBb3390TiI0KAjw0srZeamsiyvlhvshQd9J6RUZmHTeZultTIbtfYViczbOpnx2abg8kaVQ/N5kJL1Yi1OOfUjNx/mga+/oEbBMv1R8CAjYFzXfabb3z3OfQTAcZzktmXg4cRh3wR+YZA6jZL/54Ene6YMKlV9/vaZRfwONpaa8WfYYDjNqDjQYYxfo55OyTSUvW87HeqoI6Ftlcgj7o5bruUOlGPD73/+iYGuIZBcXi+3XTgbBDu0SPD8KPt2+8Ad8a1r2hcJZM+XZGyDjbLHRlkt1k7lbapewNefPI8dxme2w4W5Xjkat5OBBPB2Ero4/wLwQNOuh8OFvy8BH2q2P+7F/Pxk38eeePpS1/126DpZ9YLwJo7n4o+mO6MQvqBiFSQdC0Bl2xWoKf24I4SyYnjHbTewd+8UAF+974mBP1BRvsStLoZKGZDNmGysVjAMIwz43n2+kBwEJUezczMTrKyXEaEb8/xMjoxtUq56TIsMy6sVfClpY77cQNQv28XYCGCUjnkD+ERi2xHXdU87jjON0hX/O+DfDnLRQeyAux1nmY0RrJqN6jW7h1Hp/Q6GU9y1Yo31Yg2E+khPFzJd1wJGNQLfKofn89z1w9dxzUI+zv5y7tJg2r7Ia3AUS4vKTE9Q8/w4XkRENLpNClxhCEyhLAmSQe6FoRbbqrUgzMrhxw4XhhDIAN72hmt46NEXezqadMuK045BBfZYCOBwge77gLeFsSYAcF33dPhzzXGcP0Llots2VESr8GFq0i9F6Vci2k1bml/scTS+f6kSZ7ims+5wUO669VgcD/q/PPAdymHwnV7OTWm6pZuGMs1KBuJJsjAzWGKBaL1kqx+UjGXE2ZmjFF9JA4ioy6JiTFPU0xxF0eFDgkBydnFTmayF205fWG8IxPTlvznFNQemuf34kYFVLqMkdQHsOM5vAv8IeGtSvRDGnii7rlsKVRB3AY8Pev3CC7+DrFxGWZ0LlHiN4qkmfzf4N6+9zNmlIhKBlAKJQf3bHnoIxfuin+G2hu3E56qbLhrPRxDIKHxl9Hd0LSM8X4CMzkleK6wLSj8WhOWS2C/D/VKqa0R1DJL1baoT8XY6tC3RjsS2IFk2jeVFfR23J6538zHEfdffMY3X74d4dDQivb1pCu79yjO8cG6Nrz95nmzGpFJTdrC9BFiaMyc/gIwt6rFzm9goVttu733drTXKNJQj1NefPM9E1mJts0I31azvS3K2oFprn96p2cW4uXqlasDpixupB2QfOBzlsDiO83HgTuAAsAgsoYL7PAk8A0Rztudd1/0Jx3FeD3ySMH4L8A3g/QNYRBwFnueLR2Hze6NqhmYMafkIhL8TfRABEgK73fH1bY3HRx+8hg8BAtNQH0U/CGMMC2Vz6getH7Hm6yY/Su0+MM0fr+T50Qe6XZ3UB7LxurKprQJl73vDNfPErnDRdYTBo09daujDeCCQ7Ndk/cJ+VsNVdX0vaPyot+3fRFuFEBRyNm945SEurZR4/twG66WaipYmRVN9jPh82zLxAkkQ1MtBGEgpW9vQpv+FEOTzGTaKXkOdSLTxHW/+vvi6iajR8QBOJn7HyDF93W2z2DONASq6sGMCOAWOAs/XvnoHlCJnPXU7iN0t1TYhJZGpcblSY71UIwgCLENFjqrWPKqe3zD2grD7RbStvk+I1nEaouWRbDg3ec3o2MZyGstNbk9uM8QVez81mvHnbc8eZupY397Bqasgtpvi93+MwPfoPFWtb3/2zApfOnEK0zCYyJqsrdfYKNbI52w2y7U2U51+pr8djhHd93dK3tKPeI1T17RM2EMhLhrHBvWfiTFK4oORUICEH5Og7ceo9YPTvK9+vW77Gz84ifOEDLNDKG17EASJOrSMXZs+eo37on+GqdxSG6+TqFP8gQsazs/nLGQQUKl5KsU7KratENEYLSq7fl7bOkb7Eu01hBokRH83frTDc2TQMABo6L/wfPUxbuy/aACSsQxsUxCgUvNctbfATN7m8WcvdayrEK33SV1X6b1bBgiifp36vqDtYKP5uqYhkDIIr1t/nhrGwom2g7oH6snoZ5BEWI5ymmk/SJJM5cOQNzKIy0kO4kTid2lkBw6wfsULYM/aS2D0Nyq8/8QZVjanydomtmdw5tIG1ZqNZRnk7AlWN9t5CPW+drdJxsDj1QFmLP0euVsmQfms2RBpaxibWvWCDkdkBWMIZXalkmCKMAmlbD2wqdxe1213dLPlTX17hx0dyjKEwDCVh6cQKl7y/jD1UNULmKnY/PI/fRX/w32c755Zayi43fWSxVuGwJcycWw0G2s9RzTvi/o0FtrqDpkCpgoZ1hM66fq56qT5aRWGdK1YCT+iyqsvWuQ0GrqoXr/oThlCMlPIcHmj2vASREIcJP/2Pa+Nz60fk3yCGp+mWTM70ON1xQvgQTi/XKJYbg1xV6n5bJYgY4mejhqa7SPKMrEVtnK6lPUUREGg3Fpnp1Qg8sWVcv3aW/6i7cAzVvZYSXiqCeCLX3uOW2++isdPpmcV0MDlTouZavR7brWKH2Y6Se5rT5uPohD8+Buv4tGvv9C4NxTwAvCt+cHqnJ/q7kzQhBbACao1v2N8UUAL35RJ25zPMtVYaGrC5sBcHiBMcWNjWdXUg3tvBQl88esvkLMHm0SbCQ/A7cY2YX5PnqWVEjVfEmyxv6WEv/y7szRZsW31A9rdsLgJHb4rQZqpSTTjjxG6xm6WPW4/fqQlPfpuxgoDs5drgwm1IFBu2NtJNDqt+SqV0Ch7ulwZSF6OHC2ANZo+qYZJJC1DpUY/8Q/nWVwtc36pOFQOuXEi0qsO+h2RqAXj7QyzmazSxeXN2CNuFKQZBwK0CkKjGQiJUkV95N6/5enTq2lXZ+TE2SwGEMR+IFXC0RGU3yshaqkaxKmJdkJVvt3oEXCCAeNHa16qCGLhG5rg73r8QFlH5GxjKME2ihmASPzfDeXwsuXixgItchJstxrvSnhRNeweu70ByNomP/7Go7z3x7+/bYbjbuyZbEyKM+xzruI79Hd2PyqI3ZChRqsgEuQyFsVtUspP5W2qNZ/KgIscmvHDNOqhF68EUZzPWvzcHS/npmMLABw7PMNzZ9cIZOcEtSrfmmibHHOYPlEphETfIV5zGYuK5xP4MtRDt5a7G6xSxv8TsYPMTWW27drrxZoWvlcIpimY38ZnZaep1jzu/cozPHFyEYDbjx8hlzEbnUuaiASe7/lUBki+2YmaFwwoe2KfegAAIABJREFUMGVD+Njd+iHUAjjB2SWdz03Tm/npHKURCJ1xwQvANA0ePHEKUBYed7/1RjIJm+BocU4INf3PZSzyWSuOWDYq9ZppGuzdk+u4HqO8EAW+hJlChoxtdEzc2Q9p50jVKogEacZp1aSHQLmw9htS8cVLmy3n7/YnJ2MZnF3c5COffYxTF9ZbHJIi06+pvI2UcGihwOJqmXWU7fyw7W8OTu/7QZhrUWCbgomsSTZjxunjAwmBL/F9n2rNR0oVMCsIQlWE6K6ib3YcSd25J93iNZr0kWwtnu1uF76gVGSlqs+LlzY6eoMGElY3axhChB6AFmzREi/Z71LKUDgqV2NhCDbLXlsHKRn/R4Nqr9cYatysJ7QKQqN5iSOAjWKNQs6iWO69CB1ISdY2VTzdrDmyeiSFp5Qq3X2vz9sW4hGOBVoAazQvcSzTIJc1mS5kBp6S753Nj6wezTHGJrJWz/o0724TiG6sZyhaAA/BOH9RNeNNPmvu6PMjRHuhlOTY4WkOLxSoekHfi1Ir60onu7oxUJLygVhZLw9scm0aBgKlW56dymIa4/2+agGcoG8DdKEU/xrNoBQrfk+BOEp6eerZJg2BhfK5/paF1oo1pJSsbgyXQ67fMgZHLcYhoFTxEMLQI+Ddwp7JbM9jsrbBRMbENIzUTVg0u5OdXHkPZPfy/ADue/gkAO9+y/VctXeyr+tKCS9e3NgW4Ra9VsMYJcVZmn1Juer37diRFloAJ6jUOscCBvVg/JPjRyhM2GRsA2EILYQ1LcwU7J7HjMtzE0g4s7jJp/7iaQBuOLKn73PFNgzlM5YRR1bbyZlCWmgztAQbpe5THgk89OiLTE9mWCvWEDCSCFCaKwfTEKz3MXVO2/4UEvbLUsXFffDEKU5d6C/peBSRzDBEg0faVvEDiR8oM7TJCXtbY3QbIv37oEfACfqZ8pSrHhnLwPPCBIByvJX8mp3DNJQATvulHoTYpTgIWFwtU672FwvFMo1Yv5zPWg1rIlt5H4wwxsTcdJbZ6Rx79+Qa6zvCl80ag/CHegScwBCipzdcLmOxXqypL3W4bRe9b5o2NHtjDULSCy4IiIP07AaieiurAYOFmRzFstdXQKpqOACxLYN9c3lytsHiapmFmRy3Hz8SB/a5+8N/2VddLFPlAT92eIaNYpUo+1c+Z3PNAZtKzWdPIcOv/tTN/PxHHx5JIPWoDWm+v+l/AsaI66+a7nnMkX0FLq9vn+mNZucZlRfcbv0QSyCXtbj9+BFue91VA53nBwEr6+XYM25ls9oQ2Cdr9zdk9XwZn79W8iiWanGqp0rNx/cDbj9+hCdOLo505Jr2PdMCOIkQHc3LDANmp7Kcurg5cLxUzZVLNAoc5ryBz9mmx04IuPvHbuCmYwvcccu1A9UtkDCZt2PPuKxtNgT2qdT6E3GmIajUArK2ST5nMV3IsKeQoVj22FPI8O63XM9NxxZ48MQppicz7JnMtCxkGobYdepArYJIsLhabhuFyTQEV+2bZLNU3bZ4wZrdiUQFpvne+fWOx2QsAyklNV/Gv0sas0j0MxXerlhRyes+cXJxoFGhlLC4UmYqb5PP2RTLNVbWK5xfKvatfoBw8a3qc/rCOlIqlcRdd76Sm44t8MAjz/FfHvgOpaoX19Vok7gjCBfvomMyltFTVbEV9dMo0AI4QaXmU6y03rAgkBTLNS6vV1PXGWnGj8tr5a77o/c7uepuWwZ+4HcUqlHOs5141gRw71ee4YVza3z9yfMDn1/zA5bXK1SqPhvlGltRg0f9E0jJvV95hhP/cJ4TT19UfZHojE5lNMvS3tHR0hXAWgWRoJMZmoTQ40cyM3nlBOLWjIb1XuaLMkBKZVYV/T5TyDCRqQeyaYlp0GbbdiGEisP70KMvYobWDf2ipv0CpOqHUa1BGoaBaRqceOoSAjFUX0TmbN3IZUYXTGgYtABO0O1LqVKSQ7na3VlD055xcTwYJVGb+gmB6AeStWINyzTYU7CRCI7sn+L1L9/XaMIVXrPToGzUeuAoFnLGMihXPfwBPcdkIJku2FimGJmKxBBq1pmxDAJZVyv0kwBVpUoK69Yjeach4O633jiaSg+JVkEk6DVdkeyOPFNjyaBBVUzRNSXOKBFheYNm9o2EZK/nJolpCAzT4OfvvIlrFuqRxJ44ucin/uJpNktVEAIh6jriSABJRq8HNk0DyxRUvQDbMlheKw882lzbrDGVtzEN5fq7VROxQIId1kmZhtb7uJcAtk0Dzw8QAvbNTZCxDM5c2sAPlGA2hIj78eDcRGwulxZ6BJyg0Eds0zT1RbuZQV5JAQMJ7K1apUQv5LBXmZro7XocXbtU9TFNg/sffrZh/4MnTlEOg8eYhsBIDHWlVItO7a63VaQMmMha+H7AZM4a+MpqMUyyUaxx2+uuIpftPKYbZBYUBAG+H3D8xr1IZD0+RJdzhFDvZyBhImPGlhlG2HlBELlPhwqeMfB13pERsOM4HwV+EjgKvNJ13SfD7dcDnwbmgSXgPa7rfrfXvu0il7XZKGsVQ9pM5W0kUCp7eH188AYdubbj4NwEq8UaWdtgcbV/O2/DgGzGhB4us1ENpQTP83nuzCrv+72/plT1lI5T1rP7NtvZqFQ7AtuEWtjWbMbEEHTMXtEvWdtk/+wEtx8/wmceeobZqcxA7fcDyFiCbMbkjluu5ejBae57+CRnFzcb1Ch7JjNslGoI2X4QYxr1PkCCIYzY9Gz/3HM89OiLcV9lbEEha7FR9qhFITSFconO2iaBDFjYMxFfOwhkPIKOQm5O5e2xUCfulAriC8DHgK81bf9D4B7XdT/jOM5PA58EfqSPfdtCro8Qk9HiiGUKDoeRo84vF/G84CUzOp6fyrB3Ns9zZ9dG4pHUjMoF1l92hq2g9J9wcL7Ab/yL43zks4+xslklmzHxPL+v9DUCWOphBdHM0moFSVK1UH9uOj1Bs1NZ8jmbc4sqH93+OaW+OH1hPZ6i26Hy0w8kUqopeNY2OX1xve3imCGgMGHHnmsLM6c4v1wcqC0Anh+wr6AE3k3HFhqm9XGf2mYYnUximUr1cWAuH7fn4EIhPifyeouuc8ct13LHLdf2XZ9kmRDm+/NkbLMdSLXgfnA+/QX1HVFBuK77iOu6p5PbHMfZB9wMfC7c9DngZsdx9nbbt60VFb0NuQ2jvkgQeepMZEwkMo7i1HLZ0dc0VTbKXpy6fLuuH9lhbSXjbS/UzLSuwI1i4k5kzL5zhwVBZ5OoTsimnz2fOaH0rJWaTy5jksta8bMXx++VUU41iURy/Ma9+H6gjutQPykbsyHffvwIm0N89IIuyumoTys1P0zoqaxApiZUG3JZi1zGbOv1NizJMqWUBOENMgzVR0RzjTFIwpvmItzVwBnXdX0A13V9x3HOhttFl32XBilkfr6/+KYAVV+yfz7P+aXOo4CMbTJTyFCq+uydzXNxucjVB6Z55bF5Pv/V74KULSvY6d/m0VLzJW/+gZcxM5Pn33/qm10FUDT1s0xB1jLY7GPKXPMlVV+ydzbP6kY1zn4bxekwwwhcpgHDDMCNcJErYxvMFDLUAti7d4o3751iZibP/Q8/y8qzi2HEL4FpiJaRvm0ZapU9Iakn8zabpVrX93oia1EKnXlivXMXmzNDCDK2cijYO5vnzp+4DoD7H36Wi8tFXnZ4D/PTWR596iKlisdE1uLHf+ha3nXbDXzrqQvc//CzHZ9nCRRyFpc3qnH7/9uXXFYGzHJhCOI+bCbZpxeXi1y9fxqJpFxR78+dtza2Z9+c2vbaG/cPVIduZRqGwVReCfxauNiYvO/d6LV/q1zxVhBLSxt9h8ubm8xw+mL7cHymAZZlsm82z+pGhVot4IWzq/i+pFSuUat67N+Tw5NwcbmopoVE+rvGj+1ud+aQUvL2DzwQpwK3TEHGEpSrQd3pwICr901RLNdYXC3jhanEO5HsE8sQrK6XqdRUsBTLFMzO5FhZryCEYLpgs7RabhC+vSwRktcXAizLYHLCBiGYLWS4dEl5sl2zkOfNrznEydMrVGoetmUwXciwulnF8wKklLHLq0TpYmuhumKjWMMIQzSCKgMiIS2wTMG+2QlOnV9vHAW3qXd0/UBKap7PofkCb37NIf70oafjoDevf/k+nj61wt8+vYoMJJM5i0MLBfZNZ/nqN5/nwROnWFwtdw0y9cK5NQwh+MDH/he3Hz/CgbkJ1kvVgSxQbMtgdrLeh82srhaphSZuOdvghiN7ePrUCucubfCnDz3N7ceP8Ct33cQTJxd58MQp7vn84y1BfZqJjl1cLSvVoRCUq37Deb9y101AXSUxk0i4UKn5Dfe9E732NzOowE5TAJ8GDjuOY4YjXBM4FG4XXfZtG7OTGZ4+1X4K5geQtwSrGxXWilUmMpZyS5bK3/3C5ZLSAYfTwo2SV3+veoyId5tAlhL8xAvt+bJlISwI4OLyJlWv7s7VrY3JfUIQjzglakR86XKJXNbEEILltUrsKZasU9c6N/3t+wHLaxUKOYt3/sh18fYnTi5y71eeIWMbVDxBzQtYWi2Ry1jUkExkLco1P9T3S6BRXZGcjk9lTCxTcHm9CgRM5XNUaj52Hy6yyfr6AVy4XORTf/4U+QmbfM7i/HKRZ15cYSKj6pN8Dj/150+BEORzVvgs1jouOPkBFPL1IDrXHZomGED4GkKN6jupDKL+NE2DfM7iwuUSz7y4wnQ+w3QhE5cbeeFFx0XbgRYhnLwmSM4tlwDJ3HSu7Xm3Hz/CvV95hgp19+StqjlGRWpmaK7rXgQeB94VbnoX8Heu617qtm876/T3J5e77i9XAqq1gJlCFi+QcfI/IQxKFS8OInLV3knyobAA9YC+/ZajvP2Wow1G94ahsidkBtSlbocxfr0t/Rm8R+e1bEuMAEvVANMQ8UiwF1lbhUSUUoVHtExRb6uA+akse2fzYT0FtmVgW4YqMzywW98YAvbNTsTBc0xDMJ1Y7AFlDmaaBjOTWeamsliWgUQ5GdzxhqNcvW8yjH9rUsjZVMNRumk0lm0ZcGAuDwgOzk1wcL6gUq0XMuybnWC6YLeYZbVLoBk5Fnhhip3ItKpU9REIlfeMxuewXPUpV7z42LD72mIaUPGCOIjO359cbjF5ayb5vByaz/PL77y540g16s+43hVlyVCq+g3BeyIvvE5BfTpdc73khf1msF6stT3vpmMLvPst17cN7pM2O2WG9nHgTuAA8D8dx1lyXfcVwM8Dn3Yc59eBy8B7Eqd127ct9ApGHbkh53MWa5vVxAskKVeDWNemAq6oaWfyhYrMdO65/0n8QC1GrG42mi/1Y9Q/6rWDueksE1nVpm4ZCJJ+851eaCmjhQ6FHwSxjrhBDRD+bhqCw3sLFMseH/mFN/Cr//kb+EGAaRiAwDCVeVIgYbVYo1wtEwQBtqXSqCtrCRUAhgBsSy3oJNU/ykpJ1f3i5VKiPbC62ZhUcnG1HC9sCSEI/EDFAql4fMu9xF23Hmt4cX/2t/4y/FAIos9otBj2qz91c9s++tX//A327plgdirH6YsJFVnkcNDUn9GAtOoFFMs18jkbLzSnqgXE5RpCWSQEoS3smUsbWKYa8VmmoOZL7LCu0QjcEAIv/D3yhuv1gXeO7GlQD3xvschHPvtY23jAyf4EVT9DEJcJajZSrHihyqd+XzOWijHcTPKaUT+IsO1RO9qdF7FRqnHfXz3LZx56hoWZXMfjdoIdEcCu6/4S8Etttj8NHO9wTsd920UuUiu0QQjim7WyWcWyDHxf6QSbp9/16aXaXq56PPCNF7iwXOTZs2sEtC7URaSxMLu0VkFQ6TrsjfSmfjiVjWxWB0G2+d0Kp4RR3y7M5FjbrBLIhKtvWH654quVbKG81pbXykxO2KwXq5iGgW0Jap7fcP3Ig6xdNAE/kGyWajxxcjEWGAszahobBJLFlVLDfTq3pHKnRaEbQT0zlZpP0hckkGp7JxZmcmyUa1RrATJRQBdjgpjlMBa1FWZlMUT9XihztLq+1wj7SQYSX9SFe3KGI6nrqqteQC5j4flBx8DyAhqm+QB/8tVnQdBWdRD1Z2QSZpkGNS+IyyxVPJbXyuEotn5f56ZzGIZoKyCT16y/h/UMF8nnCRpVFkKo+wiC2SmlAkkT7QmXoFsw6pxtNKTvVqZn/TkBqIdecOKpS5im0eDlNC50WgyKEFJ5FyWr3s+3wg9avbgiDENdM6mPu/34EXJZCymD0KtJxhYQk3mbuelcWK7aroIkCWYmM8xMZtsKsGSIwmYCScN0Nbq/K+uVBuGrRrlGnDst4rbXXYUkNP9KmIF1e5ZuP34Ez5Nq1D4ARiht1zZrsenjRNYKA6NLpAwIpIztm9Xvygkh8g6Ljo2kcNIkzPeDnt5sM5OZhmn+gydOYVmio+qg2SRM1Tc03ZRRHwimJuyG+7qyXumop20wbZuwwllXwFTebmvG1qCyKNYQQgni9ZIXfxjS4oq3ghiEO265lgvLRb75nYstwuXV1y206Ar9xU02irUGS4d2C2qRK2ktqMeD3YkweLZpUBsguErX2giYzFmYhmBlozpQ3f2gjSWIAFNAsezhB5J77v82++fy3HXrMe7+sRu47+GTXFguAoIDcznWijV8P+DCcjUe4UUjutmpDPmccgcWhmgYVULvxIvJ6Wp0j++5/8l4m2XWXYOj3GkRkYPAQ4++SLnqYZkG04Usf/nYmVCvKZjJ2y2r9O+98yb+w3/9m44qp3bPkWkIZBBQ83xednCKW199iKdPrXBmcRPfl1imoFTxmZ3JIIRgrVjD85S6RgjJkf1TDcdOFzIgJeVagCXAsk0e+fZ5bKP9s7NnMkPGNrmwXKTm+SytlMhlLfbNTjQMRJIqgKg/I4uF/bMTvOlVB3n61AqLq2WkVPevMJEhW67FdZaSjnra5msenJuI+3dPIdNiPdGgsvBVfAnlkZh+XBctgBM8cXKRZ8+ukc9ZDQbphoATT19k/9xz3HHLtQ3ePh/57GM8d3aNQBLrdduhQuMp3Zuaam3/zR9E+HZDCJibzlHxAmzbZG46y/JaGRBKD9nPLCBUKeyby5Ox1ILJykYFZIBpqoArySn+b/yLRu3TB/7g6yytVVquaZkCy6qPYjKWmuImp9sqKla9jsnpt4CWae5NxxY4dng6vK8yFr6BrOdOSxJ5akVTXc+XbJZrgCCoKjVH1IfRFP0X73o1+2dznO1go9suOJBy1RUcmp+I9ct3NJ2X9AKLPkrJfGrtaLYqWNmsAZK9e3KYpsHSqlL1RMF61NqG+leu+KxsVJicqHuVNasAmr3jkvWO6gsq/1s+Z7d4wrWj3TU70aCyMI24X/tdHN5O0q/BGBFNVSJD+cYXVfDQoy+2nJOcMndTLASS2DupV+LPcUNKWNustp3GDdISwxANq+FxdoPQ2aHdFD8iitXcYqEhZdspLvFxaiG0kFgIigzIoLMJVeTpF6k/oil+rovJVfL5iYLqICPdbOMq/f0PP6viF4QVaX52moWvCjgUjou7qLCap/z9eJb1sioo5Cw2imqhU4a6KolSR0zmbdY32+dv64dh6jsovbzx0kSPgBOoaVN9gSy5kIOQba0kbjq20DBlbmdDKYTSIf/LO76fJ04ucs/9TxII2ffUsxvbbUMcveqeH5AJRwzJaVwgYTpvd7WeiIjaWyzXqCQW86JFSyHUiv/iarnB0H5hJkfVCzANVV6y37wALq2o6a5pKJOvN73qKH/+/32PQMrQkUJZeVjr5djqRADzMzl++rbO09y733oj9/3Vs1y4XEYIycxkjpxthKvnpzpOdaP+idoHdQsFUKP0i8vFBi+6ZuuQCNNQwcnVsWrPheVSw8Jhc72Bhr7r5tCQrDdE03Kle/d8uLBcZCpvk8ualEMvRtM0mA5TEMlQR7+nkOmrvOb7evvxI7z7LdcPVN9BaVFZzBdi1cueQqar5+t2owVwgpxthEbdrfgBHRN2RtOhJ04ucs+fPRm6yUajG8lUPsP+2Xqwkv2zOc4tlxBG40jHNlWsWC90l9w/l2/wmmrHVoVvN110PSC2iM2ZmqdxhiHYrHhhe9t/VJJlFcs1ltcrbesdnSuDoMF4f2WzqmIdABnLxPPbB8oJAslayePowWmOHZ5pWH0HmMjZHJwvdJyKN5Oc5sbqBdl+tR/qU92ofwyR9GhrXKXfN5fn8mr9WYuOk6jR7nWHZwBlceCHKX8idYoQdHRSaK53PySn6IYhqHl1HXtklXBwvsDhBbulT6tewNUHpmKvs240O2VEffjut1zf9z0Zlm59MkjuulGjVRBJmt2rmpjsEff1wROn1FRX1FegpVQuqg1Tqrgc0RjLVojG1W0pexrFD0Nk3iVo1I02OwZEAjWXMbntdVe1ncbJQMaz4ul89/7JZUxWN6qxEX87hFDBeJqN8vM5i0CCl7Arjs9J/B6pMEY9tW12KGhn8B9byCSsOBCRFUbjKv2dt14HUjaY2kUEgWywuIn6LFKn7JnKdnRSGIaGvmp5HkJNuZQd+zSK59CLfvrwpYYeAScoV33mpnMsrrRmBZjO27FXUScWV8tMFzLYlhGv5lqmgSEkD544FRt+r21WmZvOsV6s4fkyDOyidJkH5vLx6rby4zcwrYBm7YcRrvYPMwK2bZOcbVCu+lRqAZYBwjAIAmWor/TUSu89U7CYyNk88u3zKvqZlHiSeBp3dqmogptMRs4ctZY6GQYUcjZ3v/VG7rn/20gpsS2ToNaYlNK2DPZMZlhaLcfqjoj5mRxBUMQwTTYTOdiSd0SZWCkrhWGm4t1odiiAVoP/ZJm+H+D5ssUKIlqlf+2N+/ndz9Za4jRYhmAiZzXU8577vx27uUvg0uUSpiko9shF1y/Jel9cLjY8D6ZpMDVhUa4FHfv0tTfu7xgzIalyWN2oMjNpN4ygezlNDEo7Fcc4eLx1QgvgBPFULGPih67GgVQPYX7CZk+he/zQ6PxoNRfU4tVGqRZuV9OuKCtCFNMVWleqo1XiX/+jb3JuuYRlNqorDAFWxqRS9eNpazSiVav1JKL/q5FlEMhYtRGVubZRZTq07WyuS+RD7wUqvkXkQ5/UmzbHXs1kzAYVSvJ6yrqgrho4H+tB1Uxg/1w+DLloxeqOiKoXcM2BaX77l9/EBz72v3ju7Bo1L2hwCFEj67qVwqBT8W40OxREdWpnQdFPmd966gLlirp3tili9cPkhB26MNevt38uz5nFzYYAPsrSwuuoCx6UqN7N9xPq92+Q9kGrymFts6oyiwsRvx/t+nBYOqk4onqPI1oFkSA5hQyCyKi+1Vmg1/nJKdpGsUYhZzVMu6JV5b6mxx3UFQJiI3xDRLmuRGzuZRhGbNgOKiZqUrWRNLzvNFUfZNrdHBu5uZyko0UvI/pudYqukctaseRNjri7WSlshVGrNO5/+Fkm83ZoSRLaGUvYDGMtN9BRty5HPn0fZTubn589U1lAsrpR3RaLh92o4tAj4ATJKZaUUK35mKbgwFy+r6lMuynaZqmmDN4TTBcy+IHsa+U4Uou0qCsCyf7ZCSpV5cjgBxLLNAikwAxHu3PT2diBwRAGb339kVi1kSzz6MHpttO2zzz0zEDT7sXVcosKpblt/RrRd6pTdI3I8uTcUjFOOXNoodASq2FUjFqlEVkX2JbB2mYVz1fxGrK22XLNcq1u4igJAx6FG0Y5fYfRtrNZbTORtZQt9HqFYtkbuYqgHzVROwzR3llnJzJ5awHcRDTF2rt3auBYoMnzI9pN6apewOGF/lbjo6lvJ3VF8/WVl5LytY9UIX4QMJmzlcNAH3VuLnuYaXe7cnqV13wM1AVBNIp5cxhvdZTqhX5pLvOJk4sdg9D0Yv9cnkuXi0xkLSZC19/kVD9JPT5Go1OIIVqdSEZBr75t1rO+47YbGjI8J+vd/PyYpsGxwzPbYvXQ7/PaTC6jguQnZbCgezyPUaFVENvMVqd0vc7v5Wtfqfl4nhxqmrcTRvKdiPR5Sd35vV95hm89dWHby+6HTvV74uRiX+ffeet1ffftME4h20W7dn/y/ifatnunn59hyzuyr9AaPiDcvt2YH/rQh7a9kJTYA7y/VKoOFWGsUMhSLG49UtL+uTz7Zyd48eIGq5tV5qay3PlD1w40Uup2fvP+fXsmeOP3H6Bc9ePjf+Z/fwXOVTM7Xvet8OkHn44X4kRohxxIOHVhnde/fPh0NdtdvxcvbvDGVx7sef73XTPHZNbsq2/3z+U5tFDg9IV1Nss+QsCB+UIqMW3btVsIOHWhtd07/fwMW97nvvpsW7f9YsXnx15/zUB1KBSy//cgx2sVxDbSPFXr5HXVi15Twl4qgEHUKe3MeHpNFxvSw4SmauVaMFL9YanisbpR4eLlEh/57GPbZl7UrxnTsPrGJIOoUdJQubSjXbuzttmx3Ttd72HKK1e9Bi/LSMfeKz74KNACeJvYjSYxw9S5V6zVYduc1OdFMWOlVEJuu/pykPYPq2/c7bRrd6Xm7+p225ZBpRa6w1MPgt/J83WUaB3wNrEbTWKGqXOvWKvDtjmpz1vdCIPACJidym5bXw7S/jT142nSrt3DrjGMC0kPV9lh+3ahR8DbxCimqDvNMHXuFWt12DY3e2epVDU2hQmbmhdsS18O0v5RmGvtNq8taN/uTlYQuwUhBNN5m41SLbYumZzo7fk6CrQA3iZ24xR1mDr3irW6lTZ3887ajr4ctP1b0W9+66kLu05FFdHc7mFNNseF6L7PTueww3jSnUwCR41WQWwTu3GKOkyde8VaHUWbd6ovd/Ke3f/ws7tORXWlkua7qkfA28SoPad2gmHq3CvW6ijanCzj8kaV2cnRXLdbOdt9zy4sF8k1LfKMu4rqSuWmYwu8cG6Nhx59kYuXS+RsFf1vJ95VIYcxkt0dHAWeX1pKpP0egN0+rUqi2zJ+/N59T3DpcrEqlyhKAAAKFElEQVRt0Jvtjo07anb7PUlavxTCdGS+HwxlZ71379RAimOtgtBoUmAQTzjN9pKmxZIWwBpNCrz2xv28+y3Xs6eQoVj22FPIpOLZplHWL83xp3dKHaR1wJqu7EZTqd3CuHi3vdRJ02JJj4A1HdlqwBmNZjegrSA0Y0lSNwbK578SbtcjN82Vwk5Z2bRDC2BNR3ajN59GMwxbjQM+LFoFoenIwkyOqtcYpm/cvfk0mt2EFsCajuxGbz6NZjehVRCajuxGb74rAW158tJBC2BNV7Sp1M6yG+NIa4YndQHsOM5R4AuJTXuAadd15xzHeQEoh/8Afs113S/vaAU1mh1EW568tEhdALuu+wLw6uhvx3F+n8Z63eW67pM7Xa8rHT3NHU+05clLi9QFcBLHcTLAu4EfTbsuVzJ6mju+7MY40prhGSsBjMolecZ13ccS2+51HEcAjwAfdF13ZZALzs9PDl2ZvXunhj533Ei25av3PUE2Y5LLqNufsU3KVY+v/t1Z3vwDL0urin1zpdyXdu14x2038Mn7n8APVObhSs0HqbaPc7vHuW6DspNtGTcBfDfwqcTfP+i67mnHcbLA7wOfAH56kAvqcJStbTl3aYN8zqKWsPE1hODcpY2xb/OVcl86teOahTzvfPN1LeqhaxbyY9vuK+WewNbbMqjwHhsB7DjOIeBNwD+Ltrmuezr8WXEc5w+AB1Kq3hWFnuaON9ry5KXDODli/HPgz13XXQJwHKfgOM5M+LsA3gk8nl71rhy0g4VGMx6MzQgYJYB/KfH3fuC/O45jAibwHeAXU6jXFYd2sNBoxoOxEcCu617f9PdzwGtSqs4Vj57majTpM04qCI1Go3lJoQWwRqPRpIQWwBqNRpMSWgBrNBpNSmgBrNFoNCmhBbBGo9GkhBbAGo1GkxJaAGs0Gk1KaAGs0Wg0KaEFsEaj0aSEFsAajUaTEloAazQaTUqMTTAeze5C55TTaLaOFsCagdE55TSa0aBVEJqBSaZOF0KQtU1M0+DBE6fSrppGs6vQAlgzMIurZTJW46OjU6drNIOjBbBmYBZmclQTCT1B55TTaIZBC2DNwOicchrNaNCLcJqB0TnlNJrRoAWwZih0TjmNZutoFYRGo9GkhBbAGo1GkxJaAGs0Gk1KaAGs0Wg0KaEFsEaj0aSEFsAajUaTEloAazQaTUpcyXbAJoBhiKEvsJVzxw3dlvHjSmkH6LYkOAq8CHj9HCyklFspbJy5Bfha2pXQaDQvOV4GvNDPgVeyAM4CrwPOAX7KddFoNC8d9AhYo9Foxh29CKfRaDQpoQWwRqPRpIQWwBqNRpMSWgBrNBpNSmgBrNFoNCmhBbBGo9GkhBbAGo1GkxJaAGs0Gk1KXMmxIIbGcZzrgU8D88AS8B7Xdb+bbq0Gx3GcjwI/ifJPf6Xruk+mW6PhcBxnHvhj4BhQAZ4F3uu67qVUKzYkjuN8AeWuGgAbwL9yXffxdGs1PI7j/F/Ah9jdz9gLQDn8B/Brrut+ebvL1SPg9vwhcI/rutcD9wCfTLk+w/IF4IeA76VdkS0igY+4ruu4rnsTcBL4cMp12go/47ruq1zXfQ3wUeBTaVdoWBzHuRn4AeBU2nUZAXe5rvvq8N+2C1/QArgFx3H2ATcDnws3fQ642XGcvenVajhc133Edd3Taddjq7iuu+y67sOJTd8ErkmpOlvGdd3VxJ8zqJHwrsNxnCxqgPKLqI+kZkC0CqKVq4Ezruv6AK7r+o7jnA2378op75WE4zgG8AvAA2nXZSs4jvNHwG2AAG5PuTrD8hvAZ1zXfd5xnLTrMgrudRxHAI8AH3Rdd2W7C9QjYM1u4z+h9KafSLsiW8F13Z91XfcI8EHgt9Ouz6A4jvN6VLTBP0i7LiPiB13XfRWqTYIder60AG7lNHDYcRwTIPx5KNyuSZFwUfH7gHe4rrsrp+3NuK77x8APhwuNu4k3ATcAz4cLWFcBX3Yc57Y0KzUskarOdd0K6qPyxp0oVwvgJlzXvQg8Drwr3PQu4O9264r7lYLjOL8J/CPg7eFLsitxHGfScZyrE3+/DVgO/+0aXNf9sOu6h1zXPeq67lFUDNwfdV33oZSrNjCO4xQcx5kJfxfAO1EyYNvROuD2/Dzwacdxfh24DLwn5foMheM4HwfuBA4A/9NxnCXXdV+RcrUGxnGcV6Cm6s8A3wj1jc+7rvsTqVZsOArA5x3HKaASBSwDb3NdVy9ipcd+4L+Hs10T+A5qYXHb0QHZNRqNJiW0CkKj0WhSQgtgjUajSQktgDUajSYltADWaDSalNACWKPRaFJCC2CNRqNJCS2ANanhOM4/OI5za9r1SBvHcb7kOM7PpF0Pzc6j7YA1mh3EcZwPAde5rvvTaddFkz56BKzRjAjHcbRnqWYg9AhYkxphEJefBW4BXo7KRvATqODeP+O67rfC464GPgb8IGrQ8DnXdd8Xhqb8IPAvgQngQVR2iVXHcY4CzwN3o8ImTgL/Gvhb4P8FjqBCKb4vUZ+7gQ+gXLf/Bvg513W7BrN3HEcC7wPeD1iu677McZyPoVzAZ4DvAu93XfdrjuPcjgqjKVCZPU66rvsqx3EeDuvyR93aNFjvanYDegSsGRfuAP4E2IMSUp+AOBrd/0Bl9TgKHA6PA/jn4b8fBq5FCdnmMILHCSOoAb8P/BvgHwOvAP4Px3HeFJbzdpTguxPYC3yNelD+Xrw9LOfl4d+PAq8G5oDPomI/5FzXfRD4D8Cfuq47GYY/bKafNmmuELQA1owLj7iu+xdhIPw/BiLh9L+hwoF+wHXdTdd1y67rPhLuezfwu67rPue67gZqhPvOJlXAvw/PeQjYRI2eL7quewYlZF8THvde4D+6rvuU67oeSlC+2nGcfjJv/Mcwa0cJwHXdz7iuu+S6rue67u8AWaDfiOX9tElzhaBvqmZcOJ/4vQjkQqFzNfC9UCg2c4jGfHffQz3T+xPbLiR+L7X5ezL8/RrgY47j/E5iv0CNuHvl1GuIFe04zv+JUq0cQqXqmQYWelwjolubzvR5Dc0uQQtgzbhzGjjiOI7VRgifpTE33BHAQwnZq4Yo5zdd1713iDrGCymO4/wg8GvAm4F/cF03cBznMkqYNxzbgW5t0lxhaBWEZtz5G+Ac8OEwcHbOcZwoW8HngF9xHOdljuNMUtevthst9+IPgX8dxh7GcZwZx3H+6RDXmUIJzEuAFcaUnk7svwAcDRfb2jHKNmnGHC2ANWNNqBN+G3AdyjriRdSCGqh07n8M/DXK4qEM/Kshy/kz4LeAP3EcZw14EvgnQ1zqy8CXUMHjvxfWKami+Hz4c8lxnMfanD+yNmnGH22GptFoNCmhR8AajUaTEnoRTqPpQrio9qV2+1zXnWy3XaPpF62C0Gg0mpTQKgiNRqNJCS2ANRqNJiW0ANZoNJqU0AJYo9FoUuL/B8Td5Ozk9uGLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter of income ratio vs systolic 1\n",
    "sns.lmplot(x='income_ratio',y='systolic_1',data=df, line_kws={'color': 'orange'})\n",
    "\n",
    "#Scatter of income ratio vs systolic 2\n",
    "sns.lmplot(x='income_ratio', y='systolic_2',data=df, line_kws={'color': 'orange'})\n",
    "\n",
    "#Scatter of income ratio vs systolic 3\n",
    "sns.lmplot(x='income_ratio',y='systolic_3',data=df, line_kws={'color': 'orange'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following histogram shows the frequency of body mass indices (BMI). The most frequent BMI appears to be about 25 kg/m^2, with an upper limit of about 65 kg/m^2. \n",
    "\n",
    "The scatter plot after shows BMI compared against income ratio. Based on the regression line, we can see that there is no correlation between the two variables. This tells us that income ratio does not predict how high or low a person’s BMI is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fb8942f6e48>"
      ]
     },
     "execution_count": 11,
     "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist('bmi', bins=25)\n",
    "#Scatter of income ratio vs BMI\n",
    "sns.lmplot(x='income_ratio',y='bmi',data=df, line_kws={'color': 'orange'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This pivot table shows the average BMI for each unit of income ratio. As we can see, the averages are all fairly in the same range about 22 to 26kg/m^2. Again, showing another confirmation that income ratio does not predict BMI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>bmi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_ratio</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>27.017021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>25.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.02</th>\n",
       "      <td>25.162500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.03</th>\n",
       "      <td>29.411111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.04</th>\n",
       "      <td>25.890909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>28.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.06</th>\n",
       "      <td>31.325000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.07</th>\n",
       "      <td>26.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.08</th>\n",
       "      <td>28.275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.09</th>\n",
       "      <td>28.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>23.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.11</th>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.12</th>\n",
       "      <td>28.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.13</th>\n",
       "      <td>29.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.15</th>\n",
       "      <td>27.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.16</th>\n",
       "      <td>24.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.17</th>\n",
       "      <td>32.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.18</th>\n",
       "      <td>26.616667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.19</th>\n",
       "      <td>33.475000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.20</th>\n",
       "      <td>28.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.21</th>\n",
       "      <td>30.614286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.22</th>\n",
       "      <td>30.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.24</th>\n",
       "      <td>34.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.26</th>\n",
       "      <td>30.361538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.27</th>\n",
       "      <td>34.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.28</th>\n",
       "      <td>31.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.29</th>\n",
       "      <td>30.275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>27.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.31</th>\n",
       "      <td>31.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.53</th>\n",
       "      <td>28.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.54</th>\n",
       "      <td>29.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.55</th>\n",
       "      <td>26.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.58</th>\n",
       "      <td>25.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.61</th>\n",
       "      <td>26.205882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.63</th>\n",
       "      <td>25.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.64</th>\n",
       "      <td>30.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.66</th>\n",
       "      <td>19.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.67</th>\n",
       "      <td>32.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.69</th>\n",
       "      <td>29.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.70</th>\n",
       "      <td>27.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.71</th>\n",
       "      <td>32.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.72</th>\n",
       "      <td>30.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.77</th>\n",
       "      <td>28.812500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.79</th>\n",
       "      <td>31.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.80</th>\n",
       "      <td>23.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.81</th>\n",
       "      <td>26.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.82</th>\n",
       "      <td>23.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.83</th>\n",
       "      <td>27.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.84</th>\n",
       "      <td>29.317647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.86</th>\n",
       "      <td>41.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.87</th>\n",
       "      <td>26.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.88</th>\n",
       "      <td>23.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.90</th>\n",
       "      <td>24.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.92</th>\n",
       "      <td>31.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.93</th>\n",
       "      <td>29.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.96</th>\n",
       "      <td>26.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.97</th>\n",
       "      <td>25.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.99</th>\n",
       "      <td>21.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.00</th>\n",
       "      <td>27.564179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>413 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    bmi\n",
       "income_ratio           \n",
       "0.00          27.017021\n",
       "0.01          25.025000\n",
       "0.02          25.162500\n",
       "0.03          29.411111\n",
       "0.04          25.890909\n",
       "0.05          28.100000\n",
       "0.06          31.325000\n",
       "0.07          26.116667\n",
       "0.08          28.275000\n",
       "0.09          28.733333\n",
       "0.10          23.025000\n",
       "0.11          23.000000\n",
       "0.12          28.766667\n",
       "0.13          29.785714\n",
       "0.15          27.800000\n",
       "0.16          24.966667\n",
       "0.17          32.260000\n",
       "0.18          26.616667\n",
       "0.19          33.475000\n",
       "0.20          28.800000\n",
       "0.21          30.614286\n",
       "0.22          30.666667\n",
       "0.24          34.200000\n",
       "0.25          24.000000\n",
       "0.26          30.361538\n",
       "0.27          34.700000\n",
       "0.28          31.400000\n",
       "0.29          30.275000\n",
       "0.30          27.175000\n",
       "0.31          31.840000\n",
       "...                 ...\n",
       "4.53          28.625000\n",
       "4.54          29.300000\n",
       "4.55          26.350000\n",
       "4.58          25.100000\n",
       "4.61          26.205882\n",
       "4.63          25.600000\n",
       "4.64          30.883333\n",
       "4.66          19.900000\n",
       "4.67          32.153846\n",
       "4.69          29.900000\n",
       "4.70          27.700000\n",
       "4.71          32.775000\n",
       "4.72          30.250000\n",
       "4.77          28.812500\n",
       "4.79          31.633333\n",
       "4.80          23.400000\n",
       "4.81          26.466667\n",
       "4.82          23.700000\n",
       "4.83          27.800000\n",
       "4.84          29.317647\n",
       "4.86          41.125000\n",
       "4.87          26.400000\n",
       "4.88          23.600000\n",
       "4.90          24.675000\n",
       "4.92          31.900000\n",
       "4.93          29.160000\n",
       "4.96          26.900000\n",
       "4.97          25.250000\n",
       "4.99          21.300000\n",
       "5.00          27.564179\n",
       "\n",
       "[413 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pivot table of income vs BMI\n",
    "pv_BMI = pd.pivot_table(df, values='bmi', index=['income_ratio'])\n",
    "pv_BMI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We ran a linear model to do a statistical test to see whether or not income ratio predicts BMI and to verify our scatter plot visualizations. We calculated the P value (0.0652) using the linear model and with an alpha value of 0.05, we verified that in fact income ratio does not predict BMI. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 4.185221697e-07\n",
      "Does income ratio predict body mass index?\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "#Linear Model for Body Mass Index \n",
    "outcome_1, predictors_1 = patsy.dmatrices('bmi ~ income_ratio', df)\n",
    "mod_1 = sm.OLS(outcome_1, predictors_1)\n",
    "type(mod_1)\n",
    "res_1 = mod_1.fit()\n",
    "\n",
    "#Does income ratio predict body mass index?\n",
    "print(\"p value:\",res_1.pvalues[1])\n",
    "print(\"Does income ratio predict body mass index?\")\n",
    "print(res_1.pvalues[1] <= 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This pivot table shows the average diastolic blood pressure for each of the three readings for each unit of income ratio. As we can see, the averages are all in a range about 55 to 75mmHg. These averages seem farther apart, which led us to question our previous conclusion made from the scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diastolic_1</th>\n",
       "      <th>diastolic_2</th>\n",
       "      <th>diastolic_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_ratio</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>66.382979</td>\n",
       "      <td>66.468085</td>\n",
       "      <td>65.617021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>64.500000</td>\n",
       "      <td>58.500000</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.02</th>\n",
       "      <td>74.500000</td>\n",
       "      <td>76.500000</td>\n",
       "      <td>72.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.03</th>\n",
       "      <td>66.222222</td>\n",
       "      <td>66.444444</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.04</th>\n",
       "      <td>72.545455</td>\n",
       "      <td>73.272727</td>\n",
       "      <td>72.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.06</th>\n",
       "      <td>70.500000</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.07</th>\n",
       "      <td>69.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>67.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.08</th>\n",
       "      <td>80.500000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.09</th>\n",
       "      <td>66.666667</td>\n",
       "      <td>66.333333</td>\n",
       "      <td>64.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>63.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.11</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.12</th>\n",
       "      <td>69.333333</td>\n",
       "      <td>68.666667</td>\n",
       "      <td>72.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.13</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>69.714286</td>\n",
       "      <td>67.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.15</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.16</th>\n",
       "      <td>66.000000</td>\n",
       "      <td>60.666667</td>\n",
       "      <td>65.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.17</th>\n",
       "      <td>70.400000</td>\n",
       "      <td>72.800000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.18</th>\n",
       "      <td>69.333333</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.19</th>\n",
       "      <td>71.500000</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.20</th>\n",
       "      <td>73.333333</td>\n",
       "      <td>75.333333</td>\n",
       "      <td>70.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.21</th>\n",
       "      <td>54.571429</td>\n",
       "      <td>56.571429</td>\n",
       "      <td>56.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.22</th>\n",
       "      <td>66.000000</td>\n",
       "      <td>60.666667</td>\n",
       "      <td>51.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.24</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>72.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.26</th>\n",
       "      <td>70.153846</td>\n",
       "      <td>69.692308</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.27</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.28</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.29</th>\n",
       "      <td>72.250000</td>\n",
       "      <td>75.250000</td>\n",
       "      <td>74.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>61.250000</td>\n",
       "      <td>62.750000</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.31</th>\n",
       "      <td>64.800000</td>\n",
       "      <td>64.400000</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.53</th>\n",
       "      <td>84.500000</td>\n",
       "      <td>79.500000</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.54</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.55</th>\n",
       "      <td>71.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.58</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.61</th>\n",
       "      <td>67.294118</td>\n",
       "      <td>65.764706</td>\n",
       "      <td>64.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.63</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.64</th>\n",
       "      <td>64.666667</td>\n",
       "      <td>67.333333</td>\n",
       "      <td>67.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.66</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.67</th>\n",
       "      <td>67.538462</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>65.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.69</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.70</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.71</th>\n",
       "      <td>69.000000</td>\n",
       "      <td>67.500000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.72</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.77</th>\n",
       "      <td>67.500000</td>\n",
       "      <td>68.250000</td>\n",
       "      <td>65.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.79</th>\n",
       "      <td>80.666667</td>\n",
       "      <td>80.666667</td>\n",
       "      <td>79.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.80</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.81</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.82</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>67.333333</td>\n",
       "      <td>72.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.83</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.84</th>\n",
       "      <td>68.117647</td>\n",
       "      <td>66.823529</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.86</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>72.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.87</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.88</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>76.666667</td>\n",
       "      <td>72.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.90</th>\n",
       "      <td>66.000000</td>\n",
       "      <td>68.500000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.92</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.93</th>\n",
       "      <td>63.200000</td>\n",
       "      <td>61.200000</td>\n",
       "      <td>61.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.96</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>86.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.97</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.99</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.00</th>\n",
       "      <td>70.521240</td>\n",
       "      <td>69.786452</td>\n",
       "      <td>69.582090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>413 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              diastolic_1  diastolic_2  diastolic_3\n",
       "income_ratio                                       \n",
       "0.00            66.382979    66.468085    65.617021\n",
       "0.01            64.500000    58.500000    67.000000\n",
       "0.02            74.500000    76.500000    72.250000\n",
       "0.03            66.222222    66.444444    66.000000\n",
       "0.04            72.545455    73.272727    72.545455\n",
       "0.05            77.000000    78.000000    78.000000\n",
       "0.06            70.500000    69.500000    65.000000\n",
       "0.07            69.000000    67.000000    67.333333\n",
       "0.08            80.500000    83.000000    83.500000\n",
       "0.09            66.666667    66.333333    64.666667\n",
       "0.10            65.000000    61.000000    63.500000\n",
       "0.11            54.000000    62.000000    56.000000\n",
       "0.12            69.333333    68.666667    72.666667\n",
       "0.13            72.000000    69.714286    67.714286\n",
       "0.15            94.000000    96.000000    90.000000\n",
       "0.16            66.000000    60.666667    65.333333\n",
       "0.17            70.400000    72.800000    75.000000\n",
       "0.18            69.333333    68.000000    65.000000\n",
       "0.19            71.500000    75.500000    75.000000\n",
       "0.20            73.333333    75.333333    70.666667\n",
       "0.21            54.571429    56.571429    56.857143\n",
       "0.22            66.000000    60.666667    51.333333\n",
       "0.24            40.000000     0.000000    38.000000\n",
       "0.25            70.000000    72.000000    72.285714\n",
       "0.26            70.153846    69.692308    72.000000\n",
       "0.27            65.000000    66.000000    65.000000\n",
       "0.28            75.500000    74.000000    68.000000\n",
       "0.29            72.250000    75.250000    74.750000\n",
       "0.30            61.250000    62.750000    62.000000\n",
       "0.31            64.800000    64.400000    67.000000\n",
       "...                   ...          ...          ...\n",
       "4.53            84.500000    79.500000    84.000000\n",
       "4.54            78.000000    80.000000    76.000000\n",
       "4.55            71.000000    68.000000    72.000000\n",
       "4.58            72.000000    66.000000    66.000000\n",
       "4.61            67.294118    65.764706    64.941176\n",
       "4.63            68.000000    68.000000    72.000000\n",
       "4.64            64.666667    67.333333    67.333333\n",
       "4.66            60.000000    64.000000    58.000000\n",
       "4.67            67.538462    66.000000    65.076923\n",
       "4.69            78.000000    76.000000    78.000000\n",
       "4.70            92.000000    90.000000    88.000000\n",
       "4.71            69.000000    67.500000    72.000000\n",
       "4.72            67.000000    66.500000    69.000000\n",
       "4.77            67.500000    68.250000    65.500000\n",
       "4.79            80.666667    80.666667    79.333333\n",
       "4.80            50.000000    54.000000    62.000000\n",
       "4.81            68.000000    64.000000    68.000000\n",
       "4.82            72.000000    67.333333    72.666667\n",
       "4.83            76.000000    76.000000    74.000000\n",
       "4.84            68.117647    66.823529    68.000000\n",
       "4.86            70.000000    70.000000    72.500000\n",
       "4.87             0.000000     0.000000     0.000000\n",
       "4.88            74.000000    76.666667    72.666667\n",
       "4.90            66.000000    68.500000    63.000000\n",
       "4.92            70.000000    70.000000    70.000000\n",
       "4.93            63.200000    61.200000    61.200000\n",
       "4.96            92.000000    88.000000    86.000000\n",
       "4.97            78.000000    79.000000    77.000000\n",
       "4.99            70.000000    60.000000    62.000000\n",
       "5.00            70.521240    69.786452    69.582090\n",
       "\n",
       "[413 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pivot table for diastolic blood pressure readings \n",
    "pv_diastolic = pd.pivot_table(df, values= ['diastolic_1','diastolic_2', 'diastolic_3'], index=['income_ratio'])\n",
    "pv_diastolic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we ran a linear model to do a statistical test to see whether or not income ratio predicts diastolic blood pressure and to verify our scatter plot visualizations. We calculated the P value (9.497e-12) using the linear model and with an alpha value of 0.05, we found that income ratio does predict diastolic blood pressure. This contradicted our conclusions made previously using the scatter plot, but this test is more accurate than our visual conclusions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.00194341996729\n",
      "Does income ratio predict diastolic blood pressure reading?\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "#linear model for diastolic blood pressure to income ratio\n",
    "outcome_2, predictors_2 = patsy.dmatrices('diastolic_1 ~ income_ratio', df)\n",
    "mod_2 = sm.OLS(outcome_2, predictors_2)\n",
    "type(mod_2)\n",
    "res_2 = mod_2.fit()\n",
    "\n",
    "#Does income ratio predict diastolic blood pressure readings?\n",
    "print(\"p value:\",res_2.pvalues[1])\n",
    "print(\"Does income ratio predict diastolic blood pressure reading?\")\n",
    "print(res_2.pvalues[1] <= 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there is a relation between age and diastolic blood pressure (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2805932/), we must check whether diastolic blood pressure is still predicted by income ratio given age. According to the following patsy.dmatrices, it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.00713610236877\n",
      "Given age, does income ratio still predict diastolic blood pressure reading?\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "#linear model for diastolic blood pressure to income ratio and age\n",
    "outcome_2a, predictors_2a = patsy.dmatrices('diastolic_1 ~ income_ratio + age', df)\n",
    "mod_2a = sm.OLS(outcome_2a, predictors_2a)\n",
    "type(mod_2a)\n",
    "res_2a = mod_2a.fit()\n",
    "\n",
    "#Does income ratio still predict diastolic blood pressure readings?\n",
    "print(\"p value:\",res_2a.pvalues[1])\n",
    "print(\"Given age, does income ratio still predict diastolic blood pressure reading?\")\n",
    "print(res_2a.pvalues[1] <= 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This pivot table shows the average systolic blood pressure for each of the three readings for each unit of income ratio. As we can see, the averages are all in a range about 100 to 130mmHg. These averages seem farther apart, which led us to question our previous conclusion made from the scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>systolic_1</th>\n",
       "      <th>systolic_2</th>\n",
       "      <th>systolic_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_ratio</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>121.106383</td>\n",
       "      <td>121.872340</td>\n",
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       "    <tr>\n",
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       "      <th>0.02</th>\n",
       "      <td>126.500000</td>\n",
       "      <td>125.750000</td>\n",
       "      <td>125.500000</td>\n",
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       "    <tr>\n",
       "      <th>0.03</th>\n",
       "      <td>108.666667</td>\n",
       "      <td>109.777778</td>\n",
       "      <td>109.555556</td>\n",
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       "    <tr>\n",
       "      <th>0.04</th>\n",
       "      <td>123.454545</td>\n",
       "      <td>123.818182</td>\n",
       "      <td>124.363636</td>\n",
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       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>116.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>120.000000</td>\n",
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       "    <tr>\n",
       "      <th>0.06</th>\n",
       "      <td>110.500000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>111.000000</td>\n",
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       "    <tr>\n",
       "      <th>0.07</th>\n",
       "      <td>126.000000</td>\n",
       "      <td>129.666667</td>\n",
       "      <td>128.000000</td>\n",
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       "    <tr>\n",
       "      <th>0.08</th>\n",
       "      <td>124.500000</td>\n",
       "      <td>125.500000</td>\n",
       "      <td>127.000000</td>\n",
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       "    <tr>\n",
       "      <th>0.09</th>\n",
       "      <td>123.333333</td>\n",
       "      <td>123.000000</td>\n",
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       "      <td>119.500000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>119.500000</td>\n",
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       "      <th>0.11</th>\n",
       "      <td>116.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>116.000000</td>\n",
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       "    <tr>\n",
       "      <th>0.12</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>135.333333</td>\n",
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       "    <tr>\n",
       "      <th>0.13</th>\n",
       "      <td>120.857143</td>\n",
       "      <td>120.571429</td>\n",
       "      <td>122.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.15</th>\n",
       "      <td>156.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
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       "    <tr>\n",
       "      <th>0.16</th>\n",
       "      <td>126.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>123.333333</td>\n",
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       "    <tr>\n",
       "      <th>0.17</th>\n",
       "      <td>118.600000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.600000</td>\n",
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       "    <tr>\n",
       "      <th>0.18</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>109.666667</td>\n",
       "      <td>110.666667</td>\n",
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       "    <tr>\n",
       "      <th>0.19</th>\n",
       "      <td>118.500000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>112.000000</td>\n",
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       "    <tr>\n",
       "      <th>0.20</th>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.666667</td>\n",
       "      <td>125.333333</td>\n",
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       "    <tr>\n",
       "      <th>0.21</th>\n",
       "      <td>117.714286</td>\n",
       "      <td>118.857143</td>\n",
       "      <td>116.857143</td>\n",
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       "    <tr>\n",
       "      <th>0.22</th>\n",
       "      <td>110.666667</td>\n",
       "      <td>111.333333</td>\n",
       "      <td>111.333333</td>\n",
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       "    <tr>\n",
       "      <th>0.24</th>\n",
       "      <td>102.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>108.000000</td>\n",
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       "      <th>0.25</th>\n",
       "      <td>122.857143</td>\n",
       "      <td>120.571429</td>\n",
       "      <td>120.285714</td>\n",
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       "    <tr>\n",
       "      <th>0.26</th>\n",
       "      <td>114.923077</td>\n",
       "      <td>113.076923</td>\n",
       "      <td>114.000000</td>\n",
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       "    <tr>\n",
       "      <th>0.27</th>\n",
       "      <td>116.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>121.000000</td>\n",
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       "    <tr>\n",
       "      <th>0.28</th>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>124.500000</td>\n",
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       "    <tr>\n",
       "      <th>0.29</th>\n",
       "      <td>113.500000</td>\n",
       "      <td>113.750000</td>\n",
       "      <td>117.750000</td>\n",
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       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>111.000000</td>\n",
       "      <td>111.500000</td>\n",
       "      <td>110.500000</td>\n",
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       "    <tr>\n",
       "      <th>0.31</th>\n",
       "      <td>113.800000</td>\n",
       "      <td>116.400000</td>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.53</th>\n",
       "      <td>135.500000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>136.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.54</th>\n",
       "      <td>132.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>126.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.55</th>\n",
       "      <td>118.500000</td>\n",
       "      <td>120.500000</td>\n",
       "      <td>119.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.58</th>\n",
       "      <td>106.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>106.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.61</th>\n",
       "      <td>116.705882</td>\n",
       "      <td>117.058824</td>\n",
       "      <td>117.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.63</th>\n",
       "      <td>122.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>116.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.64</th>\n",
       "      <td>124.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>122.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.66</th>\n",
       "      <td>96.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.67</th>\n",
       "      <td>117.076923</td>\n",
       "      <td>118.307692</td>\n",
       "      <td>116.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.69</th>\n",
       "      <td>138.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.70</th>\n",
       "      <td>124.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>122.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.71</th>\n",
       "      <td>127.000000</td>\n",
       "      <td>127.500000</td>\n",
       "      <td>132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.72</th>\n",
       "      <td>108.500000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.77</th>\n",
       "      <td>122.375000</td>\n",
       "      <td>122.875000</td>\n",
       "      <td>120.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.79</th>\n",
       "      <td>137.333333</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>134.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.80</th>\n",
       "      <td>126.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>128.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.81</th>\n",
       "      <td>118.666667</td>\n",
       "      <td>115.333333</td>\n",
       "      <td>118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.82</th>\n",
       "      <td>112.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>113.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.83</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>106.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.84</th>\n",
       "      <td>118.352941</td>\n",
       "      <td>119.647059</td>\n",
       "      <td>117.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.86</th>\n",
       "      <td>122.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>123.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.87</th>\n",
       "      <td>120.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.88</th>\n",
       "      <td>116.000000</td>\n",
       "      <td>113.666667</td>\n",
       "      <td>114.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.90</th>\n",
       "      <td>101.500000</td>\n",
       "      <td>105.500000</td>\n",
       "      <td>107.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.92</th>\n",
       "      <td>124.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>128.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.93</th>\n",
       "      <td>123.600000</td>\n",
       "      <td>123.600000</td>\n",
       "      <td>122.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.96</th>\n",
       "      <td>138.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.97</th>\n",
       "      <td>114.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>113.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.99</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.00</th>\n",
       "      <td>121.866820</td>\n",
       "      <td>121.506315</td>\n",
       "      <td>120.920781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>413 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              systolic_1  systolic_2  systolic_3\n",
       "income_ratio                                    \n",
       "0.00          121.106383  121.872340  120.553191\n",
       "0.01          117.500000  117.000000  116.500000\n",
       "0.02          126.500000  125.750000  125.500000\n",
       "0.03          108.666667  109.777778  109.555556\n",
       "0.04          123.454545  123.818182  124.363636\n",
       "0.05          116.000000  117.000000  120.000000\n",
       "0.06          110.500000  111.000000  111.000000\n",
       "0.07          126.000000  129.666667  128.000000\n",
       "0.08          124.500000  125.500000  127.000000\n",
       "0.09          123.333333  123.000000  121.666667\n",
       "0.10          119.500000  122.000000  119.500000\n",
       "0.11          116.000000  114.000000  116.000000\n",
       "0.12          140.000000  134.000000  135.333333\n",
       "0.13          120.857143  120.571429  122.000000\n",
       "0.15          156.000000  158.000000  158.000000\n",
       "0.16          126.000000  124.000000  123.333333\n",
       "0.17          118.600000  120.000000  120.600000\n",
       "0.18          110.000000  109.666667  110.666667\n",
       "0.19          118.500000  120.000000  112.000000\n",
       "0.20          132.000000  132.666667  125.333333\n",
       "0.21          117.714286  118.857143  116.857143\n",
       "0.22          110.666667  111.333333  111.333333\n",
       "0.24          102.000000  104.000000  108.000000\n",
       "0.25          122.857143  120.571429  120.285714\n",
       "0.26          114.923077  113.076923  114.000000\n",
       "0.27          116.000000  119.000000  121.000000\n",
       "0.28          120.000000  120.000000  124.500000\n",
       "0.29          113.500000  113.750000  117.750000\n",
       "0.30          111.000000  111.500000  110.500000\n",
       "0.31          113.800000  116.400000  115.000000\n",
       "...                  ...         ...         ...\n",
       "4.53          135.500000  132.000000  136.000000\n",
       "4.54          132.000000  122.000000  126.000000\n",
       "4.55          118.500000  120.500000  119.500000\n",
       "4.58          106.000000  106.000000  106.000000\n",
       "4.61          116.705882  117.058824  117.882353\n",
       "4.63          122.000000  124.000000  116.000000\n",
       "4.64          124.000000  121.000000  122.000000\n",
       "4.66           96.000000   98.000000   98.000000\n",
       "4.67          117.076923  118.307692  116.923077\n",
       "4.69          138.000000  136.000000  136.000000\n",
       "4.70          124.000000  130.000000  122.000000\n",
       "4.71          127.000000  127.500000  132.000000\n",
       "4.72          108.500000  108.000000  109.000000\n",
       "4.77          122.375000  122.875000  120.250000\n",
       "4.79          137.333333  138.000000  134.666667\n",
       "4.80          126.000000  122.000000  128.000000\n",
       "4.81          118.666667  115.333333  118.000000\n",
       "4.82          112.000000  112.000000  113.333333\n",
       "4.83          110.000000  114.000000  106.000000\n",
       "4.84          118.352941  119.647059  117.882353\n",
       "4.86          122.000000  121.000000  123.000000\n",
       "4.87          120.000000  118.000000  118.000000\n",
       "4.88          116.000000  113.666667  114.666667\n",
       "4.90          101.500000  105.500000  107.000000\n",
       "4.92          124.000000  132.000000  128.000000\n",
       "4.93          123.600000  123.600000  122.400000\n",
       "4.96          138.000000  134.000000  132.000000\n",
       "4.97          114.000000  119.000000  113.000000\n",
       "4.99          108.000000  108.000000  108.000000\n",
       "5.00          121.866820  121.506315  120.920781\n",
       "\n",
       "[413 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pivot table for systolic blood pressure readings \n",
    "pv_systolic = pd.pivot_table(df, values= ['systolic_1','systolic_2', 'systolic_3'], index=['income_ratio'])\n",
    "pv_systolic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We ran a linear model to do a statistical test to see whether or not income ratio predicts systolic blood pressure and to verify our scatter plot visualizations. We calculated the P value (0.0013) using the linear model and with an alpha value of 0.05, we found that income ratio does predict systolic blood pressure. This contradicted our conclusions made previously using the scatter plot, but this test is more accurate than our visual conclusions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.647094209943\n",
      "Does income ratio predict systolic blood pressure reading?\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "#linear model for systolic blood pressure to income ratio\n",
    "outcome_3, predictors_3 = patsy.dmatrices('systolic_1 ~ income_ratio', df)\n",
    "mod_3 = sm.OLS(outcome_3, predictors_3)\n",
    "type(mod_3)\n",
    "res_3 = mod_3.fit()\n",
    "\n",
    "#Does income ratio predict systolic blood pressure readings?\n",
    "print(\"p value:\",res_3.pvalues[1])\n",
    "print(\"Does income ratio predict systolic blood pressure reading?\")\n",
    "print(res_3.pvalues[1] <= 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another way to test significant difference, we ran another P test on all three variables BMI, diastolic blood pressure, and systolic blood pressure. First, we selected all the BMI values of participants below the poverty line (income ratio less than 1) and then selected all the BMI values of participants above the poverty line (greater than or equal to 1). Then we found the T and P values and ran another P test with an alpha value of 0.05. We found that there is a significant statistical difference between BMI of people in poverty and people with higher incomes. For the second test (diastolic), we selected all the diastolic_1 values of participants below the poverty line (income ratio less than 1) and then selected all the diastolic_1 values of participants above the poverty line (greater than or equal to 1). Then we found the T and P values and ran another P test with an alpha value of 0.05. We completed this test method for all three readings of the diastolic data. We found that there is a significant statistical difference between diastolic blood pressure readings of people in poverty and people with higher incomes. Lastly, we ran a test on the systolic values by selecting all the systolic_1 values of participants below the poverty line (income ratio less than 1) and then selected all the systolic_1 values of participants above the poverty line (greater than or equal to 1). Then we found the T and P values and ran another P test with an alpha value of 0.05. We completed this test method for all three readings of the systolic data. We found that there is a significant statistical difference between systolic blood pressure readings of people in poverty and people with higher incomes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 6.67369581408e-06\n",
      "Given age, does income ratio still predict systolic blood pressure reading?\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "#linear model for diastolic blood pressure to income ratio and age\n",
    "outcome_3a, predictors_3a = patsy.dmatrices('systolic_1 ~ income_ratio + age', df)\n",
    "mod_3a = sm.OLS(outcome_3a, predictors_3a)\n",
    "type(mod_3a)\n",
    "res_3a = mod_3a.fit()\n",
    "\n",
    "#Does income ratio still predict diastolic blood pressure readings?\n",
    "print(\"p value:\",res_3a.pvalues[1])\n",
    "print(\"Given age, does income ratio still predict systolic blood pressure reading?\")\n",
    "print(res_3a.pvalues[1] <= 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.170165671643\n",
      "There is NOT a significant difference!\n"
     ]
    }
   ],
   "source": [
    "#p test for BMI poverty vs high income\n",
    "poverty_bmi = df[\"bmi\"].loc[df[\"income_ratio\"] < 1]\n",
    "highinc_bmi = df[\"bmi\"].loc[df[\"income_ratio\"] >= 1]\n",
    "\n",
    "t_val, p_val = stats.ttest_ind(poverty_bmi,highinc_bmi)\n",
    "print(\"p value:\",p_val)\n",
    "\n",
    "if p_val < 0.05:\n",
    "    print('There is a significant difference!')\n",
    "else:\n",
    "    print('There is NOT a significant difference!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.0731443054701\n",
      "There is NOT a significant difference!\n"
     ]
    }
   ],
   "source": [
    "#p test for diastolic 1 poverty vs high income\n",
    "poverty_diastolic = df[\"diastolic_1\"].loc[df[\"income_ratio\"] < 1]\n",
    "highinc_diastolic = df[\"diastolic_1\"].loc[df[\"income_ratio\"] >= 1]\n",
    "\n",
    "t_val1, p_val1 = stats.ttest_ind(poverty_diastolic, highinc_diastolic)\n",
    "print(\"p value:\",p_val1)\n",
    "\n",
    "if p_val1 < 0.05:\n",
    "    print('There is a significant difference!')\n",
    "else:\n",
    "    print('There is NOT a significant difference!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.0806494562785\n",
      "There is NOT a significant difference!\n"
     ]
    }
   ],
   "source": [
    "#p test for systolic 1 poverty vs high income\n",
    "poverty_systolic = df[\"systolic_1\"].loc[df[\"income_ratio\"] < 1]\n",
    "highinc_systolic = df[\"systolic_1\"].loc[df[\"income_ratio\"] >= 1]\n",
    "\n",
    "t_val2, p_val2 = stats.ttest_ind(poverty_systolic, highinc_systolic)\n",
    "print(\"p value:\",p_val2)\n",
    "\n",
    "if p_val2 < 0.05:\n",
    "    print('There is a significant difference!')\n",
    "else:\n",
    "    print('There is NOT a significant difference!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results of Questionaire Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the data visualization, analysis, and results obtained from the questionnaire dataset combined with the demographic dataset. For the columns from the questionnaire dataset, each column represents a question asking whether the participant was ever medically diagnosed with the condition or not. The response to the question is represented by 1 meaning yes, 2 meaning no, 3 meaning the participant didn't know or weren't sure, and 9 meaning the participant chose not to respond. The following code shows what the first row of the cleaned joint dataset looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>SEQN</th>\n",
       "      <th>income_ratio</th>\n",
       "      <th>RIDAGEYR</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>high_cholesterol</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>overweight</th>\n",
       "      <th>congestive_heart_failure</th>\n",
       "      <th>coronary_heart_disease</th>\n",
       "      <th>heart_attack</th>\n",
       "      <th>thyroid_problem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73557</td>\n",
       "      <td>0.84</td>\n",
       "      <td>69</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SEQN  income_ratio  RIDAGEYR  high_blood_pressure  high_cholesterol  \\\n",
       "0  73557          0.84        69                  1.0               1.0   \n",
       "\n",
       "   diabetes  overweight  congestive_heart_failure  coronary_heart_disease  \\\n",
       "0       1.0         1.0                       2.0                     2.0   \n",
       "\n",
       "   heart_attack  thyroid_problem  \n",
       "0           2.0              2.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joint.iloc[0:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We classified participants has high or low income by their income ratio. If their ratio of income to the poverty guidline was less than one they were classified as having a low income and if their ratio was greater than or equal to one, they were classified as having a high income. We now wanted to visualize how many people did or didn't have each condition separately depending on whether they had a high or low income. Hence, we wanted to further clean this dataframe in order to remove any rows that had the values of 3 or 9. We created 2 different dataframes, one having values of the column of the condition when the participants had a low income and the other when the participants had a high income. The following code was for the column asking about high blood pressure. After creating the new dataframes and dropping the rows with values of 3 or 9, we created 2 more dataframes with the count of the remaining values, 1 and 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "bpp = joint['high_blood_pressure'].loc[joint['income_ratio'] < 1]\n",
    "for index, value in bpp.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        bpp = bpp.drop(labels = index)\n",
    "bpp_vc = bpp.value_counts()\n",
    "        \n",
    "bpr = joint['high_blood_pressure'].loc[joint['income_ratio'] >= 1]\n",
    "for index, value in bpr.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        bpr = bpr.drop(labels = index)\n",
    "bpr_vc = bpr.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then combined both the high-income and low-income value-count dataframes, renamed the 1 values as Yes and 2 values as No, and bar-plotted the resulting dataframe as subplots for each income level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp_df = pd.DataFrame({'Low Income': bpp_vc,'High Income': bpr_vc})\n",
    "bp_df.rename(index={2.0: 'No', 1.0: 'Yes'}, inplace = True)\n",
    "bp_df.plot.bar(rot=0, subplots = True)\n",
    "plt.xlabel('Response to question')\n",
    "plt.ylabel('No. of people diagnosed with high blood pressure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing this in such a way led us to wanting to compare the ratio of No responses to Yes responses between each income level and to see if there is a significant difference between the two. Hence, we performed the chi-squared test because the variables and conditions we wanted to analyze were categorical. If the p-value from the chi-squared test was less than the significance level of 0.05, the null hypothesis was rejected and there was a significant difference between the ratio of responses in each income level. For high blood pressure, we found that there was no significant difference between having a high or low income and being diagnosed with high blood pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is no significant difference\n"
     ]
    }
   ],
   "source": [
    "chi1, p_bp, dof1, expected1 = stats.chi2_contingency(np.array([bpp_vc.values, bpr_vc.values]))\n",
    "if p_bp < 0.05:\n",
    "    print(\"There is a significant difference!\")\n",
    "else:\n",
    "    print(\"There is no significant difference\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We performed similar cleaning, visualization, and chi-squared test analysis on the rest of the conditions. The following is for being diagnosed with high cholesterol. We removed values of 3 or 9, divided the dataset by high or low income, and created dataframes with the count of the remaining values for each income level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp = joint['high_cholesterol'].loc[joint['income_ratio'] < 1]\n",
    "for index, value in cp.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        cp = cp.drop(labels = index)\n",
    "cp_vc = cp.value_counts()\n",
    "        \n",
    "cr = joint['high_cholesterol'].loc[joint['income_ratio'] >= 1]\n",
    "for index, value in cr.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        cr = cr.drop(labels = index)\n",
    "cr_vc = cr.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then again combined the new value-count dataframes, renamed the 1 and 2 values, and bar-plotted the resulting dataframe by each income level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chol_df = pd.DataFrame({'Low Income': cp_vc,'High Income': cr_vc})\n",
    "chol_df.rename(index={2.0: 'No', 1.0: 'Yes'}, inplace = True)\n",
    "chol_df.plot.bar(rot=0, subplots = True)\n",
    "plt.xlabel('Response to question')\n",
    "plt.ylabel('No. of people diagnosed with high cholesterol')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On performing the chi-squared test, we found that there was a significant difference between having a high or low income and being diagnosed with high cholesterol! By looking at the bar plot, we can see that the ratio between people who have and don't have high cholesterol is smaller among people with higher income level compared to people with lower income level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a significant difference!\n"
     ]
    }
   ],
   "source": [
    "chi2, p_chol, dof2, expected2 = stats.chi2_contingency(np.array([cp_vc.values, cr_vc.values]))\n",
    "\n",
    "if p_chol < 0.05:\n",
    "    print(\"There is a significant difference!\")\n",
    "else:\n",
    "    print(\"There is no significant difference\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We did the same cleaning on the column about whether or not the particpant has ever been diagnosed with diabetes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "dp = joint['diabetes'].loc[joint['income_ratio'] < 1]\n",
    "for index, value in dp.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        dp = dp.drop(labels = index)\n",
    "dp_vc = dp.value_counts()\n",
    "        \n",
    "dr = joint['diabetes'].loc[joint['income_ratio'] >= 1]\n",
    "for index, value in dr.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        dr = dr.drop(labels = index)\n",
    "dr_vc = dr.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualized the data for diabetes in the same way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diab_df = pd.DataFrame({'Low Income': dp_vc,'High Income': dr_vc})\n",
    "diab_df.rename(index={2.0: 'No', 1.0: 'Yes'}, inplace = True)\n",
    "diab_df.plot.bar(rot=0, subplots = True)\n",
    "plt.xlabel('Response to question')\n",
    "plt.ylabel('No. of people diagnosed with diabetes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On performing the chi-squared test, we found that there is no significant difference between being high or low income and being diagnosed with diabetes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is no significant difference\n"
     ]
    }
   ],
   "source": [
    "chi3, p_diab, dof3, expected3 = stats.chi2_contingency(np.array([dp_vc.values, dr_vc.values]))\n",
    "\n",
    "if p_diab < 0.05:\n",
    "    print(\"There is a significant difference!\")\n",
    "else:\n",
    "    print(\"There is no significant difference\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We again cleaned the data on whether or not the participant has ever been diagnosed as overweight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "owp = joint['overweight'].loc[joint['income_ratio'] < 1]\n",
    "for index, value in owp.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        owp = owp.drop(labels = index)\n",
    "owp_vc = owp.value_counts()\n",
    "        \n",
    "owr = joint['overweight'].loc[joint['income_ratio'] >= 1]\n",
    "for index, value in owr.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        owr = owr.drop(labels = index)\n",
    "owr_vc = owr.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualized the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weight_df = pd.DataFrame({'Low Income': owp_vc,'High Income': owr_vc})\n",
    "weight_df.rename(index={2.0: 'No', 1.0: 'Yes'}, inplace = True)\n",
    "weight_df.plot.bar(rot=0, subplots = True)\n",
    "plt.xlabel('Response to question')\n",
    "plt.ylabel('No. of people diagnosed as overweight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On performing the chi-squared test, we found that there is no significant difference between people diagnosed as overweight and having a high or low income level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is no significant difference\n"
     ]
    }
   ],
   "source": [
    "chi4, p_ow, dof4, expected4 = stats.chi2_contingency(np.array([owp_vc.values, owr_vc.values]))\n",
    "\n",
    "if p_ow < 0.05:\n",
    "    print(\"There is a significant difference!\")\n",
    "else:\n",
    "    print(\"There is no significant difference\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then cleaned the data for whether or not the high or low income level participant has had congestive heart failure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "chfp = joint['congestive_heart_failure'].loc[joint['income_ratio'] < 1]\n",
    "for index, value in chfp.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        chfp = chfp.drop(labels = index)\n",
    "chfp_vc = chfp.value_counts()\n",
    "        \n",
    "chfr = joint['congestive_heart_failure'].loc[joint['income_ratio'] >= 1]\n",
    "for index, value in chfr.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        chfr = chfr.drop(labels = index)\n",
    "chfr_vc = chfr.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualized this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chf_df = pd.DataFrame({'Low Income': chfp_vc,'High Income': chfr_vc})\n",
    "chf_df.rename(index={2.0: 'No', 1.0: 'Yes'}, inplace = True)\n",
    "chf_df.plot.bar(rot=0, subplots = True)\n",
    "plt.xlabel('Response to question')\n",
    "plt.ylabel('No. of people diagnosed with congestive heart failure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On performing the chi-squared test, we found that there was a significant difference between having had congestive heart failure and having a high or low income level. Looking at the bar graph, it seems that there a larger number of people with low incomes that have congestive heart failure out of the total low-income people compared to people with high income level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a significant difference!\n"
     ]
    }
   ],
   "source": [
    "chi5, p_chf, dof5, expected5 = stats.chi2_contingency(np.array([chfp_vc.values, chfr_vc.values]))\n",
    "\n",
    "if p_chf < 0.05:\n",
    "    print(\"There is a significant difference!\")\n",
    "else:\n",
    "    print(\"There is no significant difference\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then cleaned the column of data on whether or not the participant has been diagnosed with coronary heart disease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "chdp = joint['coronary_heart_disease'].loc[joint['income_ratio'] < 1]\n",
    "for index, value in chdp.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        chdp = chdp.drop(labels = index)\n",
    "chdp_vc = chdp.value_counts()\n",
    "        \n",
    "chdr = joint['coronary_heart_disease'].loc[joint['income_ratio'] >= 1]\n",
    "for index, value in chdr.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        chdr = chdr.drop(labels = index)\n",
    "chdr_vc = chdr.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualized the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chd_df = pd.DataFrame({'Low Income': chdp_vc,'High Income': chdr_vc})\n",
    "chd_df.rename(index={2.0: 'No', 1.0: 'Yes'}, inplace = True)\n",
    "chd_df.plot.bar(rot=0, subplots = True)\n",
    "plt.xlabel('Response to question')\n",
    "plt.ylabel('No. of people diagnosed with coronary heart disease')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On performing the chi-squared test, we found that there is no significant difference between people diagnosed with coronary heart disease and having a high or low income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is no significant difference\n"
     ]
    }
   ],
   "source": [
    "chi6, p_chd, dof6, expected6 = stats.chi2_contingency(np.array([chdp_vc.values, chdr_vc.values]))\n",
    "\n",
    "if p_chd < 0.05:\n",
    "    print(\"There is a significant difference!\")\n",
    "else:\n",
    "    print(\"There is no significant difference\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We cleaned the data on people who have had a heart attack."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "hap = joint['heart_attack'].loc[joint['income_ratio'] < 1]\n",
    "for index, value in hap.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        hap = hap.drop(labels = index)\n",
    "hap_vc = hap.value_counts()\n",
    "        \n",
    "har = joint['heart_attack'].loc[joint['income_ratio'] >= 1]\n",
    "for index, value in har.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        har = har.drop(labels = index)\n",
    "har_vc = har.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then visualized the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "attack_df = pd.DataFrame({'Low Income': hap_vc,'High Income': har_vc})\n",
    "attack_df.rename(index={2.0: 'No', 1.0: 'Yes'}, inplace = True)\n",
    "attack_df.plot.bar(rot=0, subplots = True)\n",
    "plt.xlabel('Response to question')\n",
    "plt.ylabel('No. of people who have had a heart attack')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On performing the chi-squared test, there was no signficant difference found between having had a heart attack and income level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is no significant difference\n"
     ]
    }
   ],
   "source": [
    "chi7, p_ha, dof7, expected7 = stats.chi2_contingency(np.array([hap_vc.values, har_vc.values]))\n",
    "\n",
    "if p_ha < 0.05:\n",
    "    print(\"There is a significant difference!\")\n",
    "else:\n",
    "    print(\"There is no significant difference\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we cleaned the data on whether or not people have been diagnosed with any thyroid problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "tp = joint['thyroid_problem'].loc[joint['income_ratio'] < 1]\n",
    "for index, value in tp.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        tp = tp.drop(labels = index)\n",
    "tp_vc = tp.value_counts()\n",
    "        \n",
    "tr = joint['thyroid_problem'].loc[joint['income_ratio'] >= 1]\n",
    "for index, value in tr.iteritems():\n",
    "    if value == 3 or value == 9:\n",
    "        tr = tr.drop(labels = index)\n",
    "tr_vc = tr.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualized the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thy_df = pd.DataFrame({'Low Income': tp_vc,'High Income': tr_vc})\n",
    "thy_df.rename(index={2.0: 'No', 1.0: 'Yes'}, inplace = True)\n",
    "thy_df.plot.bar(rot=0, subplots = True)\n",
    "plt.xlabel('Response to question')\n",
    "plt.ylabel('No. of people diagnosed with thyroid problems')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On performing the chi-squared test, we found that there was no significant difference between people diagnosed with thyroid problems and income level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is no significant difference\n"
     ]
    }
   ],
   "source": [
    "chi8, p_thy, dof8, expected8 = stats.chi2_contingency(np.array([tp_vc.values, tr_vc.values]))\n",
    "\n",
    "if p_thy < 0.05:\n",
    "    print(\"There is a significant difference!\")\n",
    "else:\n",
    "    print(\"There is no significant difference\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, we found, overall, that there is no significant difference between people who self-reported that they have been diagnosed with a condition and their income level except in the case of people with high cholesterol and congestive heart failure. There seems to be more people with higher income levels with high cholesterol and more people with lower income levels with congestive heart failure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results of Labs Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first review exactly what data is contained in our final dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>SEQN</th>\n",
       "      <th>income_ratio</th>\n",
       "      <th>age</th>\n",
       "      <th>HDL</th>\n",
       "      <th>LDL</th>\n",
       "      <th>insulin</th>\n",
       "      <th>triglyceride</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73559</td>\n",
       "      <td>4.51</td>\n",
       "      <td>72</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1.448</td>\n",
       "      <td>34.98</td>\n",
       "      <td>0.576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SEQN  income_ratio  age   HDL    LDL  insulin  triglyceride\n",
       "0  73559          4.51   72  1.55  1.448    34.98         0.576"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.iloc[0:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With that, we will first look at the HDL data, printing a histogram of it. From this, we can see that the distribution of HDL is normal, perhaps with very slight positive skew. Then, we create a scatter of the HDL data against the income ratio to the poverty line, along with the regression line of the scatter data. Moreover, we also calculate the p-value of the line, which is much smaller than the critical value of 5%. Thus, while the slope is small, there is a statistically significant correlation between income ratio and HDL levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 6.68042927729e-09\n",
      "Does income ratio predict HDL? True\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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.hist('HDL', bins=25)\n",
    "sns.lmplot(x=\"income_ratio\", y=\"HDL\", data=combined, line_kws={'color': 'orange'})\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(combined[\"income_ratio\"],\n",
    "                                                               combined[\"HDL\"])\n",
    "print(\"p value:\",p_value)\n",
    "print(\"Does income ratio predict HDL?\", p_value <= 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can further observe the difference of HDL of those above and below the poverty line of 1. By performing a chi-square test between the those two groups, we can see that the difference between them, while appearing minor, is still significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.00330397336501\n",
      "There is a significant difference!\n"
     ]
    }
   ],
   "source": [
    "#p test for HDL poverty vs high income\n",
    "poverty = combined[\"HDL\"].loc[combined[\"income_ratio\"] < 1]\n",
    "highinc = combined[\"HDL\"].loc[combined[\"income_ratio\"] >= 1]\n",
    "\n",
    "t_val, p_val = stats.ttest_ind(poverty,highinc)\n",
    "print(\"p value:\",p_val)\n",
    "\n",
    "if p_val < 0.05:\n",
    "    print('There is a significant difference!')\n",
    "else:\n",
    "    print('There is NOT a significant difference!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We explore LDL in the same fashion, observing that the histogram is also largely normal with a slight positive skew. The p-value generated for this scatter plot also falls under our critical region of .05, thus also indicating a correlation of LDL with income ratio. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.00136749977979\n",
      "Does income ratio predict LDL? True\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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.hist('LDL', bins=25)\n",
    "sns.lmplot(x=\"income_ratio\", y=\"LDL\", data=combined, line_kws={'color': 'orange'})\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(combined[\"income_ratio\"],\n",
    "                                                               combined[\"LDL\"])\n",
    "print(\"p value:\",p_value,)\n",
    "print(\"Does income ratio predict LDL?\", p_value <= 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In contrast, when observing the difference of LDL of those above versus those below the poverty line with a chi-square test, there is no significant difference shown. This provides an interesting conundrum, as the previous p-test indicates that there should be an overall trend of change as income ratio increases. The result for this test implies that either the previous test was faulty, falling within the 1% change indicating there was a correlation when there was none, or that the poverty line is not the critical breakpoint of where LDL levels shift."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.0380345223542\n",
      "There is a significant difference!\n"
     ]
    }
   ],
   "source": [
    "#p test for LDL poverty vs high income\n",
    "poverty = combined[\"LDL\"].loc[combined[\"income_ratio\"] < 1]\n",
    "highinc = combined[\"LDL\"].loc[combined[\"income_ratio\"] >= 1]\n",
    "\n",
    "t_val, p_val = stats.ttest_ind(poverty,highinc)\n",
    "print(\"p value:\",p_val)\n",
    "\n",
    "if p_val < 0.05:\n",
    "    print('There is a significant difference!')\n",
    "else:\n",
    "    print('There is NOT a significant difference!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see, if we instead compare those above and below the midpoint of the data, we can see a significant difference there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.00289645038932\n",
      "There is a significant difference!\n"
     ]
    }
   ],
   "source": [
    "#p test for LDL poverty vs high income\n",
    "poverty = combined[\"LDL\"].loc[combined[\"income_ratio\"] < 2.5]\n",
    "highinc = combined[\"LDL\"].loc[combined[\"income_ratio\"] >= 2.5]\n",
    "\n",
    "t_val, p_val = stats.ttest_ind(poverty,highinc)\n",
    "print(\"p value:\",p_val)\n",
    "\n",
    "if p_val < 0.05:\n",
    "    print('There is a significant difference!')\n",
    "else:\n",
    "    print('There is NOT a significant difference!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we observe the histogram for insulin, it first appears not to be normal due to a few outliers. Once those are accounted for, dropping the values above 400, we can again see that the insulin is definitely positive skewed. For the third time, we plot the scatter of our variable against income ratio to reveal a negatively sloped line, of which calculating the p-value for indicates that the correlation is statistically significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.00163053823815\n",
      "Does income ratio predict insulin? True\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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.hist('insulin', bins=25)\n",
    "combined[combined['insulin']<400].hist('insulin', bins=25)\n",
    "sns.lmplot(x=\"income_ratio\", y='insulin', data=combined, line_kws={'color': 'orange'})\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(combined[\"income_ratio\"],\n",
    "                                                               combined[\"insulin\"])\n",
    "print(\"p value:\",p_value)\n",
    "print(\"Does income ratio predict insulin?\", p_value <= 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to prior research, we are aware that insulin levels drop with age. Thus, there is a possibility that the correlation between income ratio and insulin is due to age instead. Thus, we perform the following patsy.dmatrices with the formula insulin ~ income_ratio + age to check whether the relationship between insulin and income ratio is still observable accounting for age. As the p-value falls belows our critical value, it is determined that relationship still exists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.00107553575585\n",
      "Given age, does income ratio still predict insulin?\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "#linear model for insulin to income ratio and age\n",
    "out, pred = patsy.dmatrices('insulin ~ income_ratio + age', combined)\n",
    "mod = sm.OLS(out, pred)\n",
    "type(mod)\n",
    "res = mod.fit()\n",
    "\n",
    "#Does income ratio still predict insulin?\n",
    "print(\"p value:\",res.pvalues[1])\n",
    "print(\"Given age, does income ratio still predict insulin?\")\n",
    "print(res.pvalues[1] <= 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, like the LDL test, there is no statistically significant difference in insulin levels of those below the poverty level and those above the poverty level. And like the conclusion for the LDL test, either the previous linear regression test was faulty, or the poverty line is not the critical breakpoint of where insulin levels shift."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.501233735198\n",
      "There is NOT a significant difference!\n"
     ]
    }
   ],
   "source": [
    "#p test for insulin poverty vs high income\n",
    "poverty = combined[\"insulin\"].loc[combined[\"income_ratio\"] < 1]\n",
    "highinc = combined[\"insulin\"].loc[combined[\"income_ratio\"] >= 1]\n",
    "\n",
    "t_val, p_val = stats.ttest_ind(poverty,highinc)\n",
    "print(\"p value:\",p_val)\n",
    "\n",
    "if p_val < 0.05:\n",
    "    print('There is a significant difference!')\n",
    "else:\n",
    "    print('There is NOT a significant difference!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be observed if we instead compare those above and below 2.5 times the poverty level, of which there is a significant difference of insulin between."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.00018619922145\n",
      "There is a significant difference!\n"
     ]
    }
   ],
   "source": [
    "#p test for insulin poverty vs high income\n",
    "poverty = combined[\"insulin\"].loc[combined[\"income_ratio\"] < 2.5]\n",
    "highinc = combined[\"insulin\"].loc[combined[\"income_ratio\"] >= 2.5]\n",
    "\n",
    "t_val, p_val = stats.ttest_ind(poverty,highinc)\n",
    "print(\"p value:\",p_val)\n",
    "\n",
    "if p_val < 0.05:\n",
    "    print('There is a significant difference!')\n",
    "else:\n",
    "    print('There is NOT a significant difference!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For one last time, we plot the histogram of our variable data, triglyceride, and observe its positive skew. Then we create a scatter plot of it against our income ratio and its corresponding linear regression line. The calculated p-value of this line indicates this income ratio does not predict triglyceride."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.311259770093\n",
      "Does income ratio predict triglyceride? False\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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.hist('triglyceride', bins=25)\n",
    "sns.lmplot(x=\"income_ratio\", y='triglyceride', data=combined, line_kws={'color': 'orange'})\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(combined[\"income_ratio\"],\n",
    "                                                               combined[\"triglyceride\"])\n",
    "print(\"p value:\",p_value)\n",
    "print(\"Does income ratio predict triglyceride?\", p_value <= 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performing a p-test for triglyceride above and below the poverty line also indicates no significant difference between the two, matching our conclusion with the previous test with the linear regression p-value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value: 0.600988191696\n",
      "There is NOT a significant difference!\n"
     ]
    }
   ],
   "source": [
    "#p test for triglyceride poverty vs high income\n",
    "poverty = combined[\"triglyceride\"].loc[combined[\"income_ratio\"] < 1]\n",
    "highinc = combined[\"triglyceride\"].loc[combined[\"income_ratio\"] >= 1]\n",
    "\n",
    "t_val, p_val = stats.ttest_ind(poverty,highinc)\n",
    "print(\"p value:\",p_val)\n",
    "\n",
    "if p_val < 0.05:\n",
    "    print('There is a significant difference!')\n",
    "else:\n",
    "    print('There is NOT a significant difference!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ethics of accessing and spreading personal information is essential in data science. Thus, we followed the Safe Harbor method. Its applications fall rather short for this dataset, however, as the research method used to collect this data stipulated that the individuals remain completely confidential. After all, it contains private physical and mental health information that would normally be completely confidential, between a patient and a doctor. In practice, there were no names or various other identifications attached. To even further obscure the data, there was no location data associated with the patients, so it would be extremely difficult to trace the information back to the individual.\n",
    "\n",
    "We can assume that because this data set comes from the Centers for Disease Control and Prevention (CDC), a governmental department, the patients consented to be involved in this dataset. Since this data is publicly available, we know that it does not violate the HIPPA law, which means that the privacy of patients was protected when creating this dataset. Additionally, we have omitted information like gender in our data, which can further protect patients’ privacy. Thus, all we needed to do to fully respect ethical concerns for privacy was to remove the data on individuals with age lower than 18, as to not investigate data on minors. Therefore, unless there was bias or misconduct in the original gathering of the data, most of the data analysis we perform on the National Health and Examination Survey data is ethical."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examination"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When comparing the income ratio to blood pressure readings (diastolic and systolic) using the scatter plots, we found that based on the regression line, income ratio did not predict blood pressure. When completing a linear model and analyzing the results, we found the converse, income ratio did predict diastolic and systolic blood pressure. Additionally, after running a p-test to find if there was a difference between low income ratio blood pressure and higher income ratio blood pressure, it was found that there is a significant difference. These contradictory results could be explained by the size of our scatter plot. Being that the scatter plot graph was so small, the visual angle of the linear regression line does not look very conclusive of a relationship between the two variables. Due to this discrepancy, we can say that the p test and linear model prediction test might be more accurate than the scatter plot. One possible explanation for having higher blood pressure readings with higher incomes, is the likelihood of having a high-stress job with a higher salary. When comparing income ratio to BMI in the scatter plot, the regression line was nearly flat, not showing a significant relationship. The linear model prediction test also verified this conclusion, with a p-value of 0.0652 income ratio that does not predict BMI. However, when conducting a p test comparing the BMI of participants below poverty and the BMI of participants above the poverty line, we found that there was a significant statistical difference. With this information and looking back at the slight angle of the regression line, we can say that people with lower incomes are likely to have higher BMIs. A higher BMI can be related to an unhealthy diet, leading to more body fat. Unhealthy diets go hand in hand with access to nutritious foods and people in poverty have barriers to the access of healthy, nutritious foods. Additionally, BMI indexes are lower for people who are more physically fit and people with higher incomes have more access, money, and time to maintain physical fitness using gyms and/or fitness programs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questionaire"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the questionnaire dataset, overall we found that there was no statistically significant difference between having any of the conditions and high or low income level of the participants. There are, however, 2 exceptions which are high cholesterol and congestive heart failure. Even among these 2 conditions, only the results of congestive heart failure are similar to our hypothesis that people with low income levels have poorer health and more diagnoses of such health conditions than people with higher income levels. In fact, the results from cholesterol show the exact opposite: more people with high income levels have high cholesterol than people with low income levels. This may be because meat and seafood are food products that tend to have high levels of cholesterol. These kinds of food products are also expensive and hence, more accessible to people with higher incomes rather than people with lower incomes. What is interesting is that even though congestive heart failure has a significant difference, the causes for the condition are hypertension, high cholesterol, diabetes, thyroid disease, and obesity, all of which were tested separately and did not show any significant difference between income levels individually. We were not able to explain this disparity and so this makes the results for congestive heart failure inconclusive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Labs "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the labs dataset, there was conclusive correlation between income ratio and HDL, LDL, and insulin. Surprisingly, there was no correlation between income ratio and triglyceride levels, given that triglycerides are a component of overall cholesterol levels, like HDL and LDL. This may be because while the correlations were significant for the former three, their overall relation was slight. Given more extensive testing with more esoteric methods, it may be revealed that some of these conclusions are incorrect or not as statistically relevant as originally thought. Ironically, however, the positive correlations indicated by HDL and LDL to income ratio point to those with higher incomes having higher cholesterol and thus being more unhealthy,  contrary to our hypothesis. The only measure indicating that those with higher incomes are healthier is the correlation of lower insulin level by income, however slight. However, there was no significant difference in insulin levels of those above the poverty line compared to those below the poverty line. Thus, the labs data overall does not support our hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limitations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One limitation of our project is that we did not have the location of our participants and therefore some of the data might be biased due to socioeconomic status of different regions. Likewise, because we did not know the locations, we could not make any definite conclusions based on their access to healthy and nutritious foods. Another limitation of our project is forgetting to account for sex. While not a primary objective of our hypothesis, further conditional tests involving patsy.dmatrices and sex may have altered our conclusions for some data from all three datasets. There were other demographic variables that we did not consider while performing our data analysis such as race that could have an effect on people’s income and nutritional health. We also did not explore the effect of age in depth which also could have had a significant effect on our data analysis.\n",
    "Finally, health is a complex issue that involves many variables. We attempted to address as many as we understood, yet the overall metrics and indeed combinations of those are better understood by those studying health, medicine, and nutrition. Our lack of knowledge regarding these topics and how they compound regarding overall health and nutrition, and therefore our conclusions may be considered rudimentary at best.\n",
    "In order to make more definite conclusions and further develop this analysis, we need to explore more variables and their possible effects on income and nutritional health."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variables that we analyzed do not support our general hypothesis. \n",
    "\n",
    "Specifically, we found that cholesterol levels (HDL, LDL) are higher for high income individuals and this is supported by the questionnaire data that said that high income individuals reported being diagnosed with high cholesterol more than low income individuals. Additionally, it was found that individuals with higher incomes had higher blood pressure readings than people below the poverty line. Unfortunately, questionnaire data for blood pressure did not show a significant difference and therefore does not support the previous conclusion. In general, individuals below the poverty line had higher body mass indices than individuals above the poverty line. There was no significant difference found between the insulin levels of people above or below the poverty line. Furthermore, there were no significant differences found between high or low income individuals in terms of diagnoses of nutrition-related health conditions such as coronary heart disease, diabetes, heart attacks, and thyroid problems. The significant difference that we found for the diagnosis of congestive heart failure is inconclusive. \n",
    "\n",
    "In conclusion, out of our nine hypothesis statements, only the BMI hypothesis was supported by the data analysis. Hence, overall, there is no significant difference between high income individuals and low income individual when looking at health factors related to nutrition. "
   ]
  }
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