Dmitri Soshnikov
3 years ago
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"## Introduction to Probability and Statistics\r\n",
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"## Assignment\r\n",
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"\r\n",
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"In this assignment, we will use the dataset of diabetes patients taken [from here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)."
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"source": [
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"import pandas as pd\r\n",
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"import numpy as np\r\n",
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"\r\n",
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"df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
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"df.head()"
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],
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" AGE SEX BMI BP S1 S2 S3 S4 S5 S6 Y\n",
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"0 59 2 32.1 101.0 157 93.2 38.0 4.0 4.8598 87 151\n",
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"1 48 1 21.6 87.0 183 103.2 70.0 3.0 3.8918 69 75\n",
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"2 72 2 30.5 93.0 156 93.6 41.0 4.0 4.6728 85 141\n",
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"3 24 1 25.3 84.0 198 131.4 40.0 5.0 4.8903 89 206\n",
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"4 50 1 23.0 101.0 192 125.4 52.0 4.0 4.2905 80 135"
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],
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>AGE</th>\n",
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" <th>SEX</th>\n",
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" <th>BMI</th>\n",
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" <th>BP</th>\n",
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" <th>S1</th>\n",
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" <th>S2</th>\n",
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" <th>S3</th>\n",
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" <th>S4</th>\n",
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" <th>S5</th>\n",
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" <th>S6</th>\n",
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" <th>Y</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>59</td>\n",
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" <td>2</td>\n",
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" <td>32.1</td>\n",
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" <td>101.0</td>\n",
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" <td>157</td>\n",
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" <td>93.2</td>\n",
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" <td>38.0</td>\n",
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" <td>4.0</td>\n",
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" <td>4.8598</td>\n",
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" <td>87</td>\n",
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" <td>151</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>48</td>\n",
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" <td>1</td>\n",
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" <td>21.6</td>\n",
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" <td>87.0</td>\n",
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" <td>183</td>\n",
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" <td>103.2</td>\n",
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" <td>70.0</td>\n",
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" <td>3.0</td>\n",
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" <td>3.8918</td>\n",
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" <td>69</td>\n",
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" <td>75</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>72</td>\n",
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" <td>2</td>\n",
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" <td>30.5</td>\n",
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" <td>93.0</td>\n",
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" <td>156</td>\n",
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" <td>93.6</td>\n",
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" <td>41.0</td>\n",
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" <td>4.0</td>\n",
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" <td>4.6728</td>\n",
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" <td>85</td>\n",
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" <td>141</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>24</td>\n",
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" <td>1</td>\n",
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" <td>25.3</td>\n",
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" <td>84.0</td>\n",
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" <td>198</td>\n",
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" <td>131.4</td>\n",
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" <td>40.0</td>\n",
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" <td>5.0</td>\n",
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" <td>4.8903</td>\n",
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" <td>89</td>\n",
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" <td>206</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>50</td>\n",
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" <td>1</td>\n",
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" <td>23.0</td>\n",
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" <td>101.0</td>\n",
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" <td>192</td>\n",
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" <td>125.4</td>\n",
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" <td>52.0</td>\n",
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" <td>4.0</td>\n",
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" <td>4.2905</td>\n",
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" <td>80</td>\n",
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" <td>135</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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]
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},
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"metadata": {},
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"execution_count": 13
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}
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],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"\r\n",
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"In this dataset, columns as the following:\r\n",
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"* Age and sex are self-explanatory\r\n",
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"* BMI is body mass index\r\n",
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"* BP is average blood pressure\r\n",
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"* S1 through S6 are different blood measurements\r\n",
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"* Y is the qualitative measure of disease progression over one year\r\n",
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"\r\n",
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"Let's study this dataset using methods of probability and statistics.\r\n",
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"\r\n",
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"### Task 1: Compute mean values and variance for all values"
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Task 2: Plot boxplots for BMI, BP and Y depending on gender"
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Task 3: What is the the distribution of Age, Sex, BMI and Y variables?"
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Task 4: Test the correlation between different variables and disease progression (Y)\r\n",
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"\r\n",
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"> **Hint** Correlation matrix would give you the most useful information on which values are dependent."
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],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women"
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],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [],
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"metadata": {}
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}
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],
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"metadata": {
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"orig_nbformat": 4,
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"language_info": {
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"name": "python",
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"version": "3.8.8",
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"mimetype": "text/x-python",
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"pygments_lexer": "ipython3",
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"nbconvert_exporter": "python",
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"file_extension": ".py"
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3.8.8 64-bit (conda)"
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},
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"interpreter": {
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"hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
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}
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"nbformat": 4,
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"nbformat_minor": 2
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@ -1,8 +1,25 @@
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# Title
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# Small Diabetes Study
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In this assignment, we will work with a small dataset of diabetes patients taken from [here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).
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| | AGE | SEX | BMI | BP | S1 | S2 | S3 | S4 | S5 | S6 | Y |
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|---|-----|-----|-----|----|----|----|----|----|----|----|----|
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| 0 | 59 | 2 | 32.1 | 101. | 157 | 93.2 | 38.0 | 4. | 4.8598 | 87 | 151 |
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| 1 | 48 | 1 | 21.6 | 87.0 | 183 | 103.2 | 70. | 3. | 3.8918 | 69 | 75 |
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| 2 | 72 | 2 | 30.5 | 93.0 | 156 | 93.6 | 41.0 | 4.0 | 4. | 85 | 141 |
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| ... | ... | ... | ... | ...| ...| ...| ...| ...| ...| ...| ... |
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## Instructions
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## Instructions
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* Open the [assignment notebook](assignment.ipynb) in a jupyter notebook environment
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* Complete all tasks listed in the notebook, namely:
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[ ] Compute mean values and variance for all values
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[ ] Plot boxplots for BMI, BP and Y depending on gender
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[ ] What is the the distribution of Age, Sex, BMI and Y variables?
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[ ] Test the correlation between different variables and disease progression (Y)
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[ ] Test the hypothesis that the degree of diabetes progression is different between men and women
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## Rubric
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## Rubric
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Exemplary | Adequate | Needs Improvement
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Exemplary | Adequate | Needs Improvement
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--- | --- | -- |
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--- | --- | -- |
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All required tasks are complete, graphically illustrated and explained | Most of the tasks are complete, explanations or takeaways from graphs and/or obtained values are missing | Only basic tasks such as computation of mean/variance and basic plots are complete, no conclusions are made from the data
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