{ "cells": [ { "cell_type": "markdown", "source": [ "## Introduction to Probability and Statistics\n", "## Assignment\n", "\n", "In this assignment, we will use the dataset of diabetes patients obtained [from here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": 13, "source": [ "import pandas as pd\r\n", "import numpy as np\r\n", "\r\n", "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n", "df.head()" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " AGE SEX BMI BP S1 S2 S3 S4 S5 S6 Y\n", "0 59 2 32.1 101.0 157 93.2 38.0 4.0 4.8598 87 151\n", "1 48 1 21.6 87.0 183 103.2 70.0 3.0 3.8918 69 75\n", "2 72 2 30.5 93.0 156 93.6 41.0 4.0 4.6728 85 141\n", "3 24 1 25.3 84.0 198 131.4 40.0 5.0 4.8903 89 206\n", "4 50 1 23.0 101.0 192 125.4 52.0 4.0 4.2905 80 135" ], "text/html": [ "
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AGESEXBMIBPS1S2S3S4S5S6Y
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" ] }, "metadata": {}, "execution_count": 13 } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "In this dataset, the columns are as follows:\n", "* Age and sex are straightforward\n", "* BMI refers to body mass index\n", "* BP represents average blood pressure\n", "* S1 to S6 are various blood measurements\n", "* Y is a qualitative indicator of disease progression over the course of one year\n", "\n", "Let's analyze this dataset using probability and statistical methods.\n", "\n", "### Task 1: Calculate the mean and variance for all values\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "### Task 2: Plot boxplots for BMI, BP, and Y depending on gender\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "### Task 3: What is the distribution of Age, Sex, BMI, and Y variables?\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "### Task 4: Test the correlation between different variables and disease progression (Y)\n", "\n", "> **Hint** The correlation matrix will provide the most valuable insights into which values are interdependent.\n" ], "metadata": {} }, { "cell_type": "markdown", "source": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "### Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women\n" ], "metadata": {} }, { "cell_type": "markdown", "source": [], "metadata": {} }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n---\n\n**Disclaimer**: \nThis document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the definitive source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.\n" ] } ], "metadata": { "orig_nbformat": 4, "language_info": { "name": "python", "version": "3.8.8", "mimetype": "text/x-python", "codemirror_mode": { "name": "ipython", "version": 3 }, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py" }, "kernelspec": { "name": "python3", "display_name": "Python 3.8.8 64-bit (conda)" }, "interpreter": { "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5" }, "coopTranslator": { "original_hash": "defe9f96b3d327a6f37d795c43ad0219", "translation_date": "2025-09-03T20:43:46+00:00", "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb", "language_code": "en" } }, "nbformat": 4, "nbformat_minor": 2 }