{ "cells": [ { "cell_type": "markdown", "source": [ "## 確率と統計の入門\n", "## 課題\n", "\n", "この課題では、[こちら](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)から取得した糖尿病患者のデータセットを使用します。\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": 13, "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\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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" ] }, "metadata": {}, "execution_count": 13 } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "このデータセットには以下の列があります：\n", "* 年齢と性別はそのまま説明不要です\n", "* BMIは体格指数を表します\n", "* BPは平均血圧を示します\n", "* S1からS6は異なる血液測定値です\n", "* Yは1年間の疾患進行の定性的な指標です\n", "\n", "このデータセットを確率と統計の手法を用いて分析してみましょう。\n", "\n", "### タスク 1: 全ての値の平均値と分散を計算する\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "### タスク2: 性別に応じたBMI、BP、Yのボックスプロットを作成\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "### タスク3: 年齢、性別、BMI、およびY変数の分布はどうなっていますか？\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "### タスク 4: 異なる変数と病気の進行 (Y) の相関をテストする\n", "\n", "> **ヒント** 相関行列は、どの値が依存しているかについて最も有用な情報を提供します。\n" ], "metadata": {} }, { "cell_type": "markdown", "source": [], "metadata": {} }, { "cell_type": "markdown", "source": [], "metadata": {} }, { "cell_type": "markdown", "source": [], "metadata": {} }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n---\n\n**免責事項**: \nこの文書は、AI翻訳サービス [Co-op Translator](https://github.com/Azure/co-op-translator) を使用して翻訳されています。正確性を期すよう努めておりますが、自動翻訳には誤りや不正確な表現が含まれる可能性があります。元の言語で記載された原文を公式な情報源としてご参照ください。重要な情報については、専門の人間による翻訳を推奨します。この翻訳の利用に起因する誤解や誤認について、当方は一切の責任を負いません。\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": "6d945fd15163f60cb473dbfe04b2d100", "translation_date": "2025-09-06T17:15:31+00:00", "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb", "language_code": "ja" } }, "nbformat": 4, "nbformat_minor": 2 }