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Merge pull request microsoft#642 from microsoft/update-translations
🌐 Update translations via Co-op Translator
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‎translations/hk/1-Introduction/01-defining-data-science/notebook.ipynb‎

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‎translations/hk/1-Introduction/01-defining-data-science/solution/notebook.ipynb‎

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‎translations/hk/1-Introduction/04-stats-and-probability/assignment.ipynb‎

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{
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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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"## 概率與統計學簡介\n",
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"## 作業\n",
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"\n",
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"在這次作業中，我們將使用[這裡](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)提供的糖尿病患者數據集。\n"
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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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"<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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" </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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" <th>2</th>\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>85</td>\n",
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" <th>3</th>\n",
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" <th>4</th>\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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"在此數據集中，列的含義如下：\n",
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"* 年齡和性別不言自明\n",
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"* BMI 是身體質量指數\n",
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"* BP 是平均血壓\n",
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"* S1 到 S6 是不同的血液測量值\n",
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"* Y 是疾病在一年內進展的定性指標\n",
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"\n",
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"讓我們使用概率和統計的方法來研究這個數據集。\n",
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"\n",
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"### 任務 1：計算所有值的平均值和方差\n"
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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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"### 任務 2：根據性別繪製 BMI、BP 和 Y 的箱型圖\n"
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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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"### 任務 3: 年齡、性別、BMI 和 Y 變量的分佈是什麼？\n"
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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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"### 任務 4：測試不同變數與疾病進展（Y）之間的相關性\n",
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"\n",
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"> **提示** 相關性矩陣可以為你提供最有用的資訊，幫助判斷哪些數值是相關的。\n"
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],
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"metadata": {}
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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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"### 任務 5：檢驗糖尿病進展程度在男性和女性之間是否存在差異的假設\n"
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],
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"metadata": {}
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n---\n\n**免責聲明**： \n本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業的人類翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。\n"
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