Data-Science-For-Beginners/translations/tw/1-Introduction/04-stats-and-probability/assignment.ipynb

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    "## 概率與統計簡介\n",
    "## 作業\n",
    "\n",
    "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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   "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()"
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       "   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"
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       "      <td>131.4</td>\n",
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    "在此數據集中，欄位如下：  \n",
    "* Age 和 sex 不需多作解釋  \n",
    "* BMI 是身體質量指數  \n",
    "* BP 是平均血壓  \n",
    "* S1 到 S6 是不同的血液測量值  \n",
    "* Y 是一年內疾病進展的定性指標  \n",
    "\n",
    "讓我們使用概率與統計的方法來研究這個數據集。\n",
    "\n",
    "### 任務 1：計算所有值的平均值和方差  \n"
   ],
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "### 任務 2：根據性別繪製 BMI、BP 和 Y 的箱型圖\n"
   ],
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   "execution_count": null,
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   "source": [
    "### 任務 3：年齡、性別、BMI 和 Y 變量的分佈是什麼？\n"
   ],
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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",
    "\n",
    "> **提示** 相關矩陣可以為您提供最有用的信息，幫助判斷哪些值是相互依賴的。\n"
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