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

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to Probability and Statistics\n",
    "## Assignment\n",
    "\n",
    "In this assignment, we will use the dataset of diabetes patients taken [from here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BP</th>\n",
       "      <th>S1</th>\n",
       "      <th>S2</th>\n",
       "      <th>S3</th>\n",
       "      <th>S4</th>\n",
       "      <th>S5</th>\n",
       "      <th>S6</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>32.1</td>\n",
       "      <td>101.00</td>\n",
       "      <td>157</td>\n",
       "      <td>93.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.8598</td>\n",
       "      <td>87</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>21.6</td>\n",
       "      <td>87.00</td>\n",
       "      <td>183</td>\n",
       "      <td>103.2</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.8918</td>\n",
       "      <td>69</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>30.5</td>\n",
       "      <td>93.00</td>\n",
       "      <td>156</td>\n",
       "      <td>93.6</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.6728</td>\n",
       "      <td>85</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>25.3</td>\n",
       "      <td>84.00</td>\n",
       "      <td>198</td>\n",
       "      <td>131.4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.8903</td>\n",
       "      <td>89</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.00</td>\n",
       "      <td>192</td>\n",
       "      <td>125.4</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.2905</td>\n",
       "      <td>80</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>28.2</td>\n",
       "      <td>112.00</td>\n",
       "      <td>185</td>\n",
       "      <td>113.8</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.9836</td>\n",
       "      <td>93</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>75.00</td>\n",
       "      <td>225</td>\n",
       "      <td>166.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.4427</td>\n",
       "      <td>102</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>99.67</td>\n",
       "      <td>162</td>\n",
       "      <td>106.6</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.77</td>\n",
       "      <td>4.1271</td>\n",
       "      <td>95</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>201</td>\n",
       "      <td>125.2</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4.79</td>\n",
       "      <td>5.1299</td>\n",
       "      <td>85</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>19.6</td>\n",
       "      <td>71.00</td>\n",
       "      <td>250</td>\n",
       "      <td>133.2</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.5951</td>\n",
       "      <td>92</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>442 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     AGE  SEX   BMI      BP   S1     S2    S3    S4      S5   S6    Y\n",
       "0     59    2  32.1  101.00  157   93.2  38.0  4.00  4.8598   87  151\n",
       "1     48    1  21.6   87.00  183  103.2  70.0  3.00  3.8918   69   75\n",
       "2     72    2  30.5   93.00  156   93.6  41.0  4.00  4.6728   85  141\n",
       "3     24    1  25.3   84.00  198  131.4  40.0  5.00  4.8903   89  206\n",
       "4     50    1  23.0  101.00  192  125.4  52.0  4.00  4.2905   80  135\n",
       "..   ...  ...   ...     ...  ...    ...   ...   ...     ...  ...  ...\n",
       "437   60    2  28.2  112.00  185  113.8  42.0  4.00  4.9836   93  178\n",
       "438   47    2  24.9   75.00  225  166.0  42.0  5.00  4.4427  102  104\n",
       "439   60    2  24.9   99.67  162  106.6  43.0  3.77  4.1271   95  132\n",
       "440   36    1  30.0   95.00  201  125.2  42.0  4.79  5.1299   85  220\n",
       "441   36    1  19.6   71.00  250  133.2  97.0  3.00  4.5951   92   57\n",
       "\n",
       "[442 rows x 11 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "In this dataset, columns as the following:\n",
    "* Age and sex are self-explanatory\n",
    "* BMI is body mass index\n",
    "* BP is average blood pressure\n",
    "* S1 through S6 are different blood measurements\n",
    "* Y is the qualitative measure of disease progression over one year\n",
    "\n",
    "Let's study this dataset using methods of probability and statistics.\n",
    "\n",
    "### Task 1: Compute mean values and variance for all values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AGE     48.518100\n",
      "SEX      1.468326\n",
      "BMI     26.375792\n",
      "BP      94.647014\n",
      "S1     189.140271\n",
      "S2     115.439140\n",
      "S3      49.788462\n",
      "S4       4.070249\n",
      "S5       4.641411\n",
      "S6      91.260181\n",
      "Y      152.133484\n",
      "dtype: float64\n",
      "\n",
      "AGE     171.846610\n",
      "SEX       0.249561\n",
      "BMI      19.519798\n",
      "BP      191.304401\n",
      "S1     1197.717241\n",
      "S2      924.955494\n",
      "S3      167.293585\n",
      "S4        1.665261\n",
      "S5        0.272892\n",
      "S6      132.165712\n",
      "Y      5943.331348\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "m, v = df.mean(), df.var()\n",
    "print(f\"{m}\\n\\n{v}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2: Plot boxplots for BMI, BP and Y depending on gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "df.boxplot(column=['BMI', 'BP', 'Y'], by=['SEX'])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7oGjH57ffftv0O9J+NwD8R8sLkIc0DLRr1y7fvQba2XXevHmm/4cebLUVRjsQ6+KB2lcnr2jfGu3smnbiO1vokGwdCaanC3WRSmfyQwD+oc8LgByjI690LhPtTJp2xt6MaKdm7Yyq847o6RTtIOrZEdWhI7P01JYvuiI4KyEDBRfhBUCO0RlgdciwzjOjo6J8zTKso2jGjRtnRk2VLVvWTGCnc4voLK86YktHzuhoJmcosLZWacuFr6UTdOSSv8siALAH4QVAjtBhuFdffbVpcVm3bp0ZruwMefakQ3512LMGEw0vesrKoZO+6bDyf/7zn2ZYug51dsKLjjxKOysvgNBAnxcAOUJnnNUWFZ38LDY21ueMtToTrgYYnfzPM7gobT3Ryeu0n0he9rUBkL8QXgDkCA0r2vdE54bR1Y619UVH26RdK0k7AWfVCZc+LAA8MVQaQNDpfDA6y7CzBpSeLtKp7XVWV8/1nZylBjKacTcrum5SRpP76ayzehoKQMFFeAEQdDqPic4Po2shOeslDRw40Cw3oOs7OaeAnFWQtUOvJx2anXahRl3xWBcfdOhq1S+//HK6x9YWHMILULARXgAElbaIOAv66QKHnqFC1/zR00ROqNHF/5zOvWnLcIKJBpwff/wx3ZIA2h/GlmUVAAQX4QVAUOkijLr4o67OnbZlRNdF0lYZJ7w4qxjrjL6edFSRE0x0WYLu3bvzKgFwI7wACHpH3c6dO8uSJUvS3aatLrpKs875oqeVtK+LngpasGCBmSrfmccFADLDfwoAQZ3+Xvuq6LIHvlZS1plvte+L0knrXnzxRdm6dSvr/ADINlpeAASNjia6dOmSz/CiixG2adNGZs+eLWPHjpWwsDDp27evmU330UcflfXr17uHTWs/mN27d5u+Lnq/tMsE+BptpHReGV0HCUDBxAy7AILmgQcekOTkZDNTri/z58+Xd999V1555RUzB4xD1zPSaf113SL9WTvk6iklHWXUo0cPKV68uHvf0aNHm5WufZk2bZrUq1ePVxYooAgvAADAKvR5AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwSr6cpE4nudIF3HRpe53ECgAAFHwul8usjXb11VdnulxIvgwvGlyio6PzuhoAACAPJCQkSOXKle0KL9ri4lReZ9lEaNFp39esWSNxcXFmHRwAoYPPf2hLTEw0jRdODrAqvDinijS4EF5C85+XrmWjrz3hBQgtfP6hsuoyQoddAABgFcILAACwCuEFAAAU7PCyaNEiueuuu6Rjx44yYMAAWb16dbp9fvnlFxk4cKB06NBBHnzwQfn111+DVV8AABDi/AovL7/8svTr109atGghTz75pFx33XVyyy23yPz58937bNu2TVq1aiUREREyfPhw+euvvyQ2NlYOHjyYE/UHAAAhxq/wsnjxYunbt688+uijpuVl3Lhx0qlTJ1myZIl7nwkTJkjDhg1l5syZ0r17d5k3b54UL15cpk2blhP1BwAAIcav8BITEyObNm2S8+fPm99PnjwpO3bskKZNm7r3+fzzz01ocYSHh0u3bt1k7dq1waw3AAAIUX7N8zJlyhQZOXKkmfWuWrVqsmfPHnNq6PHHHze3nz17Vo4dOybXXHON1/309wMHDvgsNzk52Vw8J6lxxvvrBaHFec157YHQw+c/tKVk85jvV3hZuHChzJkzR55++mlp0KCBxMfHy4svvmj6uOjpI+dBCxcu7HW/IkWKyIULF3yWO2nSJBk/fny67TrLqk5WhtBEax0Quvj8h6akpKTghxdtZdH+Ltr6onT69t9//11GjRplTifpdL46I+rx48e97qe/lylTxme5Y8aMkREjRqSbHljLZ4bd0KMhWP9xaSBmhl0gtPD5D22J/z3zErTwcvHiRdPHJe1CSfr7ihUr3P1btEXmhx9+8Nrn22+/lSZNmvgsOyoqylzS0gMXB6/QxesPhC4+/6EpMpvr2WW7w64Gk5YtW8p7770nZ86cMdt0GLQOk27durV7P53fZcGCBaYjr/r6669l3bp1ZjsAAECg/DptNGvWLDNBnZ7SqV69uuzatUuaN29u5n9xPPTQQ7J582Zp3Lix1KhRQ/bu3WtOK91+++0BVxYAYG9fBudLbWb0y/HGjRvlyiuvNNNsZKZOnTr0iwxRYS6Xy+XvnbSfy+HDh80ooooVK2a4z6FDhyQhIcGMSipXrpzf57xKlSolp06dos9LiJ7z1lOROgEipw2BguHHH3/0mlYjGLSLgk7hgYIju8d/v1pePPu5pO37kpaGGl/BBgAQWrSVJG1/yIzs3LnTDODQkay1a9fOskyEpssKLwAA+EOnvchOK0mhQoXMAI66deua7gdARlhVGgAAWIXwAgAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFiF8AIAAKwSkdcVQOhISkqSHTt2ZLnfmTNnZOPGjXLllVdK8eLFM923Tp06UrRo0SDWEgCQ3xFekGs0uDRt2jTb+0+ZMiXLfX744QeJiYkJsGYAAJsQXpBrtJVEw0ZWdu7cKSNGjJAXX3xRateunWWZAIDQQnhBrtHTO9lpJSlUqJBERUVJ3bp1pXHjxrlSNwCAPeiwCwAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFiF8AIAAKxCeAEAAFYhvAAAAKtE+LPzpk2b5Ny5c+m2ly5dWurUqeO17ejRo/L7779LtWrVzO0AAAC5Hl4mTZok+/bt89r2zTffSL9+/WTOnDnmd5fLJQ8//LC8/fbbcu2118r+/fvliSeekPHjxwelwgAAILT5FV7mz5/v9ftXX30lrVu3lnvuuce9bcaMGfLBBx/Ijz/+KPXq1ZMNGzZI+/btpUmTJtKjR4/g1RwAAISkgPq8zJo1y5wW6tChg3ubtrj06tXLBBel4UZv130BAADyLLycPn1aPv74Y7n//vslLCzMbLt48aJs3rxZmjdv7rVvixYt5Keffgq4sgAAAH6dNkp7Cik5OVnuu+8+r0CTkpIiZcqU8dq3bNmycvz4cZ9laTl6cSQmJpprLUsvCC2pqanua15/ILTw+Q9tKdk85l92eNHTQF27dpVKlSq5t0VGRpprzyCidISSc5uvjsAZdehds2aNFC1a9HKrCEslJCSY6/j4eDl48GBeVwdALuLzH9qSkpJyLrxs3brVjDJavny51/ZixYqZVpY//vjDa7v+XrVqVZ/ljRkzRkaMGOHV8hIdHS1xcXFSsmTJy6kiLKZD8lVsbKw0atQor6sDIBfx+Q9tif8985Ij4UVbXSpXrixdunRJd5t2zv3000/N8GinH8xnn30m3bp181leVFSUuaSlrTWZtdigYIqIiHBf8/oDoYXPf2iLzOYx3+8OuxcuXDBzugwaNEjCw8PT3f7000+bzrlDhw6VlStXSv/+/eXkyZMyatQofx8KAAAg8PDyww8/SI0aNWTgwIEZ3l6/fn0z/4t23p08ebLps/L111+blhoAAIBA+X3aqGXLlqYjZWYaN27snnEXAAAgmFiYEQAAWIXwAgAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFiF8AIAAKxCeAEAAFYhvAAAAKsQXgAAgFUILwAAwCqEFwAAYBXCCwAAsArhBQAAWIXwAgAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFiF8AIAAKxCeAEAAFaJuJw7ffLJJ7JixQoJCwuTO++8U9q0aeN1e0JCgkyfPl32798vtWrVkocffljKlSsXrDoDAIAQ5lfLi8vlkrvuuksefPBBqVmzpsTGxsqECRNk1apV7n327dsnMTExsn37dmnXrp18+eWX0qxZMzl+/HhO1B8AAIQYv1pe3nnnHVm0aJFs3rxZateubbYNGDBATpw44d5Hw0zlypXNfoUKFZL+/fuboPPSSy/JxIkTg/8XAACAkOJXy8vbb78t3bp1cwcXpaeOypQp4/5dTyfdfvvtJriowoULS/fu3c12AACAXG152bRpk/To0UNmz54t69evlwoVKkjPnj2lVatW5vZz587JoUOHpEqVKl7309/nzZvns9zk5GRzcSQmJprrlJQUc0FoSU1NdV/z+gN20C4DZ8+eDbicXbt2eV0HqlixYnLttdcGpSzkvOz+z4/wp79LUlKSvPbaayas3HLLLbJt2zZp37696Zw7cOBAdwApWrSo132LFy8u58+f91n2pEmTZPz48em2r1mzJl1ZKPi0w7eKj4+XgwcP5nV1AGThyJEj8o9//COoz9Po0aODVtbYsWPNl23kf5ozghpe9PRQqVKl5KqrrpKPPvrIvV2/HWvw0PCiISU8PNyrD4zSzrqlS5f2WfaYMWNkxIgRXi0v0dHREhcXJyVLlsxuFVFAaAuf0g7hjRo1yuvqAMjC1q1bzfXUqVNNH8dAaOuNDgLp0qWLaTUJxJ49e2TUqFHSvHlzqVevXkBlIXc4Z16CetpIDyRXX3211zYdCq2nirRlJiIiQurWrWs69HrS3xs2bOiz3KioKHNJKzIy0lwQWvR95Fzz+gP2fGbr1Kkj9evXD/i0wbFjx+SGG24I+PPP/xL7ZPc196vD7r333mv6uhw9etTd6rJkyRJp2bKlaZlR/fr1k48//lh+//13dyJfuXKl2Q4AABCoCH/Dy9dff21aVzQV79y504wmWrp0qXufxx57TDZu3GhaaRo3bizfffedmchOh0wDAADkanjR1pWZM2fK448/boJLpUqVTEhxmubUFVdcIcuWLTP9Fg4cOGDOf2rYAQAAyLPlAbSfi14yo6GGzpYAACDYWJgRAABYhfACAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFiF8AIAAKxCeAEAAFYhvAAAAKsQXgAAgFUILwAAwCqEFwAAYBXCCwAAsArhBQAAWIXwAgAArEJ4AQAAVonI6wqg4Pjtt9/k7NmzAZezd+9e93VEROBv0WLFikm1atUCLgcAkD8QXhC04NK+ffugPpsjR44MWlnr168nwABAAUF4QVA4LS4vv/yy1KxZM+Cyli9fLt26dTOtJoHYs2ePDB8+PCgtQgCA/IHwgqDS4FK/fv2AykhJSZFDhw5JTEyMREZGBq1uAICCgQ67AADAKoQXAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACrMM8LACBgJUuWlOTkZDl16lRA5aSmpprrxMTEgJcH0fpovVDwEF4AAAFr3bq1mVxSL4HS0BIfHx+0eqHgIbwAAAK2YcMGGTRoUMDLg2jLi5aloSPQlhddHkTLGjp0aEDlIP8hvAAAAqaneaKioqRUqVIBLw+i9HRPoMuDaH20Xih46LALAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAABTcSep0zYoTJ054bStUqJBUqVIl3b4XLlyQ48ePS/ny5QOeJREAAOCyWl5ee+01qV27trRr18596datW7r9JkyYIGXKlJE6deqY8DJ9+nR/HgYAAMAnv5tEmjRpkumCWXPnzpVJkybJqlWrpE2bNrJ48WLp3bu3XHfdddKhQwd/Hw4AACDwPi/Hjh2TM2fOZHjbm2++KbfffrsJLkp/1gW2aH0BAAB5El6+/fZbs2po2bJlpV69erJ27Vr3bZcuXZIffvhBWrVq5XUfDS/fffddUCoMAABCm1+njbS/y1dffSWxsbGmQ+64ceOke/fuJrBokDl9+rQkJyebYOOpXLlycvToUZ/l6n304nBWAdXVRZ0VRpG/6TL2znWgr5lz/2C89sGsF4Cc/5zx+Q9tKdl8//gVXu644w6vpcanTJkiixYtkjlz5sjkyZPNyCPPN7JnZcLDw32Wq31kxo8fn277mjVrpGjRov5UEXkkISHBXG/YsEH2798flDI9W/XyU70A5PznjM9/aEpKSsrWfgGNYQ4LC5OqVavKvn37zO8lSpSQUqVKyaFDh7z209+vueYan+WMGTNGRowY4dXyEh0dLXFxcVKyZMlAqohcsnXrVpk6dao5RaitcIHQsKv/uDp16iSRkZH5pl4Acv5zxuc/tCX+98xLUMPLxYsXvVpQ9DTRL7/84tXHpW3btrJ69WoZOXKke5uOPNLtvmgrjl7S0gNXoAcv5A5nLh+9DtZrFozXPyfqBSDnP2d8/kNTZDbfP36Fl5tvvlkGDRpkhkvriKPnn3/enCoaOnSoe5+nnnpKbrrpJnOb9od59913TTPi0qVL/f8rAAAAAhltpEFEO+z2799fxo4dK40aNTItL56nhFq0aGFaWr788kvp27ev7N27V9avXy+1atXy56EAAAACb3mpVq2azJgxI1stNHoBAAAINhZmBAAAViG8AAAAqxBeAACAVQgvAADAKgFNUgd40gkFdZmHU6dOBfTEODM062RFzvwRl0vrw0SHAFCwEF4QNDq7ps6mnHaG5cuhoSU+Pj5o9QIAFByEFwSNrmuikxjqquOBtrxoWRo6Am152bNnjynLcyJFAIDdCC8IGj3No8s86PpWwVhVVE/3BDrVuNYnu2tlAADsQIddAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFiF8AIAAKxCeAEAAFYhvAAAAKsQXgAAgFUILwAAwCqEFwAAYBXCCwAAsArhBQAAWIXwAgAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALBKRF5XAABgt3PnzpnrLVu2BFzW2bNn5fvvv5eKFStKsWLFAiprz549AdcHBSy87NixQ15//XVp0qSJDBo0yOu2EydOyJw5c2T//v1Sq1YtGTBggBQtWjQY9QUA5DN79+41108++WTQytRjSLAEGoJQQMLL+fPnpU+fPvLnn3/KoUOHvMLL4cOHJTY2VipXrixxcXEyc+ZMefPNN2Xjxo1SvHjxYNYdAJAP6P96VaNGDSlSpEhAZe3cuVNGjhwp06ZNk9q1awcluFSrVi3gclAAwsuIESPkxhtvlAMHDqS7beLEiVK4cGH5/PPPJSoqSv7+97+b1pdXX31VnnrqqWDUGQCQj5QpU0b69u0blLJSU1PdQah+/fpBKRMFj98ddhcvXixffPGFScUZWbZsmdxxxx0muKhSpUpJ9+7dzXYAAIBcbXnRlpahQ4fKypUrM2waTE5OloSEhHRNdNWrV5clS5b4LFfvpxdHYmKiuU5JSTEX5H/OtyW9DvQ1c+4fjNc+mPUCkPP4zIa2lGz+n47w5w115513mlNG2kk3sx7nJUqU8NpesmRJSUpK8ln2pEmTZPz48em2r1mzho6+ltDQqjZs2GA6agfD2rVr82W9AOQc5zMbHx8vBw8e5KkOMUmZZIXLCi+rV682w9caNWokDz/8sNm2detWCQ8PN78///zzJrSEhYXJyZMn040+0gDjy5gxY0wo8mx5iY6ONp3AMrsf8g99L0ydOlVat24t9erVCzh5a3Dp1KmTREZG5pt6Ach5mzZtMtc68EOPNwgtif898xK08HL99den6+eifV/04FKnTh1zrRftnLt9+3av/fR3vb8v2j/G6SPjySkT+V9ERIT7OlivWTBe/5yoF4Ccw2c2tEVm8/90tsOL9mNxWlwcq1atMiOLPLfrEOrZs2fL2LFjTQ90bapfvny5TJkyxZ/6AwAA5M7yAE888YRUqVJFmjZtKnfffbe0bNlS2rZtK/fff3+wHwoAAISggJYHeOihh0yfl7QTAn355ZdmnhcdnfTAAw+Y8KJ9YQAAAPI0vHTt2jXD7RpoOnfuHEjRAAAAGWJVaQAAYBXCCwAAsArhBQAAWIXwAgAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAAAQOjPsAo5z586Z6y1btgT8pJw9e1a+//57qVixolluIhB79uwJuD4AgPyF8IKg2Lt3r7l+8skng/aMzpkzJ2hlBRqCAAD5B+EFQREXF2eua9SoIUWKFAmorJ07d8rIkSNl2rRpUrt27aAEl2rVqgVcDgAgfyC8ICjKlCkjffv2DUpZqamp7iBUv379oJQJACg46LALAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFiF8AIAAKxCeAEAAFYhvAAAAKsQXgAAgFUILwAAwCqEFwAAYBXCCwAAsArhBQAAWIXwAgAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACrEF4AAIBVCC8AACA0wovL5cpyn0uXLl1u8QAAAIGHlwMHDsiQIUOkUqVKUrhwYbn++utl1qxZ6fZ76623pEqVKhIZGSk1a9aUhQsX+vMwAAAAwQkv7733nrRp00a2bNkip0+fljFjxsjgwYPls88+c+/z6aefyrBhw2Tq1KlmnxEjRkjfvn3lm2++8eehAAAAAg8vTz/9tNx9991StmxZueKKK6R///5yzTXXyPfff+/e56WXXpJbb71VevfuLUWLFpWhQ4dKs2bN5NVXX/XnoQAAAILT50X7upw/f16OHz8uM2bMkJMnT0qPHj3ct2kLS9u2bb3u0759e/n666/9fSgAAIB0IsRP27dvl5iYGLlw4YJERUXJzJkzpVGjRuY2PU2UlJQk5cuX97pPhQoV5PDhwz7LTE5ONhdHYmKiuU5JSTEXhJbU1FT3Na8/EFr4/Ie2lGwe8/0OL9pJV1tezp07JwsWLJD777/fnB7q2bOnz1FG+ntYWJjPMidNmiTjx49Pt33NmjWmbISWhIQEcx0fHy8HDx7M6+oAyEV8/kNbUlJSzoQXR5EiRWTAgAGydOlSefvtt014KVGihBQrVkyOHDnita/+XrFiRZ9lacdf7djr2fISHR0tcXFxUrJkycutIiy1adMmcx0bG+tu1QMQGvj8h7bE/555ybHw4tAWmPDwcPOztq7ceOONsn79ehk+fLh7n3Xr1pntvujpJ72kpUOt9YLQEhER4b7m9QdCC5//0BaZzWO+Xx12u3fvLp9//rkcPXrUzPmip3v01I6eOnI8/vjjZui09oX5888/zT6bN2/2alkBAAC4XH61vDzzzDMyceJEM6JI01G9evVk1apV0qlTJ/c+HTt2lLlz55r9NMjUqlXLzP1C8z8AAMj18NK8eXNZtmxZlvv16dPHXAAAAIKNhRkBAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFiF8AIAAKxCeAEAAFYhvAAAAKsQXgAAgFUILwAAwCqEFwAAYBXCCwAAsArhBQAAWIXwAgAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFiF8AIAAKxCeAEAAFYhvAAAAKsQXgAAgFUILwAAwCqEFwAAYBXCCwAAsArhBQAAWIXwAgAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABglQh/77Bt2zb5z3/+IykpKdK8eXOJjY1Nt8+5c+dkyZIlsn//fqlVq5b06NFDIiL8figAAIDAWl7uuusu6dOnj/z888+yY8cO6dKli9x7771e+5w4cUJuuOEGmTx5shw6dEjGjBkj7dq1k/Pnz/vzUAAAABnyqzlk8ODBJog4Bg0aJE2bNpV+/fpJx44dzbZ//vOfcvbsWdm8ebMUL15cxo4dK7Vr15bp06fLY4895s/DAQAABNby4hlcVJMmTeSKK66Q3377zb1t0aJF0rt3bxNcVIUKFaR79+6ycOFCfx4KAAAgQwF1RFm8eLFcuHDB3e9Ff9Ygo/1cPOnvK1as8FlOcnKyuTgSExPNtfar0QtCS2pqqvua1x8oGJKSkmTnzp1Z7rdr1y5zPNiyZYs5pmRGW/WLFi0axFoir2X3f/5lh5fdu3fLkCFDZNiwYdKgQQOzTU8XqVKlSnntW7p0aTlz5ozPsiZNmiTjx49Pt33NmjW8MUNQQkKCuY6Pj5eDBw/mdXUABMHevXtl5MiR2d6/f//+We4zbdo0qVGjRoA1Q34LuTkWXvbt22f6uLRv315eeeUV9/ZixYqZ61OnTnntf/LkSfdppIxop94RI0Z4tbxER0dLXFyclCxZ8nKqCItt2rTJXGuLXqNGjfK6OgCCdFBq3bp1lvvpF93Vq1dL586dMz1uKFpeCh7nzEvQw4sOf9a+LzpMet68eRIeHu6+Tfu/VKtWzbTKeNLf9U3mS1RUlLmkFRkZaS4ILc6wer3m9QcKBm2R15Go2TltcPr0abnpppv4/IegyGwe8/3qsHvgwAETXJo1aybz58/PcO6Wnj17yscff+w+TXTkyBH59NNPzXYAAIBA+dXy0qlTJzl27JjUqVNHJk6c6N6ugcYZifTUU0/JypUrpVWrVua0knbUrVevngwdOjTgygIAAPgVXrQDlTMSxJcrr7xSvvvuOzMSSVtqtDMuM+zCOeetkxtmRUck6GiD7du3y6VLlzLdV4M0ow0AILSEuVwul+TDDjt6flQ7/tJht+D48ccfzaSGwfTDDz9ITExMUMsEkHe0z4u22N9yyy30eQlBidk8/rPgEHKNtpJo2MiK9pf67LPPpGvXrlmONtAyAQChhfCCXKOnd7LTSqLfvHSNrJYtW/LNCwAQ2GgjAACAvEZ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACrEF4AAIBV8uXCjC6Xy700NkKPLsyYlJRkXv/IyMi8rg6AXMTnP7Ql/ve47+QAq8LL6dOnzXV0dHReVwUAAORBDihVqpTP28NcWcWbPHDp0iX5448/pESJEhIWFpbX1UEeJG8NrgkJCVKyZEmefyCE8PkPbS6XywSXq6++WgoVKmRXy4tWuHLlynldDeQxDS6EFyA08fkPXaUyaXFx0GEXAABYhfACAACsQnhBvhMVFSXPPvusuQYQWvj8IzvyZYddAAAAX2h5AQAAViG8AAAAqxBekOv27Nlj5nC5XHv37pUDBw4EtU4AcgeffwQD4QW5PgFV27Zt5eTJk17bk5OTZePGjfLrr79mK7zExcWZacQB2P/510lJN23a5J5dPTN8/qEIL8hVL7zwgrRs2VIaNGhgfv/rr79k9OjRUr16denUqZO8+OKLWZahwUUnMZo1a1Yu1BhATn3+16xZIzExMdKsWTMZMGCAVKxY0fw/yAyffyjCC3LNhQsXZMaMGTJw4ED3toMHD0rZsmXl559/dv9Dy4577rlHXnvttRyqKYDc+PxrK8rs2bPdLS9ffPGF+Vzrtszw+QdDpZFr1q9fb741nTp1SooWLZru9tjYWPMN7PXXX8+yrN27d8t1110nu3btklq1auVQjQHk1uffcdNNN5nPdGYBhs8/aHlBrvn222+lRo0amf7jyi7956blxMfHB6VuAPL+83/27FnZvn271KxZM9Oy+PyD8IJcc+TIEXOKKFi0rMOHDwetPAB5+/kfNmyYREREyODBg7Msj89/aMuXq0qjYCpSpIicP38+aOVpWVomgPwvq8//mDFjZMmSJbJ27VopV65cluXx+Q9thBfkGh1RFMj8Lp6SkpLMSCVthgZg9+d/3Lhx8sYbb8jq1avlhhtuyLIsPv/gtBFyTbt27eTo0aNmkip/6DesDRs2yIkTJ7zOn4eHh0vr1q1zoKYAcuvz/8wzz8irr74qq1atMsOo0+Lzj4wQXpCr37x0gqoFCxa4t126dMkEE73oBFV//vmn+fmnn35y76Pf1nQEgmfnXC2jV69eUrx4cV5BwNLP/5QpU2TixImm5cXzf8HWrVvd+/D5R0YYKo1cpZNSDRkyxAxxjoyMlHPnzpnJ6TL6R/f++++bnz/66CN58sknzSiEwoULm6GWerpo3bp10qhRI15BwNLP/yOPPOL1RcVz2oSpU6ean/n8IyOEF+S6hx9+WG699VYz50N26D8xHRp52223md/nzZsnO3bskPHjx+dwTQEEG59/BAPhBQAAWIU+LwAAwCqEFwAAYBXCCwAAsArhBQAAWIXwAgAArEJ4AQAAVmFtIwBBceHCBVm8eLH5uXTp0tKlS5c8eWZ1cb/k5GQpVKiQ9O7dO0/qACBnMc8LgKA4duyYlC9fXjp37iwNGjSQF154wX2bLqK5efNms6Ce3hYdHe1132XLlpnZltOqU6eONG7c2Cwb8e9//9uUfeWVV3rto4FJZ1x2ZlsePHiwmcFVp5lPTU3l1QUKIMILgKCGF53uXQOH47XXXpOnnnpK6tevL+XKlTPr1miAeeutt6RChQpmn4oVK8pVV10ldevW9Sqze/fucvfdd0tKSopZbVhv//DDD923v/7662ZdnE2bNknVqlXd23WRv27duhFegAKK00YAcoy2gDz66KNmSYc+ffq4t3/22Wdy9uxZr33vvPNOs4ZVRnQdHF3rqlmzZrJo0SLp2bOnWetq9OjRJgR5BhcABR/hBUCO2bZtm7hcLunQoYPX9q5du/pdlrbWPPfcc/LQQw9JixYtTIuMrnel1wBCC+EFQI5p0qSJREVFydChQ00rif4eHh6e4b7aJ2b+/Ple21q3bi2VK1d2/65lfPrpp6acIkWKyL/+9S9ePSAEEV4A5Bg9nfPJJ5+YPi/aZ6Vw4cJy4403ypAhQ6RXr15e+2pfmEuXLnltq1atmld40eCjqxJra8vzzz9vRjUBCD102AWQox12HUeOHJFvv/3W9H/RTrfTp0+XBx980N1hd/jw4T77vDhOnTolDRs2NKFl//79smXLFq9w46DDLlCwMUkdgFyhI4t0BNDcuXOlY8eOXqOGsktPP2lw+eabbyQmJkYGDhyYI3UFkL8RXgDkmKNHj6YbVaR0Ejl/T/lofxgdaaThR08/vfPOOxIfH29acACEFvq8AMgxu3fvNkOgtcVF53nRWW9XrFhhTh+tXbs2yw67Opmd9pFJSEgwo4wmT55synH600ybNk0ee+wxM3ld9erVeSWBEEF4ARBUq1evlkOHDpnlAVq1amUmkNNQouHk4sWLZtsbb7zh1VelR48ecvLkSVm6dKlXWbGxsSa8LF++XO677z4zZ4ynBx54wMwlo/PGPPLII+7lATQcASi46LALIChOnz5twoTTYuK5PEBu0uUBEhMTzcgkPcUEoOAhvAAAAKvQYRcAAFiF8AIAAKxCeAEAAFYhvAAAAKsQXgAAgFUILwAAwCqEFwAAYBXCCwAAsArhBQAAWIXwAgAArEJ4AQAAYpP/B8PDKcxI1nAnAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in ['AGE', 'BMI', 'Y']:\n",
    "    df.boxplot(column=col, by=['SEX'])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3: What is the the distribution of Age, Sex, BMI and Y variables?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    442.000000\n",
       "mean      48.518100\n",
       "std       13.109028\n",
       "min       19.000000\n",
       "25%       38.250000\n",
       "50%       50.000000\n",
       "75%       59.000000\n",
       "max       79.000000\n",
       "Name: AGE, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AGE'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([19., 28., 47., 53., 55., 85., 69., 47., 33.,  6.]),\n",
       " array([19., 25., 31., 37., 43., 49., 55., 61., 67., 73., 79.]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['AGE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1df70a816d0>,\n",
       "  <matplotlib.lines.Line2D at 0x1df70a81810>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1df70a81950>,\n",
       "  <matplotlib.lines.Line2D at 0x1df70a81a90>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1df70a81590>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1df70a81bd0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1df70a81d10>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df['AGE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Age is slightly left-skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    442.000000\n",
       "mean      26.375792\n",
       "std        4.418122\n",
       "min       18.000000\n",
       "25%       23.200000\n",
       "50%       25.700000\n",
       "75%       29.275000\n",
       "max       42.200000\n",
       "Name: BMI, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BMI'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([32., 66., 98., 90., 61., 53., 23., 12.,  5.,  2.]),\n",
       " array([18.  , 20.42, 22.84, 25.26, 27.68, 30.1 , 32.52, 34.94, 37.36,\n",
       "        39.78, 42.2 ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGdCAYAAAA44ojeAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlcelbwAAAAlwSFlzAAAPYQAAD2EBqD+naQAAHbtJREFUeJzt3Qm0l2PiB/CnupXRhtTJEiXZR1rIWjEGSYTCRDNjGIwZVAwyDv8Zy7WMsZ0GwyTrOJQyJMM4WdKQosjYDlosbWhTbtvvf573f+7vf2+3Uvxu97n3fj7nvKfe5/fcX4/3vn73e5/trZPL5XIBACAhdau6AQAAaxJQAIDkCCgAQHIEFAAgOQIKAJAcAQUASI6AAgAkR0ABAJJTFKqp1atXh88//zw0adIk1KlTp6qbAwBsgLg/7OLFi8O2224b6tatW/MCSgwnrVu3rupmAADfw6xZs8L2229f8wJK7Dkp/Q9s2rRpVTcHANgAixYtyjoYSn+O17iAUjqsE8OJgAIA1ct3Tc8wSRYASI6AAgAkZ6OHeFauXBn++c9/hjfeeCP069cvdOjQoUKdpUuXhhEjRoQZM2aE9u3bhxNPPDHUr19/o+sAALXTRvWgjB07NrRr1y7cf//94Zprrglvv/12hTpfffVV6NKlS7j55pvDggULwpVXXhm6desWli1btlF1AIDaa6MCynbbbRcmTJgQRo8evc46MbgsX748jB8/Ptx0003Zn++//34YOnToRtUBAGqvjQooe++9dxZS1ufxxx8PJ510UmjUqFF23qJFi9C7d++sfGPqAAC1V0EnyZaUlITp06eHnXfeuVx5nGMSe0g2tM663juunS57AAA1U0EDSpz4Gq25L0mzZs3CN998s8F11qa4uDirU3rYRRYAaq6CBpQ4ZBM3Xlm4cGG58jgRtnHjxhtcZ22GDBmSfU3pEXeQBQBqpoLuJNugQYPQtm3b8MEHH5Qrj+e77bbbBtdZm4YNG2YHAFDzFXyjtrifyaOPPpo9qTCaPXt2ePLJJ0Pfvn03qg4AUHvVycXnHm+gjz76KNx77735pcLHHXdc2GuvvbI9Tfr06ZMfqunevXtYtWpV9uczzzyTPa3w2WefzfeAbEid7xInyca5KHG4x7N4AKB62NCf3xsVUOLqmwcffLBCeceOHUOvXr3y599++2144oknwsyZM7PVOXEJcb169cp9zYbUKcR/IABQwwNKSgQUAKi5P78LOkkWNkabS8dUyws2/br/7y0EoHJ4mjEAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAklNU1Q2A6qbNpWNCdTP9ul5V3QSAjaIHBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcooq401ffPHFMHLkyDB37tyw7bbbhv79+4cuXbqUqzN9+vTw17/+NcyYMSO0b98+nH/++aFly5aV0RwAoLb3oAwbNiwcfvjhoVmzZqFPnz5h9erVoWvXruGpp57K1/nkk09C586dw8cffxyOOuqoMHHixCzAzJ8/v9DNAQCqoTq5XC5XyDfs0aNHaN26dXjggQfyZTGwbL311uGRRx7Jzk8//fTw1ltvhddffz3UrVs3lJSUhJ133jkMGDAgXHvttRv07yxatCgLQQsXLgxNmzYt5H8Cm0ibS8e41pvI9Ot6udZAEjb053fBe1B22WWX8NFHH2U9J9G3334bZs6cGXbfffd8nbFjx4bjjz8+CydRw4YNQ+/evbNyAICCB5S//OUvYa+99sp6RHr27JnNLznhhBPCZZddlr2+bNmyMGfOnKyXpax4Hod+1iX2ssTUVfYAAGqmggeUF154IZsge/LJJ2dDNrGnJM5LmTRpUj5oRJtvvnm5r2vcuHHW27IuxcXFWZdQ6bFmwAEAao6Cr+I555xzwplnnpkFiiiu4InjTBdccEE2GTYGkXr16oWvv/663Nd9+eWXYcstt1zn+w4ZMiQMHjw4fx57UIQUAKiZChpQVq1alQ3fxGGdsuL5c88993//YFFR2GOPPcLUqVPL1ZkyZUrYe++91/necZ5KPACAmq+gQzyxZ6RTp07hscceCytWrMjKli5dGkaNGhX23XfffL049PPoo49mk2ejt99+O/zrX//KygEACj4H5e677872N4mTZI888sjQrl27rGfl1ltvzdcZOHBg6NatW+jQoUPo3r17OOCAA7Jwcuqpp/qOAACFn4MSh2nefffdMG3atGy4Z7vttgt77rlnqFOnTr5O/fr1s4m077zzTtaLEsPMmsNCAEDtVSlb3cd5Jvvss8931ovBJR4AAGV5WCAAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACSnqKobQGG0uXSMSwlAjaEHBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAUDsCytKlS8O1114bDj744HDIIYeEO+64I+RyuXJ1xo8fH4499tjQoUOH0Ldv3zB16tTKaAoAUA0VFfoNS0pKwmGHHRZWrFgRrrnmmtCiRYtw//33hxEjRoR+/fpldSZPnhwOP/zwMHjw4DBkyJAwfPjw0K1bt/Dmm2+GnXbaqdBNglqvzaVjqt01mH5dr6puAlCTAsrNN98c3nvvvfDhhx9m4STq3LlzWLlyZb5ODC4HHXRQ1ssS7b///uHFF18MN910Uxg6dGihmwQA1PYhnn/84x+hT58++XBSqqjo/7PQuHHjwlFHHZU/r1OnTjj66KOzcgCAggeU999/P+y6667Z8E3Hjh3DkUceGe6+++6wevXq7PUlS5aEBQsWhG222abc17Vq1Sp8+umn6x06WrRoUbkDAKiZChpQ4kTY5cuXh6uvvjpsscUW4Z577gmnnXZauPjii8OVV16Z1Vm1alX2Z4MGDcp9bcOGDcsNA62puLg4NGvWLH+0bt26kE0HAGpqQIlDNVtvvXU2p+SKK67I5p4MGDAgDBo0KPztb3/L6jRp0iQLJ/Pnzy/3tfG8efPm63zvOJl24cKF+WPWrFmFbDoAUJOHePbdd9+w1VZblSuLweObb77Jeljq1q0bOnXqFF599dVydSZMmBC6dOmyzveNPSxNmzYtdwAANVPBA8pvf/vb8Mwzz4QpU6bke0biHJSePXtmPSzR2WefHUaOHBkmTpyYnY8dOzZbxXPOOecUujkAQDVU8GXGcTVOnIPSo0ePrJdj3rx5WTi5884783V++ctfZsuQu3fvntWJvSs33HBDNqEWAKBObs0tXgskrrr54osvstU5m2222VrrxGAyd+7cbEXPuuqsS1zFEyfLxvkohntCtdyIC9bHRm1QM23oz++C96CUnTPSpk2b9dZp1KhRaNu2bWU1AQCopjwsEABIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACSnqKobALA2bS4dU+0uzPTrelV1E6DG0IMCACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQCofQFl5cqVYfXq1ZX9zwAANUilBpSbb7451K9fP5x00kkVXrv33nvDTjvtFIqKisJuu+0WRo8eXZlNAQCqkUoLKJMnTw633HJL6Nq1a4XXnn766XDWWWeFq6++Onz99dfh3HPPDf369Quvv/56ZTUHAKjtAWXJkiXhZz/7WbjrrrvC1ltvXeH1m266KRx77LGhf//+oUmTJuH8888PnTp1CrfeemtlNAcAqGYqJaD85je/CT179gxHHXVUhddyuVx49dVXQ/fu3cuVH3bYYWHChAmV0RwAoJopKvQbDh8+PLz55pth0qRJa3198eLFYenSpaFFixblylu2bBnmzJmzzvctKSnJjlKLFi0qYKsBgBrbg/Lxxx+HCy+8MNx///3Z5Ne4gif2mMQj/n194kqfOnXqrPP14uLi0KxZs/zRunXrQjYdAKipPSjTpk0LCxcuDPvtt1++rHSJ8WabbRY++uijsMMOO4RGjRqFuXPnlvvaefPmhVatWq3zvYcMGRIGDx5crgdFSAGAmqmgPShx4mvsKSl7HH300eGEE07I/r7jjjtmvSQHHnhgGDduXLmvff7557PydWnYsGFo2rRpuQMAqJmqZCfZiy66KIwZMyYMGzYs6zm58cYbw9SpU8OgQYOqojkAQG0LKPXq1cuOso444ohw3333ZcGkTZs24aGHHso2auvYsWNlNwcAqI2reNb0xBNPrLU87oESDwCANXlYIACQHAEFAEiOgAIAJEdAAQCSI6AAAMkRUACA5AgoAEByBBQAIDkCCgCQHAEFAEiOgAIAJEdAAQCSI6AAAMkRUACA5AgoAEByBBQAIDkCCgCQHAEFAEiOgAIAJEdAAQCSI6AAAMkRUACA5BRVdQNS1ObSMVXdBACo1fSgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAklNU1Q0AqCnaXDomVDfTr+tV1U2AtdKDAgAkR0ABAJIjoAAAyRFQAIDaE1DmzZsXvvjii/XWWbJkSfjwww/DsmXLKqsZAEA1VPCA8vDDD4c999wz7LHHHqFjx46hdevW4fHHH69Q75JLLglbb7116NGjR2jevHm48cYbC90UAKCaKnhAeeONN8KIESOyHpTZs2eHCy64IJxyyinhvffey9cZNmxYGDp0aHjllVfCZ599FkaPHh2GDBkSxo4dW+jmAADVUMEDyp///Oew++67588HDRqU/Tl+/Ph82V133RX69u0bOnfunJ0fccQRWU9KLAcAqPRJsh988EFYsWJFNtQTrV69OkyZMiV07dq1XL0DDzwwTJ482XcEAKjcnWSXL18ezjzzzKyn5PDDD8/KFi9enJXHeSdlxfP58+ev871KSkqyo9SiRYsqseUAQI3sQVm5cmXo379/+PTTT7NJsvXq1cvKi4r+LxPFkFJWDB/169df5/sVFxeHZs2a5Y/SHhkAoOaplICyatWqcNppp4WJEyeGcePGhR122CH/WqNGjcKWW25ZYQlyPF9f6IiTaBcuXJg/Zs2aVRlNBwBqYkApDScTJkwIL7zwQthpp50q1Dn00EPD008/nT/P5XLZeSxfl4YNG4amTZuWOwCAmqngc1BOP/308NRTT4WHHnooLF26NEybNi0rb9myZXZEl19+eTjggAPCxRdfHHr37h3uu+++MGfOnHDRRRcVujkAQDVU8IDyzjvvhB133DFcdtll5crPPffc7IjiBm5x6OeGG24IAwcODO3btw8vv/xyaNOmTaGbAwBUQwUPKBu6VDj2oIwaNarQ/zwAUAN4WCAAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQIKABAcgQUACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgOQUVXUDAKg6bS4dU+0u//TrelV1E9gE9KAAAMkRUACA5AgoAEByBBQAIDkCCgCQHAEFAEiOgAIAJEdAAQCSI6AAAMkRUACA5AgoAEByBBQAIDkCCgCQHAEFAEiOgAIAJEdAAQCSI6AAAMkRUACA5AgoAEByBBQAIDkCCgCQHAEFAEiOgAIAJEdAAQCSU1TVDQCAjdHm0jHV7oJNv65XVTeh2tGDAgAkR0ABAJIjoAAAyRFQAIDkCCgAQHIEFAAgOQIKAJAcAQUASI6AAgAkR0ABAJJjq3sAqGS25994elAAgORUaQ/KW2+9FWbMmBHat28fdtttt6psCgCQkCrpQVm+fHno06dPOPTQQ8Mtt9wS9ttvv3DGGWeEXC5XFc0BABJTJT0oMZS88sorYerUqWH77bcP77zzTujSpUvo0aNHGDBgQFU0CQCo7T0oDzzwQDj55JOzcBLtueeeoWfPnlk5AMAm70FZuXJlePfdd8N5551XrnzvvfcOd9555zq/rqSkJDtKLVy4MPtz0aJFBW/j6pKlBX9PAKhOFlXCz9ey7/td0zo2eUBZsmRJWLVqVdhyyy3LlTdv3jwsWLBgnV9XXFwc/vjHP1Yob926daW0EwBqs2a3VO77L168ODRr1iydgNKwYcPsz6VLl1YILpttttk6v27IkCFh8ODB+fPVq1eHr776Kgs2derUCTVdTJwxjM2aNSs0bdq0qptTK7jmrnlt4D53zTe12HMSw8m222673nqbPKD86Ec/Cq1atQozZ84sVx7Pd9ppp/UGm9JwU2qLLbYItU0MJwKKa17Tuc9d89qgNt/nzdbTc1Klk2TjhNjHH3886wWJvv322/Dkk09m5QAAVRJQrrjiivDpp5+GE088Mdx9992hV69eoaioqNwQDgBQe1VJQGnTpk144403st1jX3jhhXDIIYeE119/PZtPwtrF4a0rr7yywjAXlcc13/Rcc9e8NnCfb5g6Odu3AgCJ8bBAACA5AgoAkBwBBQBITpU8LJB1++CDD8L7778fttlmm9CpU6dQt27FDBm3+Y8PW4wb1B188MGhSZMmLukPEFeUvfXWW9m+Oh07dsz26inr3//+d5g/f36Fid7777+/6/49ffPNN2Hy5MnZn/FZXDvssEOFOnHH6ddeey3MnTs3exTG+vZJYsOeIv/mm2+GefPmhfbt24ddd9213OvxESTxAa5l1a9fP1ttyQ8Tt9QYOXJk2HzzzbNVq2v68MMPs4fmxj3C9ttvv7V+7tdGAkoi4ofDr3/96+zDY5dddgnTpk3LbuYnnngi7Lzzzvl6zz//fOjbt29WJ36Af/LJJ2HUqFGhW7duVdr+6iiGjnjN44qyvfbaK9ssMO5O/NBDD2VP1i51+eWXZzsf77HHHvmyuPJMQPl+7rjjjnD99ddn93X8IB4/fnz4+c9/npWX7gr95ZdfhiOPPDLMmTMnW+03YcKEcNFFF631cRd8t7jP1IUXXhhatmyZBfGXX345++VmxIgR+UAeP2tuuOGGcMQRR+S/Lu7uLaD8cFdddVW49tprs93A1wwo8b6Oz6E78MADs8/9tm3bhrFjx9baDdzKiat4qHqvvfZa7pVXXsmfL1++PNejR4/coYcemi9bunRprlWrVrkLL7wwX3b22WfnWrdundVn40yfPj03atSo3OrVq8tdz5YtW5Yr69q1a+6qq65yeQtkxIgRuSVLluTPJ06cGJ8Ylnv22WfzZWeccUZuzz33zC1evDg7f+6557I6L7/8su/D9zBmzJjc3Llz8+eff/55rnHjxrnbbrstX1ZcXJzr3Lmz61tgL730Uq5t27a53/3ud7l27dqVe23s2LG5unXr5l599dXs/KuvvsrqDhw40Pchl8vpR0pE7NaLCbps12rv3r3DlClTyvWezJ49OwwcODBfFn8ris/neemllzZ5m6u7HXfcMfTp06fcs5xOOOGEbEjhiy++KFc3XuPYUxWHHOLOx3x/8TfyRo0a5c9jb2D8HsRnc5Q+8fyRRx4JZ511VmjcuHFWdvjhh2fDPLF3i4139NFHhxYtWuTP41BC3Gq89JqXWrZsWRgzZkw2rBn/P+CHiT2yAwYMCMOHD1/r1u4PPvhgOOigg0LXrl2z8/gQ3dNPPz0rxxBP0uKHRBx6KPX2229nN/D222+fL4tjybEbNr72k5/8pIpaWrOuedwwMM4BKuuZZ54Jn332Wfjvf/+b/QB94IEHQvfu3ausndVdvJZxmCHOp7rvvvuyYHjsscdmr8Vhyzg3pey9H/34xz/O7nO+nxhGYviI1zYO58SAfvbZZ1cI4rfddlv2fYm/HMXNIeODWvl+fvWrX4X+/ftnQ/DPPvtshdfj/RwDypr3eRx+nj17dhYkazNzUBJ11113ZTf0uHHj8mXxQ2OrrbaqUDeWLViwYBO3sOaJuxrfcsstYejQoeV6VeK8h5/+9KfZfIk47yf+Zn/SSSdlE5o35IFXVBR7qEaPHp3Nufr444/DcccdF+rVq5e/z6M17/UYHMv2KLLxASVe83h94wTlU089Nd9DFcV5V+ecc07+IayxxzAGx3322cdz0r6H22+/PQvijz322DrrrO0zvXRH9QULFggo3+fCU7keffTRcN5554W///3v2WTMstsjL1mypEL9WBZ7Ufj+4qMW4g/JQYMGZRNny4qTNUvFH6L/8z//E4YNG5YN95SdUMiG69KlSzaME8WVI3HCcXz0euwOL32cw5r3uvv8h4nXt/Sax9/OO3funE3Ev+aaa7KyNSd9H3/88dlKQg9y3XixB+T3v/99uOyyy7LVO1HsfY33cPwexM/17bbbbq2f6aXnm/lMtw9KauKs+vghHWd1/+IXvyj3Wrt27bLVDXFFSamYshctWmQJ5g8wadKkLGjEYBJXl3yX0tn18bd/frgOHTpk80vikE8UVzHEHqy4qqqsGTNmuM8LJA4dxHk9pdd8ffe6+3zjxSfIxPltMZTEXqt4xO0j4vBa/HvpHLf4mb62+7xBgwblhvJrLVOF0zFy5MhcgwYNcvfcc89aX48z7+vXr5978MEH82V33nlnrmHDhrn58+dvwpbWHJMmTcptscUW5VZGlbVgwYJs9VRZw4YNy9WpUyf33nvvbaJW1hwlJSW5L7/8ssI1bt68ee5Pf/pTvqx79+65Pn36VLj3hw8fvknbW1PE61fWypUrcx06dMj1799/nXVmzpyZa9SoUe7666/fZO2syf7whz9UWMVz++23Z6upvv7663xZXLl5zDHHVEEL02OIJxEvvvhiOOWUU7LhhLjCobQrNorzHeL8hzhxM05YO/fcc7PUHedDFBcXZ0MOngS98aZPn571nMTJgmWHHKL4fYgTkuNvOnHVSRyLj7/Zx+GIOD/okksuqbDRFd+tpKQk23/jmGOOyfY3iasc4gqHuMIk3tel4n4ccRJynGQYhyLiNY/DDXHeBBsv3sNxLknciDCuQovDyPHeLnvP9+vXL5uYHK9z/L7EuVhx75+y3xcK68wzzwz33HNPNsctrt6JewLF4ea47w+eZpyMuDFPXM2wNnHFSFx2XCp2EcbZ+LEbPK58iB/2bLy4KdLVV1+91tfipkqlO5fG8fr4QzROio3j+HH5d+myQL7fZM14r8cJr3EX5BhATj755HL3eOnmhXEeVhxiiD9c4wTONXf5ZcOsWLEiPPzww+E///lPKCoqynbvjUPJZSfJlq0Tr/O+++6b/dJkV9PCiKEwBo84EX/NOSdxk8L4eRSH3uJQc9nNOWuzOrEbpaobAQBQlo3aAIDkCCgAQHIEFAAgOQIKAJAcAQUASI6AAgAkR0ABAJIjoAAAyRFQAIDkCCgAQHIEFAAgOQIKABBS87+bGBmuUHqDMAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['BMI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1df71c3a990>,\n",
       "  <matplotlib.lines.Line2D at 0x1df71c3aad0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1df71c3ac10>,\n",
       "  <matplotlib.lines.Line2D at 0x1df71c3ad50>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1df71c3a850>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1df71c3ae90>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1df71c3afd0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df['BMI'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> BMI is Right-skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    442.000000\n",
       "mean     152.133484\n",
       "std       77.093005\n",
       "min       25.000000\n",
       "25%       87.000000\n",
       "50%      140.500000\n",
       "75%      211.500000\n",
       "max      346.000000\n",
       "Name: Y, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Y'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([38., 80., 68., 62., 50., 41., 38., 42., 17.,  6.]),\n",
       " array([ 25. ,  57.1,  89.2, 121.3, 153.4, 185.5, 217.6, 249.7, 281.8,\n",
       "        313.9, 346. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1df706d9310>,\n",
       "  <matplotlib.lines.Line2D at 0x1df706d9450>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1df706d9590>,\n",
       "  <matplotlib.lines.Line2D at 0x1df706d96d0>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1df706d91d0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1df706d9810>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1df706d9950>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df['Y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Y is positively skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    442.000000\n",
       "mean       1.468326\n",
       "std        0.499561\n",
       "min        1.000000\n",
       "25%        1.000000\n",
       "50%        1.000000\n",
       "75%        2.000000\n",
       "max        2.000000\n",
       "Name: SEX, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SEX'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SEX\n",
       "1    235\n",
       "2    207\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = df['SEX'].value_counts()\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='SEX'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Males are slightly higher in number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in ['AGE', 'SEX', 'BMI', 'Y']:\n",
    "    plt.hist(df[col])\n",
    "    plt.xlabel(col)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Age and BMI have normal distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Sex has Uniform distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Cann't tell about Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4: Test the correlation between different variables and disease progression (Y)\n",
    "\n",
    "> **Hint** Correlation matrix would give you the most useful information on which values are dependent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(df['AGE'], df['Y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> There's no correlation between the two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(df['SEX'], df['Y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> There's no correlation between the two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(df['BMI'], df['Y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(df['S5'], df['Y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ['BMI', 'BP', 'S5']:\n",
    "    plt.scatter(df[col], df['Y'])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> There's notable correlation between BMI, BP and S5 vs. Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_confidence_interval(data, confidence=0.95):\n",
    "    a = 1.0 * data\n",
    "    n = len(a)\n",
    "    m, se = np.mean(a), scipy.stats.sem(a)\n",
    "    h = se * scipy.stats.t.ppf((1+confidence)/2, n-1)\n",
    "    return m, h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for p in [0.85, 0.9, 0.95]:\n",
    "    m1, h1 = mean_confidence_interval(df[df['SEX'] == 1]['Y'], p)\n",
    "    m2, h2 = mean_confidence_interval(df[df['SEX'] == 2]['Y'], p)\n",
    "    print(f\"Male progression: {m1-h1:.2f}..{m1+h1:.2f}, Female progression: {m2-h2:.2f}..{m2+h2:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Confidence intervals are overlaping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tval, pval = ttest_ind(df[df['SEX'] == 1]['Y'], df[df['SEX'] == 2]['Y'], equal_var=False)\n",
    "print(f\"t-value: {tval:.2f}, p-value: {pval:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Since p-value is above the threshold (0.05), we failed to reject Null Hypothesis (H0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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