DS_Assignment_1.ipynb

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      "source": [
        "Basics statastics volume 1"
      ],
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import scipy\n",
        "from scipy import stats\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy.stats import norm"
      ],
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        "Q6) Calculate the Expected number of candies for a randomly selected child Below are the probabilities of count of candies for children (ignoring the nature of the child-Generalized view)\n",
        "\n",
        "Child A – probability of having 1 candy = 0.015 Child B – probability of having 4 candies = 0.20 Child C – probability of having 3 candies = 0.65 Child D – probability of having 5 candies = 0.005 Child E – probability of having 6 candies = 0.01 Child F – probability of having 2 candies = 0.120"
      ],
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      "source": [
        "#Ans: Expected number of candies for a randomly selected child :\n",
        "import numpy as np\n",
        "Probability=[0.015,0.20,0.65,0.005,0.01,0.120]\n",
        "Candies_count=[1,4,3,5,6,2]\n",
        "x=0\n",
        "for i in Probability:\n",
        "    for j in Candies_count:\n",
        "        if Probability.index(i)==Candies_count.index(j):\n",
        "            x=x+(i*j)\n",
        "print(x)"
      ],
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          "text": [
            "3.09\n"
          ]
        }
      ]
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      "source": [
        "Q7) Calculate Mean, Median, Mode, Variance, Standard Deviation, Range & comment about the values / draw inferences, for the given dataset\n",
        "\n",
        "For Points,Score,Weigh> Find Mean, Median, Mode, Variance, Standard Deviation, and Range and also Comment about the values/ Draw some inferences."
      ],
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      "source": [
        "df=pd.read_csv(\"/content/Q7.csv\")"
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        "df.head()"
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              "          Unnamed: 0  Points  Score  Weigh\n",
              "0          Mazda RX4    3.90  2.620  16.46\n",
              "1      Mazda RX4 Wag    3.90  2.875  17.02\n",
              "2         Datsun 710    3.85  2.320  18.61\n",
              "3     Hornet 4 Drive    3.08  3.215  19.44\n",
              "4  Hornet Sportabout    3.15  3.440  17.02"
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              "        count       mean       std     min       25%     50%    75%     max\n",
              "Points   32.0   3.596563  0.534679   2.760   3.08000   3.695   3.92   4.930\n",
              "Score    32.0   3.217250  0.978457   1.513   2.58125   3.325   3.61   5.424\n",
              "Weigh    32.0  17.848750  1.786943  14.500  16.89250  17.710  18.90  22.900"
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              "             Unnamed: 0  Points  Score  Weigh\n",
              "0           AMC Javelin    3.07   3.44  17.02\n",
              "1    Cadillac Fleetwood    3.92    NaN  18.90\n",
              "2            Camaro Z28     NaN    NaN    NaN\n",
              "3     Chrysler Imperial     NaN    NaN    NaN\n",
              "4            Datsun 710     NaN    NaN    NaN\n",
              "5      Dodge Challenger     NaN    NaN    NaN\n",
              "6            Duster 360     NaN    NaN    NaN\n",
              "7          Ferrari Dino     NaN    NaN    NaN\n",
              "8              Fiat 128     NaN    NaN    NaN\n",
              "9             Fiat X1-9     NaN    NaN    NaN\n",
              "10       Ford Pantera L     NaN    NaN    NaN\n",
              "11          Honda Civic     NaN    NaN    NaN\n",
              "12       Hornet 4 Drive     NaN    NaN    NaN\n",
              "13    Hornet Sportabout     NaN    NaN    NaN\n",
              "14  Lincoln Continental     NaN    NaN    NaN\n",
              "15         Lotus Europa     NaN    NaN    NaN\n",
              "16        Maserati Bora     NaN    NaN    NaN\n",
              "17            Mazda RX4     NaN    NaN    NaN\n",
              "18        Mazda RX4 Wag     NaN    NaN    NaN\n",
              "19             Merc 230     NaN    NaN    NaN\n",
              "20            Merc 240D     NaN    NaN    NaN\n",
              "21             Merc 280     NaN    NaN    NaN\n",
              "22            Merc 280C     NaN    NaN    NaN\n",
              "23           Merc 450SE     NaN    NaN    NaN\n",
              "24           Merc 450SL     NaN    NaN    NaN\n",
              "25          Merc 450SLC     NaN    NaN    NaN\n",
              "26     Pontiac Firebird     NaN    NaN    NaN\n",
              "27        Porsche 914-2     NaN    NaN    NaN\n",
              "28       Toyota Corolla     NaN    NaN    NaN\n",
              "29        Toyota Corona     NaN    NaN    NaN\n",
              "30              Valiant     NaN    NaN    NaN\n",
              "31           Volvo 142E     NaN    NaN    NaN"
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              "      <td>AMC Javelin</td>\n",
              "      <td>3.07</td>\n",
              "      <td>3.44</td>\n",
              "      <td>17.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cadillac Fleetwood</td>\n",
              "      <td>3.92</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Camaro Z28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Chrysler Imperial</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Datsun 710</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Dodge Challenger</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Duster 360</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Ferrari Dino</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Fiat 128</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Fiat X1-9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Ford Pantera L</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Honda Civic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Hornet 4 Drive</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Hornet Sportabout</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Lincoln Continental</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Lotus Europa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Maserati Bora</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Mazda RX4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Mazda RX4 Wag</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Merc 230</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Merc 240D</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Merc 280</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Merc 280C</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Merc 450SE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Merc 450SL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Merc 450SLC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Pontiac Firebird</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Porsche 914-2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Toyota Corolla</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Toyota Corona</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Valiant</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Volvo 142E</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1c214f9-cac5-4eae-8fcf-7a99ce2f537e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d1c214f9-cac5-4eae-8fcf-7a99ce2f537e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5OJz5FfbgfDp",
        "outputId": "275f0965-a8f4-4375-a9f9-813866950723"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Points     3.695\n",
              "Score      3.325\n",
              "Weigh     17.710\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.var()"
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          "base_uri": "https://localhost:8080/"
        },
        "id": "eVzcSsrqglB6",
        "outputId": "47a52c8f-77d3-44dd-cd4c-158cab082cb3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Points    0.285881\n",
              "Score     0.957379\n",
              "Weigh     3.193166\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Points.max()-df.Points.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wichIjOigpwX",
        "outputId": "84e3156e-6b79-4a4f-b13c-9f219b7b6cb7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.17"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Score.max()-df.Score.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G7D2wd_9gyjy",
        "outputId": "3e72deac-3cd4-4579-c324-efa296533ae5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.9110000000000005"
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          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Weigh.max()-df.Weigh.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KTkPq7zbhWtC",
        "outputId": "25be6b9b-04f4-45c4-f53d-67050d4bf3dc"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.399999999999999"
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          },
          "metadata": {},
          "execution_count": 12
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      ]
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    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(df.Points, vert=False)\n",
        "plt.boxplot(df.Score, vert=False)\n",
        "plt.boxplot(df.Weigh, vert=False)\n",
        "sns.jointplot(df.Points, kind='kde')\n",
        "sns.jointplot(df.Score, kind='kde')\n",
        "sns.jointplot(df.Weigh, kind='kde')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yiMosyWBhbMR",
        "outputId": "603f91bf-4854-4f6c-9eff-403bd2b24a41"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7fbf41443b90>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q8) Calculate Expected Value for the problem below a) The weights (X) of patients at a clinic (in pounds), are 108, 110, 123, 134, 135, 145, 167, 187, 199 Assume one of the patients is chosen at random. What is the Expected Value of the Weight of that patient?"
      ],
      "metadata": {
        "id": "BbFtkV3mh1Cm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Weights=(108, 110, 123, 134, 135, 145, 167, 187, 199)\n",
        "Expected_value=sum(Weights)/len(Weights)\n",
        "print('Expected Value of the Weight of that patient is:', Expected_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JrLpNjEBh6yh",
        "outputId": "524929e3-0758-4103-f125-e29c65a30690"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected Value of the Weight of that patient is: 145.33333333333334\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q9) Calculate Skewness, Kurtosis & draw inferences on the following data Cars speed and distance"
      ],
      "metadata": {
        "id": "9TAktCdViFbL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/Q9_a.csv\")"
      ],
      "metadata": {
        "id": "Ww9B_F6jiMMu"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VMPOFuEziS-B",
        "outputId": "7590e982-1604-4d3d-c2c1-34173ace10a3"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Index  speed  dist\n",
              "0      1      4     2\n",
              "1      2      4    10\n",
              "2      3      7     4\n",
              "3      4      7    22\n",
              "4      5      8    16"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Index</th>\n",
              "      <th>speed</th>\n",
              "      <th>dist</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>10</td>\n",
              "    </tr>\n",
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              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
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              "      <td>7</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>16</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5152382-117a-474c-93c1-81132ff691de')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f5152382-117a-474c-93c1-81132ff691de button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f5152382-117a-474c-93c1-81132ff691de');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## for calculating skewness\n",
        "df.skew()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PTR-diJDiZbQ",
        "outputId": "d2b63395-2663-4bce-ebc5-9925595c2331"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index    0.000000\n",
              "speed   -0.117510\n",
              "dist     0.806895\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### for calculating kurtosis\n",
        "df.kurt()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zstiUMDiigaq",
        "outputId": "b5289e26-b856-46be-91bb-87fc66fc8ea1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index   -1.200000\n",
              "speed   -0.508994\n",
              "dist     0.405053\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.jointplot(df.speed, kind='kde')\n",
        "sns.jointplot(df.dist, kind='kde')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 954
        },
        "id": "-euGGAfGiowM",
        "outputId": "8954e577-cecd-4a1f-de9a-223e103997ee"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  B) SP and Weight(WT)\n",
        "import pandas as pd\n",
        "data=pd.read_csv('/content/Q9_b.csv')"
      ],
      "metadata": {
        "id": "tgwQCEgRiyXP"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Skewness of Index is:', scipy.stats.skew(data.SP))\n",
        "print('Skewness of Speed is:', scipy.stats.skew(data.WT))\n",
        "print('Kurtosis of Index is:', scipy.stats.kurtosis(data.SP))\n",
        "print('Kurtosis of Speed is:', scipy.stats.kurtosis(data.WT))\n",
        "\n",
        "plt.hist(data.SP, color='red')\n",
        "plt.hist(data.WT, color='blue')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "nphb_0hdi7RX",
        "outputId": "163760c4-3224-4db2-a975-09dff17d1998"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Skewness of Index is: 1.5814536794423764\n",
            "Skewness of Speed is: -0.6033099322115126\n",
            "Kurtosis of Index is: 2.7235214865269244\n",
            "Kurtosis of Speed is: 0.8194658792266849\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q11)Suppose we want to estimate the average weight of an adult male in Mexico. We draw a random sample of 2,000 men from a population of 3,000,000 men and weight them. We find that the average person in our sample weights 200 pounds, and the standard deviation of the sample is 30 pounds. Calculate 94%,98%,96% confidence interval?"
      ],
      "metadata": {
        "id": "C65gjTvBi_9m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import stats\n",
        "print('Confidence Interval for 94% is:', stats.norm.interval(0.94,200,30/(2000**0.5)))\n",
        "print('Confidence Interval for 98% is:', stats.norm.interval(0.98,200,30/(2000**0.5)))\n",
        "print('Confidence Interval for 96% is:', stats.norm.interval(0.96,200,30/(2000**0.5)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uhfVYG8SjEgx",
        "outputId": "e8fb7fde-331e-46b2-9007-752904602675"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confidence Interval for 94% is: (198.738325292158, 201.261674707842)\n",
            "Confidence Interval for 98% is: (198.43943840429978, 201.56056159570022)\n",
            "Confidence Interval for 96% is: (198.62230334813333, 201.37769665186667)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q12) Below are the scores obtained by a student in tests 34,36,36,38,38,39,39,40,40,41,41,41,41,42,42,45,49,56\n",
        "\n",
        "1) Find mean, median, variance, standard deviation. 2) What can we say about the student marks? Ans:"
      ],
      "metadata": {
        "id": "DjexufJUjMms"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  1)\n",
        "data=np.array([34,36,36,38,38,39,39,40,40,41,41,41,41,42,42,45,49,56])\n",
        "mean=np.mean(data)\n",
        "print('Mean is:', mean)\n",
        "median=np.median(data)\n",
        "print('Median is:', median)\n",
        "var=np.var(data)\n",
        "print('Variance is:', var)\n",
        "std=np.std(data)\n",
        "print('Standard Deviation is:', std)\n",
        "\n",
        "plt.boxplot(data)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "Hy_N7UmEjNm2",
        "outputId": "b1914616-ab00-4370-d311-fcf409ee4601"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean is: 41.0\n",
            "Median is: 40.5\n",
            "Variance is: 24.11111111111111\n",
            "Standard Deviation is: 4.910306620885412\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q 20) Calculate probability from the given dataset for the below cases Calculate the probability of MPG of Cars for the below cases. MPG <- Cars$MPG a. P(MPG>38) b. P(MPG<40) c. P (20<MPG<50)"
      ],
      "metadata": {
        "id": "aBdVmviIjZU4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cars=pd.read_csv(\"/content/Cars.csv\")\n",
        "cars"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "8agw3i1djck8",
        "outputId": "a0edfd28-ff5b-4b17-fe43-9f2dc3f23af4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     HP        MPG  VOL          SP         WT\n",
              "0    49  53.700681   89  104.185353  28.762059\n",
              "1    55  50.013401   92  105.461264  30.466833\n",
              "2    55  50.013401   92  105.461264  30.193597\n",
              "3    70  45.696322   92  113.461264  30.632114\n",
              "4    53  50.504232   92  104.461264  29.889149\n",
              "..  ...        ...  ...         ...        ...\n",
              "76  322  36.900000   50  169.598513  16.132947\n",
              "77  238  19.197888  115  150.576579  37.923113\n",
              "78  263  34.000000   50  151.598513  15.769625\n",
              "79  295  19.833733  119  167.944460  39.423099\n",
              "80  236  12.101263  107  139.840817  34.948615\n",
              "\n",
              "[81 rows x 5 columns]"
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              "      <td>139.840817</td>\n",
              "      <td>34.948615</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## P(MPG>38)\n",
        "1-stats.norm.cdf(38,cars.MPG.mean(),cars.MPG.std())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GchHBZaLjjEA",
        "outputId": "56017aa2-6e27-4abd-85c4-d023191e9502"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.34759392515827137"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# P(MPG<40)\n",
        "stats.norm.cdf(40,cars.MPG.mean(),cars.MPG.std())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6f5KrtpDjojM",
        "outputId": "5f5bec54-14e8-4d63-82ab-89008a0f49aa"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7293498762151609"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# P (20<MPG<50)\n",
        "stats.norm.cdf(0.50,cars.MPG.mean(),cars.MPG.std())-stats.norm.cdf(0.20,cars.MPG.mean(),cars.MPG.std())          "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZRTsyYfhjsG9",
        "outputId": "933e0905-b22b-4216-f6da-17542be0b425"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.2430968797327491e-05"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(cars.MPG)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "KGa4P9mKj0WI",
        "outputId": "a956ce22-275d-40e6-d0c8-49f8b833a6ce"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbf3dffbad0>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q 21) Check whether the data follows normal distribution a) Check whether the MPG of Cars follows Normal Distribution Dataset: Cars.csv\n"
      ],
      "metadata": {
        "id": "JLKcHeZaj7mG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(cars.MPG,label='cars-MPG')\n",
        "plt.xlabel('MPG')\n",
        "plt.ylabel('Density')\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "vYKWBrqlkAsv",
        "outputId": "99fa2908-9e59-4a1d-8afc-b406bbda2ff2"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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v2L4byDHGZANLgMdFJMBdWZUajOc2lFDZ2M7Plpzm1eMeTmV6aozO8OrD3NmCmAcUGmOKjDEdwCpgWa9jlgHPOe6/BpwvImKMaTXGdDm2hwA6taTyKHUtHTz64QHOmZTI/PHxVsdxm4jgADKTIthRUY/Npj+GvsadBSIFKO/xuMKxzekxjoLQAMQDiMh8EckHdgE/6FEwviQi3xeRXBHJraqqcsO3oJRzf3hvL83tXfzikilWR3G77LQYGo51sqWk1uooaph5bCe1MWaTMWYqMBf4uYiEODnmCWNMjjEmJzHRe+e+Ud5l98EGXtlcxg0LMpg8OtLqOG43ZUwUgf7Cm3mHrI6ihpk7C8RBIK3H41THNqfHOPoYooGangcYYwqAZmCa25Iq1U82m+Hev+8mPjyIH18w0eo4wyI4wJ+pydG8s+swHV06w6svcWeB2AJMFJFxIhIEXAOs7XXMWuAGx/2rgPXGGON4TgCAiIwFTgNK3JhVqX752/aDbCur566LpxAVEmh1nGEzM9V+munDvUetjqKGkdsKhKPP4DbgXaAAeNUYky8ivxaRpY7DngLiRaQQuBM4finsmcAOEckD3gD+yxhT7a6sSvVHXUsHv/tnAbPTY7hi1sgaFNeXzKQI4sOD+LueZvIpbr101BjzDvBOr2339rjfBix38rwXgBfcmU2pgfrl33fTcKyT31w2fURMyDcQ/n7C12eMYdWWchrbOn2q9eTLPLaTWilPsnbHId7eeZgfL55EVnKU1XEssWxWCu1dNv61+4jVUdQw0cFnyuO9vKnMra9/7fz0U+6vbGzjnjd3Mys9hpvPHu/WLJ5sVloMY+PD+HveQa7OSev7CcrraQtCqVMwxvCz13fS3tXNn5bPJMDfd39kRIRlM5PZcKCGykadesMX+O6nXal+ePTDA3y4t4qfXzyF8YkRVsex3LJZKRgDb+3QzmpfoAVCqZNY/0Ulf3xvL0tnJvOtBWOtjuMRJiRGMD0lmjfzeg9pUiORFgilnCg82syPXskja0wU9185Y8ROxjcYy7KT2X2wkcKjTVZHUW6mBUKpXupbO/j+C7kEBfjxxLdyCA3ytzqSR1manYy/n/D6Nm1FjHT9KhAi8jcR+ZqIaEFRI1pLexc3PruFitpjPHrdbFJiQq2O5HGSIkM4e2ICb2w7SLfO8Dqi9fcX/qPAtcB+EfmdiEx2YyalLNHa0cV3n8tlR3k9D187a0RP4z1UV85J5UhjGxsO6AQHI1m/CoQx5n1jzHXAbOxzIr0vIhtE5EYR0SGVyus1HOvkhqc3s6m4hgeuzuaiqaOtjuTRFk8ZRVRIAK9vrbA6inKjfp8yEpF44NvAd4HtwEPYC8Y6tyRTapiU17Zy1WMbyCuv5+EVs7nMx+ZZGoyQQH8unZnMv/KP0NTWaXUc5Sb97YN4A/gECAMuNcYsNcasNsbcDujF4cpr7a9sYulfPqWysY3nvzOfr80YY3Ukr3HlnFTaOm38c5dOvTFS9XeqjScdE+99SUSCjTHtxpgcN+RSyq3au7pZt6eSzw/UkBQVzHXzxlJc3UJxdYvV0bzGrLQYxieE89q2Cq6eq1NvjET9PcX0GyfbPndlEKWGy77KJh76YD8bDtQwf3wct5yTSUJksNWxvI6IcOWcVDYX11JW02p1HOUGpywQIjJaROYAoSIyS0RmO26LsJ9uUsprHKw7xnMbSnh2QwmBfn7cfPZ4ls5MIShAr94erMtnpSACr2/TzuqRqK9TTBdh75hOBR7osb0J+IWbMinlMsYYymtb+Xh/NXsONxIa6M+FWaNYmJlAoA9PvOcqyTGhnDEhnr9tr+BH50/0uXUyRrpTFghjzHPAcyJypTHm9WHKpNSQdXbb2FFez8biGg7VtxES6Mf5U5JYOCGBkEAdGe1KV85O5c5Xd7ClpFbHjowwpywQIvJNY8yLQIaI3Nl7vzHmASdPU8oytS0dbCqqIbe0jmOd3YyKCmZZdjLZaTEEB2hhcIcl00Zzz5u7eX1bhRaIEaavU0zhjq96KavyaOW1rXy0r4qCw42IQFZyNKePj2NcfLhOtOdmYUEBXDJ9DG/vPMz/XjqV8GBdh2yk6OsU0+OOr78anjhKDUxJdQvvF1RSVN1CSKAf50xOZP64eKJDdYD/cLpmXhprtlbw9s7DesnrCNKvUi8iv8d+qesx4F/ADOAOx+knpYZdY1sn/9p9hLzyeqJCArhk2mjmZsQRrP0LlpidHsuExHBWbSnTAjGC9PcyjguNMY3A17HPxZQJ/NRdoZQ6GWMMm4preHDdPnYdbGDR5ETuvGAyZ05M1OJgIRHhmrnpbCurZ3+lrhMxUvS3QBxvaXwNWGOMaXBTHqVOqr2zm5c3l/H3vEOkxYXxo/MncmHWaB3H4CEun51CoL+weku51VGUi/T3J+sfIvIFMAf4QEQSAV21XA2b+tYOHv+4iILDjVw8bTTfPiODhAgd/exJEiKCWTxlFH/bfpCOLpvVcZQL9He677uAM4AcY0wn0AIsc2cwpY6raW7n8Y+LqGvt4IYFGZw1MRE/vTLJI31jbhq1LR28X1BpdRTlAgO5Hu007OMhej7neRfnUeorymtbefKTIrpshu+dNZ5kXeHNo501MZHk6BBWbSnnkuk6M6636+9VTC8AE4A8oNux2aAFQrlRTXM733p6M53d9uIwOjrE6kiqD/5+wlU5aTy8fj8H64/pkq1err8tiBwgyxijC9CqYdHe1c33ns/lUP0xvn1GhhYHL7J8TioPr9/Pmtxyfrx4ktVx1BD0t5N6N6BrMKph86u39rCtrJ4Hrs5mbHx4309QHiMtLowzMxNYk1tBt03/pvRm/S0QCcAeEXlXRNYev7kzmPJdb2yv4OVNZdyyaIKu8OalvjE3jYP1x/i0sNrqKGoI+nuKaaU7Qyh1XHltK/e8mc/cjFh+cuFkq+OoQbogaxRx4UG8sqmMcyYlWh1HDVJ/L3P9CPsI6kDH/S3ANjfmUj7IZjP896s7EOCBq7Px17UFvFZwgD/Lc1JZV1DJkQYdMuWt+lUgROR7wGvA445NKcCb7gqlfNNLm8vYXFLLPZdmkRanCxZ6u+vmjaXbZnRktRfrbx/ErcBCoBHAGLMfSHJXKOV7jjS0cf8/v2BhZjzL56RaHUe5QHp8GGdPSmTVljK6unVktTfqbx9EuzGm4/i8+o7Bcnp5gnKZ+94poKPbxn2XT9f1GzzUy5vKBvyc9NhQPt5Xxcq1e8hKjurz+Gvnpw8mmnKT/rYgPhKRXwChInIBsAZ4y32xlC/ZXFzL2h2H+ME5E/SS1hFm8ugookIC2FxSY3UUNQj9LRB3AVXALuBm4B3gl+4KpXyHzWb41Vv5JEeHcMs5E6yOo1zM30+YmxHH/spmals6rI6jBqi/VzHZsHdK/5cx5ipjzJP9GVUtIktEZK+IFIrIXU72B4vIasf+TSKS4dh+gYhsFZFdjq/nDezbUt7ind2HyT/UyE+XTCY0SNdzGIlyMuIQsbcUlXc5ZYEQu5UiUg3sBfaKSJWI3NvXC4uIP/AIcDGQBawQkaxeh90E1BljMoEHgfsd26uBS40x04EbgBcG8k0p79DVbeOBdfuYNCqCpTNTrI6j3CQ6NJDTRkextbRWO6u9TF8tiDuwX7001xgTZ4yJA+YDC0Xkjj6eOw8oNMYUGWM6gFWcOEX4MuA5x/3XgPNFRIwx240xhxzb87H3fejk/yPM37YfpKiqhTsvmKxjHka4+ePjaOnoJv9Qo9VR1AD0VSCuB1YYY4qPbzDGFAHfBL7Vx3NTgJ4XQFc4tjk9xhjTBTQA8b2OuRLYZoxp7/0GIvJ9EckVkdyqqqo+4ihP0t7VzUPv72dGajQXTR1ldRzlZhMSI4gLD2JTsXZWe5O+CkSgMeaEyVSMMVVAoHsi/YeITMV+2ulmZ/uNMU8YY3KMMTmJiTqc35us3lLOwfpj/PeFk/WyVh/gJ8L8cXGU1LTqyGov0leBONVlB31dknAQSOvxONWxzekxjrEV0UCN43Eq8AbwLWPMgT7eS3mR9q5u/rK+kHkZcZw9McHqOGqYzBkbS6C/8HmRTuDnLfoqEDNFpNHJrQmY3sdztwATRWSciAQB1wC9Z4Bdi70TGuAqYL0xxohIDPA2cJcx5rOBfUvK0725/SBHm9r54fkTtfXgQ8KCApiZGkNeeT2tHV1Wx1H9cMoCYYzxN8ZEOblFGmNOeYrJ0adwG/AuUAC8aozJF5Ffi8hSx2FPAfEiUgjciX28BY7nZQL3ikie46ZTe4wANpvh8Y+LmJocxcLM3t1NaqRbMCGezm7D1tI6q6OofhjImtQDZox5B/ugup7b7u1xvw1Y7uR5vwF+485syhrvF1RSVNXCn1fM0taDDxoTHUpGfDgbi2pYmJmAn34GPFp/R1Ir5RKPf1xEamwol0zTBQp91YIJ8dS1drL3SJPVUVQftECoYbOlpJatpXV876zxBPjrR89XZY2JIjo0kA0HtLPa0+lPqRo2j390gNiwQJbn6HTevszfz37J64GqFiob9ZJXT6YFQg2L4uoW3i84yvWnjyUsyK1dX8oLzM2II8BP2FikA+c8mRYINSxe+LyUAD/hm6ePtTqK8gDhwQHMSI1he1k9bZ3dVsdRJ6EFQrldS3sXa7aWc/H0MSRFhVgdR3mIBRPi6ei2kauXvHosLRDK7d7YfpCmti5uWKCtB/UfKTGhjI0P4/MD1dj6Xj1AWUALhHIrYwzPf17C1OQo5oyNtTqO8jBnZiZQ19qps7x6KC0Qyq02FtWyr7KZGxZk6MA4dYIpY6KICw/is0K95NUTaYFQbvXchhJiwgJZmp1sdRTlgfxEOGNCPGW1rZTVtFgdR/Wi1xuqIXl5U9lJ9zUc6+S9PUdYmJnA37b1nshXKbs5Y2N5v6CST7UV4XG0BaHcZmtpLTYD8zLirI6iPFhwgD/zMuLJP9RIeW2r1XFUD1oglFvYjGFLSR2ZiRHER+hqserUFkyIRwSe/qy474PVsNECodxif2UTDcc6mTtOWw+qb9GhgcxIjeHVLeU0HOu0Oo5y0AKh3GJzSR3hwQFMGRNpdRTlJRZmJtDS0c3qLSfv11LDSwuEcrmGY53sPdLInPRYAvz0I6b6JyUmlNPHx/HMZyV0dtusjqPQAqHcYGtpHTYDczN0YJwamO+dNZ7DDW38Y+chq6MotEAoF7MZQ25pLRMSw7VzWg3YuZOTmDQqgsc+PIDNptNvWE0LhHKp/ZXN1Ld2Mm+crjetBs7PT/jBORPYV9nMv/cetTqOz9MCoVxqS0kt4UH+2jmtBu3SmcmkxITy2IcHrI7i87RAKJdpPNbJF0camTNWO6fV4AX6+/G9s8aRW1rHlpJaq+P4NP0pVi6ztex457SOfVBD84256cSFB2krwmJaIJRL2EdOa+e0co3QIH9uPCOD9V8cpeCwTgVuFS0QyiUKj9o7p7X1oFzlWwsyCA/y5/GPtBVhFS0QyiU2F9s7p7OSo6yOokaI6LBArp2fzls7D+skfhbRAqGGrLFNO6eVe9x05nj8BJ74uMjqKD5Jf5rVkOWWaOe0co/R0SFcOTuV1bnlVDa2WR3H52iBUENyvHM6M0mn9Vbu8V+LMum2Gb2iyQJaINSQ7Dtin9ZbFwVS7pIeH8YVs1J4ZXMZR7UVMay0QKgh2VRcS2RIAFPGaOe0cp/bzsuky2Z4XPsihpUWCDVo5bWt7KtsImdsHP5+YnUcNYKNjQ/nsuwUXtpUSlVTu9VxfEaA1QGU91rlWNhFp/VWrvLyppMvFjQ2Poz2Tht3rM7jkuljBvX6185PH2w0n6QtCDUoHV02Vm+pYPLoSGLCgqyOo3xAQkQw2WkxbCquobm9y+o4PkELhBqUdXsqqW5uZ76uOa2G0aLJSXR1Gz7ZX2V1FJ+gBUINykubSkmNDWXiKJ3WWw2fxMhgZqRGs7FIWxHDQQuEGrADVc1sOFDDinnp+Il2Tqvhde5p9lbEx/u0FeFuWiDUgL28qYwAP+HqnDSroygflBQZwqz0WDYW1VDf2mF1nBHNrQVCRJaIyF4RKRSRu5zsDxaR1Y79m0Qkw7E9XkT+LSLNIvIXd2ZUA9PS3sWrueVcNG00iW/xmwoAABEgSURBVJE6clpZ4/wpSRhg/Re6LKk7ua1AiIg/8AhwMZAFrBCRrF6H3QTUGWMygQeB+x3b24B7gJ+4K58anNe3VdDU1sV3Fo6zOoryYbFhQcwfF8fW0jqONunoandxZwtiHlBojCkyxnQAq4BlvY5ZBjznuP8acL6IiDGmxRjzKfZCoTyEzWZ49rMSZqbFMDs9xuo4ysctmpxEoL8f7++ptDrKiOXOApEClPd4XOHY5vQYY0wX0ADE9/cNROT7IpIrIrlVVdph5W4f7auiqLqF7yzMQLRzWlksIjiAMycmsPtQIxV1ul6EO3h1J7Ux5gljTI4xJicxMdHqOCPe058VMyoqmIunDW4Uq1KudmZmAmFB/rynrQi3cGeBOAj0vMwl1bHN6TEiEgBEAzVuzKQGaV9lE5/sr+b608cSFODVf1eoESQk0J9FkxIpPNrMgapmq+OMOO78Sd8CTBSRcSISBFwDrO11zFrgBsf9q4D1xhjjxkxqkJ75rITgAD9WzNO5bJRnmT8+nujQQN7NP4L++nAttxUIR5/CbcC7QAHwqjEmX0R+LSJLHYc9BcSLSCFwJ/DlpbAiUgI8AHxbRCqcXAGlhsnRpjZe31bBFbNTdFEg5XEC/f1YPGUUFXXH2FnRYHWcEcWts7kaY94B3um17d4e99uA5Sd5boY7s6n+e+qTYrq6bdx89gSroyjl1Kz0GD4/UM2/8o+QlRxFoL+eBnUF/VdUp1Tf2sGLG0v5+oxkMhLCrY6jlFN+IlwyYwwNxzr5tLDa6jgjhhYIdUrPbSilpaObWxZp60F5tvEJEWSNieKjvVU0tnVaHWdE0AKhTqqlvYtnNhSzeEqSLimqvMLF00bTbTOs08teXUILhDqpVzaXUd/ayX+dm2l1FKX6JT4imAUT4tlWWkd5rQ6eGyotEMqp1o4uHv+4iAXj45mdrkuKKu9x3mlJRIQEsHbHIWx62euQaIFQTj39aTFVTe385KJJVkdRakBCAv25ZNoYDtYfY0tJrdVxvJoWCHWCupYOHv+oiAuyRjFnrC4pqrzPjNRoxiWE815+pa48NwRaINQJHvl3IS0dXfzPRZOtjqLUoIgIS2cm097Vzbv5R6yO47W0QKivqKhr5fnPS7lqTqquN6282qioEBZmJrC1tI6iap2naTC0QKiveHDdfhD48WLte1De7/zTRhEbFsgb2w7S2W2zOo7X0QKhvrS1tJbXt1Vw48IMkmNCrY6j1JAFBfhx+axUalo6+KBAx0YMlBYIBUBnt42739hNcnQIPzxvotVxlHKZzKQIcsbG8sn+anZW1Fsdx6togVAAPPNZMV8caWLl0qmEB7t1Dkelht3F08YQERLA/7y2k44uPdXUX1ogFBV1rTy4bj+Lp4ziwqmjrY6jlMuFBvlzWXYKXxxp4oF1+6yO4zW0QPg4Ywwr1+YDsHKpLrmhRq4pY6JYMS+Nxz8+wOcHdOHK/tAC4eNWbSnn/YKj/PeFk0iNDbM6jlJudc/XsxgXH86dr+bR0KozvvZFC4QP21/ZxK/eyufMzAS+s3Cc1XGUcruwoAD+75psqpra+cUbu3SJ0j5ogfBRbZ3d3P7KdsKDAnjg6pn4+YnVkZQaFjNSY7jjgkm8veswL20qszqOR9MC4aN++3YBXxxp4o9XzyQpKsTqOEoNqx+cM4FFkxP51Vv5bCurszqOx9IC4YPW5JbzwsZSvnvmOM6dnGR1HKWGnb+f8H/fyGZ0dAi3vLiVqqZ2qyN5JC0QPmZzcS13v7GbhZnx/Ozi06yOo5RlYsKCePybOTQc6+S2l7fpVBxOaIHwITsr6rnp2S2kxoXy6LVzCPTX/37l27KSo/h/V0xnU3Etd2un9Ql0yKyPKDjcyPVPbSY6LJAXb5pPdFig1ZGU8giXz0qluKqFP68vZEx0KHdcoBNVHqcFwgcUHm3m+qc2ERroz8vfPV0n4lOqlzsumMThhjYe+mA/yTEhfGNuutWRPIIWiBFuU1ENN7+4lQA/4aXvzSc9XgfDKdWbiHDfFdOpbGrnF2/sJjo0kCXTxlgdy3J6EnoEe2N7Bd98ahNx4UG8fssZTEiMsDqSUh4r0N+PR6+bzczUaG59eTtv7zxsdSTLaYEYgTq7bdz/ry+4Y/UO5oyN5Y1bFjI2PtzqWEp5vIjgAJ6/aT6z02P44art/D3voNWRLKUFYoQ5UNXMlY9t4LEPD7BiXhrPf0c7pJUaiIjgAJ69cR45Y2O5Y3UeL24stTqSZbRAjBBd3Tae/ayYr//5U8pqW3nsutn8vytmEBSg/8VKDVR4cADP3DiXcyYl8ss3d7NybT5dPjhOQjupR4DPD9Twq7fy+eJIE2dPSuQPV81gVFQIL+s8M0oNWlhQAH+9YS6/fbuApz8rpqSmhYeumUV0qO+0yLVAeLH8Qw38+YP9vJtfSUpMKI9dN5sl00YjohPvKeUK/n7CvZdmMSEpnP/9ez6XPPQJD1w9k/nj462ONiy0QHihvPJ6/rK+kPcLKokMDuCOxZO4+ZzxhAT6Wx1NqRHpuvljmTImijtW53HNkxv5wTkTuGPxpBF/ClcLhJdo6+zm7Z2Hef7zEnZUNBAVYi8M316Y4VNNXqWsMjs9lnd+eBa/fmsPj314gHd3H+Ger2dx7mkjd8JLLRAezBjD1tI63th+kH/sPEzDsU4mJIaz8tIsrpyTSmSIFgalhlN4cAD3XzWDJdNH8/+9tYcbn93CosmJ/GzJaUwZE2V1PJfTAuFhOrpsbCqu4YOCo6zbU8nB+mOEBPpx0dTRXJ2TxhkT4rWPQSmLnTs5iYUTEnj+8xIeen8/Fz/0CYsmJ3Lz2RM4fXzciPkZ1QJhMWMMB6pa2FJSyyf7q/h4XzXN7V2EBPpxZmYCd14wiYumjSYiWP+rlPIkQQF+fPes8Syfk8YLG0t45rMSVjy5kcmjIrlidgrLslMYHe3di3HJSJneNicnx+Tm5lodo08NrZ3sOdxI/qEGckvq2FJSS01LBwBJkcGcPyWJxVNGccaEBEKDhtbprJe5KvVV18533yR8bZ3d/G3bQdZsLWd7WT0ikDM2lrMnJnL2pESmp0R75NK+IrLVGJPjbJ9b/ywVkSXAQ4A/8FdjzO967Q8GngfmADXAN4wxJY59PwduArqBHxpj3nVnVlfq6rZxqL6NstpWSmtbKKttpbCymYLDjRxqaPvyuLS4UM6ZnMi8jDjmjYtjXEL4iGmaKuVrQgL9uXZ+OtfOT6eoqpk38w6x/otK/rRuH39at4/I4ACmpUQzIy2arDFRZMSHkxEf7tEzHbitQIiIP/AIcAFQAWwRkbXGmD09DrsJqDPGZIrINcD9wDdEJAu4BpgKJAPvi8gkY0y3q3MaY+iyGTq7bXR2H/9qo7PL0Gn7z/2ObhutHV00t3XR1G7/2txuv9U0d1Dd3E5VUzvVze3UtHTQbftPyyzQX8iID2fuuDimjIly3CJJivTu5qdSyrnxiRHcecEk7rxgEtXN7Xy6v5rc0lp2VjTw9KfFdHb/5/dDTFggY+PCSIwMISEiiLhw+y02LIiwIH9CgvwJC/QnLCiA0CB/QoP8CfQTAvz9CPAXAv38CPS3P3Y1d7Yg5gGFxpgiABFZBSwDehaIZcBKx/3XgL+I/U/oZcAqY0w7UCwihY7X+9zVIXdUNHDZI58N+vnBAX7EhQeRGBnM6OgQpqdEkxgZTFpcKOlx4aTHhzE6KgR/D2xaKqXcLyEimMtmpXDZrBTAfiqqrLaVkuoWSmtaKamxf62oa2VHRT11LR102QZ26v9rM8bwyLWzXZ7dnQUiBSjv8bgCmH+yY4wxXSLSAMQ7tm/s9dyU3m8gIt8Hvu942CwieweQLwGoHsDxnsSbs4N359fs1hly/utcFGQQ3Ppv/yjw6OC/ubEn2+HVl8YYY54AnhjMc0Uk92QdM57Om7ODd+fX7Nbx5vzemt2d48QPAmk9Hqc6tjk9RkQCgGjsndX9ea5SSik3cmeB2AJMFJFxIhKEvdN5ba9j1gI3OO5fBaw39utu1wLXiEiwiIwDJgKb3ZhVKaVUL247xeToU7gNeBf7Za5PG2PyReTXQK4xZi3wFPCCoxO6FnsRwXHcq9g7tLuAW91wBdOgTk15CG/ODt6dX7Nbx5vze2X2ETNQTimllGuN7LlqlVJKDZoWCKWUUk75ZIEQkSUisldECkXkLqvznIqIPC0iR0Vkd49tcSKyTkT2O77GWpnxZEQkTUT+LSJ7RCRfRH7k2O7x+UUkREQ2i8gOR/ZfObaPE5FNjs/OascFGB5JRPxFZLuI/MPx2Juyl4jILhHJE5FcxzaP/9wAiEiMiLwmIl+ISIGILPCW7L35XIHoMQXIxUAWsMIxtYenehZY0mvbXcAHxpiJwAeOx56oC/hvY0wWcDpwq+Pf2hvytwPnGWNmAtnAEhE5Hft0MA8aYzKBOuzTxXiqHwEFPR57U3aAc40x2T3GD3jD5wbs88/9yxhzGjAT+/+Bt2T/KmOMT92ABcC7PR7/HPi51bn6yJwB7O7xeC8wxnF/DLDX6oz9/D7+jn1uLq/KD4QB27DPBFANBDj7LHnSDfvYoQ+A84B/AOIt2R35SoCEXts8/nODfSxXMY4LgLwpu7Obz7UgcD4FyAnTeHi4UcaYw477R4BRVobpDxHJAGYBm/CS/I5TNHnAUWAdcACoN8Z0OQ7x5M/O/wH/A9gcj+PxnuwABnhPRLY6ptQB7/jcjAOqgGccp/f+KiLheEf2E/higRhRjP1PEo++VllEIoDXgR8bYxp77vPk/MaYbmNMNva/xucBp1kcqV9E5OvAUWPMVquzDMGZxpjZ2E8F3yoiZ/fc6cGfmwBgNvCYMWYW0EKv00kenP0EvlggRsI0HpUiMgbA8fWoxXlOSkQCsReHl4wxf3Ns9pr8AMaYeuDf2E/LxDimhQHP/ewsBJaKSAmwCvtppofwjuwAGGMOOr4eBd7AXqC94XNTAVQYYzY5Hr+GvWB4Q/YT+GKB6M8UIJ6u5xQlN2A/t+9xHFO3PwUUGGMe6LHL4/OLSKKIxDjuh2LvOynAXiiuchzmkdmNMT83xqQaYzKwf77XG2OuwwuyA4hIuIhEHr8PXAjsxgs+N8aYI0C5iEx2bDof+4wQHp/dKas7Qay4AZcA+7CfU77b6jx9ZH0FOAx0Yv/r5Cbs55M/APYD7wNxVuc8SfYzsTeldwJ5jtsl3pAfmAFsd2TfDdzr2D4e+7xghcAaINjqrH18H4uAf3hTdkfOHY5b/vGfUW/43DhyZgO5js/Om0Cst2TvfdOpNpRSSjnli6eYlFJK9YMWCKWUUk5pgVBKKeWUFgillFJOaYFQSinllBYIpYZARIyIvNjjcYCIVPWYQfXbjsd5jlltv9fj2CWOGWO/cOxfLSLpVnwfSjnjtiVHlfIRLcA0EQk1xhzDPqCu9wjl1caY20QkCcgXkbVAIvAwsNQYUwAgIkuxT8xYNmzplToFbUEoNXTvAF9z3F+BfXDjCYx92ogDwFjgZ8B9x4uDY/9aY8zHbs6qVL9pgVBq6FYB14hICPYR2JucHSQi47GPEi4EpmKfQlwpj6UFQqkhMsbsxH5qaAX21kRv33BMG/4KcLMxprbnThGJd/RB7BORn7g9sFL9pH0QSrnGWuCP2Oc+iu+1b7Ux5rZe2/Kxz/K5wxhTA2Q7ikOEu4Mq1V9aIJRyjaexL8izS0QW9eP43wNviMjGHv0QYW5Lp9QgaIFQygWMMRXAnwdw/C4R+RHwvIhEYV8OtAz4XzdFVGrAdDZXpZRSTmkntVJKKae0QCillHJKC4RSSimntEAopZRySguEUkopp7RAKKWUckoLhFJKKaf+f65lK/LLl0ROAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cars.MPG.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPA31sy2li1C",
        "outputId": "a9afab6a-5690-42b7-f17b-587e6355700a"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "34.42207572802469"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cars.MPG.median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lH3F48Tylm5b",
        "outputId": "6f5f9588-7049-4f6d-a33d-19cd9a480a2e"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35.15272697"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "b) Check Whether the Adipose Tissue (AT) and Waist Circumference(Waist) from wc-at data set follows Normal Distribution Dataset: wc-at.csv"
      ],
      "metadata": {
        "id": "Wrn19iNwlw6S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wtat=pd.read_csv('/content/wc-at.csv')\n",
        "wtat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "spsRe_KUlx-m",
        "outputId": "8cd7c1b9-2d4a-4c8c-de40-ef72862a0069"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Waist      AT\n",
              "0     74.75   25.72\n",
              "1     72.60   25.89\n",
              "2     81.80   42.60\n",
              "3     83.95   42.80\n",
              "4     74.65   29.84\n",
              "..      ...     ...\n",
              "104  100.10  124.00\n",
              "105   93.30   62.20\n",
              "106  101.80  133.00\n",
              "107  107.90  208.00\n",
              "108  108.50  208.00\n",
              "\n",
              "[109 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-26b839fa-9d65-49db-b914-0fa2ae0ee2a6\">\n",
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              "      <th></th>\n",
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              "    <tr>\n",
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              "      <td>81.80</td>\n",
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              "      <th>3</th>\n",
              "      <td>83.95</td>\n",
              "      <td>42.80</td>\n",
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              "      <th>4</th>\n",
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              "      <td>29.84</td>\n",
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              "      <td>107.90</td>\n",
              "      <td>208.00</td>\n",
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              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>108.50</td>\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-26b839fa-9d65-49db-b914-0fa2ae0ee2a6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-26b839fa-9d65-49db-b914-0fa2ae0ee2a6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " ## plotting distribution for Waist circumference (Waist)\n",
        " sns.distplot(wtat.Waist)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "rYHb2MOjl4rD",
        "outputId": "1f75e595-6163-40cc-fc3c-e46163516c6a"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbf3d716e10>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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qvrffy7ee20VBegJfvaZkzM8/b0Iagm+VTBMZAplMCoDqQa9rnG3DllHVfqAVGNpv8ePA26ra45QfvDLRcMc0IarcGfxXEkLtJYNdWphBZlIsa/ccd32cxaPrKthf1853Vs52Zc369MRYJmcmsqu2bczPbdwR1A3wIjIbX9XXFy9g37tFpExEyhoagrfbpvm7g/UdJMVGk5cW73YoFyQ6Srh2Vi51bT2uthdUNHTwg1fLWTE3j2tm5boWx6z8NI63dVtVV4QIZDKpBQZ3H5ngbBu2jIh4gDSgyXk9AXgOuENVKwaVn3COYwKgqo+qaqmqlmZnZ4/wo5hA86pysK6dktyUoFgI60LNKUgjPz2el/fUubL6oNerfOMPO4mPieL/Wzm0iXJsnVqHZu8xuzuJBIFMJluBEhEpEpFY4BZgzZAya/A1sAPcBLymqioi6cBfgPtUdf2pwqp6DGgTkcVOL647gD8G8DOYMVLX1k1n7wDF2aHXXjJYlAgr5ubTerLPla7CT5dVs6WqmW+tmElOirt3eBlJseSlxbPHkklECFgycdpA7gHWAnuBZ1R1t4g8KCIrnWKPAZkiUg7cC5zqPnwPUAzcLyI7nEeO894/AD8HyoEK4MVAfQYzdiobfF1qg2khrAtVlJXExZPG8VZ5A3Vt3WN23tqWk/zvF/ayeEoGnxyjMSXnMjMvlcNNXTaAMQIEtGVOVV8AXhiy7f5Bz7uBTwyz33eB757hmGXAnNGN1LitoqGDzKRY0hNj3Q5lVCyfM569x9r4446jfOGKIgI9HGrAq9z79A68XuWhj88L+Pn8NSsvldf21bPvWBulhRluh2MCKKgb4E1kGPAqVY2dYXFXckpynIflc8ZzqKmT7UcC3xj/03UVbK5q5oGVs5mcGTzXMS8tnvTEGKvqigCWTIzrjrWepKffy5QQby8Z6pLJ45iUkcgLu47R3t0XsPO8W9PC9/96gBVz87jpkgnn3mEMiQiz81Ipr++gp98WzQpnlkyM6063l4TQFCr+iBLhYwsK6O338vu3awIyMr6jp5+vrd5Bdkoc37txTtBUbw02Mz+Vfq9ysM6mpQ9nlkyM6yoaOshJiSMlPsbtUEZdTmo8183N40BdB+srmkb12F6v8vWnd3C4uYuHb74oaNubJmckkRgbbVVdYc6SiXHVgFc53NQVVu0lQy0uymBWXiov7TpG5SguGvX9lw/w8p46/ueKmSyeErgFr0YqOkqYnpvCgbp212cGMIFjycS4quaEb8r5KVnh1V4ymIhw0yUTyEyK46ktR2hsH/mI8Cc2HOKR18u55dKJfOaywpEHGWAlucl09Q5wtOWk26GYALFkYlxVEabtJUPFx0Rz+5LJCPDY+ipaunov+FhPbT7CA3/azbWzcvnuDcHZTjJUcY5vvrUD1m4StiyZGFdVNnaQlxZPYtzYT0Y41rKS4/js5UV09w3w6LpK6s9zQKOq8qM3yvnmczu5anoO/3XrAjzRofErnBznoSA9gYP17s+obAIjNL6JJiz1DXg50tQV9nclg+WnJ/CFK6bQ71V+sq6CF3ce82u/lq5e7nlqO//3pf18dH4+P/n0JcSH2JovJTnJVDd30d1nXYTDkSUT45qaEyfp92rYjS85l/z0BL70galkJsXx337zNl/+zdtUnKFhvrtvgF9vOszV//E3Xtp9nPuum8EPbrmIWE/o/eqW5Kbg1b8vNWDCS/jXLZigVdXoay+ZnJnociRjLyMpli9+YArNnb08uq6Sv+w8xoJJ6SwqyiQ3NY6u3gH2HmvjrfJGWrr6uHhSOk/eMJdZ+WO7YuJompSRSJwnioP1HcwpSHM7HDPKLJkY1xxu6mR8arwrizcFA09UFF+7ZhqfXjyZp7dW89Ku4zz+VhW9A76p63NT47hqeg43XzqRRUUZIdHQfjbRUcLU7GQO1rWjqiH/ecx7ReZvsXFd/4CXw01dLJiU7nYorstKjuPLVxXz5auKGfAqLV29JMV5Qq5NxB8lucnsOdZGQ0eP61Pkm9EVehWvJizsPtpG74CXoghqfPdHdJSQmRwXlokEYJrTRdimVgk/fiUTEfmDiKwQEUs+ZlRsPdQMQGEQzXBrAm9cUixZybHWRTgM+ZscfgR8CjgoIv8qItMDGJOJAJurmslIiiU1Ifzm4zJnV5yTQlVjJ/3esV/W2ASOX8lEVV9R1duAi4FDwCsiskFEPisi9tfAnBevV9l6qJkiuyuJSFOzk+gbUGqabWqVcOJ3tZWIZAKfAT4PbAf+E19yeTkgkZmwVd7QQUtXH4XWXhKRirKSEKCi0dpNwom/bSbPAW8CicBHVXWlqj6tql8BImvEmRmxzVW+9hJrfI9MibEe8tMTqKjvdDsUM4r87Rr8M2c999NEJE5Ve1S1NABxmTC2paqZ3NQ4xiVaDWmkmpKdxIbyJnr7rd0kXPhbzfXdYbZtHM1ATGRQVbZWNbOwKNMGrUWwqdnJDKhyuNnuTsLFWe9MRGQ8UAAkiMgC4NRvfyq+Ki9jzkt180mOt3WzsCjD7VCCwlObj7gdgismZyYSJX9fstmEvnNVc30YX6P7BOD7g7a3A98MUEwmjG2u8i1du7Awg22HT7gcjXFLnCeaiRmJZ5zg0oSesyYTVX0CeEJEPq6qvx+jmEwY23qomfTEGEpyki2ZRLip2cm8vq+e1pN9pNl4o5B3rmquT6vqr4FCEbl36Puq+v1hdjPmjLZUNXNpYQZRUdZeEummZCfx2j7fd+LaWbluh2NG6FwN8Kf6biYDKcM8jPFbfVs3h5q6WFho7SUGJo1LxBMlbKhodDsUMwrOVc31U+fnd8YmHBPOtjjzcVnjuwHwREdRmOnrImxCn7+DFv+viKSKSIyIvCoiDSLyaT/2Wy4i+0WkXETuG+b9OBF52nl/s4gUOtszReR1EekQkUeG7POGc8wdziPHv49q3LalqpnE2Ghmh/ACT2Z0Tc1OYn9dOw3tPW6HYkbI33EmH1LVNuB6fHNzFQP/fLYdRCQa+CFwHTALuFVEZg0pdhdwQlWLgYeBh5zt3cC3gf9+hsPfpqoXOY96Pz+DcdmWqmYumTwOT7RNPm18Ti3ZvKnS7k5Cnb+/1aeqw1YAv1PVVj/2WQiUq2qlqvYCq4FVQ8qsAp5wnj8LXC0ioqqdqvoWvqRiwkBLVy/769qtvcS8R356AilxHjZUWDIJdf4mkz+LyD7gEuBVEcnm3H/oC4DqQa9rnG3DllHVfqAVyPQjnl84VVzfFhtGHRLKDp1A1dpLzHtFRwmLpmRYI3wY8HcK+vuAy4BSVe0DOnn/XcZYuU1V5wJXOI/bhyskIneLSJmIlDU0NIxpgOb9thxqJjY6ivkTbZle816XTc3icFMXNSe63A7FjMD5VF7PAG4WkTuAm4APnaN8LTBx0OsJzrZhy4iIB0gDznq/q6q1zs924Cl81WnDlXtUVUtVtTQ7O/scoZpA21LVzPyJaWG7HK25cJcV+yojrKortPnbm+tJ4N+BpcClzuNcswVvBUpEpEhEYoFbgDVDyqwB7nSe3wS8pqp6ljg8IpLlPI/B1yFglz+fwbins6efXbWtVsVlhjUtJ4XMpFg2WTIJaf5OQV8KzDrbH/qhVLVfRO4B1gLRwOOqultEHgTKVHUN8BjwpIiUA834Eg4AInII34SSsSJyA747ocPAWieRRAOvAD/zNybjju1HWuj3Kpda47sZRlSUsHhqJhsqmlBVm006RPmbTHYB44Fj53NwZw2UF4Zsu3/Q827gE2fYt/AMh73kfGIw7ttS1USUQKklE3MGl03N5C/vHqOqsfN0d2ETWvxNJlnAHhHZApweXaSqKwMSlQkrm6uamVOQRnKcv183E2kum5oF+NpNLJmEJn9/ux8IZBAmfPX0D7C9uoU7Fk92OxQTxAozE8lLi2djZROftu9KSPIrmajq30RkMlCiqq+ISCK+NgtjzurdmlZ6+73W+G7OSkRYMjWTv+1vwOtVm1U6BPnbm+sL+Eao/9TZVAA8H6igTPjYUuWb3NEa3825XDY1i6bOXg7Ut7sdirkA/o4z+TJwOdAGoKoHAZtg0ZzT5qpmpuemMC4p1u1QTJBbMtUZb2KzCIckf5NJjzO/FnB6gKHf3YRNZOof8LLtULNVcRm/FKQnUJiZaIMXQ5S/yeRvIvJNIEFErgV+B/wpcGGZcLDnWBudvQOWTIzflkzNZHNlE/0DXrdDMefJ32RyH9AA7AS+iG/syP8MVFAmPJxqL7FkYvy1ZGoW7T397D7a5nYo5jz525vLKyLPA8+rqs2aaPyyuaqZwsxEclPj3Q7FhIglU/4+T5dNChpaznpnIj4PiEgjsB/Y76yyeP/Z9jPG61W2WnuJOU/ZKXFMy022KelD0Lmqub6OrxfXpaqaoaoZwCLgchH5esCjMyHrYH0HLV19LCzyZ3kaY/7usqlZbD3UTG+/tZuEknMlk9uBW1W16tQGVa0EPg3cEcjATGjbUuXrkbPI7kzMeVoyNZPuPi87qlvcDsWch3MlkxhVfd/9ptNuEhOYkEw42FTVTF5aPBPGJbgdigkxi4syEYGN1kU4pJwrmfRe4HsmgqkqW6p87SU2nbg5X2mJMczJT7N2kxBzrt5c80VkuD56AlgXHTOsQ01dNLT3WOO7uWCXTc3k8fVVdPX2kxhrs02HgrPemahqtKqmDvNIUVWr5jLDsvYSM1JLS7LoG1A2O2OVTPA7nzXgjfHL5qpmMpJimWrrUpgLdGlhBnGeKN48YFVdocKSiRl1myubWVho7SXmwsXHRLOwKIM3D9oY6VBhycSMqurmLmpbTp6eAdaYC3VlSTYH6zs43trtdijGD5ZMzKg61QPHkokZqaUlvqV87e4kNFgyMaNqY0UTWcmxlORYe4kZmRnjU8hKjuPNg9ZuEgosmZhRo6psrGxi8ZRMay8xIyYiXFmSxVvljXi9tnxSsLNkYkZNZWMndW09XDY1y+1QTJi4YloWzZ297DlmU9IHO0smZtScmv7C2kvMaLm8+FS7iVV1BTtLJmbUbKxoIi8tnsLMRLdDMWEiJyWeGeNTrBE+BFgyMaPC61U2VTaxxNpLzCi7clo2ZYdO0NnT73Yo5iwsmZhRcaC+nabOXqviMqPug9Oz6R3wsr7cqrqCWUCTiYgsF5H9IlIuIvcN836ciDztvL9ZRAqd7Zki8rqIdIjII0P2uUREdjr7/EDs3+CgYO0lJlAuLcwgJc7Da/vq3Q7FnEXAkomIRAM/BK4DZgG3isisIcXuAk6oajHwMPCQs70b+Dbw34c59I+BLwAlzmP56EdvztfGiiYmZSQyYZy1l5jRFRMdxZXTsnltX711EQ5igbwzWQiUq2qlqvYCq4FVQ8qsAp5wnj8LXC0ioqqdqvoWvqRymojkAamquklVFfgVcEMAP4PxQ/+A93R7iTGBsGxGDvXtPew+al2Eg1Ugk0kBUD3odY2zbdgyqtoPtAJn+4tU4BznbMc0Y+zd2lbauvu5YpqNLzGB8cHp2YjAq/vq3A7FnEHYNsCLyN0iUiYiZQ0N1q0wkNYdaEAELrfBiiZAMpPjWDAxndet3SRoBTKZ1AITB72e4GwbtoyIeIA04GwLP9c6xznbMQFQ1UdVtVRVS7Ozs88zdHM+3jzYyLwJ6YxLinU7FBPGls3I4Z2aVurbbRbhYBTIZLIVKBGRIhGJBW4B1gwpswa403l+E/Ca0xYyLFU9BrSJyGKnF9cdwB9HP3Tjr9aTfeyobuHKErsrMYG1bEYuAG/ss5qGYBSwZOK0gdwDrAX2As+o6m4ReVBEVjrFHgMyRaQcuBc43X1YRA4B3wc+IyI1g3qC/QPwc6AcqABeDNRnMOe2saKJAa9yRYnd/ZnAmpmXQl5avLWbBClPIA+uqi8ALwzZdv+g593AJ86wb+EZtpcBc0YvSjMS6w42kBznYcGkdLdDMWFORFg2I4fnttfS3TdAfEy02yGZQcK2Ad4Enqqy7kADS6ZmEhNtXyUTeNfMyqWrd4C3bOLHoGN/AcwFO9zURc2Jk9ZeYsbM5VOzSI338MLOY26HYoawZGIu2DpnJldrLzFjJdYTxYdmj+flvXX09A+4HY4ZxJKJuWDrDjQyKSORwkI+f0MAABPWSURBVKwkt0MxEeQjc8fT3t1vEz8GGUsm5oL09A+wsaKRK6yKy4yxpcXZpMR7+Mu7x90OxQxiycRckM2VzXT2DrBsRo7boZgIE+uJ4tpZuby85zi9/V63wzEOSybmgry6t474mKjTy6oaM5ZWzM2jrbuf9RVW1RUsLJmY86aqvLK3nqXF2dbX37hiaUkWKXEeXnjXenUFC0sm5rztO95ObctJrplpVVzGHXGeaK6Zlctf99RZVVeQsGRizture33TWSyzZGJcdP28PFpP9vG3AzZXVzCwZGLO28t765k/MZ2clHi3QzER7Mpp2WQlx/K7supzFzYBZ8nEnJf69m7eqW7hWrsrMS6LiY7ixgUFvLavnsaOHrfDiXiWTMx5ObU40dUzc12OxBj4ROlE+r3K89uHXdbIjCFLJua8vLynnoL0BGaMT3E7FGOYlpvC/AlpPLuthrMshWTGgCUT47eu3n7eKm/g6pk5+NYmM8Z9N5VOZN/xdnYfbXM7lIhmycT47bV99XT3ebluTp7boRhz2sp5+cR6oqwh3mWWTIzf/vzOMbJT4lhYlOF2KMaclpYYw4dnj+eP7xy1mYRdZMnE+KWjp5/X99ezYm4e0VFWxWWCyydLJ9DS1cdfbES8ayyZGL+8sqeOnn4v18+zKi4TfJYWZ1Gck8xjb1VZQ7xLLJkYv/z53aPkpcVz8aRxbodizPuICJ+7vIjdR9vYXNXsdjgRyZKJOafWLt+UFSvm5hFlVVwmSH3s4gLGJcbw+FtVbocSkSyZmHNau+c4fQPK9fPz3Q7FmDOKj4nmtkWTeXlvHYebOt0OJ+JYMjHn9Od3jzExI4H5E9LcDsWYs7pjyWQ8UcIv1h9yO5SIY8nEnFVjRw/ryxtZMTffBiqaoJeTGs9H5+Xzu7JqWk/2uR1ORLFkYs7qD2/XMOBVbrqkwO1QjPHLXVcU0dk7wC/WW9vJWLJkYs5IVVm9tZrSyeMozrG5uExomJ2fxvLZ43nszSpOdPa6HU7EsGRizmjroRNUNnRy86UT3Q7FmPNy74em0dHbz0/XVbodSsQIaDIRkeUisl9EykXkvmHejxORp533N4tI4aD3vuFs3y8iHx60/ZCI7BSRHSJSFsj4I93qrUdIjvOwwgYqmhAzLTeFVfPz+eWGKurbu90OJyIELJmISDTwQ+A6YBZwq4jMGlLsLuCEqhYDDwMPOfvOAm4BZgPLgR85xzvlKlW9SFVLAxV/pGs92ccLO4+x8qJ8EmM9bodjzHn72jXT6BtQfvR6hduhRIRA3pksBMpVtVJVe4HVwKohZVYBTzjPnwWuFl+XoVXAalXtUdUqoNw5nhkja945Snefl1usisuEqMKsJD5ZOoGnNh+h5kSX2+GEvUAmkwJg8JzQNc62Ycuoaj/QCmSeY18F/ioi20Tk7gDEbYCntx5hZl4qcwtsbIkJXV9ZVkJUFHz3z3vdDiXshWID/FJVvRhf9dmXReTK4QqJyN0iUiYiZQ0NDWMbYYjbUd3Crto2brl0oo0tMSEtPz2Brywr4aXdx08vOW0CI5CV4bXA4DqSCc624crUiIgHSAOazravqp76WS8iz+Gr/lo39OSq+ijwKEBpaalNIzrEU5uPnPG91VuPEOeJwuvVs5YzZjQE+juWEu8hOzmOf/rdO3z16hJiot//P/SnFk0KaAyRIJB3JluBEhEpEpFYfA3qa4aUWQPc6Ty/CXhNffNHrwFucXp7FQElwBYRSRKRFAARSQI+BOwK4GeIOC1dveyqbeXSwgziYqLPvYMxQc4TFcXKi/Jp7uzljf1WSxEoAbszUdV+EbkHWAtEA4+r6m4ReRAoU9U1wGPAkyJSDjTjSzg45Z4B9gD9wJdVdUBEcoHnnKoXD/CUqr4UqM8QiTZVNqEKS6Zkuh2KMaNmanYyF01MZ93BBi6amE52SpzbIYWdgPb5VNUXgBeGbLt/0PNu4BNn2Pd7wPeGbKsE5o9+pAbgZO8Am6uamVOQxrikWLfDMWZUXTdnPPuPt/NMWTVf+sBUWzF0lIViA7wJkI2VTfT0e/ng9Gy3QzFm1KXEx3DjggJqW07yyt46t8MJO5ZMDAA9/QNsqGhkxvgU8tIS3A7HmICYU5BG6eRxrDvQQGVDh9vhhBVLJgaAjRVNdPUO8MHpOW6HYkxAXT8vn8zkWH63rYau3n63wwkblkwMJ3sHWHewgRnjU5iUkeh2OMYEVKwniptLJ9HR3c8zZdV41UYOjAZLJoZ1Bxvo7vNy7axct0MxZkwUjEtg5fx8DtR18NKu426HExYsmUS4E529rC9v5KKJ6dZWYiLKpUUZLJ6SyVvljTy7rcbtcEKeJZMI9+Lu44jAh+yuxESgFXPzmJqdxDf/sJOyQ81uhxPSLJlEsIqGDnbVtnJlSTbpiTauxESe6Cjh1oWTKBiXwF1PlLHveJvbIYUsSyYRqm/Ay/Pba8lIiuWKEhtXYiJXYqyHX31uIQkx0dz+2BaONNl09RfCkkmEen1/PU2dvay6KJ9Yj30NTGSbmJHIk3ctpG/Ay22PbaKuzVZnPF/2VyQCbT9ygnUHGlgwMZ2SnBS3wzEmKJTkpvDEZxfS3NHLrY9u4ljrSbdDCimWTCJMV28/9z7zDqnxMXx0fr7b4RgTVOZPTOeJzy2kvr2HT/50I9XNVuXlL0smEURV+dZzuzjU1MlNl0wg3qaYN+Z9Sgsz+PXnF9Ha1cfNP91IVWOn2yGFBEsmEeTXm4/w3PZa7r1mGlOyk90Ox5igddHEdH5792K6+73c9OMN7KhucTukoGfJJEKsL2/kO2t2c9X0bL58VbHb4RgT9Gbnp/G7Ly0hMS6aWx7dyMt7bKbhs7FkEgEO1LXzpV9vY0p2Ev956wKibB0HY/wyNTuZP/y3y5mWm8IXnyzjl+urUJvLa1iWTMLcocZObvv5ZhJionn8M5eSGh/jdkjGhJTslDhW372YZTNyeeBPe/iXZ9+lu2/A7bCCjiWTMFbZ0MGnfraJ/gEvv/n8IiaMsxmBjbkQibEeHr39Ev5xWTG/21bDzdZ1+H0smYSpXbWtfPKnm+jp9/Lrzy+iJNfGkxgzElFRwr0fms5Pb7+E8rp2VvzgLV61FRtPs2QShp7fXsvHf7yB2Gjh6S8uYXZ+mtshGRM2Pjx7PH+8ZynjU+O564kyHliz26q9sGQSVvoHvHzvL3v42tM7mD8xnTVfWUpxjnUBNma0Feck89yXL+Nzlxfxyw2HWPnIW2w/csLtsFxlySRMHKhr56afbORnb1Zx55LJ/Obzi8hKjnM7LGPCVpwnmvs/OotffPZS2rv7+diPN/C//rwnYpcC9rgdgBmZ3n4vP36jgkdeP0hKfAw/uHUBK22aFGPGzFXTc/jr16/koZf28dhbVby06zj/snw6K+fnIxI53fDtziREDXiV32+r4ervv8HDrxzgI3PzePnrV1oiMcYFKfExfPeGuTzzxSWkJcTw1dU7uPFHG9gaQQtu2Z1JiOnpH+DFncf54evlHKzvYHZ+Kk98bi4fmGZrkhjjtoVFGfzpK0v5w9s1/Nva/XziJxtZMiWTe5YVc9nUzLC+U7FkEiKqm7t4ems1q7ceobGjl+KcZH5028Usnz3eRrQbE0Sio4RPlE5kxbw8ntp8hEfXVXLbzzczf0Iaty2ezPXz8kiMDb8/vRIJUwOUlpZqWVmZ22GctyNNXfx1z3H+9O4x3qluQQSunpHDHUsKWVqcNaIk8tTmI6MYqTGh7VOLJgXs2N19Azy7rYZfrK+ioqGTlDgPKy/KZ8XcPBYWZeCJDt7WBhHZpqql/pQNv/QYorxepaqpk121rWyqbGZ9eSNHnLUU5hakcd91M7h+Xp6NYjcmxMTHRPPpxZO5bdEkth46wW+3HOH3b9fwm81HGJcYw9Uzc7m8OJPFUzLJS0twO9wLFtBkIiLLgf8EooGfq+q/Dnk/DvgVcAnQBNysqoec974B3AUMAP+oqmv9OWaw6xvwcqylm8PNnRxp7qKyoZOdta3sOdpGR4+vS2FKnIfFUzP5/BVFfGBaNpMzk1yO2hgzUiLCwqIMFhZl8L0b57DuQAMv7TrOy3vqeHZbDQCTMhKZW5DGrPxUZuWlMjkzkYJxCcR5gn/toYAlExGJBn4IXAvUAFtFZI2q7hlU7C7ghKoWi8gtwEPAzSIyC7gFmA3kA6+IyDRnn3Mdc9SoKn0DSr/X6/s54KXfq/QNeOkftL2n30tnTz8dPf10nnr0DtB2so/Gjh6aOnpp7Oylsb2H423dDHj/XrUY54liVn4qH7u4gDkFacwtSKMkJzmob32NMSOTGOth+Zw8ls/Jw+tV9h5vY1NlM1urmtlZ28pfdh47XVYEclPimZiRQF5aAhlJsaQnxjg/Y0mKjSYhJpr42GjiPdEkOK9jPVF4ooWUOM+YNPwH8s5kIVCuqpUAIrIaWAUM/sO/CnjAef4s8Ij4PvUqYLWq9gBVIlLuHA8/jjlqrvn+36houPBV1jxRQmZyLJlJcWQmxzIlK4mC9AQmZSQyKTORSRmJjE+NtwZ0YyJYVJQwOz+N2flp3LW0CIDWk30cqGunurmLI81dVDefpLq5i3drWmju7KWt2/+Bkfv+1/IxWVU1kMmkAKge9LoGWHSmMqraLyKtQKazfdOQfQuc5+c6JgAicjdwt/OyQ0T2X8BnGLGKsTtVFtA4dqcLenY93s+uyXudvh63uRxIICU85HfRod+P8/quhG0DvKo+CjzqdhxjRUTK/O11EQnseryfXZP3suvxXiO9HoGsmK8FJg56PcHZNmwZEfEAafga4s+0rz/HNMYYM8YCmUy2AiUiUiQisfga1NcMKbMGuNN5fhPwmvoGvqwBbhGROBEpAkqALX4e0xhjzBgLWDWX0wZyD7AWXzfex1V1t4g8CJSp6hrgMeBJp4G9GV9ywCn3DL6G9X7gy6o6ADDcMQP1GUJMxFTp+cmux/vZNXkvux7vNaLrEREj4I0xxgSWDWYwxhgzYpZMjDHGjJglkxAlIuki8qyI7BORvSKyREQyRORlETno/BzndpxjRUS+LiK7RWSXiPxWROKdjhqbRaRcRJ52Om2EJRF5XETqRWTXoG3Dfh/E5wfOdXlXRC52L/LAOMP1+Dfn9+VdEXlORNIHvfcN53rsF5EPuxN14Ax3PQa9908ioiKS5by+oO+HJZPQ9Z/AS6o6A5gP7AXuA15V1RLgVed12BORAuAfgVJVnYOvc8ap6XkeVtVi4AS+6XvC1S+B5UO2nen7cB2+HpIl+Ab2/niMYhxLv+T91+NlYI6qzgMOAN8AGDJ903LgR850UOHkl7z/eiAiE4EPAYOnEb+g74clkxAkImnAlfh6w6Gqvaragm9qmSecYk8AN7gToSs8QIIzXikROAYswzdND4T59VDVdfh6RA52pu/DKuBX6rMJSBeRvLGJdGwMdz1U9a+qemoekk34xqnBoOmbVLUKGDx9U1g4w/cD4GHgX4DBPbEu6PthySQ0FQENwC9EZLuI/FxEkoBcVT01Q9xxINe1CMeQqtYC/47vv6tjQCuwDWgZ9Mdj8JQ8keJM34fhpjqKtGvzOeBF53lEXg8RWQXUquo7Q966oOthySQ0eYCLgR+r6gKgkyFVWs7gz4jo9+20BazCl2TzgSSGuaWPZJH0fTgXEfkWvvFrv3E7FreISCLwTeD+0TqmJZPQVAPUqOpm5/Wz+JJL3anbUednvUvxjbVrgCpVbVDVPuAPwOX4bs9PDcyNxKl3zvR9iNhpiUTkM8D1wG3690F2kXg9puL75+sdETmE7zO/LSLjucDrYckkBKnqcaBaRKY7m67GN1vA4Olp7gT+6EJ4bjgCLBaRRGcJg1PX43V80/RAZF2PU870fVgD3OH02lkMtA6qDgtb4ltY71+AlaraNeitM03fFLZUdaeq5qhqoaoW4vsH9WLnb8uFfT9U1R4h+AAuAsqAd4HngXH4pu9/FTgIvAJkuB3nGF6P7wD7gF3Ak0AcMAXfH4Vy4HdAnNtxBvDz/xZfe1Gf84fhrjN9HwDBt8hcBbATXy841z/DGFyPcnxtATucx08Glf+Wcz32A9e5Hf9YXI8h7x8Cskby/bDpVIwxxoyYVXMZY4wZMUsmxhhjRsySiTHGmBGzZGKMMWbELJkYY4wZMUsmxowSEXlYRL426PVaEfn5oNf/ISL3nmHfB0XkmrMc+wZnQkJjgpIlE2NGz3rgMgARiQKy8M1Ee8plwIbhdlTV+1X1lbMc+wbAkokJWpZMjBk9G4AlzvPZ+AZQtovIOBGJA2YCHxKRrc66K486I/YRkV+KyE3O838VkT3OWhL/LiKXASuBfxORHSIydew/mjFn5zl3EWOMP1T1qIj0i8gkfHchG/HNtroE30zGO4FHVPVBABF5Et88UX86dQwRyQRuBGaoqopIuqq2iMga4M+q+izGBCG7MzFmdG3Al0hOJZONg16vB65yVn/ciW+9ldlD9m8FuoHHRORjQBfGhABLJsaMrlPtJnPxVXNtwndncqq95EfATao6F/gZED94Z/Wtv7IQ30zQ1wMvjVnkxoyAJRNjRtcGfEmgWVUHVLUZSMeXUE41vjeKSDJ/n9H4NGd7mqq+AHwd35LMAO1ASqCDN+ZCWTIxZnTtxNeLa9OQba2q2ojvbmQXsBbYOsz+KcCfReRd4C3gVFfi1cA/OytrWgO8CTo2a7AxxpgRszsTY4wxI2bJxBhjzIhZMjHGGDNilkyMMcaMmCUTY4wxI2bJxBhjzIhZMjHGGDNi/z+dB3EnzFTAzAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## plotting distribution for adipose tissue (AT)\n",
        "sns.distplot(wtat.AT)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "k5f0Nha-l9aA",
        "outputId": "73479654-3629-4158-cf14-f44445efc1f4"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbf3d680510>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q 22) Calculate the Z scores of 90% confidence interval,94% confidence interval, 60% confidence interval"
      ],
      "metadata": {
        "id": "g1cCvkOOmBoX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#for 90% ,  \n",
        "z_score = 1-0.90\n",
        "print('Z_Score for 90 % is:', z_score)\n",
        "#Z0.1 = 1.28  (Using Z-Table)\n",
        "#For 94 % ,\n",
        "z_score1 = (1-0.94)\n",
        "print('Z_Score for 90 % is:', z_score1)\n",
        "#Z0.06 = 1.55  (Using Z-Table)\n",
        "#For 60% ,  \n",
        "z_score2 = (1-0.60)\n",
        "print('Z_Score for 90 % is:', z_score2)\n",
        "#Z0.4 = 0.25  (Using Z-Table)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l4DTHUCHmEhT",
        "outputId": "e84a8a81-da6c-4874-fd07-e21950a5a4b5"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Z_Score for 90 % is: 0.09999999999999998\n",
            "Z_Score for 90 % is: 0.06000000000000005\n",
            "Z_Score for 90 % is: 0.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q 23) Calculate the t scores of 95% confidence interval, 96% confidence interval, 99% confidence interval for sample size of 25"
      ],
      "metadata": {
        "id": "mOPppHrzmJWn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "t_score= (1-0.95)/2\n",
        "print('t_score of 95% is:',t_score)\n",
        "#t0.025= 2.060\n",
        "t_score1= (1-0.96)/2\n",
        "print('t_score of 96% is:',t_score1)\n",
        "#t0.02= 2.485\n",
        "t_score2= (1-0.99)/2\n",
        "print('t_score of 99% is:',t_score2)\n",
        "#t0.005= 2.787"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-uK2xfZmNeU",
        "outputId": "aa6d42b0-f7af-474d-ea04-208233f01056"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "t_score of 95% is: 0.025000000000000022\n",
            "t_score of 96% is: 0.020000000000000018\n",
            "t_score of 99% is: 0.0050000000000000044\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q 24) A Government company claims that an average light bulb lasts 270 days. A researcher randomly selects 18 bulbs for testing. The sampled bulbs last an average of 260 days, with a standard deviation of 90 days. If the CEO's claim were true, what is the probability that 18 randomly selected bulbs would have an average life of no more than 260 days Hint:\n",
        "rcode  pt(tscore,df)\n",
        "df  degrees of freedom"
      ],
      "metadata": {
        "id": "-9PW0aLimStB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#H0= average life >= 260 days\n",
        "#H1 = average life < 260 days\n",
        "# tscore = x - µ / (ϭ / √n)\n",
        "tscore = (260-270) / (90/ np.sqrt(18))\n",
        "print('T_Score is', tscore)\n",
        "p_value = stats.t.cdf(-0.4714, df = 17)\n",
        "print('P_value is:', p_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4OiwZOhdmW9N",
        "outputId": "50df7f8c-648d-4737-e3b0-a08691fea12e"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T_Score is -0.4714045207910317\n",
            "P_value is: 0.32167411684460556\n"
          ]
        }
      ]
    }
  ]
}