Linear_regression1.ipynb

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        "##import data set"
      ],
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      "source": [
        "import pandas as pd\n",
        "data= pd.read_csv(\"/content/NewspaperData.csv\")\n",
        "data.head()\n"
      ],
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              "            Newspaper    daily   sunday\n",
              "0       Baltimore Sun  391.952  488.506\n",
              "1        Boston Globe  516.981  798.298\n",
              "2       Boston Herald  355.628  235.084\n",
              "3  Charlotte Observer  238.555  299.451\n",
              "4   Chicago Sun Times  537.780  559.093"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Newspaper</th>\n",
              "      <th>daily</th>\n",
              "      <th>sunday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Baltimore Sun</td>\n",
              "      <td>391.952</td>\n",
              "      <td>488.506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Boston Globe</td>\n",
              "      <td>516.981</td>\n",
              "      <td>798.298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Boston Herald</td>\n",
              "      <td>355.628</td>\n",
              "      <td>235.084</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Charlotte Observer</td>\n",
              "      <td>238.555</td>\n",
              "      <td>299.451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Chicago Sun Times</td>\n",
              "      <td>537.780</td>\n",
              "      <td>559.093</td>\n",
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              "            '<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",
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              "                         Newspaper     daily    sunday\n",
              "0                    Baltimore Sun   391.952   488.506\n",
              "1                     Boston Globe   516.981   798.298\n",
              "2                    Boston Herald   355.628   235.084\n",
              "3               Charlotte Observer   238.555   299.451\n",
              "4                Chicago Sun Times   537.780   559.093\n",
              "5                  Chicago Tribune   733.775  1133.249\n",
              "6              Cincinnati Enquirer   198.832   348.744\n",
              "7                      Denver Post   252.624   417.779\n",
              "8              Des Moines Register   206.204   344.522\n",
              "9                 Hartford Courant   231.177   323.084\n",
              "10               Houston Chronicle   449.755   620.752\n",
              "11                Kansas City Star   288.571   423.305\n",
              "12         Los Angeles Daily  News   185.736   202.614\n",
              "13               Los Angeles Times  1164.388  1531.527\n",
              "14                    Miami Herald   444.581   553.479\n",
              "15        Minneapolis Star Tribune   412.871   685.975\n",
              "16      New Orleans Times-Picayune   272.280   324.241\n",
              "17             New York Daily News   781.796   983.240\n",
              "18                  New York Times  1209.225  1762.015\n",
              "19                         Newsday   825.512   960.308\n",
              "20              Omaha World Herald   223.748   284.611\n",
              "21          Orange County Register   354.843   407.760\n",
              "22           Philadelphia Inquirer   515.523   982.663\n",
              "23                Pittsburgh Press   220.465   557.000\n",
              "24              Portland Oregonian   337.672   440.923\n",
              "25     Providence Journal-Bulletin   197.120   268.060\n",
              "26  Rochester Democrat & Chronicle   133.239   262.048\n",
              "27             Rocky Mountain News   374.009   432.502\n",
              "28                  Sacramento Bee   273.844   338.355\n",
              "29         San Francisco Chronicle   570.364   704.322\n",
              "30         St. Louis Post-Dispatch   391.286   585.681\n",
              "31          St. Paul Pioneer Press   201.860   267.781\n",
              "32                   Tampa Tribune   321.626   408.343\n",
              "33                 Washington Post   838.902  1165.567"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Newspaper</th>\n",
              "      <th>daily</th>\n",
              "      <th>sunday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Baltimore Sun</td>\n",
              "      <td>391.952</td>\n",
              "      <td>488.506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Boston Globe</td>\n",
              "      <td>516.981</td>\n",
              "      <td>798.298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Boston Herald</td>\n",
              "      <td>355.628</td>\n",
              "      <td>235.084</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Charlotte Observer</td>\n",
              "      <td>238.555</td>\n",
              "      <td>299.451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Chicago Sun Times</td>\n",
              "      <td>537.780</td>\n",
              "      <td>559.093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Chicago Tribune</td>\n",
              "      <td>733.775</td>\n",
              "      <td>1133.249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Cincinnati Enquirer</td>\n",
              "      <td>198.832</td>\n",
              "      <td>348.744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Denver Post</td>\n",
              "      <td>252.624</td>\n",
              "      <td>417.779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Des Moines Register</td>\n",
              "      <td>206.204</td>\n",
              "      <td>344.522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Hartford Courant</td>\n",
              "      <td>231.177</td>\n",
              "      <td>323.084</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Houston Chronicle</td>\n",
              "      <td>449.755</td>\n",
              "      <td>620.752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Kansas City Star</td>\n",
              "      <td>288.571</td>\n",
              "      <td>423.305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Los Angeles Daily  News</td>\n",
              "      <td>185.736</td>\n",
              "      <td>202.614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Los Angeles Times</td>\n",
              "      <td>1164.388</td>\n",
              "      <td>1531.527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Miami Herald</td>\n",
              "      <td>444.581</td>\n",
              "      <td>553.479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Minneapolis Star Tribune</td>\n",
              "      <td>412.871</td>\n",
              "      <td>685.975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>New Orleans Times-Picayune</td>\n",
              "      <td>272.280</td>\n",
              "      <td>324.241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>New York Daily News</td>\n",
              "      <td>781.796</td>\n",
              "      <td>983.240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>New York Times</td>\n",
              "      <td>1209.225</td>\n",
              "      <td>1762.015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Newsday</td>\n",
              "      <td>825.512</td>\n",
              "      <td>960.308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Omaha World Herald</td>\n",
              "      <td>223.748</td>\n",
              "      <td>284.611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Orange County Register</td>\n",
              "      <td>354.843</td>\n",
              "      <td>407.760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Philadelphia Inquirer</td>\n",
              "      <td>515.523</td>\n",
              "      <td>982.663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Pittsburgh Press</td>\n",
              "      <td>220.465</td>\n",
              "      <td>557.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Portland Oregonian</td>\n",
              "      <td>337.672</td>\n",
              "      <td>440.923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Providence Journal-Bulletin</td>\n",
              "      <td>197.120</td>\n",
              "      <td>268.060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Rochester Democrat &amp; Chronicle</td>\n",
              "      <td>133.239</td>\n",
              "      <td>262.048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Rocky Mountain News</td>\n",
              "      <td>374.009</td>\n",
              "      <td>432.502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Sacramento Bee</td>\n",
              "      <td>273.844</td>\n",
              "      <td>338.355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>San Francisco Chronicle</td>\n",
              "      <td>570.364</td>\n",
              "      <td>704.322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>St. Louis Post-Dispatch</td>\n",
              "      <td>391.286</td>\n",
              "      <td>585.681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>St. Paul Pioneer Press</td>\n",
              "      <td>201.860</td>\n",
              "      <td>267.781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Tampa Tribune</td>\n",
              "      <td>321.626</td>\n",
              "      <td>408.343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Washington Post</td>\n",
              "      <td>838.902</td>\n",
              "      <td>1165.567</td>\n",
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              "          document.querySelector('#df-10f8a7c7-7660-4c99-8355-bf7f94d79203 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-10f8a7c7-7660-4c99-8355-bf7f94d79203');\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": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_nPjnJSKZAmK",
        "outputId": "f5c79ec5-8a0b-4523-9035-60e6346bd163"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 34 entries, 0 to 33\n",
            "Data columns (total 3 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Newspaper  34 non-null     object \n",
            " 1   daily      34 non-null     float64\n",
            " 2   sunday     34 non-null     float64\n",
            "dtypes: float64(2), object(1)\n",
            "memory usage: 944.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#correlation"
      ],
      "metadata": {
        "id": "LJFQIuj6Z9S4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "OM3kdwCeZ0rQ",
        "outputId": "b1bbce04-7918-445d-f146-8376fb4a56ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           daily    sunday\n",
              "daily   1.000000  0.958154\n",
              "sunday  0.958154  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fe8ba38a-97ea-4973-a929-63d708320107\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>daily</th>\n",
              "      <th>sunday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>daily</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.958154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sunday</th>\n",
              "      <td>0.958154</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe8ba38a-97ea-4973-a929-63d708320107')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \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",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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-fe8ba38a-97ea-4973-a929-63d708320107 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-fe8ba38a-97ea-4973-a929-63d708320107');\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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns \n",
        "sns.displot(data['daily'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "l0Md2lMwZ08g",
        "outputId": "42082075-dfd1-477b-ecc5-a23ed235a89d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f4487a38700>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(data ['sunday'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "-6F3LTiYZ0_q",
        "outputId": "b489e58a-a146-4eac-ff57-0f55b33555b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f44b1c63220>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#fitting a linear regression model"
      ],
      "metadata": {
        "id": "9yIDx7gTfcXK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.formula.api as smf # model - Regression model, \"Y~X\", OR \"Dependent_variable~Independent_Variable\"\n",
        "model=smf.ols(\"sunday~daily\",data=data).fit()  # Ordinary Least Squares, y Dep. variable - sunday, then give ~ symbol and then x Ind.Variable - daily, \n",
        "                                                  # dataset - data, fit() - fit the regression line"
      ],
      "metadata": {
        "id": "6VtXejFhZ1FW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Coefficients are B0 and B1\n",
        "model.params\n",
        "# Reg. Equn: Sunday = B0+B1*daily\n",
        "# In output: Intercept - B0, daily - coefficient of daily i.e.B1\n",
        "# Sunday = 13.83 + 1.33 * daily\n",
        "# we assumed daily circulation as 200\n",
        "# So Sunday = 13.83 + 1.33 * 200"
      ],
      "metadata": {
        "id": "HnaeTvAnZ1Ir",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1074fad9-7a09-4754-b615-615501c5f692"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intercept    13.835630\n",
              "daily         1.339715\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sun=13.83+1.33*200\n",
        "sun"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MYngUVFmhhrT",
        "outputId": "5ddb32a7-f0d6-4539-bf01-0fc32e1f4a6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "279.83"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#R squared values - 0.92 - Good reg. equn.\n",
        "(model.rsquared,model.rsquared_adj)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BFToe6Zlhhuq",
        "outputId": "73fcc26e-e015-4322-ba1c-d294e49a5a25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9180596895873295, 0.9154990548869336)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# model.summary() # OLS - Ordinary Least Squares"
      ],
      "metadata": {
        "id": "7CXdt6WEhhyN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x=\"daily\",y=\"sunday\", data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "2z3-CLRzhh1Z",
        "outputId": "110203ca-f343-4b52-e7a1-31610383f931"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f44791274f0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "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": "code",
      "source": [
        "#t and p value\n",
        "print(model.tvalues, '\\n', model.pvalues)\n",
        "#we have estimate B0=13.8,, B1=1.33. We have t-value 0.38 and 18.93, p-value - 0.7, 6.01 is approx.= 0\n",
        "#focus on B1. B1 is slope. Define H0 and H1."
      ],
      "metadata": {
        "id": "F1Vj99T9hh41",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97e2a19f-56f6-4f17-84bb-6cb2a640b6a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept     0.386427\n",
            "daily        18.934840\n",
            "dtype: float64 \n",
            " Intercept    7.017382e-01\n",
            "daily        6.016802e-19\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1100 manual calculation\n",
        "(1.3*1100)+13.835630"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E0g-zeUH-n8d",
        "outputId": "d4d86221-b7a6-4a39-896b-4a41be50bbb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1443.83563"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#predict for new data"
      ],
      "metadata": {
        "id": "sXqcvPFMHYYN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Predict for 200 and 300 daily circulation\n",
        "newdata=pd.Series([200,300]) # x value is (daily circulation) 200 at one branch and 300 at another branch"
      ],
      "metadata": {
        "id": "6adhpW5w-oG6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newdata"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ScuHwqh0-oKU",
        "outputId": "d70bda70-5515-43c8-c9c1-2a8d4694a23d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    200\n",
              "1    300\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_pred=pd.DataFrame(newdata,columns=['daily'])"
      ],
      "metadata": {
        "id": "O7chLtUC-oNb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "jVsW8t53-oQP",
        "outputId": "205cb926-cb87-4a0e-80ca-af32e024394a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   daily\n",
              "0    200\n",
              "1    300"
            ],
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              "      <div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\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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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-6070f605-986d-4d5d-ba43-3089f8764476 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-6070f605-986d-4d5d-ba43-3089f8764476');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(data_pred) # model - regression model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IiBQZ4dtHs2V",
        "outputId": "6272df5a-1660-43a4-cd8c-12e7113979b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    281.778581\n",
              "1    415.750057\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RDOJm_VmHs5P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l9IKksdmHs80"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}