SimpleLinearRegression_Assignment5_LearnerFacing.ipynb

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        "<a href=\"https://colab.research.google.com/github/Joykareko/Data-Science-Projects/blob/main/SimpleLinearRegression_Assignment5_LearnerFacing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Activity: Run simple linear regression"
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        "## Introduction\n",
        "\n",
        "In this lab, you will explore a marketing and sales dataset and conduct simple linear regression. Linear regression is one of the approaches for modeling the relationship between two variables. This activity will deepen your understanding of linear regression and prepare you for future scenarios where you will use regression to draw insights about the relationship between variables.\n",
        "\n",
        "For this activity, imagine that you are part of an analytics team that provides insights about marketing and sales. You have been assigned to a project that focuses on the use of influencer marketing, and you would like to explore the relationship between radio promotion budget and sales. The dataset provided includes information about marketing campaigns across TV, radio, and social media, as well as how much revenue in sales was generated from these campaigns. Based on your analysis, leaders in your company will make decisions about where to focus future marketing efforts, so it is critical to have a clear understanding of the relationship between types of marketing and the revenue they generate."
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      "source": [
        "## Step 1. Imports"
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      "source": [
        "# Import relevant Python libraries and modules\n",
        "import pandas as pd\n",
        "### YOUR CODE HERE ###\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb"
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      "source": [
        "### Import relevant packages.\n",
        "\n",
        "In this section, you will start by importing relevant libraries and modules."
      ]
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        "### Now load the dataset into a DataFrame. \n",
        "\n",
        "The dataset provided is a csv file (named `marketing_sales_data.csv`) containing information about promotional marketing conducted in collaboration with influencers, along with the corresponding sales. This is a fictional dataset that was created for educational purposes and modified for this lab. Assume that the numerical variables in the data are expressed in millions of dollars."
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              "    TV     Radio  Social Media Influencer      Sales\n",
              "0  Low  3.518070      2.293790      Micro  55.261284\n",
              "1  Low  7.756876      2.572287       Mega  67.574904"
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              "      <th></th>\n",
              "      <th>TV</th>\n",
              "      <th>Radio</th>\n",
              "      <th>Social Media</th>\n",
              "      <th>Influencer</th>\n",
              "      <th>Sales</th>\n",
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              "      <th>0</th>\n",
              "      <td>Low</td>\n",
              "      <td>3.518070</td>\n",
              "      <td>2.293790</td>\n",
              "      <td>Micro</td>\n",
              "      <td>55.261284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Low</td>\n",
              "      <td>7.756876</td>\n",
              "      <td>2.572287</td>\n",
              "      <td>Mega</td>\n",
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              "  "
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      "source": [
        "# Load the dataset into a DataFrame and save in a variable\n",
        "data = pd.read_csv('/content/marketing_sales_data.csv')\n",
        "### YOUR CODE HERE ###\n",
        "data.head(2)"
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        "tags": [],
        "id": "lXPOJ3LO-v7J"
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      "source": [
        "<details>\n",
        "  <summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "You may refer back to what you learned about loading data in Python.\n",
        "\n",
        "</details>"
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      "id": "lXPOJ3LO-v7J"
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        "tags": [],
        "id": "O5TEL8Ty-v7L"
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      "source": [
        "<details>\n",
        "  <summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "There is a function in the `pandas` library that allows you to read data from a csv file and load the data into a DataFrame.\n",
        " \n",
        "\n",
        "</details>"
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      "id": "O5TEL8Ty-v7L"
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        "id": "kEx2SsH2-v7M"
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      "source": [
        "<details>\n",
        "  <summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "Use the `read_csv()` function from the `pandas` library. \n",
        "\n",
        "</details>"
      ],
      "id": "kEx2SsH2-v7M"
    },
    {
      "cell_type": "markdown",
      "id": "18981762-ab15-441e-8cc7-16848fd6c9d2",
      "metadata": {
        "id": "18981762-ab15-441e-8cc7-16848fd6c9d2"
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      "source": [
        "## Step 2. Data exploration"
      ]
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    {
      "cell_type": "markdown",
      "id": "07264b51-9724-4cba-b5f0-3f508e392675",
      "metadata": {
        "id": "07264b51-9724-4cba-b5f0-3f508e392675"
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      "source": [
        "### Start to learn about the data by displaying the first 10 rows of the data."
      ]
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              "       TV      Radio  Social Media Influencer       Sales\n",
              "0     Low   3.518070      2.293790      Micro   55.261284\n",
              "1     Low   7.756876      2.572287       Mega   67.574904\n",
              "2    High  20.348988      1.227180      Micro  272.250108\n",
              "3  Medium  20.108487      2.728374       Mega  195.102176\n",
              "4    High  31.653200      7.776978       Nano  273.960377\n",
              "5     Low   5.561586      3.530209       Nano   39.992087\n",
              "6  Medium  13.951808      4.477142      Micro  147.150178\n",
              "7  Medium  28.352592      4.695376       Mega  229.141912\n",
              "8  Medium  15.322175      4.379750      Macro  222.696768\n",
              "9    High  26.914287      6.074165       Mega  322.466797"
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              "      <th>TV</th>\n",
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              "      <th>0</th>\n",
              "      <td>Low</td>\n",
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              "      <th>1</th>\n",
              "      <td>Low</td>\n",
              "      <td>7.756876</td>\n",
              "      <td>2.572287</td>\n",
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              "      <th>2</th>\n",
              "      <td>High</td>\n",
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              "      <td>Micro</td>\n",
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              "      <th>3</th>\n",
              "      <td>Medium</td>\n",
              "      <td>20.108487</td>\n",
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              "      <th>5</th>\n",
              "      <td>Low</td>\n",
              "      <td>5.561586</td>\n",
              "      <td>3.530209</td>\n",
              "      <td>Nano</td>\n",
              "      <td>39.992087</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Medium</td>\n",
              "      <td>13.951808</td>\n",
              "      <td>4.477142</td>\n",
              "      <td>Micro</td>\n",
              "      <td>147.150178</td>\n",
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              "      <td>Medium</td>\n",
              "      <td>28.352592</td>\n",
              "      <td>4.695376</td>\n",
              "      <td>Mega</td>\n",
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              "      <th>8</th>\n",
              "      <td>Medium</td>\n",
              "      <td>15.322175</td>\n",
              "      <td>4.379750</td>\n",
              "      <td>Macro</td>\n",
              "      <td>222.696768</td>\n",
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              "      <th>9</th>\n",
              "      <td>High</td>\n",
              "      <td>26.914287</td>\n",
              "      <td>6.074165</td>\n",
              "      <td>Mega</td>\n",
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              "\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": 3
        }
      ],
      "source": [
        "# Display the first 10 rows of the data\n",
        "data.head(10)\n",
        "### YOUR CODE HERE ###\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "49881dfd-4532-424f-9729-f5378d984df0"
      },
      "source": [
        "<details>\n",
        "  <summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "You may find it helpful to refer back to what you learned about exploring datasets in Python.\n",
        "\n",
        "</details>"
      ],
      "id": "49881dfd-4532-424f-9729-f5378d984df0"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "k2QxFOi9HW1Q"
      },
      "source": [
        "<details>\n",
        "  <summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "  There is a function in the `pandas` library that allows you to get a specific number of rows from the top of a dataframe.\n",
        " \n",
        "\n",
        "</details>"
      ],
      "id": "k2QxFOi9HW1Q"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "ptkclWFzHjHv"
      },
      "source": [
        "<details>\n",
        "  <summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "Use the `head()` function from the `pandas` library. \n",
        "\n",
        "</details>"
      ],
      "id": "ptkclWFzHjHv"
    },
    {
      "cell_type": "markdown",
      "id": "70e40948-980d-41b1-a17d-78ecfd62ab21",
      "metadata": {
        "id": "70e40948-980d-41b1-a17d-78ecfd62ab21"
      },
      "source": [
        "#### **Question 1**: What do you observe about the different variables included in the data?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Double-click (or enter) to edit"
      ],
      "metadata": {
        "id": "Eh2HFr3CiPiC"
      },
      "id": "Eh2HFr3CiPiC"
    },
    {
      "cell_type": "markdown",
      "id": "d7dafc3d-ae53-471e-a91e-2b177dee576a",
      "metadata": {
        "id": "d7dafc3d-ae53-471e-a91e-2b177dee576a"
      },
      "source": [
        "### To learn about the data, identify the number of rows and the number of columns in the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "829c217b-5504-4b01-9f2f-ef4530c5d6a5",
      "metadata": {
        "id": "829c217b-5504-4b01-9f2f-ef4530c5d6a5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "337f18f1-46b6-4efb-aa89-b16b1ddcd8ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(572, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Display number of rows, number of columns\n",
        "data.shape\n",
        "### YOUR CODE HERE ###\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "AlENNCbBChZd"
      },
      "source": [
        "<details>\n",
        "  <summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "You may find it helpful to refer back to what you learned about exploring datasets in Python.\n",
        "\n",
        "</details>"
      ],
      "id": "AlENNCbBChZd"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "Wq72auJsChZf"
      },
      "source": [
        "<details>\n",
        "  <summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "There is a property that every DataFrame in `pandas` has; it gives you access to the number of rows and the number of columns as a tuple.\n",
        "\n",
        "</details>"
      ],
      "id": "Wq72auJsChZf"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "QhI8U7wkChZg"
      },
      "source": [
        "<details>\n",
        "  <summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "Use the `shape` property.\n",
        "\n",
        "</details>"
      ],
      "id": "QhI8U7wkChZg"
    },
    {
      "cell_type": "markdown",
      "id": "3c5721af-2d79-4d2c-a0b1-d38d2b154d47",
      "metadata": {
        "tags": [],
        "id": "3c5721af-2d79-4d2c-a0b1-d38d2b154d47"
      },
      "source": [
        "#### **Question 2**: What do you observe about the number of rows and number of columns in the data?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Double-click (or enter) to edit"
      ],
      "metadata": {
        "id": "OyHSIQkxiQp5"
      },
      "id": "OyHSIQkxiQp5"
    },
    {
      "cell_type": "markdown",
      "id": "be18e6af-bf42-44ac-86e8-e87e2d4a4ecf",
      "metadata": {
        "tags": [],
        "id": "be18e6af-bf42-44ac-86e8-e87e2d4a4ecf"
      },
      "source": [
        "### Check for missing values in the rows of the data.\n",
        "This is an important check since missing values are not that meaningful when modeling the relationship between two variables."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a087e249-4392-4168-80ee-153dfbd9660f",
      "metadata": {
        "id": "a087e249-4392-4168-80ee-153dfbd9660f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "c37f29d8-0d92-4df5-ba73-f736c87f2c4c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        TV  Radio  Social Media  Influencer  Sales\n",
              "0    False  False         False       False  False\n",
              "1    False  False         False       False  False\n",
              "2    False  False         False       False  False\n",
              "3    False  False         False       False  False\n",
              "4    False  False         False       False  False\n",
              "..     ...    ...           ...         ...    ...\n",
              "567  False  False         False       False  False\n",
              "568  False  False         False       False  False\n",
              "569  False  False         False       False  False\n",
              "570  False  False         False       False  False\n",
              "571  False  False         False       False  False\n",
              "\n",
              "[572 rows x 5 columns]"
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              "<p>572 rows × 5 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# Step 1. Start with .isna() to get booleans indicating whether each value in the data is missing\n",
        "data.isna()\n",
        "### YOUR CODE HERE ###\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3bd09ee1-60c5-4f88-bd50-17491f715a39",
      "metadata": {
        "id": "3bd09ee1-60c5-4f88-bd50-17491f715a39"
      },
      "source": [
        "If you would like to read more about the `isna()` function, refer to its documentation in the references section of this lab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f08b6e59-daf5-4aca-a63e-7b7f0c188449",
      "metadata": {
        "id": "f08b6e59-daf5-4aca-a63e-7b7f0c188449",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9100913c-3227-4b03-f5e0-6a833a62fd61"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      False\n",
              "1      False\n",
              "2      False\n",
              "3      False\n",
              "4      False\n",
              "       ...  \n",
              "567    False\n",
              "568    False\n",
              "569    False\n",
              "570    False\n",
              "571    False\n",
              "Length: 572, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Step 1. Start with .isna() to get booleans indicating whether each value in the data is missing\n",
        "# Step 2. Use .any(axis=1) to get booleans indicating whether there are any missing values along the columns in each row\n",
        "\n",
        "### YOUR CODE HERE ###\n",
        "data.isna().any(axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e6bc866e-b4da-4562-b066-3f1201b0e0c1",
      "metadata": {
        "id": "e6bc866e-b4da-4562-b066-3f1201b0e0c1"
      },
      "source": [
        "If you would like to read more about the `any()` function, refer to its documentation in the references section of this lab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8e403e86-0a72-44a9-8d92-dd57f9c0caca",
      "metadata": {
        "id": "8e403e86-0a72-44a9-8d92-dd57f9c0caca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6fb9e4d4-d631-421f-e367-3a14bb7d1694"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Step 1. Start with .isna() to get booleans indicating whether each value in the data is missing\n",
        "# Step 2. Use .any(axis=1) to get booleans indicating whether there are any missing values along the columns in each row\n",
        "# Step 3. Use .sum() to get the number of rows that contain missing values\n",
        "\n",
        "### YOUR CODE HERE ###\n",
        "data.isna().any(axis=1).sum()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8e75b7b7-8474-4eb7-9616-3f1dc017db86",
      "metadata": {
        "id": "8e75b7b7-8474-4eb7-9616-3f1dc017db86"
      },
      "source": [
        "If you would like to read more about the `sum()` function, refer to its documentation in the references section of this lab."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1b0da416-2142-4109-b1ca-48d0e620f2b2",
      "metadata": {
        "tags": [],
        "id": "1b0da416-2142-4109-b1ca-48d0e620f2b2"
      },
      "source": [
        "#### **Question 3**: What do you observe about the number of rows containing missing values?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Double-click (or enter) to edit"
      ],
      "metadata": {
        "id": "f9ActhhsiR0p"
      },
      "id": "f9ActhhsiR0p"
    },
    {
      "cell_type": "markdown",
      "id": "378974cf-892b-43f9-80fc-7b7271531915",
      "metadata": {
        "tags": [],
        "id": "378974cf-892b-43f9-80fc-7b7271531915"
      },
      "source": [
        "### Drop the rows that contain missing values.\n",
        "This is an important step in data cleaning since it makes the data more usable for the analysis and regression that you will conduct next."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fed80cba-aa90-4c9d-a9d9-d995fa9a649d",
      "metadata": {
        "id": "fed80cba-aa90-4c9d-a9d9-d995fa9a649d"
      },
      "outputs": [],
      "source": [
        "# Step 1. Use .dropna(axis=0) to indicate that you want rows which contain missing values to be dropped\n",
        "# Step 2. To update the DataFrame, reassign it to the result\n",
        "\n",
        "### YOUR CODE HERE ###\n",
        "data = data.dropna(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "540f9c5d-5036-4df3-976b-2c1348a235fe",
      "metadata": {
        "id": "540f9c5d-5036-4df3-976b-2c1348a235fe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b33957d-e385-438d-99df-05ff957fc992"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Check to make sure that the data does not contain any rows with missing values now\n",
        "\n",
        "# Step 1. Start with .isna() to get booleans indicating whether each value in the data is missing\n",
        "# Step 2. Use .any(axis=1) to get booleans indicating whether there are any missing values along the columns in each row\n",
        "# Step 3. Use .sum() to get the number of rows that contain missing values\n",
        "\n",
        "### YOUR CODE HERE ###\n",
        "data.isna().any(axis=1).sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "04271393-b4a7-40b8-8cfd-6dfd815f52bd",
      "metadata": {
        "tags": [],
        "id": "04271393-b4a7-40b8-8cfd-6dfd815f52bd"
      },
      "source": [
        "### Check model assumptions.\n",
        "You would like to explore the relationship between radio promotion budget and sales. You could model the relationship using linear regression. To do this, you want to check if the model assumptions for linear regression can be made in this context. Some of the assumptions can be addressed before the model is built --- you will address those in this section. After the model is built, you can finish checking the assumptions."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0781c130-3f65-4fcb-a137-cd6b3e057187",
      "metadata": {
        "id": "0781c130-3f65-4fcb-a137-cd6b3e057187"
      },
      "source": [
        "### Start by creating a plot of pairwise relationships in the data.\n",
        "This will help you visualize the relationships between variables in the data and help you check model assumptions. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cfb20968-e969-495b-9b34-1de2d7b110f6",
      "metadata": {
        "id": "cfb20968-e969-495b-9b34-1de2d7b110f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "outputId": "edf6e01b-18c7-4b57-812f-20e20d4ff646"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x540 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Create plot of pairwise relationships\n",
        "\n",
        "### YOUR CODE HERE ###\n",
        "sb.pairplot(data);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aba2029f-5371-4214-8497-9a8be492c68b",
      "metadata": {
        "tags": [],
        "id": "aba2029f-5371-4214-8497-9a8be492c68b"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "  You may find it helpful to refer back to what you learned about creating a plot that shows the \n",
        "  relationships between pairs of variables.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eecb9ac6-6cae-4b86-b2dc-b6f07b19a6bf",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "eecb9ac6-6cae-4b86-b2dc-b6f07b19a6bf"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "  There is a function in the `seaborn` library that you can call to create a plot that shows the \n",
        "  relationships between pairs of variables.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "edfdd31e-85f3-4d25-837f-72c09e93110d",
      "metadata": {
        "tags": [],
        "id": "edfdd31e-85f3-4d25-837f-72c09e93110d"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "  You can call the `pairplot()` function from the `seaborn` library.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4119330d-82f9-4075-8c12-3db273bdf9b3",
      "metadata": {
        "tags": [],
        "id": "4119330d-82f9-4075-8c12-3db273bdf9b3"
      },
      "source": [
        "#### **Question 4**: Is the assumption of linearity met?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Double-click (or enter) to edit"
      ],
      "metadata": {
        "id": "bp7NAEpyiTXH"
      },
      "id": "bp7NAEpyiTXH"
    },
    {
      "cell_type": "markdown",
      "id": "1be09689-a693-4059-adeb-945ce8e4e117",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "1be09689-a693-4059-adeb-945ce8e4e117"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "  You may find it helpful to refer back to what you learned about checking model assumptions for linear regression.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e88fc7fe-ce13-4a05-8b46-68f6e82516e7",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "e88fc7fe-ce13-4a05-8b46-68f6e82516e7"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "  Use the scatter plot of `Sales` over `Radio` found in the above plot of pairwise relationships. \n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9234580a-503a-47b4-83d2-ba8ee46ec407",
      "metadata": {
        "tags": [],
        "id": "9234580a-503a-47b4-83d2-ba8ee46ec407"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "  Check the scatter plot of `Sales` over `Radio` found in the above plot of pairwise relationships. If the data points cluster around a line, that would indicate that the assumption of linearity is met. Alternatively, if the data points resemble a random cloud or a curve, then a linear model may not fit the data well.  \n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "39ab5dd9-ea2a-46ce-b3a3-8077d52a833a",
      "metadata": {
        "id": "39ab5dd9-ea2a-46ce-b3a3-8077d52a833a"
      },
      "source": [
        "## Step 3. Model building"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3cd2422c-0f5e-4ac7-89ed-52803d971555",
      "metadata": {
        "id": "3cd2422c-0f5e-4ac7-89ed-52803d971555"
      },
      "source": [
        "### Start by selecting only the columns that are needed for the model that you will build from the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "17701ff3-82f0-4300-90c0-bca6ccce01eb",
      "metadata": {
        "id": "17701ff3-82f0-4300-90c0-bca6ccce01eb"
      },
      "outputs": [],
      "source": [
        "# Select relevant columns\n",
        "# Save resulting DataFrame in a separate variable to prepare for regression\n",
        "\n",
        "### YOUR CODE HERE ###\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e4c39136-f4d6-43a2-950a-e76db253f8c5",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "e4c39136-f4d6-43a2-950a-e76db253f8c5"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "  You may find it helpful to refer back to what you learned about selecting multiple columns from a DataFrame.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1a342164-70eb-4a3f-9ddf-e9a8a22a9ad1",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "1a342164-70eb-4a3f-9ddf-e9a8a22a9ad1"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "  Use two pairs of square brackets around the names of the columns that should be selected.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "260ce4a1-10b9-420b-be6d-b3e109227bd1",
      "metadata": {
        "tags": [],
        "id": "260ce4a1-10b9-420b-be6d-b3e109227bd1"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "  Make sure to column names are spelled exactly as they are in the data.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1ea8080e-7f93-4ea6-a0b6-bedeaed3a856",
      "metadata": {
        "id": "1ea8080e-7f93-4ea6-a0b6-bedeaed3a856"
      },
      "source": [
        "### Display the first 10 rows of the new DataFrame to ensure it looks right."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "39a7ccec-e572-46a8-9139-c4e159e75aae",
      "metadata": {
        "id": "39a7ccec-e572-46a8-9139-c4e159e75aae"
      },
      "outputs": [],
      "source": [
        "# Display first 10 rows of the new DataFrame\n",
        "\n",
        "### YOUR CODE HERE ###\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dcd2bea2-dbbd-4e07-85b7-ac3f3121fc20",
      "metadata": {
        "tags": [],
        "id": "dcd2bea2-dbbd-4e07-85b7-ac3f3121fc20"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "  You may find it helpful to refer back to what you learned about displaying contents of a DataFrame.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "119e5c4b-e37f-4832-bca5-7ea9170a1e60",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "119e5c4b-e37f-4832-bca5-7ea9170a1e60"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "  There is a function in the `pandas` library that allows you to display the first n number of rows of a DataFrame, where n is a number of your choice.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "68ec36bd-8617-43bd-be30-132fcb369db1",
      "metadata": {
        "tags": [],
        "id": "68ec36bd-8617-43bd-be30-132fcb369db1"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "  Call the `head()` function from the `pandas` library and pass in the number of rows from top that you want to display. \n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "be7f9869-c67e-4c87-b186-7461a00a9b37",
      "metadata": {
        "id": "be7f9869-c67e-4c87-b186-7461a00a9b37"
      },
      "source": [
        "### Write the linear regression formula for modeling the relationship between the two variables of interest."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f186ff9a-2c18-4cc6-acb3-3b7bf0c0c942",
      "metadata": {
        "id": "f186ff9a-2c18-4cc6-acb3-3b7bf0c0c942"
      },
      "outputs": [],
      "source": [
        "# Write the linear regression formula\n",
        "# Save it in a variable\n",
        "y = mx+c\n",
        "### YOUR CODE HERE ###\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "69f2b790-4859-4bf6-8eba-45998ddcc599",
      "metadata": {
        "tags": [],
        "id": "69f2b790-4859-4bf6-8eba-45998ddcc599"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "  You may find it helpful to refer back to what you learned where model building for linear regression is discussed. \n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4bf1a70c-61a8-4802-a16d-e6d8b08cc9ca",
      "metadata": {
        "tags": [],
        "id": "4bf1a70c-61a8-4802-a16d-e6d8b08cc9ca"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "  Save the formula as string.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "67d7406a-0074-4029-9bda-9f97f653a979",
      "metadata": {
        "tags": [],
        "id": "67d7406a-0074-4029-9bda-9f97f653a979"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "  Use a tilde to separate the y variable from the x variable so that the computer understands which is which. Make sure the spelling of each variable exactly matches the corresponding column from the data.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d855f4df-61d5-44a9-996c-9c4080d2be27",
      "metadata": {
        "id": "d855f4df-61d5-44a9-996c-9c4080d2be27"
      },
      "source": [
        "### Implement the Ordinary Least Squares (OLS) approach for linear regression."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0722345c-df7c-4563-a112-d30ea25a9ed8",
      "metadata": {
        "id": "0722345c-df7c-4563-a112-d30ea25a9ed8"
      },
      "outputs": [],
      "source": [
        "# Implement OLS\n",
        "\n",
        "### YOUR CODE HERE ###\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b5569bad-28e8-45da-bee5-990ab592ff70",
      "metadata": {
        "tags": [],
        "id": "b5569bad-28e8-45da-bee5-990ab592ff70"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "  You may find it helpful to refer back to what you learned where model building for linear regression is discussed.  \n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8884acac-75a5-44e7-a4bc-e162d8753a93",
      "metadata": {
        "tags": [],
        "id": "8884acac-75a5-44e7-a4bc-e162d8753a93"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "  There is a function from the `statsmodels` library that can be called to implement OLS.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "89a8c604-1e36-40e5-bc6a-7d85f24c372b",
      "metadata": {
        "tags": [],
        "id": "89a8c604-1e36-40e5-bc6a-7d85f24c372b"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "  You can call the `ols()` function from the `statsmodels` library.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d480f1b9-e772-4c45-aab0-d18600d43d3b",
      "metadata": {
        "id": "d480f1b9-e772-4c45-aab0-d18600d43d3b"
      },
      "source": [
        "### Create a linear regression model for the data and fit the model to the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0927ac02-4f5d-4406-a4c1-56d2e5dbd223",
      "metadata": {
        "id": "0927ac02-4f5d-4406-a4c1-56d2e5dbd223"
      },
      "outputs": [],
      "source": [
        "# Fit the model to the data\n",
        "# Save the fitted model in a variable\n",
        "\n",
        "### YOUR CODE HERE ###\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ae5677e2-f2de-4f7e-99c2-df0bd23f32c0",
      "metadata": {
        "tags": [],
        "id": "ae5677e2-f2de-4f7e-99c2-df0bd23f32c0"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "  You may find it helpful to refer back to what you learned where model building for linear regression is discussed.  \n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4c3cbaf2-f372-4f4d-be61-28f80de823d7",
      "metadata": {
        "tags": [],
        "id": "4c3cbaf2-f372-4f4d-be61-28f80de823d7"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "  There is a function from the `statsmodels` library that can be called to fit the model.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ce5ed8ce-050a-4f99-905b-676444dd1f02",
      "metadata": {
        "tags": [],
        "id": "ce5ed8ce-050a-4f99-905b-676444dd1f02"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "  You can call the `fit()` function from the `statsmodels` library.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e4bb9926-05aa-45da-be69-0d373b9abb66",
      "metadata": {
        "id": "e4bb9926-05aa-45da-be69-0d373b9abb66"
      },
      "source": [
        "## Step 4. Results and evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "91275e23-3c8a-4218-8525-13268d62c14b",
      "metadata": {
        "id": "91275e23-3c8a-4218-8525-13268d62c14b"
      },
      "source": [
        "### Get a summary of the results from the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "de2160b6-3bdf-41a7-9743-f7cd6d4c38f8",
      "metadata": {
        "id": "de2160b6-3bdf-41a7-9743-f7cd6d4c38f8"
      },
      "outputs": [],
      "source": [
        "# Get summary of results\n",
        "\n",
        "### YOUR CODE HERE ###\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "95984c14-a8b4-4d85-854b-0215b9be0419",
      "metadata": {
        "tags": [],
        "id": "95984c14-a8b4-4d85-854b-0215b9be0419"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "  You may find it helpful to refer back to what you learned where getting model results is discussed.  \n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "45dddaed-a2f2-4328-b1c6-839cff1cbfe0",
      "metadata": {
        "tags": [],
        "id": "45dddaed-a2f2-4328-b1c6-839cff1cbfe0"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "  There is a function from the `statsmodels` library that can be called to get the summary of results from a model.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0fe50838-54b9-404f-b441-57fbe2be8b2d",
      "metadata": {
        "tags": [],
        "id": "0fe50838-54b9-404f-b441-57fbe2be8b2d"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "  You can call the `summary()` function from the `statsmodels` library.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "557f7ef1-e1ee-4602-aa34-fb04bf5c3158",
      "metadata": {
        "id": "557f7ef1-e1ee-4602-aa34-fb04bf5c3158"
      },
      "source": [
        "### Take a look at the bottom table from the results summary.\n",
        "\n",
        "Based on that table, identify the coefficients that the model determined would generate the line of best fit, the coefficients here being the y-intercept and the slope. "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "29ec7e9e-ded4-4621-9ef1-62836b90b3a6",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "29ec7e9e-ded4-4621-9ef1-62836b90b3a6"
      },
      "source": [
        "#### **Question 5**: What is the y-intercept? "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Double-click (or enter) to edit"
      ],
      "metadata": {
        "id": "3bV7OGngiVs2"
      },
      "id": "3bV7OGngiVs2"
    },
    {
      "cell_type": "markdown",
      "id": "dbdbfc1c-fce0-4813-aafe-c58bfaa436b6",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "dbdbfc1c-fce0-4813-aafe-c58bfaa436b6"
      },
      "source": [
        "#### **Question 6**: What is the slope? "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Double-click (or enter) to edit"
      ],
      "metadata": {
        "id": "QG_N49E5iWhM"
      },
      "id": "QG_N49E5iWhM"
    },
    {
      "cell_type": "markdown",
      "id": "3521b6db-6c35-4574-9bea-17af4ef3dd96",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "3521b6db-6c35-4574-9bea-17af4ef3dd96"
      },
      "source": [
        "#### **Question 7**: What is the linear equation you would write to express the relationship between sales and radio promotion budget in the form of y = slope * x + y-intercept? \n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Double-click (or enter) to edit"
      ],
      "metadata": {
        "id": "GGDkSwpWiXbA"
      },
      "id": "GGDkSwpWiXbA"
    },
    {
      "cell_type": "markdown",
      "id": "fcf820e2-426e-4554-a1a6-20cec9cacd12",
      "metadata": {
        "id": "fcf820e2-426e-4554-a1a6-20cec9cacd12"
      },
      "source": [
        "#### **Question 8**: What do you think the slope in this context means?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Double-click (or enter) to edit"
      ],
      "metadata": {
        "id": "oAadvPHziYWI"
      },
      "id": "oAadvPHziYWI"
    },
    {
      "cell_type": "markdown",
      "id": "729bb783-ffba-4799-a915-79128060f180",
      "metadata": {
        "id": "729bb783-ffba-4799-a915-79128060f180"
      },
      "source": [
        "### Finish checking model assumptions.\n",
        "\n",
        "Now that you've built the linear regression model and fit it to the data, you can finish checking the model assumptions. This will help confirm your findings."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c8ebe78d-32ba-4213-b58b-3f0aad1774aa",
      "metadata": {
        "id": "c8ebe78d-32ba-4213-b58b-3f0aad1774aa"
      },
      "source": [
        "### Plot the OLS data with the best fit regression line."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a41ef379-d2a6-474b-80ce-61dc7a6ad7dd",
      "metadata": {
        "id": "a41ef379-d2a6-474b-80ce-61dc7a6ad7dd"
      },
      "outputs": [],
      "source": [
        "# Plot the OLS data with the best fit regression line\n",
        "\n",
        "### YOUR CODE HERE ###\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3684ed67-e472-4800-a0b0-b43774541afb",
      "metadata": {
        "tags": [],
        "id": "3684ed67-e472-4800-a0b0-b43774541afb"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "  You may find it helpful to refer back to what you learned where plotting the data with the best fit regression line is discussed.  \n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "516bc013-1786-4551-91a5-088cf27c9cb1",
      "metadata": {
        "tags": [],
        "id": "516bc013-1786-4551-91a5-088cf27c9cb1"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "  There is a function from the `seaborn` library that can be useful here.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "098a9e51-5138-411b-bde8-0c622ce10323",
      "metadata": {
        "tags": [],
        "id": "098a9e51-5138-411b-bde8-0c622ce10323"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "  You can call the `regplot()` function from the `seaborn` library.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ad67e686-dd0e-4582-b867-47fe112a53f0",
      "metadata": {
        "id": "ad67e686-dd0e-4582-b867-47fe112a53f0"
      },
      "source": [
        "#### **Question 9**: What do you observe from the regression plot above?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Double-click (or enter) to edit"
      ],
      "metadata": {
        "id": "AhZ1_iEiiZrS"
      },
      "id": "AhZ1_iEiiZrS"
    },
    {
      "cell_type": "markdown",
      "id": "72138441-ca7c-4afe-bfe4-aaf0e7aada24",
      "metadata": {
        "id": "72138441-ca7c-4afe-bfe4-aaf0e7aada24"
      },
      "source": [
        "### Check the normality assumption.\n",
        "Get the residuals from the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "78650cbb-eb48-469f-88b7-f91914b79ea4",
      "metadata": {
        "id": "78650cbb-eb48-469f-88b7-f91914b79ea4"
      },
      "outputs": [],
      "source": [
        "# Get the residuals from the model\n",
        "\n",
        "### YOUR CODE HERE ###\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "253395c5-75ef-4791-85b3-a6be7a2683ce",
      "metadata": {
        "tags": [],
        "id": "253395c5-75ef-4791-85b3-a6be7a2683ce"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "  You may find it helpful to refer back to what you learned where accessing residuals is discussed.  \n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3ab36332-ff4d-4ff0-89d9-96c15a86d005",
      "metadata": {
        "tags": [],
        "id": "3ab36332-ff4d-4ff0-89d9-96c15a86d005"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "  There is an attribute from the `statsmodels` library that can be called to get the residuals from a fitted model.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3d6c0477-af7c-4850-8a4e-db8cde350c6d",
      "metadata": {
        "tags": [],
        "id": "3d6c0477-af7c-4850-8a4e-db8cde350c6d"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "  You can call the `resid` attribute from the `statsmodels` library.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "666b1d10-0aaa-46c8-ba9f-fc096e1ec989",
      "metadata": {
        "id": "666b1d10-0aaa-46c8-ba9f-fc096e1ec989"
      },
      "source": [
        "#### Visualize the distribution of the residuals."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1ea40581-121a-4d71-b421-7a888fd3b538",
      "metadata": {
        "id": "1ea40581-121a-4d71-b421-7a888fd3b538"
      },
      "outputs": [],
      "source": [
        "# Visualize the distribution of the residuals\n",
        "\n",
        "### YOUR CODE HERE ###\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4c3bb284-6e46-4c13-84ea-d40f3898ec0c",
      "metadata": {
        "tags": [],
        "id": "4c3bb284-6e46-4c13-84ea-d40f3898ec0c"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "  You may find it helpful to refer back to what you learned where visualizing residuals is discussed.  \n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e7124d39-997b-4bee-b6b5-e42d10f7f83e",
      "metadata": {
        "tags": [],
        "id": "e7124d39-997b-4bee-b6b5-e42d10f7f83e"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "  There is a function from the `seaborn` library that can be called to create a histogram.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "693b2c61-7196-4e72-86e7-f35722739f0f",
      "metadata": {
        "tags": [],
        "id": "693b2c61-7196-4e72-86e7-f35722739f0f"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "  You can call the `histplot()` function from the `seaborn` library.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a7eca70a-cbe7-4e61-b712-3eae5cf6da34",
      "metadata": {
        "id": "a7eca70a-cbe7-4e61-b712-3eae5cf6da34"
      },
      "source": [
        "#### **Question 10**: Based on the visualization above, what do you observe about the distribution of the residuals?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Double-click (or enter) to edit"
      ],
      "metadata": {
        "id": "nymTKHlGia2z"
      },
      "id": "nymTKHlGia2z"
    },
    {
      "cell_type": "markdown",
      "id": "368b2874-1289-465d-a2cd-c0b24dacf682",
      "metadata": {
        "id": "368b2874-1289-465d-a2cd-c0b24dacf682"
      },
      "source": [
        "### Create a Q-Q plot to confirm the assumption of normality."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "475aa7cd-c410-4116-93eb-6b6607f75409",
      "metadata": {
        "id": "475aa7cd-c410-4116-93eb-6b6607f75409"
      },
      "outputs": [],
      "source": [
        "# Create a Q-Q plot \n",
        "\n",
        "### YOUR CODE HERE ###\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2f5278be-49cd-4cb3-80a2-dccae87192fd",
      "metadata": {
        "tags": [],
        "id": "2f5278be-49cd-4cb3-80a2-dccae87192fd"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "  You may find it helpful to refer back to what you learned where creating a Q-Q plot is discussed.  \n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e1d4e7e6-55df-4fa3-97b1-5306b38ceb67",
      "metadata": {
        "tags": [],
        "id": "e1d4e7e6-55df-4fa3-97b1-5306b38ceb67"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "  There is a function from the `seaborn` library that can be called to create a Q-Q plot.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "48c76f2c-459a-497e-ab7b-4a812166c627",
      "metadata": {
        "tags": [],
        "id": "48c76f2c-459a-497e-ab7b-4a812166c627"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "  You can call the `qqplot()` function from the `seaborn` library.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b343172c-32e9-4d71-a353-5b769460afe1",
      "metadata": {
        "id": "b343172c-32e9-4d71-a353-5b769460afe1"
      },
      "source": [
        "#### **Question 11**: Is the assumption of normality met?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Double-click (or enter) to edit"
      ],
      "metadata": {
        "id": "rZCW2Sfsib7T"
      },
      "id": "rZCW2Sfsib7T"
    },
    {
      "cell_type": "markdown",
      "id": "f0cd9598-a10f-4ba4-8103-a4fe017017ce",
      "metadata": {
        "id": "f0cd9598-a10f-4ba4-8103-a4fe017017ce"
      },
      "source": [
        "### Check the assumptions of independentobservation and homoskedasticity.\n",
        "\n",
        "Start by getting the fitted values from the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7a1df62d-2597-409b-9e1b-5a0b72a01359",
      "metadata": {
        "id": "7a1df62d-2597-409b-9e1b-5a0b72a01359"
      },
      "outputs": [],
      "source": [
        "# Get fitted values\n",
        "\n",
        "### YOUR CODE HERE ###\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9026e8bb-07a0-4263-b664-0448b6fb934e",
      "metadata": {
        "tags": [],
        "id": "9026e8bb-07a0-4263-b664-0448b6fb934e"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "  You may find it helpful to refer back to what you learned where calculating fitted values is discussed.  \n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a6123afa-c5e8-4d45-a2d6-e6750d748f95",
      "metadata": {
        "tags": [],
        "id": "a6123afa-c5e8-4d45-a2d6-e6750d748f95"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "  There is a function from the `statsmodels` library that can be called to calculate fitted values from the model.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b8034b30-f8e9-4b38-8afb-c7ed38b94711",
      "metadata": {
        "tags": [],
        "id": "b8034b30-f8e9-4b38-8afb-c7ed38b94711"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 3</strong></h4></summary>\n",
        "\n",
        "  You can call the `predict()` function from the `statsmodels` library. Make sure to pass in the column from `ols_data` corresponding to the x variable.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "309d0258-0c94-4857-be28-ae67b282ce3d",
      "metadata": {
        "id": "309d0258-0c94-4857-be28-ae67b282ce3d"
      },
      "source": [
        "Create a scatterplot of the residuals against the fitted values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "baec4c75-1b60-4c3f-bcb2-56abea7088bd",
      "metadata": {
        "id": "baec4c75-1b60-4c3f-bcb2-56abea7088bd"
      },
      "outputs": [],
      "source": [
        "# Create a scatterplot of residuals against fitted values\n",
        "\n",
        "### YOUR CODE HERE ###\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "afe532da-1b40-496c-b948-47e4b426246c",
      "metadata": {
        "tags": [],
        "id": "afe532da-1b40-496c-b948-47e4b426246c"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 1</strong></h4></summary>\n",
        "\n",
        "  You may find it helpful to refer back to what you learned where visualizing residuals against fitted values is discussed.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a3b18175-7f72-44a7-8a17-046ba30dc4c6",
      "metadata": {
        "tags": [],
        "id": "a3b18175-7f72-44a7-8a17-046ba30dc4c6"
      },
      "source": [
        "<details>\n",
        "<summary><h4><strong>HINT 2</strong></h4></summary>\n",
        "\n",
        "  There is a function from the `seaborn` library that can be called to create a scatterplot.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "81b77acb-1e0b-44b4-89be-123d3843e2a6",
      "metadata": {
        "tags": [],
        "id": "81b77acb-1e0b-44b4-89be-123d3843e2a6"
      },
      "source": [
        "<details>\n",
        "  <summary><h4>HINT 3</h4></summary>\n",
        "\n",
        "  You can call the `scatterplot()` function from the `seaborn` library.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "93d6469f-8fc1-4cbb-b551-d7be8f506f3e",
      "metadata": {
        "id": "93d6469f-8fc1-4cbb-b551-d7be8f506f3e"
      },
      "source": [
        "#### **Question 12**: Are the assumptions of independent observation and homoskedasticity met?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Double-click (or enter) to edit"
      ],
      "metadata": {
        "id": "qceg6rqUidGl"
      },
      "id": "qceg6rqUidGl"
    },
    {
      "cell_type": "markdown",
      "id": "29f336ab-8b70-446e-9498-b56740728e87",
      "metadata": {
        "id": "29f336ab-8b70-446e-9498-b56740728e87"
      },
      "source": [
        "## Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**What are some key takeaways from this lab?**"
      ],
      "metadata": {
        "id": "B8QDsK1eZ691"
      },
      "id": "B8QDsK1eZ691"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Double-click (or enter) to edit"
      ],
      "metadata": {
        "id": "s9QOOQVViyS8"
      },
      "id": "s9QOOQVViyS8"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**What results can be presented from this lab?**"
      ],
      "metadata": {
        "id": "TAf52zLaiqys"
      },
      "id": "TAf52zLaiqys"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Double-click (or enter) to edit"
      ],
      "metadata": {
        "id": "fSVZtCPhizkd"
      },
      "id": "fSVZtCPhizkd"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**How would you frame your findings to external stakeholders?**"
      ],
      "metadata": {
        "id": "Wi_DUs07it_J"
      },
      "id": "Wi_DUs07it_J"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Double-click (or enter) to edit"
      ],
      "metadata": {
        "id": "gedSOV7ni0YB"
      },
      "id": "gedSOV7ni0YB"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**References**\n",
        "\n",
        "Pandas.DataFrame.Any — Pandas 1.4.3 Documentation. https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.any.html.\n",
        "\n",
        "Pandas.DataFrame.Isna — Pandas 1.4.3 Documentation. https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isna.html.\n",
        "\n",
        "Pandas.Series.Sum — Pandas 1.4.3 Documentation. https://pandas.pydata.org/docs/reference/api/pandas.Series.sum.html.\n",
        "\n",
        "Saragih, H.S. *Dummy Marketing and Sales Data*. https://www.kaggle.com/datasets/harrimansaragih/dummy-advertising-and-sales-data."
      ],
      "metadata": {
        "id": "TLd689mcydX2"
      },
      "id": "TLd689mcydX2"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "acUurV2B5LZX"
      },
      "id": "acUurV2B5LZX",
      "execution_count": null,
      "outputs": []
    }
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