Create scatterplots using the data in the spreadsheet linked above and display th equation for the regression line. What is the equation for the regression line that predicts the probability of default percent using credit score as the explanatory variable? \( \hat{Y}= \) What is the interpretation of the slope? What is the interpretation of the intercept? Pick

It looks like there's no spreadsheet currently linked or attached to your message. However, I can guide you through the process of creating scatterplots with regression lines, deriving the regression equation, and interpreting its components using a typical dataset involving credit scores and the probability of default. Here's a step-by-step guide: ### **1. Creating Scatterplots and Adding a Regression Line** **Using Microsoft Excel:** 1. **Enter Your Data:** - **Column A:** Credit Scores (Explanatory Variable, X) - **Column B:** Probability of Default (%) (Response Variable, Y) 2. **Create the Scatterplot:** - Select both columns of data. - Go to the **Insert** tab. - Click on **Scatter** in the Charts group and choose the first scatterplot option. 3. **Add the Regression Line:** - Click on any data point in the scatterplot to select the data series. - Right-click and choose **Add Trendline**. - In the **Format Trendline** pane, select **Linear**. - Check the boxes for **Display Equation on chart** and **Display R-squared value on chart** (optional). **Using Google Sheets:** 1. **Enter Your Data:** - **Column A:** Credit Scores (X) - **Column B:** Probability of Default (%) (Y) 2. **Create the Scatterplot:** - Select both columns of data. - Go to **Insert** > **Chart**. - In the Chart Editor, choose **Scatter chart** as the chart type. 3. **Add the Regression Line:** - Within the Chart Editor, go to the **Customize** tab. - Expand the **Series** section. - Check the box for **Trendline**. - Choose **Linear**. - Optionally, check **Show R²** and **Label** to display the equation on the chart. ### **2. Deriving the Regression Equation** After adding the regression (trend) line to your scatterplot, the equation will typically appear in the form: \[ \hat{Y} = a + bX \] Where: - \( \hat{Y} \) = Predicted probability of default (%) - \( a \) = Intercept - \( b \) = Slope coefficient - \( X \) = Credit Score **Example:** Suppose the trendline equation displayed is: \[ \hat{Y} = 50 - 0.3X \] ### **3. Interpretation of the Regression Coefficients** **a. Intercept (\( a \))** - **What It Represents:** The intercept is the predicted value of \( Y \) when \( X = 0 \). - **Interpretation in Context:** - In our example, when the credit score is 0, the model predicts a 50% probability of default. - **Caution:** In practical scenarios, a credit score of 0 may be outside the range of your data and not meaningful. The intercept serves as a starting point for the regression line but may not always have a meaningful real-world interpretation. **b. Slope (\( b \))** - **What It Represents:** The slope indicates the change in the predicted value of \( Y \) for a one-unit increase in \( X \). - **Interpretation in Context:** - In our example, for each additional point in the credit score, the probability of default decreases by 0.3%. - This suggests a negative relationship: higher credit scores are associated with lower probabilities of default. ### **4. Understanding the Relationship** - **Negative Slope:** Indicates an inverse relationship between credit score and default probability. As credit scores increase, the likelihood of default decreases. - **Positive Slope:** Would indicate a direct relationship. (Not applicable in this context.) ### **5. Additional Considerations** - **R-squared Value:** This statistic indicates how well the regression line fits the data. It ranges from 0 to 1, with higher values representing a better fit. - **Assumptions:** Ensure that your data meets the assumptions of linear regression, including linearity, independence, homoscedasticity, and normality of residuals. ### **6. Example Summary** Given the example equation: \[ \hat{Y} = 50 - 0.3X \] - **Intercept (50):** When the credit score is 0, the model predicts a 50% probability of default. (Note: This may not be practically meaningful.) - **Slope (-0.3):** For each additional point in the credit score, the probability of default decreases by 0.3%. ### **Final Notes** - **Data Verification:** Always verify that the regression model is appropriate for your data by checking residual plots and other diagnostic measures. - **Practical Implications:** Use the model to understand risk factors and make informed decisions, but complement it with other analyses and domain knowledge. If you can provide the specific data or share the spreadsheet, I can offer more tailored assistance!