The coefficient of determination, $R^2$, measures the proportion of the variability in the dependent variable (weekly revenue) that is explained by the independent variable (advertising expenditures).

The formula for $R^2$ is:

$R^2 = \frac{\text{SS}_{\text{Regression}}}{\text{SS}_{\text{Total}}}$

Given the summary output:

• $\text{SS}_{\text{Regression}} = 15.15403768$
• $\text{SS}_{\text{Total}} = 19.87238347$

Now, let's calculate $R^2$.

$R^2 = \frac{15.15403768}{19.87238347}$The coefficient of determination, $R^2$, for this model is 0.7626. This means that approximately 76.26% of the variability in weekly revenue is explained by the advertising expenditures.

Do you want more details or have any questions?

Here are 5 related questions:

1. How do we interpret an $R^2$ value close to 1?
2. What is the significance of the F-statistic in the regression model?
3. How can we improve the $R^2$ value in this model?
4. What are the assumptions of a linear regression model?
5. How does multicollinearity affect the $R^2$ value?

Tip: A high $R^2$ value indicates a good fit, but it's essential to check other statistics like p-values and residual plots for a thorough model evaluation.