The Mean Square Error (MSE) is a measure of the average squared difference between the observed actual outcomes and the outcomes predicted by the model. It is calculated by dividing the sum of squares for the residuals (SS Residual) by the degrees of freedom for the residuals (df Residual).

Formula:

$\text{MSE} = \frac{\text{SS Residual}}{\text{df Residual}}$

From the provided output:

• SS Residual (also known as Residual Sum of Squares) = 41,391.301190
• df Residual = 13

Calculation:

$\text{MSE} = \frac{41,391.301190}{13} = 3,183.946245$

Since the value is already calculated and rounded in the output, the Mean Square Error (MSE) for the model is:

$\text{MSE} = 3,183.9462 \text{ (rounded to four decimal places)}$

This value represents the average squared difference between the observed revenue values and those predicted by the regression model.

Would you like more details or have any other questions?

Next Steps:

1. How does MSE impact the accuracy of the model?
2. What is the difference between MSE and RMSE?
3. How can we reduce the MSE in a regression model?
4. What is the significance of the coefficients in the regression output?
5. How does the R-squared value relate to MSE?
6. Can high MSE values indicate overfitting?
7. What are some common causes of high MSE?
8. How does MSE compare with other error metrics like MAE?

Tip: The lower the MSE, the better the model fits the data. However, always consider other metrics like R-squared and residual plots to get a full picture of model performance.