*** PLEASE USE MICROSOFT EXCEL WITH FORMULAS ***

A sample data of 21 house prices was collected, along with the house size and number of offers active for a house. A linear regression model has been developed to predict the house price, using house size (SqFt) and number of offers (Offers) as independent variables. The regression output is present below. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA df MS F (e) (g) Regression Residual (b) (d) Total (a) Coefficients t Stat P-value Intercept -15.6918 43.2327 -0.3630 0.7209 SqFt 88.0389 25.4766 3.4557 0.0028 Offers -11.5047 4.9777 4.9777 0.0328 There are many missing information in the output report. Answer the following questions to fill in the blanks. MUST show detailed formulas and computations. Providing the final results only will receive at least 50% penalty. 4 (a) What is the total degrees of freedom? (b) What is the degrees of freedom of residuals? (c) What is the SSE? (d) What is the MSE? (e) What is the MSR? (f) What is the R square? (g) What is the F statistics? (h) Test the model significance at the level of significance of 0.05. State the hull hypothesis and alternate hypothesis. What is the critical F value used? Can you reject the null hypothesis? (i) Determine if the house size variable is significant at the level of significance of 0.05. Can you reject the null hypothesis? (j) Determine if the Offers variable is significant at the level of significance of 0.05. Can you reject the null hypothesis? (k) What is the adjusted R square for the model? 0.6326 (f) (m) 19.2793 21 2 SS 4464.49 (c) 11154.95 Standard Error