Hi please can I get some help with Question 3 and 4

QUESTION 2 [22] Consider the data and the results obtained in Question 1. (2.1) Calculate the mean absolute deviation. (6) (2.2) Calculate the mean squared residuals. ( 9 ) (2.3) Calculate the coefficient of determination, R2. (6) (2.4) Calculate the adjusted coefficient of determination, Radj. (4) QUESTION 3 [16] The following model (M) was proposed for testing whether there was a significant interaction be- tween two predictor variables X1 and X2. y = Pot Bixit B2x2 + B3x2 + BAXIxzTE. (M) The regression ANOVA table for the model without interaction is the following: df SS MS F Significance F Regression 3 7474.7333 2491.5778 846.1803 2.8653 x 10-8 Residual 17.6667 2.945 Total 7492.4 The regression ANOVA table for the full model is the following: dif SS MS F Significance F Regression 7487.7803 1871.9451 2026.1339 3.3025 x 10-8 Residual 4.6197 0.9239 Total CO 7492.4 (3.1) Do the data provide sufficient evidence to support the model without interaction instead of the full model? Use a = 0.05. Show all necessary calculations before explaining your answer. (10) (3.2) Repeat the appropriate steps in part (3.1) using a = 0.01. (4) (3.3) Do we obtain the same conclusions in parts (3.1) and (3.2)? Explain your answer. (2) 3QUESTION 4 [24] The quarterly sales (ya), where t denotes time. of a product for four consecutive years are given in the following table: Year Quarter y, 1 4O 52 54 65 44 53 58 68 50 57 62 70 62 69 73 75 in) hWNHhWN-'hWN-'Jhwmd Assume that the model that ts the data is given by equation y!=0+lt+2Q2+3Q3+4Q4+Et (E) where 92, Qg, and Q4 are the appropriately defined dummy variables for quarters 2. 3 and 4, r: l,2,...,16. (4.1) Plot y; versus :. Use Excel. (4) (4.2) What types of trend and seasonal variation appear to exist? (4) (4.3) Fit model (E), then write down the predictive equation. Use Excel. (6) (4.4) Use the results in pan (4.3) to calculate the predicted value 3?\" and its 95% predictive interval. (10) Total: [94]