Question

its 8.17 the one that is highlighted and I have also attached the models.

Xi2: 0 1 0 a. Explain how each regression coefficient in model (8.33) is interpreted hene. b. Fit the regression model and state the estimated regression function. c. Test whether the X2 variable can be dropped from the regression model; use α 01 St ate the alternatives, decision rule, and conclusion. d. Obtain the residuals for regression model (8.33) and plot them against XiXz. Is there any evidence in your plot that it would be helpful to include an interaction term in the model? 8.17. Refer to regression models (8.33) and (8.49). Would the conclusion thata 0have the seme 8.18. Refer to regression model (8.49). Portray graphically the response curves for this model if implication for each of these models? Explain. 12.5, and β.-.05. Describe the nature of the interaction effect 25, β:: .30, β *8.19. Refer to Copier maintenance Problems 1.20 and 8.15. a. Fit regression model (8.49) and state the estimated regression function e interaction term can be dropped from the model; control the a wsk at.10 State the alternatives, decision rule, and conclusion. What is the P-value of the test? If the interaction term cannot be dropped from the model, describe the nature of effect. 8.20. Refer to Grade point average Problems I.19 and 8.16 a. Fit regression model (8.49) and state the estimated regression function. b. Test whether the interaction term can be dropped from the model; use a05. State the lternatives, decision rule, and conctusion. If the interuction term cannot be dropped from 3·4) Model (8.33) Model (. 49)

Answer 1

No, the implications will not be the same,

the first equation will transform into a simple linear regression model with one predictor variable where as the second equation will have a interaction effect between two predictor variables and the main effect of the second predictor variable will be null.