QUESTION 3 [Regression: Cu rvilinear] Estimate a curvilinear regression model to test the effect of Salary on SC Index. Based on computer regression output there is evidence of relationship between the two variables. 0 no 0 cubic O quadratic 0 linear 0 rst degree QUESTION 4 [Regression: Curvilinear] Estimate a curvilinear regression model to test the effect of Salary on SC Index. The estimated cubic regression model is given by the following equation while the proportion ofunexplained variation is . O Y = 2.0 01 Salary + 0.002 Salary2 5.0 Saiary3; 0.77 O Y = 2.0 01 Salary + 0.002 Salary2 0.000005 Salary3; 0.70 O Y = 2.0 01 Salary + 0.002 Salary2 0.000000 Salary3; 0.35 O Y = 2.0 01 Salary + 0.01 Salary2 0.000000 Salary3; 0.35 O Y = 2.0 01 Salary + 0.002 Salary2 5.0 Salary3; 0.70 QUESTION 7 [Regression: Interaction term] Estimation a regression model predicting SC Index with Salary and Cars as predictors. Test the hypothesis that there is a significant independent variable interaction effcet on SC index. Our conclusion for this test is that . This conclusion is supported by a p value(s) on partial slope coefficientls) of 0 no interaction effect exists; large; Salary.r and Cars 0 no interaction effect exists; large, interaction term 0 an interaction effect exists; large, Salary.r and Cars 0 an interaction effect exists; large; interaction term 0 an interaction effect exists; small, interaction term QUESTION 9 [Regression: Multiple] Estimate a multiple regression model with SC Index as the dependent variable and Salary. Cars, Home. and Savings as independent variables. Based on reported coefcients and their corresponding 95% confidence intervals in the computer output, we can conclude that the following predictors have a statistically signica nt relationship with SC Index. 0 SalaryI CarsI Home, Savings O Intercept. Salary, Cars, Home, Savings Intercept. Salary, Home. Savings 0 Salary. Home,Savings 0 Cars