Developed a multiple regression model to predict GMAT scores

Developed a multiple regression model to predict GMAT scores from grade point average(UGPA) and months of business experience(EXPER) by sex. Data were collected for ten students and are presented in the accompanying table.

The last two independent variables (SEX and UMAJOR) are as follows: SEX = 1 if male, SEX = 0 if female and . UMAJOR = 1 if business background in undergraduate, UMAJOR=0 if not.

NOTE: UMAJOR SHOULD BE EXLUDED FROM REGRESSION MODEL

6.What’s the computed t-value for the Undergraduate GPA (UGPA) and how it is computed (show it directly from the computer output) ?

7. Is there sufficient evidence to indicate that the multiple regression model contributes information for predicting the GMAT score that will be related? If yes, at what percent level of significance? Prove it by your calculation.

8. Use the model to predict(estimate) the mean GMAT score for all students with 5 months business experience after holding all other variable constant.

9. Explain the partial correlation R (GMAT UGPA. SEX EXPER) =0.8089. What does this value stand for?

10. Would you explain how is GMAT score influenced by SEX? Why?

11. Is there any implication of multicollinearity problem in this model? If yes, explain it using your criteria

12. Run the regression again, using only the independent variables that are significant at least at the 5 percent level of significance. Does it make a difference whether our MBA student has a business major in the undergraduate or not ?

If it does, how much more point in GMAT does a business background in UMAJOR have than a non-business background in UMAJOR?

GMAT UGPA EXPER SEX UMAJOR 1 C) C) 1 C) 1 C) 1 630 3.53 520 80 535 3.25 518 2.5 8.5 7.1 5.6 1 1 C) C) 1 1 C) C) C) 1 2.58 2.75 495 2.53 560 610 540 3.26 505 2.68 4.8 6.5 4.9 3.8 2.3 3.72 3.78 1

Solution