Develop a simple linear regression equation for starting salaries using an independent variable that has the closest relationship with the salaries. Explain how you chose this variable. (2) Then, present the simple linear regression equation, identify and explain the coefficient of determination, intercept and regression coefficient, and significance of the F-test. Based on this analysis, is your regression equation “good for use”? Explain. (3) Then, Provide a numeric example of how this regression equation can be used to predict students’ starting salaries. (4) Then, Develop a multiple regression equation for starting salaries using School_Ranking, GPA, and Experience as independent variables. Is this regression equation “good for use”? Explain (5) If the multiple regression equation in the previous question is not “good for use”, how would you suggest improving this multiple regression equation? Present the improved multiple regression equation. Give a numeric example of how this improved multiple regression equation may be used to predict students’ starting salaries

-data-

Student	School_Ranking	GPA	Experience	Salary
1	78	2.92	3	73,590
2	56	3.84	9	87,000
3	23	3.04	6	76,970
4	67	3.20	6	79,320
5	56	3.61	7	79,530
6	78	2.99	5	71,040
7	68	3.78	8	82,050
8	89	3.20	5	78,890
9	37	3.42	7	82,170
10	67	3.05	5	76,120
11	48	3.12	4	77,500
12	78	3.56	7	83,920
13	56	3.01	5	71,800
14	25	3.15	6	77,000
15	68	3.05	7	79,000
16	36	3.24	5	77,800
17	76	3.25	6	80,600
18	78	3.78	9	87,000
19	67	3.12	4	78,450
20	67	3.24	8	80,600
21	15	2.98	5	74,900
22	29	3.24	6	79,200
23	49	3.08	4	77,000
24	67	3.00	6	77,900
25	39	2.95	4	76,950
26	81	3.01	5	76,800
27	54	3.23	7	79,300
28	72	3.01	2	72,120
29	73	3.45	7	83,900
30	78	3.85	8	85,200
31	51	3.00	5	77,300
32	86	3.23	6	83,500
33	76	3.80	7	77,000
34	30	3.08	5	75,000
35	58	3.15	7	79,200
36	86	3.35	7	80,400
37	34	3.09	7	80,200
38	72	3.35	9	84,800
39	38	3.16	3	72,800
40	89	2.76	7	75,000