Run a regression analysis to estimate staring salary based on academic performance (GMAT score and GPA), and years of work experience, including interaction with the gender variable.

1. What does your final model reveal about the relationship between salary, academic performance, and work experience? Think about the relationship of each of the variables as well as any significant interactions you have identified.
2. How good is your model?

gmat_tot	salary	work_yrs	sex	Gender_DV	Gender_GMAT	Gender_GPA	Gender_WorkYear
660	85000	1	2	0	0	0	0
700	85000	2	2	0	0	0	0
680	86000	2	2	0	0	0	0
650	88000	3	2	0	0	0	0
710	92000	2	1	1	710	3.4	2
620	93000	5	2	0	0	0	0
670	95000	0	1	1	670	3.28	0
560	95000	1	2	0	0	0	0
530	95000	3	2	0	0	0	0
650	96000	1	1	1	650	3.4	1
590	96000	4	2	0	0	0	0
650	100000	2	2	0	0	0	0
560	100000	2	1	1	560	3.54	2
610	100000	6	1	1	610	3.4	6
650	105000	2	1	1	650	3.15	2
550	105000	3	1	1	550	3.5	3
570	105000	3	1	1	570	3.65	3
580	105000	2	1	1	580	3.48	2
600	105000	5	1	1	600	3.28	5
570	105000	6	1	1	570	3.55	6
620	106000	8	1	1	620	3.25	8
680	106000	6	2	0	0	0	0
630	107500	3	1	1	630	3.28	3
600	108000	3	1	1	600	3.38	3
640	110000	6	2	0	0	0	0
600	112000	16	1	1	600	3.55	16
570	115000	4	1	1	570	3.85	4
710	115000	1	1	1	710	3.55	1
620	118000	10	1	1	620	3.5	10
600	120000	3	1	1	600	3.25	3
700	120000	5	1	1	700	3.9	5
600	120000	5	1	1	600	3.38	5
670	120000	8	2	0	0	0	0
630	146000	15	1	1	630	2	15
700	162000	1	1	1	700	3.68	1
670	82000	1	2	0	0	0	0
620	92000	2	2	0	0	0	0
540	93000	8	1	1	540	3.05	8
670	95000	2	1	1	670	3.35	2
610	95000	3	1	1	610	3.26	3
560	96000	2	1	1	560	3.23	2
500	96500	2	1	1	500	2.88	2
590	98000	2	1	1	590	3.23	2
570	98000	2	1	1	570	3.23	2
570	98000	3	2	0	0	0	0
580	99000	5	2	0	0	0	0
580	100000	2	2	0	0	0	0
560	100000	4	1	1	560	3.05	4
620	101000	2	2	0	0	0	0
620	103000	3	1	1	620	3.1	3
560	104000	4	1	1	560	3.23	4
680	105000	3	1	1	680	3.23	3
620	105000	3	1	1	620	3	3
550	105000	16	1	1	550	3	16
600	107000	2	1	1	600	3.3	2
670	112000	6	1	1	670	3.05	6
620	115000	1	1	1	620	3.13	1
630	115000	4	1	1	630	3.1	4
650	130000	4	1	1	650	3.23	4
620	145800	2	1	1	620	3.05	2
520	78256	1	2	0	0	0	0
620	88500	3	1	1	620	2.73	3
580	90000	2	1	1	580	3.08	2
630	90000	2	1	1	630	2.6	2
610	93000	4	1	1	610	2.73	4
560	95000	5	2	0	0	0	0
620	97000	4	1	1	620	2.83	4
580	97000	3	1	1	580	2.9	3
670	98000	2	2	0	0	0	0
560	98000	2	2	0	0	0	0
580	98000	2	2	0	0	0	0
680	98000	2	1	1	680	2.83	2
610	98000	4	1	1	610	2.85	4
710	98000	7	1	1	710	3.08	7
710	100000	3	1	1	710	2.95	3
630	100000	2	2	0	0	0	0
600	101000	2	2	0	0	0	0
660	101100	8	1	1	660	2.9	8
670	102500	6	1	1	670	2.78	6
580	105000	2	1	1	580	2.87	2
680	106000	6	1	1	680	2.95	6
660	107300	2	1	1	660	3.2	2
570	108000	4	1	1	570	2.95	4
680	112000	2	1	1	680	2.78	2
560	64000	2	2	0	0	0	0
660	77000	2	1	1	660	2.75	2
720	85000	3	2	0	0	0	0
620	85000	2	1	1	620	2.2	2
630	86000	3	2	0	0	0	0
650	90000	5	1	1	650	2.33	5
660	92000	2	1	1	660	3.33	2
610	95000	2	1	1	610	2.58	2
600	96000	2	1	1	600	2.5	2
570	98000	2	1	1	570	2.4	2
600	100000	2	2	0	0	0	0
650	100000	2	2	0	0	0	0
630	100400	3	1	1	630	2.55	3
630	101600	3	1	1	630	2.68	3
530	104000	4	1	1	530	2.58	4
580	105000	2	1	1	580	2.1	2
540	115000	3	1	1	540	2.55	3
550	126710	3	1	1	550	2.68	3
500	220000	15	2	0	0	0	0