Pivot table of management level versus gender

Question

Discussion:

Q1. A study indicated where different age groups primarily get their news: At the 0.05 level of significance, is there evidence of a significant relationship between the age group and where people primarily get their news? If so, explain the relationship

Use the Chi Square file

Age Group
Media Under 36 36-50 50+
Local TV 107 119 133
National TV 73 102 127
Radio 75 97 109
Local newspaper 52 79 107
Internet 95 83 76

Q2. Create a pivot table of management level versus gender. Then copy the data from the table into a chi square table to see if a relationship exists between employee genders and their management level. What can you conclude at the .05 significance level?

Salary (dollars)	Gender	Age (years)	Experience (years)	Management Level	Education Level
41,711	F	30	0	2	0
27,739	F	26	1	1	0
67,977	M	42	3	3	1
32,950	F	38	6	1	0
72,899	F	43	4	3	1
30,096	F	32	4	1	0
42,513	F	32	2	2	0
48,630	F	38	5	2	0
51,450	M	37	7	2	1
46,319	F	36	5	2	0
74,425	M	47	4	2	2
62,426	M	41	1	3	0
29,495	F	30	3	1	0
58,297	F	43	13	2	1
40,470	M	44	13	1	0
77,395	F	50	7	2	2
41,410	M	37	15	1	1
47,300	F	30	0	2	1
26,074	M	23	2	1	0
26,451	F	24	3	1	0
25,367	F	21	0	1	0
25,820	F	22	1	1	0
32,008	M	37	5	1	0
27,996	M	28	2	1	0
45,999	M	42	19	1	1
48,924	F	33	0	2	1
46,986	F	37	5	2	0
79,550	M	50	0	3	2
72,464	F	45	0	2	2
39,932	F	33	12	1	1
55,869	F	41	9	2	1
52,045	F	39	8	2	1
26,208	M	23	0	1	0
30,344	F	33	4	1	0
58,054	M	42	11	2	1
68,133	M	51	14	2	1
31,529	F	35	5	1	0
35,565	F	39	7	1	0
35,986	M	40	8	1	0
24,539	F	21	0	1	0
25,623	M	21	0	1	0
44,956	F	41	18	1	1
31,298	F	34	4	1	0
28,141	F	28	2	1	0
29,926	M	31	3	1	0
60,136	F	46	10	2	1
26,527	F	25	4	1	0
56,140	F	41	10	2	1
43,992	M	34	3	2	0
28,429	F	29	2	1	0
54,800	M	54	19	1	2
37,474	M	41	10	1	0
29,631	F	31	3	1	0
55,784	F	40	9	2	1
31,497	F	35	5	1	0
54,429	F	40	8	2	1
31,795	F	36	5	1	0
94,541	M	55	9	3	2
36,716	F	21	0	1	1
62,361	F	47	11	2	1
69,750	M	52	15	2	1
44,314	M	39	17	1	1
49,405	F	42	14	1	2
76,986	M	48	6	2	2
31,995	F	36	5	1	0
31,301	M	35	5	1	0
39,850	F	42	11	1	0
59,644	M	44	15	2	1
49,073	M	39	7	2	0
37,128	F	28	6	1	1
66,708	F	49	13	2	1
38,666	F	29	7	1	1
44,690	F	35	4	2	0
47,955	M	32	0	2	1
31,002	F	34	4	1	0
42,204	M	31	1	2	0
80,456	M	54	10	2	2
49,042	F	36	7	2	1
77,154	M	45	5	3	1
25,019	M	21	0	1	0
64,741	M	40	0	3	1
46,047	F	40	0	1	2
52,175	F	49	18	1	2
30,621	M	33	4	1	0
49,357	F	40	7	2	0
27,514	M	26	5	1	0
27,987	F	27	1	1	0
25,738	F	22	0	1	0
64,756	F	48	12	2	1
41,077	F	36	14	1	1
37,118	F	41	9	1	0
51,759	F	38	8	2	1
97,431	M	57	10	3	2
85,594	F	53	8	3	2
85,457	M	52	6	3	2
32,589	M	37	6	1	0
28,643	M	30	3	1	0
51,987	F	45	16	1	2
33,803	F	39	6	1	0
43,186	M	38	16	1	1

Q3. Develop a model to predict the employee's salary using the employee's age, years of experience, and gender. Since gender is nominal, use 0 for male and 1 for female. The 'Multiple Regression' file has already been pre-loaded with the data, so you need only interpret the output.

Salary (dollars)	Gender	Age (years)	Experience (years)	Management Level	Education Level
41,711	F	30	0	2	0
27,739	F	26	1	1	0
67,977	M	42	3	3	1
32,950	F	38	6	1	0
72,899	F	43	4	3	1
30,096	F	32	4	1	0
42,513	F	32	2	2	0
48,630	F	38	5	2	0
51,450	M	37	7	2	1
46,319	F	36	5	2	0
74,425	M	47	4	2	2
62,426	M	41	1	3	0
29,495	F	30	3	1	0
58,297	F	43	13	2	1
40,470	M	44	13	1	0
77,395	F	50	7	2	2
41,410	M	37	15	1	1
47,300	F	30	0	2	1
26,074	M	23	2	1	0
26,451	F	24	3	1	0
25,367	F	21	0	1	0
25,820	F	22	1	1	0
32,008	M	37	5	1	0
27,996	M	28	2	1	0
45,999	M	42	19	1	1
48,924	F	33	0	2	1
46,986	F	37	5	2	0
79,550	M	50	0	3	2
72,464	F	45	0	2	2
39,932	F	33	12	1	1
55,869	F	41	9	2	1
52,045	F	39	8	2	1
26,208	M	23	0	1	0
30,344	F	33	4	1	0
58,054	M	42	11	2	1
68,133	M	51	14	2	1
31,529	F	35	5	1	0
35,565	F	39	7	1	0
35,986	M	40	8	1	0
24,539	F	21	0	1	0
25,623	M	21	0	1	0
44,956	F	41	18	1	1
31,298	F	34	4	1	0
28,141	F	28	2	1	0
29,926	M	31	3	1	0
60,136	F	46	10	2	1
26,527	F	25	4	1	0
56,140	F	41	10	2	1
43,992	M	34	3	2	0
28,429	F	29	2	1	0
54,800	M	54	19	1	2
37,474	M	41	10	1	0
29,631	F	31	3	1	0
55,784	F	40	9	2	1
31,497	F	35	5	1	0
54,429	F	40	8	2	1
31,795	F	36	5	1	0
94,541	M	55	9	3	2
36,716	F	21	0	1	1
62,361	F	47	11	2	1
69,750	M	52	15	2	1
44,314	M	39	17	1	1
49,405	F	42	14	1	2
76,986	M	48	6	2	2
31,995	F	36	5	1	0
31,301	M	35	5	1	0
39,850	F	42	11	1	0
59,644	M	44	15	2	1
49,073	M	39	7	2	0
37,128	F	28	6	1	1
66,708	F	49	13	2	1
38,666	F	29	7	1	1
44,690	F	35	4	2	0
47,955	M	32	0	2	1
31,002	F	34	4	1	0
42,204	M	31	1	2	0
80,456	M	54	10	2	2
49,042	F	36	7	2	1
77,154	M	45	5	3	1
25,019	M	21	0	1	0
64,741	M	40	0	3	1
46,047	F	40	0	1	2
52,175	F	49	18	1	2
30,621	M	33	4	1	0
49,357	F	40	7	2	0
27,514	M	26	5	1	0
27,987	F	27	1	1	0
25,738	F	22	0	1	0
64,756	F	48	12	2	1
41,077	F	36	14	1	1
37,118	F	41	9	1	0
51,759	F	38	8	2	1
97,431	M	57	10	3	2
85,594	F	53	8	3	2
85,457	M	52	6	3	2
32,589	M	37	6	1	0
28,643	M	30	3	1	0
51,987	F	45	16	1	2
33,803	F	39	6	1	0
43,186	M	38	16	1	1

Complete the following tasks:

a.State the multiple regression equation.

b. Interpret the meaning of the slopes in the equation.

c. Predict the salary for a 40-year-old male employee with five years' experience.

d. At the .05 level of significance, determine whether each explanatory variable makes a significant contribution to the regression model.

e. Interpret the meaning of the r-squared.

f. Interpret your findings. What changes would you make to the model, given the results?

Attachment:- Regression.rar