Question

1. Show all of your work in each question. In parts (d), (e), and (g) make sure to set up your null and alternative hypotheses and write your conclusions. Also, please round your numbers to 2 decimal points. Write legibly and neatly.
2. You can use p-value approach or critical-value approach in writing the conclusions of your hypotheses.

A large firm employing tens of thousands of workers has been accused of discriminating against its female managers. The accusation is based on a random sample of 40 managers. The mean annual salary of the 20 female managers is $79,500 while the mean annual salary of the 20 male managers is $103,250. The president of the firm points out that the company has a strict policy of equal pay for equal work and that the difference may be due to other variables. Accordingly, he found and recorded the number of years of education and the number of years of experience for each of the 40 managers in the sample. Also recorded are the salary and gender (1 = female and 0 = male). The data are in attached Excel document. The president wanted to know whether a regression analysis would shed some light on the issue.

Use Microsoft Excel to run a regression of annual salary on years of education, years of experience, and gender and round up numbers in your regression results to 2 decimal points. Please use the level of significance of 10 percent (i.e. α = 0.10). On the basis of your Excel results answer following questions.

. Clearly interpret the numerical values of estimated coefficients of our explanatory  

variables which are years of education, years of experience and gender.

Annual Salary(in $1000.00)	Education(in years)	Experience(in years)	Gender
130	20	18	0
80	19	16	1
62	18	8	1
176	18	30	0
44	14	9	1
139	16	22	0
165	18	27	0
112	19	20	1
92	16	17	1
84	18	13	1
106	14	17	0
103	22	24	1
98	16	18	0
93	17	14	1
79	16	14	1
92	16	14	0
79	14	16	0
98	20	18	1
111	19	25	1
45	15	10	0
82	16	13	0
100	19	21	1
88	15	15	0
76	18	13	1
123	16	21	0
120	17	22	0
50	18	7	1
30	17	4	1
135	16	19	0
84	14	17	0
50	16	6	1
65	15	9	0
83	17	12	1
105	21	21	1
70	17	10	1
99	15	12	0
89	18	8	0
93	15	14	0
68	17	11	1
57	15	10	0

Answer 1

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.92
R Square	0.84
Adjusted R Square	0.83
Standard Error	12.80
Observations	40
ANOVA
	df	SS	MS	F	Significance F
Regression	3	32022.63	10674.21	65.14	0.00
Residual	36	5898.74	163.85
Total	39	37921.38
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	-2.90	20.29	-0.14	0.89	-44.05	38.25	-44.05	38.25
Education(in years)	2.49	1.45	1.72	0.09	-0.45	5.42	-0.45	5.42
Experience(in years)	4.01	0.42	9.51	0.00	3.15	4.86	3.15	4.86
Gender	-18.78	5.35	-3.51	0.00	-29.64	-7.92	-29.64	-7.92

Years of education: Significant at 90% since p value is less than 0.1

For every additional year in education there is an average increase of $2490 salary provided all other factors are the same

Experience : Significant at 90% since p value is less than 0.1

For every additional year of experience, there is an average increase of $4010 of salary provided all other factors are the same

Gender: Significant at 90% since p value is less than 0.1

Females on an average earn 1878 dollars less than males provided all other factors remain the same