Week 2 – Problem Set

Data

ID	Salary	Compa	Midpoint	Age	Performance Rating	Service	Gender	Raise	Degree	Gender1	Grade	Do not manipuilate Data set on this page, copy to another page to make changes
1	56.5	0.992	57	34	85	8	0	5.7	0	M	E	The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)?
2	26.5	0.854	31	52	80	7	0	3.9	0	M	B	Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
3	34.2	1.103	31	30	75	5	1	3.6	1	F	B
4	61.3	1.076	57	42	100	16	0	5.5	1	M	E	The column labels in the table mean:
5	49.4	1.030	48	36	90	16	0	5.7	1	M	D	ID – Employee sample number	Salary – Salary in thousands
6	72.3	1.079	67	36	70	12	0	4.5	1	M	F	Age – Age in years	Performance Rating – Appraisal rating (employee evaluation score)
7	41.5	1.037	40	32	100	8	1	5.7	1	F	C	Service – Years of service (rounded)	Gender – 0 = male, 1 = female
8	22.4	0.976	23	32	90	9	1	5.8	1	F	A	Midpoint – salary grade midpoint	Raise – percent of last raise
9	73.3	1.094	67	49	100	10	0	4	1	M	F	Grade – job/pay grade	Degree (0= BS\BA 1 = MS)
10	23.6	1.024	23	30	80	7	1	4.7	1	F	A	Gender1 (Male or Female)	Compa-ratio – salary divided by midpoint
11	23.1	1.003	23	41	100	19	1	4.8	1	F	A
12	61.7	1.082	57	52	95	22	0	4.5	0	M	E
13	41.9	1.048	40	30	100	2	1	4.7	0	F	C
14	23.4	1.016	23	32	90	12	1	6	1	F	A
15	22.9	0.994	23	32	80	8	1	4.9	1	F	A
16	41.3	1.032	40	44	90	4	0	5.7	0	M	C
17	65.7	1.153	57	27	55	3	1	3	1	F	E
18	35.6	1.148	31	31	80	11	1	5.6	0	F	B
19	23.5	1.023	23	32	85	1	0	4.6	1	M	A
20	35.4	1.141	31	44	70	16	1	4.8	0	F	B
21	77.3	1.153	67	43	95	13	0	6.3	1	M	F
22	58.3	1.215	48	48	65	6	1	3.8	1	F	D
23	22.3	0.970	23	36	65	6	1	3.3	0	F	A
24	47.2	0.984	48	30	75	9	1	3.8	0	F	D
25	23.9	1.041	23	41	70	4	0	4	0	M	A
26	24.4	1.059	23	22	95	2	1	6.2	0	F	A
27	44.2	1.105	40	35	80	7	0	3.9	1	M	C
28	76.2	1.138	67	44	95	9	1	4.4	0	F	F
29	77.3	1.154	67	52	95	5	0	5.4	0	M	F
30	48.9	1.018	48	45	90	18	0	4.3	0	M	D
31	24.4	1.062	23	29	60	4	1	3.9	1	F	A
32	27.4	0.883	31	25	95	4	0	5.6	0	M	B
33	58	1.018	57	35	90	9	0	5.5	1	M	E
34	27.6	0.890	31	26	80	2	0	4.9	1	M	B
35	22.4	0.975	23	23	90	4	1	5.3	0	F	A
36	22.7	0.985	23	27	75	3	1	4.3	0	F	A
37	23.9	1.037	23	22	95	2	1	6.2	0	F	A
38	59.5	1.043	57	45	95	11	0	4.5	0	M	E
39	35.1	1.132	31	27	90	6	1	5.5	0	F	B
40	25	1.087	23	24	90	2	0	6.3	0	M	A
41	40.9	1.022	40	25	80	5	0	4.3	0	M	C
42	22.7	0.987	23	32	100	8	1	5.7	1	F	A
43	73.9	1.103	67	42	95	20	1	5.5	0	F	F
44	65	1.140	57	45	90	16	0	5.2	1	M	E
45	52.4	1.092	48	36	95	8	1	5.2	1	F	D
46	60.6	1.063	57	39	75	20	0	3.9	1	M	E
47	61.1	1.072	57	37	95	5	0	5.5	1	M	E
48	68.7	1.206	57	34	90	11	1	5.3	1	F	E
49	60	1.052	57	41	95	21	0	6.6	0	M	E
50	59.5	1.043	57	38	80	12	0	4.6	0	M	E

Week2

Week 2: Identifying Significant Differences – part 1
To Ensure full credit for each question, you need to show how you got your results. This involves either showing where the data you used is located
or showing the excel formula in each cell.	Be sure to copy the appropriate data columns from the data tab to the right for your use this week.
As with our examination of compa-ratio in the lecture, the first question we have about salary between the genders involves equality – are they the same or different?
What we do, depends upon our findings.
1	As with the compa-ratio lecture example, we want to examine salary variation within the groups – are they equal?	Use Cell K10 for the Excel test outcome location.
a	What is the data input ranged used for this question:
b	Which is needed for this question: a one- or two-tail hypothesis statement and test ?
Answer:
Why:
c. Step 1:	Ho:
Ha:
Step 2:	Significance (Alpha):
Step 3:	Test Statistic and test:
Why this test?
Step 4:	Decision rule:
Step 5:	Conduct the test – place test function in cell k10
Step 6:	Conclusion and Interpretation
What is the p-value:
What is your decision: REJ or NOT reject the null?
Why?
What is your conclusion about the variance in the population for male and female salaries?
2	Once we know about variance quality, we can move on to means: Are male and female average salaries equal?	Use Cell K35 for the Excel test outcome location.
(Regardless of the outcome of the above F-test, assume equal variances for this test.)
a	What is the data input ranged used for this question:
b	Does this question need a one or two-tail hypothesis statement and test?
Why:
c. Step 1:	Ho:
Ha:
Step 2:	Significance (Alpha):
Step 3:	Test Statistic and test:
Why this test?
Step 4:	Decision rule:
Step 5:	Conduct the test – place test function in cell K35
Step 6:	Conclusion and Interpretation
What is the p-value:
What is your decision: REJ or NOT reject the null?
Why?
What is your conclusion about the means in the population for male and female salaries?
3	Education is often a factor in pay differences.
Do employees with an advanced degree (degree = 1) have higher average salaries?	Use Cell K60 for the Excel test outcome location.
Note: assume equal variance for the salaries in each degree for this question.
a	What is the data input ranged used for this question:
b	Does this question need a one or two-tail hypothesis statement and test?
Why:
c. Step 1:	Ho:
Ha:
Step 2:	Significance (Alpha):
Step 3:	Test Statistic and test:
Why this test?
Step 4:	Decision rule:
Step 5:	Conduct the test – place test function in cell K60
Step 6:	Conclusion and Interpretation
What is the p-value:
Is the t value in the t-distribution tail indicated by the arrow in the Ha claim?
What is your decision: REJ or NOT reject the null?
Why?
What is your conclusion about the impact of education on average salaries?
4	Considering both the compa-ratio information from the lectures and your salary information, what conclusions can you reach about equal pay for equal work?
Your findings:
The lecture’s related findings:
Overall conclusion:
Why – what statistical results support this conclusion?

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