Week 2 Problem Set

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Week2ProblemSet.xlsx
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Data

ID Salary Compa-ratio 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 62.5 1.096 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 27.8 0.897 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 35.9 1.158 31 30 75 5 1 3.6 1 F B
4 65.4 1.147 57 42 100 16 0 5.5 1 M E The column labels in the table mean:
5 49.1 1.023 48 36 90 16 0 5.7 1 M D ID – Employee sample number Salary – Salary in thousands
6 75.6 1.129 67 36 70 12 0 4.5 1 M F Age – Age in years Performance Rating - Appraisal rating (employee evaluation score)
7 40.2 1.005 40 32 100 8 1 5.7 1 F C Service – Years of service (rounded) Gender – 0 = male, 1 = female
8 23.3 1.013 23 32 90 9 1 5.8 1 F A Midpoint – salary grade midpoint Raise – percent of last raise
9 78.5 1.171 67 49 100 10 0 4 1 M F Grade – job/pay grade Degree (0= BS\BA 1 = MS)
10 22.9 0.997 23 30 80 7 1 4.7 1 F A Gender1 (Male or Female) Compa-ratio - salary divided by midpoint
11 24.2 1.050 23 41 100 19 1 4.8 1 F A
12 64 1.122 57 52 95 22 0 4.5 0 M E
13 40.8 1.021 40 30 100 2 1 4.7 0 F C
14 24.1 1.046 23 32 90 12 1 6 1 F A
15 23.8 1.034 23 32 80 8 1 4.9 1 F A
16 40.9 1.023 40 44 90 4 0 5.7 0 M C
17 66.5 1.167 57 27 55 3 1 3 1 F E
18 35.2 1.136 31 31 80 11 1 5.6 0 F B
19 23.9 1.039 23 32 85 1 0 4.6 1 M A
20 33.5 1.080 31 44 70 16 1 4.8 0 F B
21 74.2 1.107 67 43 95 13 0 6.3 1 M F
22 55.8 1.162 48 48 65 6 1 3.8 1 F D
23 23 1.001 23 36 65 6 1 3.3 0 F A
24 55.9 1.164 48 30 75 9 1 3.8 0 F D
25 25 1.085 23 41 70 4 0 4 0 M A
26 22.6 0.983 23 22 95 2 1 6.2 0 F A
27 46.3 1.157 40 35 80 7 0 3.9 1 M C
28 75.2 1.122 67 44 95 9 1 4.4 0 F F
29 77.8 1.161 67 52 95 5 0 5.4 0 M F
30 45.6 0.949 48 45 90 18 0 4.3 0 M D
31 22.8 0.991 23 29 60 4 1 3.9 1 F A
32 28.7 0.927 31 25 95 4 0 5.6 0 M B
33 64 1.122 57 35 90 9 0 5.5 1 M E
34 27.9 0.899 31 26 80 2 0 4.9 1 M B
35 23.8 1.034 23 23 90 4 1 5.3 0 F A
36 22.7 0.987 23 27 75 3 1 4.3 0 F A
37 23.7 1.032 23 22 95 2 1 6.2 0 F A
38 60.7 1.065 57 45 95 11 0 4.5 0 M E
39 36.2 1.169 31 27 90 6 1 5.5 0 F B
40 24.3 1.058 23 24 90 2 0 6.3 0 M A
41 41.4 1.035 40 25 80 5 0 4.3 0 M C
42 23 1.001 23 32 100 8 1 5.7 1 F A
43 75.7 1.130 67 42 95 20 1 5.5 0 F F
44 61 1.071 57 45 90 16 0 5.2 1 M E
45 54.8 1.141 48 36 95 8 1 5.2 1 F D
46 62 1.087 57 39 75 20 0 3.9 1 M E
47 61 1.071 57 37 95 5 0 5.5 1 M E
48 71.6 1.257 57 34 90 11 1 5.3 1 F E
49 60.6 1.063 57 41 95 21 0 6.6 0 M E
50 57.7 1.012 57 38 80 12 0 4.6 0 M E

Week 2

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?
Why - what statistical results support this conclusion?