busness question

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Data

ID Salary Compa-ratio Midpoint Age Performance Rating Service Gender Raise Degree Gender1 Grade Copy Employee Data set to this page.
3 34.3 1.105 31 30 75 5 1 3.6 1 F B 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)?
7 41.9 1.048 40 32 100 8 1 5.7 1 F C Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
8 23.4 1.018 23 32 90 9 1 5.8 1 F A
10 24.2 1.054 23 30 80 7 1 4.7 1 F A The column labels in the table mean:
11 23.7 1.032 23 41 100 19 1 4.8 1 F A ID – Employee sample number Salary – Salary in thousands
13 41.4 1.034 40 30 100 2 1 4.7 0 F C Age – Age in years Performance Rating – Appraisal rating (Employee evaluation score)
14 23.7 1.030 23 32 90 12 1 6 1 F A SERvice – Years of service Gender: 0 = male, 1 = female
15 23 1.001 23 32 80 8 1 4.9 1 F A Midpoint – salary grade midpoint Raise – percent of last raise
17 63.6 1.115 57 27 55 3 1 3 1 F E Grade – job/pay grade Degree (0= BS\BA 1 = MS)
18 34.3 1.107 31 31 80 11 1 5.6 0 F B Gender1 (Male or Female) Compa-ratio - salary divided by midpoint
20 35.5 1.144 31 44 70 16 1 4.8 0 F B
22 49 1.020 48 48 65 6 1 3.8 1 F D t-Test: Two-Sample Assuming Equal Variances
23 23.1 1.005 23 36 65 6 1 3.3 0 F A
24 56.8 1.183 48 30 75 9 1 3.8 0 F D Variable 1 Variable 2
26 23.1 1.003 23 22 95 2 1 6.2 0 F A Mean 1.06684 1.04836
28 76.4 1.141 67 44 95 9 1 4.4 0 F F Variance 0.00430164 0.00648099
31 23.1 1.006 23 29 60 4 1 3.9 1 F A Observations 25 25
35 23.7 1.032 23 23 90 4 1 5.3 0 F A Pooled Variance 0.005391315
36 25.1 1.093 23 27 75 3 1 4.3 0 F A Hypothesized Mean Difference 0
37 23.4 1.016 23 22 95 2 1 6.2 0 F A df 48
39 36.7 1.184 31 27 90 6 1 5.5 0 F B t Stat 0.8898352784
42 23 1.001 23 32 100 8 1 5.7 1 F A P(T<=t) one-tail 0.188996287
43 75 1.120 67 42 95 20 1 5.5 0 F F t Critical one-tail 1.6772241961
45 47.4 0.988 48 36 95 8 1 5.2 1 F D P(T<=t) two-tail 0.3779925741
48 65.9 1.156 57 34 90 11 1 5.3 1 F E t Critical two-tail 2.0106347576
1 63.9 1.121 57 34 85 8 0 5.7 0 M E
2 27.7 0.892 31 52 80 7 0 3.9 0 M B
4 57.6 1.010 57 42 100 16 0 5.5 1 M E
5 47.4 0.987 48 36 90 16 0 5.7 1 M D
6 75.2 1.122 67 36 70 12 0 4.5 1 M F
9 74 1.105 67 49 100 10 0 4 1 M F
12 58.3 1.022 57 52 95 22 0 4.5 0 M E
16 41.5 1.037 40 44 90 4 0 5.7 0 M C
19 24.7 1.073 23 32 85 1 0 4.6 1 M A
21 78.1 1.166 67 43 95 13 0 6.3 1 M F
25 24.2 1.053 23 41 70 4 0 4 0 M A
27 45.3 1.132 40 35 80 7 0 3.9 1 M C
29 74.6 1.113 67 52 95 5 0 5.4 0 M F
30 48.2 1.005 48 45 90 18 0 4.3 0 M D
32 27.8 0.898 31 25 95 4 0 5.6 0 M B
33 61.7 1.083 57 35 90 9 0 5.5 1 M E
34 27.5 0.886 31 26 80 2 0 4.9 1 M B
38 60.1 1.054 57 45 95 11 0 4.5 0 M E
40 25.1 1.090 23 24 90 2 0 6.3 0 M A
41 38.8 0.971 40 25 80 5 0 4.3 0 M C
44 64.4 1.129 57 45 90 16 0 5.2 1 M E
46 68.7 1.205 57 39 75 20 0 3.9 1 M E
47 59.6 1.045 57 37 95 5 0 5.5 1 M E
49 69.1 1.213 57 41 95 21 0 6.6 0 M E
50 65.2 1.143 57 38 80 12 0 4.6 0 M E

Sheet1

SUMMARY OUTPUT SUMMARY OUTPUT
Regression Statistics Regression Statistics
Multiple R 0.7050179484 Multiple R 0.9931286935
R Square 0.4970503076 R Square 0.9863046018
Adjusted R Square 0.4132253589 Adjusted R Square 0.9840220355
Standard Error 0.0561252686 Standard Error 2.4352822665
Observations 50 Observations 50
ANOVA ANOVA
df SS MS F Significance F df SS MS F Significance F
Regression 7 0.1307500775 0.0186785825 5.9296225662 0.0000782906 Regression 7 17938.424611863 2562.632087409 432.1033638177 5.29906273684337E-37
Residual 42 0.1323019225 0.0031500458 Residual 42 249.085188137 5.9305997175
Total 49 0.263052 Total 49 18187.5098
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 0.9486238772 0.0817167716 11.6086803119 0 0.7837127557 1.1135349987 0.7837127557 1.1135349987 Intercept -4.8714544587 3.54570071 -1.3739045839 0.1767599037 -12.0269681853 2.2840592678 -12.0269681853 2.2840592678
Mid 0.0034995027 0.0006492568 5.3900133356 0.0000029767 0.0021892495 0.0048097559 0.0021892495 0.0048097559 Mid 1.2284155048 0.0281713308 43.6051641629 1.32019333894083E-36 1.1715634576 1.2852675521 1.1715634576 1.2852675521
Age 0.0005527738 0.0014459446 0.3822925256 0.7041721007 -0.0023652605 0.0034708081 -0.0023652605 0.0034708081 Age 0.0368279425 0.0627397124 0.5869957178 0.5603489282 -0.0897859231 0.1634418081 -0.0897859231 0.1634418081
EES -0.0018462553 0.0010252155 -1.8008461371 0.0789105539 -0.0039152239 0.0002227133 -0.0039152239 0.0002227133 EES -0.0821579785 0.0444842245 -1.8469014451 0.0718147225 -0.171930778 0.007614821 -0.171930778 0.007614821
SR -0.0004182288 0.0018278101 -0.2288141345 0.820123898 -0.004106899 0.0032704414 -0.004106899 0.0032704414 SR -0.0778484529 0.079308905 -0.9815852701 0.3319249969 -0.2379003029 0.0822033971 -0.2379003029 0.0822033971
G 0.0646649961 0.0183396697 3.5259629624 0.001034866 0.0276540443 0.101675948 0.0276540443 0.101675948 G 2.9145083112 0.7957605113 3.6625445343 0.000693549 1.3085985836 4.5204180389 1.3085985836 4.5204180389
Raise 0.0146549564 0.0139088976 1.0536389608 0.2980722322 -0.0134143354 0.0427242483 -0.0134143354 0.0427242483 Raise 0.6763294824 0.6035087689 1.1206622295 0.2687988764 -0.5416005215 1.8942594864 -0.5416005215 1.8942594864
Deg 0.0014675988 0.0161098249 0.0910996125 0.9278465471 -0.0310433441 0.0339785418 -0.0310433441 0.0339785418 Deg 0.0345044482 0.6990072742 0.0493620731 0.9608647532 -1.3761493419 1.4451582383 -1.3761493419 1.4451582383

Week 3

ANOVA Three Questions
Remember to show how you got your results in the appropriate cells. For questions using functions, show the input range when asked.
1 Group name: G1 G2 G3 G4 G5 G6
One interesting question is are the average compa-ratios equal across salary ranges of 10K each. Salary Intervals: 22-29 30-39 40-49 50-59 60-69 70-79
While compa-ratios remove the impact of grade on salaries, are they different for different pay levels, Compa-ratio values:
that is are people at different levels paid differently relative to the midpoint? (Put data values at right.)
What is the data input ranged used for this question:
Step 1: Ho:
Ha:
Step 2: Decision Rule:
Step 3: Statistical test:
Why?
Step 4: Conduct the test - place cell b16 in the output location box.
Step 5: Conclusions and Interpretation
What is the p-value?
Is P-value < 0.05?
What is your decision: REJ or NOT reject the null?
If the null hypothesis was rejected, what is the effect size value (eta squared)?
If calculated, what does the effect size value tell us about why the null hypothesis was rejected?
What does that decision mean in terms of our equal pay question?
2
If the null hypothesis in question 1 was rejected, which pairs of means differ? Why?
Groups Compared Diff T +/- Term Low to High Difference Significant? Why?
G1 G2
G1 G3
G1 G4
G1 G5
G1 G6
G2 G3
G2 G4
G2 G5
G2 G6
G3 G4
G3 G5
G3 G6
G4 G5
G4 G6
G5 G6
3
Since compa is already a measure of pay for equal work, do these results impact
your conclusion on equal pay for equal work? Why or why not?

Week 4

Regression and Corellation Five Questions Compa-ratio Midpoint Age Performance Rating Service Raise Degree Gender
Remember to show how you got your results in the appropriate cells. For questions using functions, show the input range when asked.
1 Create a correlation table using Compa-ratio and the other interval level variables, except for Salary.
Suggestion, place data in columns T - Y.
What range was placed in the Correlation input range box:
Place C9 in output box.
b What are the statistically significant correlations related to Compa-ratio? T = Significant r =
c Are there any surprises - correlations you though would be significant and are not, or non significant correlations you thought would be?
d Why does or does not this information help answer our equal pay question?
2 Perform a regression analysis using compa as the dependent variable and the variables used in Q1 along with
including the dummy variables. Show the result, and interpret your findings by answering the following questions.
Suggestion: Place the dummy variables values to the right of column Y.
What range was placed in the Regression input range box:
Note: be sure to include the appropriate hypothesis statements.
Regression hypotheses
Ho:
Ha:
Coefficient hyhpotheses (one to stand for all the separate variables)
Ho:
Ha:
Place B36 in output box.
Interpretation:
For the Regression as a whole:
What is the value of the F statistic:
What is the p-value associated with this value:
Is the p-value < 0.05?
What is your decision: REJ or NOT reject the null?
What does this decision mean?
For each of the coefficients: Midpoint Age Perf. Rat. Service Gender Degree
What is the coefficient's p-value for each of the variables:
Is the p-value < 0.05?
Do you reject or not reject each null hypothesis:
What are the coefficients for the significant variables?
Using the intercept coefficient and only the significant variables, what is the equation? Compa-ratio =
Is gender a significant factor in compa-ratio?
Regardless of statistical significance, who gets paid more with all other things being equal?
How do we know?
3 What does regression analysis show us about analyzing complex measures?
4 Between the lecture results and your results, what else would you like to know
before answering our question on equal pay? Why?
5 Between the lecture results and your results, what is your answer to the question
of equal pay for equal work for males and females? Why?