Excel Assignment

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ExcelSpreadsheetAssignment4.xlsx
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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

Week 4

Week 4: Identifying relationships - correlations and regression
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.
1 What is the correlation between and among the interval/ratio level variables with salary? (Do not include compa-ratio in this question.)
a. Create the correlation table. Use Cell K08 for the Excel test outcome location.
i. What is the data input ranged used for this question:
ii. Create a correlation table in cell K08.
b. Technically, we should perform a hypothesis testing on each correlation to determine
if it is significant or not. However, we can be faithful to the process and save some
time by finding the minimum correlation that would result in a two tail rejection of the null.
We can then compare each correlation to this value, and those exceeding it (in either a
positive or negative direction) can be considered statistically significant.
i. What is the t-value we would use to cut off the two tails? T =
ii. What is the associated correlation value related to this t-value? r =
c. What variable(s) is(are) significantly correlated to salary?
d. Are there any surprises - correlations you though would be significant and are not, or non significant correlations you thought would be?
e. Why does or does not this information help answer our equal pay question?
2 Perform a regression analysis using salary as the dependent variable and all of the variables used in Q1. Add the
two dummy variables - gender and education - to your list of independent variables. Show the result, and interpret your findings by answering the following questions.
Suggestion: Add the dummy variables values to the right of the last data columns used for Q1.
What is the multiple regression equation predicting/explaining salary using all of our possible variables except compa-ratio?
a. What is the data input ranged used for this question:
b. Step 1: State the appropriate hypothesis statements: Use Cell M34 for the Excel test outcome location.
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 M34
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 factors influencing the population salary values?
c. If we rejected the null hypothesis, we need to test the significance of each of the variable coefficients.
Step 1: State the appropriate coefficient hypothesis statements: (Write a single pair, we will use it for each variable separately.)
Ho:
Ha:
Step 2: Significance (Alpha):
Step 3: Test Statistic and test:
Why this test?
Step 4: Decision rule:
Step 5: Conduct the test
Note, in this case the test has been performed and is part of the Regression output above.
Step 6: Conclusion and Interpretation
Place the t and p-values in the following table
Identify your decision on rejecting the null for each variable. If you reject the null, place the coefficient in the table.
Midpoint Age Perf. Rat. Seniority Raise Gender Degree
t-value:
P-value:
Rejection Decision:
If Null is rejected, what is the variable's coefficient value?
Using the intercept coefficient and only the significant variables, what is the equation?
Salary =
d. Is gender a significant factor in salary?
e. Regardless of statistical significance, who gets paid more with all other things being equal?
f. How do we know?
3 After considering the compa-ratio based results in the lectures and your salary based results, what else would you like to know
before answering our question on equal pay? Why?
4 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?
Your findings:
The lecture's related findings:
Overall conclusion:
5 What does regression analysis show us about analyzing complex measures?