Problem Set Week Four
This week we get to answer our equal pay for equal work question by looking at relationships between and among the different variables.
The first question this week looks at correlations and the creation of a correlation table for our variables. The second question asks for a regression equation showing how the different variables impact the compa-ratio measure. The third questions asks you to discuss the benefits of using a regression equation approach over the single variable tests we have been doing.
The forth question asks for what other information you would have liked to have analyzed in our research. The fifth question asks for your answer to the equal pay for equal work question of: Is the company paying fairly or not? If not, who benefits and why?
Regression and Corellation
Remember to show how you got your results in the appropriate cells. For questions using functions, show the input range when asked.
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- Create a correlation table using Compa-ratio and the other interval level variables, except for Salary.
Suggestion, place data in columns T – Y
a 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?
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