Part -1:
1. One interesting question is are the average comparatios equal across salary ranges of 10K each.
While comparatios remove the impact of grade on salaries, are they different for different pay levels, 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?
2. If the null hypothesis in question 1 was rejected, which pairs of means differ?
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?
Remember to show how you got your results in the appropriate cells. For questions using functions, show the input range when asked.
Part -2:
1 Create a correlation table using Compa-ratio and the other interval level variables, except for Salary. Suggestion, place data in columns T - Y.
Place C9 in output box.
b What are the statistically significant correlations related to Comparatio?
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.
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:
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?
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?
Attachment:- DATA SHEET.rar