1 Using the Excel Analysis ToolPak function descriptive statistics, generate and show the descriptive statistics for each appropriate variable in the sample data set.

a. For which variables in the data set does this function not work correctly for? Why?

2 Sort the data by Gen or Gen 1 (into males and females) and find the mean and standard deviation for each gender for the following variables:

sal, compa, age, sr and raise. Use either the descriptive stats function or the Fx functions (average and stdev).

3 What is the probability for a:

a. Randomly selected person being a male in grade E?

b. Randomly selected male being in grade E?

c. Why are the results different?

4 Find:

a. The z score for each male salary, based on only the male salaries.

b. The z score for each female salary, based on only the female salaries.

c. The z score for each female compa, based on only the female compa values.

d. The z score for each male compa, based on only the male compa values.

e. What do the distributions and spread suggest about male and female salaries?

Why might we want to use compa to measure salaries between males and females?

5 Based on this sample, what conclusions can you make about the issue of male and female pay equality?

1 Are all of the results consistent with your conclusion? If not, why not?

2 Based on our sample results, perform a 2-sample t-test to see if the population male and female salaries could be equal to each other.

3 Based on our sample results, can the male and female compas in the population be equal to each other? (Another 2-sample t-test.)

4 What other information would you like to know to answer the question about salary equity between the genders? Why?

5 If the salary and compa mean tests in questions 3 and 4 provide different results about male and female salary equality,

which would be more appropriate to use in answering the question about salary equity? Why?

What are your conclusions about equal pay at this point?