Reference no: EM13720082

Question 1) Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)

a) Reviewing the data levels, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?

b) Place table here (C8):

c) Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables are
significantly related to Salary?

d) Looking at the above correlations - both significant or not - are there any surprises -by that I
mean any relationships you expected to be meaningful and are not and vice-versa?

e) Does this help us answer our equal pay for equal work question?

Question 2) Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint, age, performance rating, service, gender, and degree variables. (Note: since salary and compa are different ways of expressing an employee's salary, we do not want to have both used in the same regression.)

Question 3) Perform a regression analysis using compa as the dependent variable and the same independent variables as used in question 2. Show the result, and interpret your findings by answering the same questions.

Note: be sure to include the appropriate hypothesis statements.

Question 4) Based on all of your results to date,

Do we have an answer to the question of are males and females paid equally for equal work?

Which is the best variable to use in analyzing pay practices - salary or compa? Why?

What is most interesting or surprising about the results we got doing the analysis?

Question 5) Why did the single factor tests and analysis (such as t and single factor ANOVA tests on salary equality) not provide a complete answer to our salary equality question?

What outcomes in your life or work might benefit from a multiple regression examination rather than a simpler one variable test?