BUS 308 Full Week 2
BUS 308 ASSIGNMENT WEEK 2
Problem Set Week Two
Complete the problems below and submit your work in an Excel document. Be sure to show all of your work and clearly label all calculations. All statistical calculations will use the Employee Salary Data Set. Included in the Week Two tab of the Employee Salary Data Set are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
- Below are 2 one-sample t-test comparing male and female average salaries to the overall sample mean. Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female salaries?
- Based on our sample data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other. (Since we have not yet covered testing for variance equality, assume the data sets have statistically equal variances.)
- Based on our sample data set, can the male and female compas in the population be equal to each other? (Another 2-sample t-test.)
- Since performance is often a factor in pay levels, is the average Performance Rating the same for both genders?
- If the salary and compa mean tests in questions 2 and 3 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??
BUS-308 Discussion 2-1/ Hypotheses: What is a hypothesis test? Why do we need to use them to make decisions about relating sample results to the population; why can’t we just make our decisions by the sample value?
BUS 308 Discussion 2-2/ Variation: Variation exists in virtually all parts of our lives. We often see variation in results in what we spend (utility costs each month, food costs, business supplies, etc.). Consider the measures and data you use (in either your personal or job activities). When are differences (between one time period and another, between different production lines, etc.) between average or actual results important? How can you or your department decide whether or not the observed differences over time are important? How could using a mean difference test help?