BUS 308 Full Week 3

BUS 308 ASSIGNMENT WEEK 3
Problem Set Week Three

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 (in Appendix section). (Note: Questions 1- 4 have additional elements to respond to below the analysis results.)

1. Last week, we found that the average performance ratings do not differ between males and females in the population. Now we need to see if they differ among the grades. Is the average performance rating the same for all grades? (Assume variances are equal across the grades for this ANOVA.)
2. While it appears that average salaries per grade differ, we need to test this assumption. Is the average salary the same for each of the grade levels? (Assume equal variances, and use the Analysis toolpak function ANOVA.) Use the input table to the right to list salaries under each grade level.
3. The table and analysis below demonstrate a 2-way ANOVA with replication. Please interpret the results.
4. Many companies consider the midpoint to be the “market rate” – what is needed to hire a new employee. Does the company, on average, pay its existing employees at or above the market rate?
5. Using the results through this week, what are your conclusions about gender equal pay for equal work at this point?

BUS.308 Discussion 3-1/ ANOVA: In many ways, comparing multiple sample means is simply an extension of what we covered last week. Just as we had 3 versions of the t-test (1 sample, 2 sample (with and without equal variance), and paired; we have several versions of ANOVA – single factor, factorial (called 2-factor with replication in Excel), and within-subjects (2-factor without replication in Excel). What examples (professional, personal, social) can you provide on when we might use each type? What would be the appropriate hypotheses statements for each example?

BUS 308 Discussion 3-2/ Effect Size: Several statistical tests have a way to measure effect size. What is this, and when might you want to use it in looking at results from these tests on job related data?

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