1. HR Analysis April 4, 2014 MBP Professor Judson Glenice Booker-Butler, Mark Dominik, Tammi Dorion & Fred Paul

2. Team Shenanigans  Glenice Booker-Butler  Mark Dominik  Tammi Dorion  Fred Paul

3. Table of Contents 1. Executive Summary 2. Problem Statement 3. Purpose Statement & Research Question 4. Business Case 5. Variables Analyzed 6. Methods 7. Demographics 8. Hypothesis 9. Analysis & Results 10. Conclusions

4. Executive Summary  The analysis of key demographic information is important to the new executive team in order to understand if any policies need immediate review. This study will provide the following:  General overview of company demographics  Significance between key factors  Statistical analysis for determination of potential bias  Descriptions of analysis methods utilized  Ethical considerations

5. Problem Statement  Problem: The new executive team wants to better understand the critical issues related to demographics and processes such as compensation and job grade.

6. Purpose & Research Question Statement of Purpose: The purpose of this presentation is to outline key HR statistical data for the new executive team to understand if the various demographics affect salary. Research Question: Which demographic(s) within the company most affects the salary of the employees?

7. Business Case  Close in-depth look into demographics  Benefits of analysis  Forward looking…

8. Variables Analyzed  Gender  Cultural Identity  Age  Grade  Effectiveness  Years of Education  Years of Experience  Company Experience  ESL

9. Methods  Demographics  Scatter Plot  Correlations  Regression Analysis  Voice of the data – ethical considerations

16. AA = 36% $79,512.17 H = 37% $76,016.11 E = 27% $80,612.93

17. F = 53% $73,939.53 M = 47% $83,673.19

18. ESL N = 47% $82,330.30 ESL Y = 53% $75,130.32

19. Basic Hypothesis: There is no relationship between the independent variables (1-8) and the dependent variable salary. Independent Variables 1. Gender 2. Cultural Identity 3. Age 4. Effectiveness 5. Years of Education 6. Years of Experience 7. Company Experience 8. ESL Dependent Variable  Salary Hypothesis

20. Variables Related to Salary

21. Correlations – Pearson’s

22. Correlations – Kendall Tau’s

23. Multi Regression Independent Variables  Age  Grade  Effectiveness  Years of Education  Years of Experience  Company Experience  Satisfaction with company  Gender  Cultural Identity  ESL

24. Multiple Regression – All Variables Gender and ESL are the only statistically significant variables.

25. R-Square – All Variables 22.9% of the variation within salary.

26. R-Square – ESL & Gender 14.7% of the variation within salary.

27. R-Square - Gender 10.2% of the variation within salary.

28. Salary & Job Grade Analysis Job “grade” is not statistically significant, nor is it predictive of an individual’s salary. Job grade is directly correlated with “Age”.

30. Conclusions  ESL and gender are the demographics that are statistically significant related to salary.  No strong predictive model for salary.  Grade of individuals is based on age of employees.  Overall determination of why differences exist would need to be investigated further.