## 09 Apr BUS308 Statistics for Managers Week 1

BUS308 Statistics for Managers Week 1 Assignment ID Salary Compa Midpoint Age Performance Rating Service Gender Raise Degree Gender1 Grade 1 60.2 1.056 57 34 85 8 0 5.7 0 M E 2 27.7 0.893 31 52 80 7 0 3.9 0 M B 3 35.5 1.145 31 30 75 5 1 3.6 1 F B 4 56.1 0.985 57 42 100 16 0 5.5 1 M E 5 48.9 1.018 48 36 90 16 0 5.7 1 M D 6 74.1 1.106 67 36 70 12 0 4.5 1 M F 7 42.2 1.055 40 32 100 8 1 5.7 1 F C 8 21.4 0.929 23 32 90 9 1 5.8 1 F A 9 77.1 1.151 67 49 100 10 0 4 1 M F 10 22.6 0.983 23 30 80 7 1 4.7 1 F A 11 23.8 1.036 23 41 100 19 1 4.8 1 F A 12 67.4 1.183 57 52 95 22 0 4.5 0 M E 13 40.2 1.004 40 30 100 2 1 4.7 0 F C 14 23.7 1.032 23 32 90 12 1 6 1 F A 15 23 1.000 23 32 80 8 1 4.9 1 F A Do not manipuilate Data set on this page, copy to another page to make changes The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work. The column labels in the table mean: ID – Employee sample number Salary – Salary in thousands Age – Age in years Performance Rating – Appraisal rating (employee evaluation score) Service – Years of service (rounded) Gender – 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raise Grade – job/pay grade Degree (0= BSBA 1 = MS) Gender1 (Male or Female) Compa-ratio – salary divided by midpoint Week 1: Descriptive Statistics, including Probability While the lectures will examine our equal pay question from the compa-ratio viewpoint, our weekly assignments will focus on examining the issue using the salary measure. The purpose of this assignmnent is two fold: 1. Demonstrate mastery with Excel tools. 2. Develop descriptive statistics to help examine the question. 3. Interpret descriptive outcomes The first issue in examining salary data to determine if we – as a company – are paying males and females equally for doing equal work is to develop some descriptive statistics to give us something to make a preliminary decision on whether we have an issue or not. 1 Descriptive Statistics: Develop basic descriptive statistics for Salary The first step in analyzing data sets is to find some summary descriptive statistics for key variables. Suggestion: Copy the gender1 and salary columns from the Data tab to columns T and U at the right. Then use Data Sort (by gender1) to get all the male and female salary values grouped together. a. Use the Descriptive Statistics function in the Data Analysis tab Place Excel outcome in Cell K19 to develop the descriptive statistics summary for the overall group’s overall salary. (Place K19 in output range.) Highlight the mean, sample standard deviation, and range. b. Using Fx (or formula) functions find the following (be sure to show the formula and not just the value in each cell) asked for salary statistics for each gender: Male Female Mean: Sample Standard Deviation: Range: 2 Develop a 5-number summary for the overall, male, and female SALARY variable. For full credit, use the excel formulas in each cell rather than simply the numerical answer. Overall Males Females Max 3rd Q Midpoint 1st Q Min 3 Location Measures: comparing Male and Female midpoints to the overall Salary data range. For full credit, show the excel formulas in each cell rather than simply the numerical answer. Using the entire Salary range and the M and F midpoints found in Q2 Male Female a. What would each midpoint’s percentile rank be in the overall range? Use Excel’s =PERCENTRANK.EXC function b. What is the normal curve z value for each midpoint within overall range? Use Excel’s =STANDARDIZE function 4 Probability Measures: comparing Male and Female midpoints to the overall Salary data range For full credit, show the excel formulas in each cell rather than simply the numerical answer. Using the entire Salary range and the M and F midpoints found in Q2, find Male Female a. The Empirical Probability of equaling or exceeding (=>) that value for Show the calculation formula = value/50 or =countif(range,”>=”&cell)/50 b. The Normal curve Prob of => that value for each group Use “=1-NORM.S.DIST” function Note: be sure to use the ENTIRE salary range for part a when finding the probability. 5 Conclusions: What do you make of these results? Be sure to include findings from this week’s lectures as well. In comparing the overall, male, and female outcomes, what relationship(s) see, to exist between the data sets? Your findings: The lecture’s related findings: Overall conclusion: What does this suggest about our equal pay for equal work question?