•

0 likes•4 views

See comments at the right of the data set. ID Salary Compa Midpoint Age Performance Rating Service Gender Raise Degree Gender1 Grade 8 23 1.000 23 32 90 9 1 5.8 0 F A 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)? 10 22 0.956 23 30 80 7 1 4.7 0 F A Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work. 11 23 1.000 23 41 100 19 1 4.8 0 F A 14 24 1.043 23 32 90 12 1 6 0 F A The column labels in the table mean: 15 24 1.043 23 32 80 8 1 4.9 0 F A ID – Employee sample number Salary – Salary in thousands 23 23 1.000 23 36 65 6 1 3.3 1 F A Age – Age in years Performance Rating – Appraisal rating (Employee evaluation score) 26 24 1.043 23 22 95 2 1 6.2 1 F A Service – Years of service (rounded) Gender: 0 = male, 1 = female 31 24 1.043 23 29 60 4 1 3.9 0 F A Midpoint – salary grade midpoint Raise – percent of last raise 35 24 1.043 23 23 90 4 1 5.3 1 F A Grade – job/pay grade Degree (0= BS\BA 1 = MS) 36 23 1.000 23 27 75 3 1 4.3 1 F A Gender1 (Male or Female) Compa - salary divided by midpoint 37 22 0.956 23 22 95 2 1 6.2 1 F A 42 24 1.043 23 32 100 8 1 5.7 0 F A 3 34 1.096 31 30 75 5 1 3.6 0 F B 18 36 1.161 31 31 80 11 1 5.6 1 F B 20 34 1.096 31 44 70 16 1 4.8 1 F B 39 35 1.129 31 27 90 6 1 5.5 1 F B 7 41 1.025 40 32 100 8 1 5.7 0 F C 13 42 1.050 40 30 100 2 1 4.7 1 F C 22 57 1.187 48 48 65 6 1 3.8 0 F D 24 50 1.041 48 30 75 9 1 3.8 1 F D 45 55 1.145 48 36 95 8 1 5.2 0 F D 17 69 1.210 57 27 55 3 1 3 0 F E 48 65 1.140 57 34 90 11 1 5.3 1 F E 28 75 1.119 67 44 95 9 1 4.4 1 F F 43 77 1.149 67 42 95 20 1 5.5 1 F F 19 24 1.043 23 32 85 1 0 4.6 1 M A 25 24 1.043 23 41 70 4 0 4 0 M A 40 25 1.086 23 24 90 2 0 6.3 0 M A 2 27 0.870 31 52 80 7 0 3.9 0 M B 32 28 0.903 31 25 95 4 0 5.6 0 M B 34 28 0.903 31 26 80 2 0 4.9 1 M B 16 47 1.175 40 44 90 4 0 5.7 0 M C 27 40 1.000 40 35 80 7 0 3.9 1 M C 41 43 1.075 40 25 80 5 0 4.3 0 M C 5 47 0.979 48 36 90 16 0 5.7 1 M D 30 49 1.020 48 45 90 18 0 4.3 0 M D 1 58 1.017 57 34 85 8 0 5.7 0 M E 4 66 1.157 57 42 100 16 0 5.5 1 M E 12 60 1.052 57 52 95 22 0 4.5 0 M E 33 64 1.122 57 35 90 9 0 5.5 1 M E 38 56 0.982 57 45 95 11 0 4.5 0 M E 44 60 1.052 57 45 90 16 0 5.2 1 M E 46 65 1.140 57 39 75 20 0 3.9 1 M E 47 62 1.087 57 37 95 5 0 5.5 1 M E 49 60 1.052 57 41 95 21 0 6.6 0 M E 50 66 1.157 57 38 80 12 0 4.6 0 M E 6 76 1.134 67 36 70 12 0 4.5 1 M F 9 77 1.149 67 49 100 10 0 4 1 M F 21 76 1.134 67 43 95 13 0 6.3 1 M F 29 72 1.074 67 52 95 5 0 5.4 0 M F Score: Week 1. Measurement and Description - chapters 1 and 2 .

- 1. See comments at the right of the data set. ID Salary Compa Midpoint Age Performance Rating Service Gender Raise
- 2. Degree Gender1 Grade 8 23 1.000 23 32 90 9 1 5.8 0 F A 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)? 10 22 0.956 23 30 80 7 1 4.7
- 3. 0 F A Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work. 11 23 1.000 23 41 100 19 1 4.8 0 F A 14 24 1.043 23 32 90 12 1 6 0 F A
- 4. The column labels in the table mean: 15 24 1.043 23 32 80 8 1 4.9 0 F A ID – Employee sample number Salary – Salary in thousands 23 23 1.000 23 36 65 6 1 3.3 1 F A Age – Age in years
- 5. Performance Rating – Appraisal rating (Employee evaluation score) 26 24 1.043 23 22 95 2 1 6.2 1 F A Service – Years of service (rounded) Gender: 0 = male, 1 = female 31 24 1.043 23 29 60 4 1 3.9 0 F A Midpoint – salary grade midpoint
- 6. Raise – percent of last raise 35 24 1.043 23 23 90 4 1 5.3 1 F A Grade – job/pay grade Degree (0= BSBA 1 = MS) 36 23 1.000 23 27 75 3 1 4.3 1 F A Gender1 (Male or Female) Compa - salary divided by midpoint 37 22 0.956 23 22
- 26. Score: Week 1. Measurement and Description - chapters 1 and 2
- 30. <1 point> 1 Measurement issues. Data, even numerically coded variables, can be one of 4 levels -
- 32. nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, as
- 33. this impact the kind of analysis we can do with the data. For example, descriptive statistics
- 35. such as means can only be done on interval or ratio level data.
- 36. Please list under each label, the variables in our data set that belong in each group.
- 49. b. For each variable that you did not call ratio, why did you make that decision?
- 58. <1 point> 2 The first step in analyzing data sets is to find some summary
- 59. descriptive statistics for key variables.
- 60. For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.
- 61. You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions.
- 63. (the range must be found using the difference between the =max and =min functions with Fx) functions.
- 64. Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.
- 65. Salary Compa
- 67. Overall Mean
- 70. Range
- 72. Female Mean
- 75. Range
- 77. Male Mean
- 80. Range
- 83. <1 point> 3 What is the probability for a: Probability
- 85. a. Randomly selected person being a male in grade E?
- 86. b. Randomly selected male being in grade E?
- 88. Note part b is the same as given a male, what is probabilty of being in grade E?
- 89. c. Why are the results different?
- 92. <1 point> 4 For each group (overall, females, and males) find: Overall Female Male
- 94. a. The value that cuts off the top 1/3 salary in each group. Hint: can use these Fx functions
- 95. b. The z score for each value: Excel's standize function
- 97. c. The normal curve probability of exceeding this score: 1-normsdist function
- 98. d. What is the empirical probability of being at or exceeding this salary value?
- 100. e. The value that cuts off the top 1/3 compa in each group.
- 101. f. The z score for each value:
- 103. g. The normal curve probability of exceeding this score:
- 104. h. What is the empirical probability of being at or exceeding this compa value?
- 105. i.
- 106. How do you interpret the relationship between the data sets? What do they mean about our equal pay for equal work question?
- 112. <2 points> 5. What conclusions can you make about the issue of male and female pay equality? Are all of the results consistent?
- 113. What is the difference between the sal and compa measures of pay?
- 118. Conclusions from looking at salary results:
- 123. Conclusions from looking at compa results:
- 127. Do both salary measures show the same results?
- 132. Can we make any conclusions about equal pay for equal work yet?