1.Using the Excel Analysis ToolPak function Descriptive Statistics, generate descriptive statistics for the salary data. Which variables does this function not work properly for, even though we have some generated results?

ata

ompa

8 0

0 M E

7 0

0 M

5 1

1 M E

16 0 5.7 1 M D

36 70

1 M F

8 1 5.7 1 F C

(0 = male, 1 = female)

23 32 90 9 1

1 F A

67 49 100 10 0 4 1 M F

– job/pay grade

23 30 80 7 1

1 F A

(Male or Female)

23 1.000 23 41 100

1 F A

57 52 95 22 0 4.5 0 M E

13 42

40 30 100 2 1 4.7 0 F C

23 32 90 12 1 6 1 F A

15 24

23 32 80 8 1

1 F A

16 47

90 4 0 5.7 0 M C

57 27

3 1 3 1 F E

31 31 80 11 1

0 F B

19 24 1.043 23 32 85 1 0

1 M A

31 44 70 16 1 4.8 0 F B

21 76

67 43 95 13 0

1 M

F
22 57

48 48

6 1

1 F D

23 23 1.000 23 36 65 6 1

0 F A

24 50

48 30 75 9 1 3.8 0 F D

25 24 1.043 23 41 70 4 0 4 0 M A

24 1.043 23 22 95 2 1

0 F A

27 40 1.000 40

80 7 0

1 M C

67 44 95 9 1

0 F F

67 52 95 5 0

0 M F

30 49

90 18 0

0 M D

31 24 1.043 23 29 60 4 1 3.9 1 F A
32 28

31 25 95 4 0 5.6 0 M B

57 35 90 9 0 5.5 1 M E

34 28 0.903 31 26 80 2 0 4.9 1 M B
35 24 1.043 23 23 90 4 1

0 F A

36 23 1.000 23 27 75 3 1 4.3 0 F A

23 22 95 2 1 6.2 0 F A

57 45 95 11 0 4.5 0 M E

31 27 90 6 1 5.5 0 F B

40 25

23 24 90 2 0 6.3 0 M A

41 43

40 25 80 5 0 4.3 0 M C

42 24 1.043 23 32 100 8 1 5.7 1 F A
43 77

67 42 95 20 1 5.5 0 F F

44 60 1.052 57 45 90 16 0

1 M E

45 55

48 36 95 8 1 5.2 1 F D

57 39 75 20 0 3.9 1 M E

57 37 95 5 0 5.5 1 M E

48 65 1.140 57 34 90 11 1 5.3 1 F E
49 60 1.052 57 41 95 21 0

0 M E

50 66

57 38 80 12 0 4.6 0 M E

																																										ID	Sal	C	M	A	E	SR	G	Raise	Deg	Gen	1	Gr
					5					8	1.																							0	7	57	3	4	85	5.7	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)?
				27	0.8			70								31			52								80	3.		9	B	Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
				34	1.09				6					30					75	3.6													F
	66	1.	15				42				10					16					5.5	The column labels in the table mean:
		47	0.979						48						36											90	ID – Employee sample number	Sal – Salary in thousands
	76	1.		13					67				12			4.5	Age – Age in years	EES – Appraisal rating (Employee evaluation score)
					41	1.0			25							40							32			100	SER – Years of service	G –	Gender
																			23					1.000	5.8	Mid – salary grade midpoint	Raise – percent of last raise
			77	1.1			49		Grade	Deg (0= BS\BA 1 = MS)
					22	0.											95		4.7		Gen1	Compa – salary divided by midpoint
			11		19		4.8
				60			1.052
1.0		50
14									24			1.0			43
				1.043		4.9
1.	17				44
69	1.		21			55
	18	1.161		5.6
	4.6
		20	1.096
1.134		6.3
1.187				65		3.8
3.3
1.041
	26		6.2
			35			3.9
		28	1.119	4.4
	29	72	1.074	5.4
1.020				45			4.3
	0.903
33	64	1.122
	5.3
	37	0.9	56
	38	0.982
	39	1.129
1.086
1.075
1.149
	5.2
1.145
46		1.140
62	1.087
6.6
1.157

Week 1

Week 1.	Describing the data.
1. Using the Excel Analysis ToolPak function descriptive statistics, generate descriptive statistics for the salary data.
Which variables does this function not work properly for, even though we have some excel generated results?
2. Sort the data by Gen or Gen 1 (into males and females) and find the mean and standard deviation for each gender for the following variables:
sal, compa, age, sr and raise.	Use the descriptive stats function for one gender and the Fx functions (average and stdev) for the other.
3. What is the probability distribution table for a:
a. Randomly selected person being a male in a specific grade?
b. Randomly selected person being in a specific grade?
4. Find:
a. The z score for each male salary, based on only the male salaries.
b. The z score for each female salary, based on only the female salaries.
5. Repeat question 4 for compa for each gender.
6. What conclusions can you make about the issue of male and female pay equality? Are all of the results consistent? If not, why not?

Week 2

Testing means
Is either the male or female salary equal to the overall mean salary?	(Two hypotheses tests – 1 sample tests)
Are the male and female salaries statistically equal to each other?
Are the male and female compas equal to each other?
4. If the salary and compa mean tests in questions 3 and 4 provide different equality results,
which would be more appropriate to use in answering the question about salary equity? Why?
5. What other information would you like to know to answer the question about salary equity between the genders? Why?

Week 3

Week 3
A B C D E F
Grade
Gender A B C D E F
M

Grade
Gender A B C D E F
M
F
For each empty cell randomly pick a male or female salary from each grade.

1. Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.)
Set up the input table/range to use as follows: Put all of the salary values for each grade under the appropriate grade label.
2. The factorial ANOVA with only 2 variables can be done with the Analysis ToolPak function 2-Way ANOVA with replication. Set up a data input table like the following:
	For each empty cell randomly pick a male or female salary from each grade.
Interpret the results. Are the average salaries for each gender (listed as sample) equal?
Are the average salaries for each grade (listed as column) equal?
3. Repeat question 2 for the compa values.
Interpret the results. Are the average compas for each gender (listed as sample) equal?
Are the average compas for each grade (listed as column) equal?
4. Pick any other variable you are interested in and do a simple 2-way ANOVA without replication. Why did you pick this variable and what do the results show?
5. What are your conclusions about salary equity now?

Week 4

Gr Deg Gen1 Sal

A 0 F 34

A 0 F 41

C 0 F 77

C 0 F 55

D 1 M 77

D 1 M 60

Confidence Intervals and Chi Square (CHs 11 – 12)	Q1	Q2
Let’s look at some other factors that might influence pay.
1. Is the probability of having a graduate degree independent of the grade the employee is in?
2. Construct a 95% confidence interval on the mean service for each gender? Do they intersect?
3. Are males and females distributed across grades in a similar pattern?
4. Do 95% confidence intervals on the mean length of service for each gender intersect?
5. How do you interpret these results in light of our equity question?

Week 5

Week 5 Correlation and Regression

1. Create a correlation table for the variables in our data set. (Use analysis ToolPak function Correlation.)

2. Create a multiple regression equation (using the Analysis ToolPak function Regression) to predict either salary or compa using the mid

(a substitute variable for grade level), age, ees, sr, raise, and deg variables. (Note: since salary and compa are different ways of

expressing an employee’s salary, we do not want to have both used in the same regression.)

3. Based on all of your results to date, is gender a factor in the pay practices of this company? Why or why not?

4. In looking at equal pay issues across an entire company, which is a better variable to use – compa or salary? Why?

5. Why did the single factor tests and analysis (such as t and single factor ANOVA tests on salary equality) not provide a complete answer to our salary equality question?

What outcomes in your life or work might benefit from a multiple regression examination rather than a simpler one varable test?

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