See comments at the right of the data set..docx

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= 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

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 point>
1
Measurement issues.
Data, even numerically coded variables, can be one of 4 levels -

nominal, ordinal, interval, or ratio.
It is important to identify which level a variable is, as

this impact the kind of analysis we can do with the data.
For example, descriptive statistics

such as means can only be done on interval or ratio level data.

Please list under each label, the variables in our data set that
belong in each group.

Nominal
Ordinal
Interval
Ratio

b.
For each variable that you did not call ratio, why did you make
that decision?

<1 point>
2
The first step in analyzing data sets is to find some summary

descriptive statistics for key variables.

For salary, compa, age, performance rating, and service; find
the mean, standard deviation, and range for 3 groups: overall
sample, Females, and Males.

You can use either the Data Analysis Descriptive Statistics tool
or the Fx =average and =stdev functions.

(the range must be found using the difference between the =max
and =min functions with Fx) functions.

Note: Place data to the right, if you use Descriptive statistics,
place that to the right as well.

<1 point>
3
What is the probability for a:
Probability

a. Randomly selected person being a male in grade E?

b. Randomly selected male being in grade E?

Note part b is the same as given a male, what is probabilty of
being in grade E?

c.
Why are the results different?

<1 point>
4
For each group (overall, females, and males) find:
Overall
Female
Male

a.
The value that cuts off the top 1/3 salary in each group.
Hint: can use these Fx functions

b.
The z score for each value:
Excel's standize function

c.
The normal curve probability of exceeding this score:
1-normsdist function

d.
What is the empirical probability of being at or exceeding this
salary value?

e.
The value that cuts off the top 1/3 compa in each group.

f.
The z score for each value:

g.
The normal curve probability of exceeding this score:

h.
What is the empirical probability of being at or exceeding this
compa value?

How do you interpret the relationship between the data sets?
What do they mean about our equal pay for equal work
question?

<2 points>
5.
What conclusions can you make about the issue of male and
female pay equality?
Are all of the results consistent?

What is the difference between the sal and compa measures of
pay?

Conclusions from looking at salary results:

Conclusions from looking at compa results:

Do both salary measures show the same results?

Can we make any conclusions about equal pay for equal work
yet?

See comments at the right of the data set..docx

See comments at the right of the data set..docx

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See comments at the right of the data set..docx