# Calculating Descriptive Statistics

For
each variable in the data set provided, classify the variable as
Nominal, Ordinal, Interval or Ratio. Explain why you made the decision
for each.Find
the Mean, Standard Deviation, and Range for The Salary variable for the
entire data set and for the male and females separately. Find
the first, second, and third quartiles and the interquartile range for
the entire data set, the males and females separately. Compare the
results of the male and females and comment on the results.To
test if the data is approximately normal, use the empirical rule for
the normal distribution. Find the percentage of male salaries within
one, two, and three standard deviation of the mean. Compare the
percentages to those predicted by the empirical rule for the normal
distribution. Repeat the calculations for the female salaries. Comment
on the results.
copy_of_salary.xlsm

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ID
Salary
Compa
Midpoint
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
62.9
27.2
34.9
64.6
49.2
77.7
41.2
22
73.4
23.9
21.8
60.8
41.8
23.3
24.6
47.4
66.6
34.8
24.5
35.9
77.1
58.9
23.8
55.1
24.6
24.1
45.3
75.2
78.7
48.3
24.2
27.6
61.2
27
23.8
24.4
23.5
61.3
34.4
24.7
1.104
57
31
31
57
48
67
40
23
67
23
23
57
40
23
23
40
57
31
23
31
67
48
23
48
23
23
40
67
67
48
23
31
57
31
23
23
23
57
31
23
34
52
30
42
36
36
32
32
49
30
41
52
30
32
32
44
27
31
32
44
43
48
36
30
41
22
35
44
52
45
29
25
35
26
23
27
22
45
27
24
0.879
1.127
1.134
1.026
1.160
1.029
0.956
1.095
1.039
0.947
1.066
1.044
1.012
1.070
1.186
1.169
1.124
1.065
1.159
1.150
1.228
1.036
1.147
1.069
1.047
1.133
1.122
1.175
1.006
1.050
0.889
1.074
0.872
1.036
1.060
1.023
1.075
1.109
1.076
Performance Service Gende Raise
Rating
r
85
80
75
100
90
70
100
90
100
80
100
95
100
90
80
90
55
80
85
70
95
65
65
75
70
95
80
95
95
90
60
95
90
80
90
75
95
95
90
90
8
7
5
16
16
12
8
9
10
7
19
22
2
12
8
4
3
11
1
16
13
6
6
9
4
2
7
9
5
18
4
4
9
2
4
3
2
11
6
2
0
0
1
0
0
0
1
1
0
1
1
0
1
1
1
0
1
1
0
1
0
1
1
1
0
1
0
1
0
0
1
0
0
0
1
1
1
0
1
0
5.7
3.9
3.6
5.5
5.7
4.5
5.7
5.8
4
4.7
4.8
4.5
4.7
6
4.9
5.7
3
5.6
4.6
4.8
6.3
3.8
3.3
3.8
4
6.2
3.9
4.4
5.4
4.3
3.9
5.6
5.5
4.9
5.3
4.3
6.2
4.5
5.5
6.3
41
42
43
44
45
46
47
48
49
50
45.8
22.4
75.6
61.3
59.7
55.1
63.8
62.8
64
66.3
1.145
0.975
1.129
1.075
1.244
0.967
1.119
1.101
1.122
1.163
40
23
67
57
48
57
57
57
57
57
25
32
42
45
36
39
37
34
41
38
80
100
95
90
95
75
95
90
95
80
5
8
20
16
8
20
5
11
21
12
0
1
1
0
1
0
0
1
0
0
4.3
5.7
5.5
5.2
5.2
3.9
5.5
5.3
6.6
4.6
Degree Gender
1
0
0
1
1
1
1
1
1
1
1
1
0
0
1
1
0
1
0
1
0
1
1
0
0
0
0
1
0
0
0
1
0
1
1
0
0
0
0
0
0
M
M
F
M
M
M
F
F
M
F
F
M
F
F
F
M
F
F
M
F
M
F
F
F
M
F
M
F
M
M
F
M
M
M
F
F
F
M
F
M
Gr
E
B
B
E
D
F
C
A
F
A
A
E
C
A
A
C
E
B
A
B
F
D
A
D
A
A
C
F
F
D
A
B
E
B
A
A
A
E
B
A
The column labels in the table mean:
ID  Employee sample number
Salary  Salary in thousands
Age  Age in years
Performance Rating – Appraisal rating (em
Service  Years of service (rounded) Gender  0 = male, 1 = female
Raise  percent of last raise
Degree (0= BSBA 1 = MS)
Gender1 (Male or Female)
Compa – salary divided by midpoint
0
1
0
1
1
1
1
1
0
0
M
F
F
M
F
M
M
F
M
M
C
A
F
E
D
E
E
E
E
E
ance Rating – Appraisal rating (employee evaluation score)

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