Homework SourseQuestion 1 Create a correlation table for the variables in o
Question 1)
Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)
What variables can be used in a Pearson\'s Correlation table (which is what Excel produces)?
b )Place table here (C8):
c )Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables are significantly related to Salary?
To compa?
d )Looking at the above correlations - both significant or not - are there any surprises -by that I mean any relationships you expected to be meaningful and are not and vice-versa?
e )Does this help us answer our equal pay for equal work question?
ID
Salary
Compa
Midpoint
Age
Performance Rating
Service
Gender
Raise
Degree
Gender1
Gr
1
66.1
1.159
57
34
85
8
0
5.7
0
M
E
2
25.9
0.834
31
52
80
7
0
3.9
0
M
B
3
35.2
1.135
31
30
75
5
1
3.6
1
F
B
4
55.3
0.971
57
42
100
16
0
5.5
1
M
E
5
49.6
1.033
48
36
90
16
0
5.7
1
M
D
6
78.3
1.168
67
36
70
12
0
4.5
1
M
F
7
42.3
1.058
40
32
100
8
1
5.7
1
F
C
8
22.8
0.990
23
32
90
9
1
5.8
1
F
A
9
78
1.164
67
49
100
10
0
4
1
M
F
10
23.3
1.014
23
30
80
7
1
4.7
1
F
A
11
23.6
1.025
23
41
100
19
1
4.8
1
F
A
12
60.8
1.067
57
52
95
22
0
4.5
0
M
E
13
40.6
1.014
40
30
100
2
1
4.7
0
F
C
14
21.7
0.943
23
32
90
12
1
6
1
F
A
15
21.8
0.949
23
32
80
8
1
4.9
1
F
A
16
37.4
0.934
40
44
90
4
0
5.7
0
M
C
17
57
1.000
57
27
55
3
1
3
1
F
E
18
33.5
1.081
31
31
80
11
1
5.6
0
F
B
19
23
1.000
23
32
85
1
0
4.6
1
M
A
20
36
1.162
31
44
70
16
1
4.8
0
F
B
21
76
1.135
67
43
95
13
0
6.3
1
M
F
22
43.7
0.911
48
48
65
6
1
3.8
1
F
D
23
25.3
1.098
23
36
65
6
1
3.3
0
F
A
24
48.9
1.019
48
30
75
9
1
3.8
0
F
D
25
25.8
1.122
23
41
70
4
0
4
0
M
A
26
23.3
1.013
23
22
95
2
1
6.2
0
F
A
27
42.3
1.057
40
35
80
7
0
3.9
1
M
C
28
75.2
1.122
67
44
95
9
1
4.4
0
F
F
29
80.9
1.208
67
52
95
5
0
5.4
0
M
F
30
49
1.020
48
45
90
18
0
4.3
0
M
D
31
24.2
1.054
23
29
60
4
1
3.9
1
F
A
32
27.5
0.886
31
25
95
4
0
5.6
0
M
B
33
63.6
1.115
57
35
90
9
0
5.5
1
M
E
34
28.6
0.922
31
26
80
2
0
4.9
1
M
B
35
22.4
0.976
23
23
90
4
1
5.3
0
F
A
36
23.6
1.026
23
27
75
3
1
4.3
0
F
A
37
24.3
1.057
23
22
95
2
1
6.2
0
F
A
38
63
1.105
57
45
95
11
0
4.5
0
M
E
39
34.8
1.123
31
27
90
6
1
5.5
0
F
B
40
24.3
1.057
23
24
90
2
0
6.3
0
M
A
41
42.8
1.071
40
25
80
5
0
4.3
0
M
C
42
23
0.998
23
32
100
8
1
5.7
1
F
A
43
75.4
1.125
67
42
95
20
1
5.5
0
F
F
44
60.7
1.065
57
45
90
16
0
5.2
1
M
E
45
57.9
1.206
48
36
95
8
1
5.2
1
F
D
46
62.2
1.091
57
39
75
20
0
3.9
1
M
E
47
62.2
1.091
57
37
95
5
0
5.5
1
M
E
48
70.1
1.230
57
34
90
11
1
5.3
1
F
E
49
61.7
1.083
57
41
95
21
0
6.6
0
M
E
50
61.4
1.077
57
38
80
12
0
4.6
0
M
E
| ID | Salary | Compa | Midpoint | Age | Performance Rating | Service | Gender | Raise | Degree | Gender1 | Gr |
| 1 | 66.1 | 1.159 | 57 | 34 | 85 | 8 | 0 | 5.7 | 0 | M | E |
| 2 | 25.9 | 0.834 | 31 | 52 | 80 | 7 | 0 | 3.9 | 0 | M | B |
| 3 | 35.2 | 1.135 | 31 | 30 | 75 | 5 | 1 | 3.6 | 1 | F | B |
| 4 | 55.3 | 0.971 | 57 | 42 | 100 | 16 | 0 | 5.5 | 1 | M | E |
| 5 | 49.6 | 1.033 | 48 | 36 | 90 | 16 | 0 | 5.7 | 1 | M | D |
| 6 | 78.3 | 1.168 | 67 | 36 | 70 | 12 | 0 | 4.5 | 1 | M | F |
| 7 | 42.3 | 1.058 | 40 | 32 | 100 | 8 | 1 | 5.7 | 1 | F | C |
| 8 | 22.8 | 0.990 | 23 | 32 | 90 | 9 | 1 | 5.8 | 1 | F | A |
| 9 | 78 | 1.164 | 67 | 49 | 100 | 10 | 0 | 4 | 1 | M | F |
| 10 | 23.3 | 1.014 | 23 | 30 | 80 | 7 | 1 | 4.7 | 1 | F | A |
| 11 | 23.6 | 1.025 | 23 | 41 | 100 | 19 | 1 | 4.8 | 1 | F | A |
| 12 | 60.8 | 1.067 | 57 | 52 | 95 | 22 | 0 | 4.5 | 0 | M | E |
| 13 | 40.6 | 1.014 | 40 | 30 | 100 | 2 | 1 | 4.7 | 0 | F | C |
| 14 | 21.7 | 0.943 | 23 | 32 | 90 | 12 | 1 | 6 | 1 | F | A |
| 15 | 21.8 | 0.949 | 23 | 32 | 80 | 8 | 1 | 4.9 | 1 | F | A |
| 16 | 37.4 | 0.934 | 40 | 44 | 90 | 4 | 0 | 5.7 | 0 | M | C |
| 17 | 57 | 1.000 | 57 | 27 | 55 | 3 | 1 | 3 | 1 | F | E |
| 18 | 33.5 | 1.081 | 31 | 31 | 80 | 11 | 1 | 5.6 | 0 | F | B |
| 19 | 23 | 1.000 | 23 | 32 | 85 | 1 | 0 | 4.6 | 1 | M | A |
| 20 | 36 | 1.162 | 31 | 44 | 70 | 16 | 1 | 4.8 | 0 | F | B |
| 21 | 76 | 1.135 | 67 | 43 | 95 | 13 | 0 | 6.3 | 1 | M | F |
| 22 | 43.7 | 0.911 | 48 | 48 | 65 | 6 | 1 | 3.8 | 1 | F | D |
| 23 | 25.3 | 1.098 | 23 | 36 | 65 | 6 | 1 | 3.3 | 0 | F | A |
| 24 | 48.9 | 1.019 | 48 | 30 | 75 | 9 | 1 | 3.8 | 0 | F | D |
| 25 | 25.8 | 1.122 | 23 | 41 | 70 | 4 | 0 | 4 | 0 | M | A |
| 26 | 23.3 | 1.013 | 23 | 22 | 95 | 2 | 1 | 6.2 | 0 | F | A |
| 27 | 42.3 | 1.057 | 40 | 35 | 80 | 7 | 0 | 3.9 | 1 | M | C |
| 28 | 75.2 | 1.122 | 67 | 44 | 95 | 9 | 1 | 4.4 | 0 | F | F |
| 29 | 80.9 | 1.208 | 67 | 52 | 95 | 5 | 0 | 5.4 | 0 | M | F |
| 30 | 49 | 1.020 | 48 | 45 | 90 | 18 | 0 | 4.3 | 0 | M | D |
| 31 | 24.2 | 1.054 | 23 | 29 | 60 | 4 | 1 | 3.9 | 1 | F | A |
| 32 | 27.5 | 0.886 | 31 | 25 | 95 | 4 | 0 | 5.6 | 0 | M | B |
| 33 | 63.6 | 1.115 | 57 | 35 | 90 | 9 | 0 | 5.5 | 1 | M | E |
| 34 | 28.6 | 0.922 | 31 | 26 | 80 | 2 | 0 | 4.9 | 1 | M | B |
| 35 | 22.4 | 0.976 | 23 | 23 | 90 | 4 | 1 | 5.3 | 0 | F | A |
| 36 | 23.6 | 1.026 | 23 | 27 | 75 | 3 | 1 | 4.3 | 0 | F | A |
| 37 | 24.3 | 1.057 | 23 | 22 | 95 | 2 | 1 | 6.2 | 0 | F | A |
| 38 | 63 | 1.105 | 57 | 45 | 95 | 11 | 0 | 4.5 | 0 | M | E |
| 39 | 34.8 | 1.123 | 31 | 27 | 90 | 6 | 1 | 5.5 | 0 | F | B |
| 40 | 24.3 | 1.057 | 23 | 24 | 90 | 2 | 0 | 6.3 | 0 | M | A |
| 41 | 42.8 | 1.071 | 40 | 25 | 80 | 5 | 0 | 4.3 | 0 | M | C |
| 42 | 23 | 0.998 | 23 | 32 | 100 | 8 | 1 | 5.7 | 1 | F | A |
| 43 | 75.4 | 1.125 | 67 | 42 | 95 | 20 | 1 | 5.5 | 0 | F | F |
| 44 | 60.7 | 1.065 | 57 | 45 | 90 | 16 | 0 | 5.2 | 1 | M | E |
| 45 | 57.9 | 1.206 | 48 | 36 | 95 | 8 | 1 | 5.2 | 1 | F | D |
| 46 | 62.2 | 1.091 | 57 | 39 | 75 | 20 | 0 | 3.9 | 1 | M | E |
| 47 | 62.2 | 1.091 | 57 | 37 | 95 | 5 | 0 | 5.5 | 1 | M | E |
| 48 | 70.1 | 1.230 | 57 | 34 | 90 | 11 | 1 | 5.3 | 1 | F | E |
| 49 | 61.7 | 1.083 | 57 | 41 | 95 | 21 | 0 | 6.6 | 0 | M | E |
| 50 | 61.4 | 1.077 | 57 | 38 | 80 | 12 | 0 | 4.6 | 0 | M | E |
Solution
Question 1)
Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)
What variables can be used in a Pearson\'s Correlation table (which is what Excel produces)?
Variables used are salary, compa, midpoint, age, performance rating, service, gender, raise and degree
b )Place table here (C8):
Salary
Compa
Midpoint
Age
Performance Rating
Service
Gender
Raise
Degree
Salary
1
Compa
0.6133
1
Midpoint
0.9855
0.4803
1
Age
0.5422
0.1794
0.5671
1
Performance Rating
0.2022
0.0695
0.1918
0.1392
1
Service
0.4569
0.2272
0.4711
0.5651
0.2257
1
Gender
-0.3911
-0.0250
-0.4282
-0.3918
-0.1504
-0.1837
1
Raise
-0.0052
0.0292
-0.0289
-0.1804
0.6737
0.1028
-0.0676
1
Degree
0.0587
-0.0269
0.0647
-0.0147
-0.0619
0.0777
0.0400
-0.0722
1
c )Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables are significantly related to Salary?
significantly related to Salary are :
compa, midpoint, age, service and gender
significantly related to compa are
Salary and midpoint
d )Looking at the above correlations - both significant or not - are there any surprises -by that I mean any relationships you expected to be meaningful and are not and vice-versa?
Performance rating and raise is positive and significant.
Salary with age and service are positive and significant.
e )Does this help us answer our equal pay for equal work question?
Yes, the correlation between salary and gender is -0.3911 and is significant.
| Salary | Compa | Midpoint | Age | Performance Rating | Service | Gender | Raise | Degree | |
| Salary | 1 | ||||||||
| Compa | 0.6133 | 1 | |||||||
| Midpoint | 0.9855 | 0.4803 | 1 | ||||||
| Age | 0.5422 | 0.1794 | 0.5671 | 1 | |||||
| Performance Rating | 0.2022 | 0.0695 | 0.1918 | 0.1392 | 1 | ||||
| Service | 0.4569 | 0.2272 | 0.4711 | 0.5651 | 0.2257 | 1 | |||
| Gender | -0.3911 | -0.0250 | -0.4282 | -0.3918 | -0.1504 | -0.1837 | 1 | ||
| Raise | -0.0052 | 0.0292 | -0.0289 | -0.1804 | 0.6737 | 0.1028 | -0.0676 | 1 | |
| Degree | 0.0587 | -0.0269 | 0.0647 | -0.0147 | -0.0619 | 0.0777 | 0.0400 | -0.0722 | 1 |
