Statistics Regression
Data was collected for a company making a highly technical product, which has recently had a number of employees expressing dissatisfaction with their compensation.
The data is summarised attached excel file called “Data File IF1202 CW March17”.
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a)Â Â Â By conducting a simple regression on the variables Salary and Gender, discuss which of the values of the dummy variable represents Male workers and whether
you believe there is a significant difference between pay of Male and Female workers.       Â
b)Â Â Â Â Assuming you were going to conduct a multiple regression identify and justify:
1.    Which variable you believe will be the dependent variable; and
2.    Which independent variables you believe will be useful in a multiple regression.
c)Â Â Â Construct a multiple regression model with all independent variables and clearly indicate your regression equation;
d)Â Â Â Â Indicate and justify which variables are significant and non-significant in the regression model and compare with your answer to part c2) above;
e)Â Â Â Â Construct another multiple regression model including only the significant variables from the model in d) above and discuss whether it is a better model
than the one fund in part d) or not.
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age Experience Years Jr Years Senior Gender Salary Market Value
57 30 19 0 0 51825 0 Experience = number of years of work experience
57 26 13 0 0 45225 0 Years Jr = number of years experience as a junior analyst
47 20 9 0 0 51000 0 Years Senior = number of years experience as a senior analyst
36 8 2 0 0 39000 0 Market Value: 1 – Highly skilled = high value; 0 – low skill = low value
30 5 0 0 1 32250 0
38 15 5 0 0 44025 0
47 15 7 0 1 46500 0
36 5 0 0 1 32700 0
45 18 0 0 1 43800 0
42 5 0 0 0 32700 0
46 13 2 0 0 41250 0
56 26 4 16 0 63450 0
63 35 9 18 0 60000 0
51 23 7 0 0 53400 0
50 22 11 0 0 48900 0
52 24 5 0 0 55725 0
59 27 22 0 0 49500 0
46 19 8 3 0 52875 0
44 11 0 0 1 38475 0
40 13 0 0 1 40200 0
46 7 0 0 1 37500 0
59 34 10 20 0 70800 1
49 16 7 2 0 54525 1
48 21 11 3 0 54525 1
32 4 0 0 0 40350 1
40 10 2 0 0 43800 1
41 7 0 0 0 40950 1
45 8 0 0 1 41700 1
35 6 0 0 1 37500 1
32 3 0 0 1 41925 1
35 3 0 0 0 38025 1
32 4 0 0 1 43500 1
34 2 0 0 0 31125 0
53 5 0 0 1 31500 0
44 4 0 0 1 33000 0
29 8 0 0 0 31500 0
63 31 9 9 0 55800 0
57 21 10 3 0 53400 0
53 24 8 9 0 53175 0
53 23 17 0 1 52350 0
48 17 9 0 0 48375 0
41 15 5 0 0 47700 0
38 10 0 0 0 37725 0
51 6 0 0 0 35550 0
45 7 0 0 1 34800 0
49 5 0 0 1 33525 0
39 5 0 0 1 32625 0
49 22 6 11 0 67500 0
55 29 11 12 0 63150 0
54 28 7 16 0 62175 0
52 26 7 15 0 61575 0
50 22 8 6 0 53325 0
55 28 20 0 0 52125 0
43 17 7 1 0 47100 0
38 9 3 0 0 42900 0
32 6 0 0 0 37350 0
54 27 5 17 0 63300 0
46 22 6 12 0 60600 0
49 21 11 4 0 57150 0
50 21 10 3 0 55125 0
42 16 5 2 0 51600 0
49 22 16 0 0 49800 0
41 13 6 0 0 46050 0
49 15 7 0 0 45825 0
49 18 2 0 0 42750 0
37 10 2 0 0 42000 0
37 10 2 0 1 40650 0
35 7 0 0 0 39000 0
36 6 0 0 0 37350 0
34 5 0 0 0 36750 0
29 3 0 0 0 36000 0
30 6 0 0 0 36000 0
30 8 0 0 1 35550 0
30 5 0 0 0 35550 0
43 6 0 0 1 33600 0
56 27 9 16 0 62325 0
48 23 12 7 0 57375 0
41 13 5 0 0 45000 0
36 9 2 0 0 40500 0
38 11 0 0 0 38550 0
31 5 0 0 1 36000 0
33 4 0 0 0 35250 0
56 25 7 13 0 60150 0
36 10 4 0 0 42000 0
31 2 0 0 0 29250 0
57 26 8 12 0 61575 0
51 24 8 11 0 55725 0
49 23 11 7 0 54900 0
58 31 14 8 0 54525 0
48 22 13 4 0 54450 0
46 22 8 0 0 47625 0
45 19 10 0 0 47400 0
48 17 7 0 1 43800 0
54 14 6 0 1 43200 0
40 13 3 0 1 41550 0
39 10 0 0 0 39600 0
36 9 0 0 1 35400 0
38 9 0 0 0 34575 0
34 9 0 0 0 31500 0
52 23 4 16 0 61200 0
45 13 5 0 0 44175 0
49 21 2 0 0 43950 0
44 13 0 0 1 39675 0
33 2 0 0 1 29250 0
65 35 7 18 0 69525 0
53 19 6 4 0 51450 0
40 6 0 0 1 33900 0
39 6 0 0 0 30750 0
50 22 9 2 0 49950 0
41 19 6 2 0 45525 0
53 16 6 0 0 42450 0
43 16 6 0 0 41700 0
42 8 2 0 1 40050 0
35 9 0 0 1 38925 0
59 17 0 0 1 38250 0
32 3 0 0 1 30750 0
33 2 0 0 1 30000 0
41 2 0 0 0 30000 0
55 23 9 0 0 48750 0
40 9 0 0 0 38475 0
38 8 0 0 0 34200 0
65 41 9 9 0 57750 0
59 32 7 15 0 55950 0
58 30 16 0 0 52500 0
52 20 6 0 0 44625 0
41 10 3 0 0 43500 0
51 4 0 0 1 31500 0
62 36 5 17 0 66750 0
58 32 10 12 0 62625 0
46 17 7 4 0 59250 0
65 39 19 4 0 52950 0
58 32 10 3 1 52500 0
50 22 14 1 0 51300 0
45 16 8 0 0 50475 0
56 26 11 0 0 48750 0
48 21 9 0 0 47625 0
54 24 10 0 1 47400 0
55 22 7 0 0 46125 0
45 14 3 0 0 43950 0
40 12 2 0 0 41925 0
40 15 2 0 1 41850 0
38 10 0 0 1 41700 0
39 11 0 0 0 40950 0
32 6 0 0 0 39450 0
36 9 1 0 1 35700 0
36 5 0 0 0 31650 0
62 38 8 20 0 61800 0
44 19 7 2 0 49725 0
33 6 0 0 0 33975 0
32 6 0 0 1 31950 0
