Simple and Multiple Regressions
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3) A company has recently conducted a survey of its employees on their job satisfaction. The survey team wants to find out, among other things, if job satisfaction increases with tenure. The data is in file jobsatis.dta, where the first column has job satisfaction scores (0 - 100; the higher, the more satisfied), the second, numbers of years each employee has been working in the company, the third, salary and the fourth a dummy which is 1 for men, 0 for women.
(a) Regress job satisfaction against the number of years working, salary and the dummy variable. Interpret the coefficients.
(b) Does the regression support the claim that on average, employees with longer tenure at the company are more satisfied with their job, holding salaries and gender fixed? Use a 5% significance level.
(c) What else can one say about the relationship between years at the company and job satisfaction?
score years salary sex
68 20 17 1
76 23 17.2 1
67 15 15 0
52 11 13.4 0
73 15 15 0
51 6 10.4 0
40 3 10.2 0
65 14 14.6 1
58 6 11.4 1
43 1 9.4 1
89 30 20 1
72 23 17.2 0
77 16 15.4 0
34 2 8.8 1
75 20 17 0
33 1 9.4 0
75 23 18.2 0
59 11 12.4 1
56 15 15 1
61 7 11.8 1
39 2 9.8 0
74 21 16.4 0
55 6 10.4 0
53 4 9.6 0
88 29 20.6 1
66 12 12.8 0
90 27 19.8 0
84 27 18.8 0
50 10 12 1
33 3 9.2 1
59 14 13.6 1
57 15 14 0
59 12 12.8 0
40 5 11 1.
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Solution Summary
The solution gives detailed steps on running one simple regression and one multiple regression with the same group of data. Results are also interpreted.
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Please check the attachment.
(a) Regress job satisfaction against the number of years working, salary and the dummy variable. Interpret the coefficients.
We first run the regression with score as dependent variable and other 3 variables as independent variables. The results are shown on the attached excel file.
We can see that the p-value of year which is 0.2688 is very large, so can say that the coefficient of year is not significant.
We ...
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