6) The human resources manager of DataCom, Inc., wants to predict the annual sal

Question

6) The human resources manager of DataCom, Inc., wants to predict the annual salaries of given employees using the potential explanatory variables in the file Q6.xlsx.

a. Estimate an appropriate multiple regression equation to predict the annual salary of a given DataCom employee using all of the data in columns C-H. What is the regression equation? Interpret the estimated regression coefficients.

b. Comment on the overall fitness and usefulness of the model. Justify your answers.

2 c. According to the estimated regression model, is there a difference between the mean salaries earned by male and female employees at DataCom? If so, how large is the difference?

d. According to the estimated regression model, in which department are DataCom employees paid the highest mean salary (after controlling for other explanatory variables)? In which department are DataCom employees paid the lowest mean salary?

e. Given the estimated regression model, predict the annual salary of a female employee who served in a similar department at another company for 10 years prior to coming to work at DataCom. This woman, a graduate of a fouryear collegiate business program, has been supervising 12 subordinates in the purchasing department since joining the organization five years ago.

Link for Q6.xls - https://drive.google.com/file/d/1cjqn89-_vnr9VB9DvXgkEvIlLdNuZ-hX/view?usp=sharing

Explanation / Answer

a)

Salary=19589.47-106.55*yrs previous exp+621.06*yrs employed+1631.83*yrs education-1654.07*gender+2134.29*department+134.01*number supervised

For every increase of 1 in yrs previous exp, salary would go down by 106.55 keeping all other things constant

For every increase of 1 in yrs employed, salary would go up by 621.06 keeping all other things constant

For every increase of 1 in yrs education, salary would go up by 1631.83 keeping all other things constant

For every increase of 1 in gender, salary would go down by 1654.07 keeping all other things constant

For every increase of 1 in department, salary would go up by 2134.29 keeping all other things constant

For every increase of 1 in number supervised, salary would go up by 134.01 keeping all other things constant

b)

We can see that r^2 is 0.82 which means 82% of the variation in dependent variable is explained for by the independent variable. Also, we can see that F is 29.47 which is greater than Significance F and hence reject the null hypothesis of no association. We can prove that the dependent variable is affected by variation in independent variable

c)

Yes. It is 1654.07

d)

Highest in department 4 and lowest in department 1

SUMMARY OUTPUT Regression Statistics Multiple R 0.91 R Square 0.82 Adjusted R Square 0.79 Standard Error 5022.37 Observations 46.00 ANOVA df SS MS F Significance F Regression 6 4460508914.97 743418152.49 29.47 0.00 Residual 39 983742487.99 25224166.36 Total 45 5444251402.96 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 19589.47 2862.64 6.84 0.00 13799.24 25379.70 Years Previous Experience -106.55 213.08 -0.50 0.62 -537.54 324.45 Years Employed 621.06 125.41 4.95 0.00 367.38 874.73 Years Education 1631.83 362.76 4.50 0.00 898.09 2365.58 Gender -1654.07 1558.11 -1.06 0.29 -4805.66 1497.51 Department 2134.29 624.77 3.42 0.00 870.58 3398.00 Number Supervised 134.01 88.14 1.52 0.14 -44.27 312.29

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