Absenteeism. Data on 77 employees of the ABX Company have been collected. The dependent variable is absenteeism (ABSENT). The possible explanatory variables are COMPLX = measure of job complexity
SENIOR =seniority
SATIS= response to "How satisfied are you with your foreman?"

These data are available in a file named ABSENT7 on the CD. In this exercise, use SENINV I/SENIOR, which is the reciprocal of the seniority variable, and COMPLX as two of the explanatory variables. The variable SATIS should be transformed into indicator variables as follows:

FS1= 1 if SATIS = 1 (very dissatisfied) = 0 otherwise
FS2 = 1 if SATIS =2 (somewhat dissatisfied) = 0 otherwise
FS2 = 1 if SATIS =3 (neither satisfied nor dissatisfied) = 0 otherwise
FS4= 1 if SATIS = 1 (somewhat satisfied) = 0 otherwise
FS5= 1 if SATIS = 5 (very satisfied) = 0 otherwise

Five indicator variables are created to represent all five supervisor satisfaction categories. Recall that only four need to be used in the regression. Run the regression with the explanatory variables described here. Answer the following questions:

a. Is there a difference in average absenteeism for employees in different supervisor satisfaction groups? Perform a hypothesis test to answer this question. State the hypotheses to be tested, the decision rule, the test statistic, and your decision. use a 5% level of significance.
b. Using the model chosen in part a (and keeping the variables COMPLX and SENINV in the model), what would be your estimate of the average absenteeism rate for all employees with COMPLX 60 and SENIOR 30 who were very dissatisfied with their supervisor? What if they were very satisfied with their supervisor, but COMPLX and SENIOR were the same values?
c. How do you account for the differences in the estimates in part b?
d. How could this equation be used to help identify employees who might be prone to absenteeism?