A B C D 1 ABSENT COMPLX SATIS SENIOR 2 0 45 4 3 3 1 76 4 10 4 0 56 1 9 5 2 76 3 69 0 70 3 74 14 1 69 3 9 8 1 56 4 9 1 56 4 10 2 43 1 319 11 1 76 3 1 12 3 30 2 1 13 2 50 4 9 14 1 10 4 1 15 3 69 2 4 16 2 67 3 3 17 0 69 1 4 18 4 70 2 8 19 7 13 2 1 20 3 16 21 2 52 22 2 23 4 24 2 22236 3 1 52 1 16 2 3 25 0 67 3 356246 26 3 10 3 27 3 89 3 28 3 21 2 29 0 34 3 1824 30 2 12 4 6 31 3 70 2 32 1 69 3 33 st 4 13 2 34 2 30 4 35 1 43 2 36 3 00 8 2 37 2 69 2 2113122 38 4 30 4 1 39 st 4 23 2 1 40 4 16 4 1 41 3 11 3 1 42 43 26 2 16 3 1 50 1 2 44 A B D 3 50 3 45 1 69 3 46 2 10 3 47 1 43 3 2426 48 1 12 4 1 49 3 76 2 50 2 56 3 51 0 6 3 52 0 8 5 53 1 45 st 4 54 3 43 3 55 6 23 3 56 3 1 5 52 3257 8 57 2 82 3 58 2 1 3 59 4 1 10 5 60 3 70 3 61 0 76 3 62 0 82 3 63 1 50 3 64 1 70 3 65 1 81 3 66 2 67 3 27 70 3 1 4 st 68 2 00 8 5 69 2 23 4 70 2 21 st 4 71 2 82 3 72 1 67 4 28 73 0 81 3 18 74 1 43 3 111467285221227 6 9 9 75 4 6 3 76 3 13 2 77 78 23 52 1 3 07 8 52 3 1 Human Resources Management: The ABX Company is interested in conducting a study of the factors that affect absenteeism among its production employees. Data on 77 employees of the ABX Company have been collected. The variable ABSENT is the number of distinct occasions that the worker was absent during 2018. (Each occasion consists of one or more consecutive days of absence.) The following possible explanatory variables are: COMPLX = A measure of job complexity. A rating ranging from 0 to 100. SENIOR = Number of complete years with the company on December 31, 2018. (Seniority) SATIS = response to "How satisfied are you with your foreman?" However, there is evidence to suggest that an inverse relationship exists between ABSENT and SENIOR. Therefore, use the reciprocal of SENIOR in the model. Furthermore, the variable SATIS has five categories (coded 1 = Very Dissatisfied (VD), 2 = Somewhat Dissatisfied (SD), 3 = Neutral (N), 4 = Somewhat Satisfied (SS), 5 = Very Satisfied (VS)). Recode the SATIS variable for regression analysis such that "Very Satisfied" serves as the reference level. These data are available in the worksheet entitled "ABSENT7R". (a) State the model equation. O ABSENT =B0+BCOMPLX + BSENIOR + B3VD + BSD + BN + BSS + B-VS ABSENT = 0 + BCOMPLX + BSENIOR + 3VD + BSD + BN + BESS 1 O ABSENT B+BCOMPLX + B2 110R + BVD + B SD + BN +855 SENIOR O ABSENT = + BCOMPLX + BSENIOR + SATIS O ABSENT B+BCOMPLX + B 1 O ABSENT B+BCOMPLX + SENIOR 1 + B3VD + BSD + BN + BSS + BVS SENIOR SATIS Run the regression with the explanatory variables described here. Answer the following questions. (b) Is there a difference in average absenteeism for employees in different supervisor satisfaction groups? Perform a hypothesis test to answer this question. Use a 5% level of significance. State the hypotheses to be tested. H: B3 = B = B = 86 = 0 H: None of the coefficients are equal to 0. O H: None of the coefficients are equal to 0. HB3 B4 B = B = 0 = O H: At least one of the coefficients is not equal to 0. H: B3 B4 B=B = 0 O H: B3 = B = B = 86 = 0 H.: At least one of the coefficients is not equal to 0. Interpret the hypotheses you specified above. O H: None of the explanatory variables are important in explaining/predicting absenteeism. H: All of the explanatory variables are important in explaining/predicting absenteeism. O H: There is no significant difference in absenteeism among the different satisfaction groups after accounting for job complexity and seniority. H: There is a significant difference in absenteeism among the different satisfaction groups after accounting for job complexity and seniority. O H: There is a significant difference in absenteeism among the different satisfaction groups after accounting for job complexity and seniority. H.: There is no significant difference in absenteeism among the different satisfaction groups after accounting for job complexity and seniority. O H: None of the explanatory variables are important in explaining/predicting absenteeism. H: At least one explanatory variable is important in explaining/predicting absenteeism. O H: At least one explanatory variable is important in explaining/predicting absenteeism. H.: None of the explanatory variables are important in explaining/predicting absenteeism. State the decision rule. O Reject H if p < 0.025. Do not reject H. if p 0.025. O Reject H if p > 0.025. Do not reject H, if p 0.025. O Reject H, if p < 0.05. Do not reject H, if p 0.05. O Reject H, if p>0.05. Do not reject H if p 0.05. State the reduced model equation. O ABSENT = B+BCOMPLX + B 1 SENIOR O ABSENT = B + BSATIS ABSENT = 0 + BCOMPLX + BSENIOR O ABSENT = B + COMPLX + 1 SENIOR ASATIS 1 Bo + BCOMPLX + B SENIOR + B3VD + BSD ABSENT = B + BVD + BSD State the appropriate test statistic name, degrees of freedom, test statistic value, and the associated p-value (Enter the degrees of freedom as a whole number, the test statistic value to three decimal places, and the p-value to four decimal places). --Select- State your decision. P ---Select-- Do not reject the null hypothesis. There is a difference in average absenteeism for employees in different supervisor satisfaction groups. Reject the null hypothesis. There is not a difference in average absenteeism for employees in different supervisor satisfaction groups. O Reject the null hypothesis. There is a difference in average absenteeism for employees in different supervisor satisfaction groups. Do not reject the null hypothesis. There is not a difference in average absenteeism for employees in different supervisor satisfaction groups. (c) Regardless of your conclusions above, use the full model specified in part a (with all the variables including COMPLX and SENINV in the model) to answer the following questions. What would be your estimate of the average absenteeism rate for all employees with a job complexity rating of 75 and 40 complete years with the company who were very dissatisfied with their supervisor? (Round your answer to three decimal places.) absences What if they were neutral with respect to their supervisor, but COMPLX and SENIOR were the same values as in the previous question part? (Round your answer to three decimal places.) absences What if they were very satisfied with their supervisor, but COMPLX and SENIOR were the same values as in the previous question part? (Round your answer to three decimal places.) absences (d) How do you account for the differences in the estimates in part (b)? O Employees who are more satisfied with their supervisor are absent more often than those who are less satisfied. Supervisor satisfaction does affect employee absenteeism, however, it is unclear from the results how. Supervisor satisfaction does not affect employee absenteeism. O Employees who are more satisfied with their supervisor are absent less often than those who are less satisfied.