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- HW 6-Multiple Reg1 Due Wed July 14th 1 The ABX Company is interested in conducting a study of the factors that affect absenteeism among its

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HW 6-Multiple Reg1 Due Wed July 14 th . 1. The ABX Company is interested in conducting a study of the factors that affect absenteeism among its production employees. After reviewing the literature on absenteeism and interviewing several production supervisors and a number of employees, the researcher in charge of the project defined the following variables Variable Description Absenteeism The number of distinct occasions that the worker was absent during 2003. Each occasion consists of one or more consecutive days of absence. Job Complexity An index ranging from 0 to 100, a higher value indicates more job complexity Base Pay Base hourly pay rate in dollars Seniority Number of complete years with the company on December 31, 2003 Age Employee’s age on December 31, 2003 Dependents Determined by employee response to the question: “How many individuals other than yourself depend on you for most of their financial support?” We consider a multiple regression model for predicting absenteeism based on job complexity, base pay, seniority, age and dependents. Multivariate Correlations Absenteeism Job Complexity Base Pay Seniority Age Dependents Absenteeism 1.0000 -0.3617 -0.2254 -0.3356 -0.3100 -0.0510 Job Complexity -0.3617 1.0000 0.5020 0.3735 0.2768 -0.0792 Base Pay -0.2254 0.5020 1.0000 0.4940 0.3259 0.0590 Seniority -0.3356 0.3735 0.4940 1.0000 0.7530 0.1518 Age -0.3100 0.2768 0.3259 0.7530 1.0000 0.1478 Dependents -0.0510 -0.0792 0.0590 0.1518 0.1478 1.0000
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Response Absenteeism Whole Model Actual by Predicted Plot Summary of Fit RSquare 0.187444 RSquare Adj 0.130222 Root Mean Square Error 1.379833 Mean of Response 2.090909 Observations (or Sum Wgts) 77 Analysis of Variance Source DF Sum of Squares Mean Square F Ratio Model 5 31.18388 6.23678 3.2757 Error 71 135.17976 1.90394 Prob > F C. Total 76 166.36364 0.0102 Parameter Estimates Term Estimate Std Error t Ratio Prob>|t| VIF Intercept 3.6266094 1.387114 2.61 0.0109 . Job Complexity -0.015955 0.006883 -2.32 0.0233 1.4100426 Base Pay 0.0448386 0.166481 0.27 0.7885 1.5803544 Seniority -0.0381 0.04824 -0.79 0.4323 2.7925213 Age -0.025584 0.032503 -0.79 0.4338 2.3335252 Dependents -0.040499 0.123385 -0.33 0.7437 1.051738 (a) If one were to use only one of the five explanatory variables to predict absenteeism, which would provide the best predictions? -1 0 1 2 3 4 5 6 7 8 Absenteeism Actual 0 1 2 3 4 5 6 7 Absenteeism Predicted P=0.0102 RSq=0.19 RMSE=1.3798
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(b) Based on the multiple regression model, predict Absenteeism for an employee with Job Complexity 30, Base Pay $7 per hour, Seniority 5 years, Age 35 and 1 Dependent.
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This note was uploaded on 07/13/2010 for the course MATH 2261 taught by Professor Umashangerthayasivam during the Summer '10 term at Rowan.

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- HW 6-Multiple Reg1 Due Wed July 14th 1 The ABX Company is interested in conducting a study of the factors that affect absenteeism among its

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