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HW 6Multiple 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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View Full Document 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
(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.
 Summer '10
 UmashangerThayasivam
 Statistics, Factors

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