Interpret the slope coefficient for wage based on this MLR model.

College Algebra
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Author:Jay Abramson
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Chapter6: Exponential And Logarithmic Functions
Section6.8: Fitting Exponential Models To Data
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Absenteeism:  Absenteeism can be a serious employment problem.  It is estimated that absenteeism reduces potential output by more than 10%.  Two economists launched a research project to learn more about the problem.  They randomly selected 100 organizations to participate in a 1-year study.  For each organization, they recorded the average number of days absent per employee and several variables thought to affect absenteeism.  The file ‘absenteeism FinEx’ contains the data on these selected 100 organizations.

Clearly write the linear regression equation below without using X’s and Y’s. 

Interpret the slope coefficient for wage based on this MLR model. 

 

Column 1, Wage:
Average employee wage
Column 2, Pct PT:
Percentage of part-time employees
Column 3, Pct U :
Percentage of unionized employees
Column 4: Av Shift : Availability of shiftwork
1= Yes
0 = No
Column 5: U/M Rel : Union-management relationship
1= good 0 = Not good
Column 6: Absent: Average number of days absent per employee
Transcribed Image Text:Column 1, Wage: Average employee wage Column 2, Pct PT: Percentage of part-time employees Column 3, Pct U : Percentage of unionized employees Column 4: Av Shift : Availability of shiftwork 1= Yes 0 = No Column 5: U/M Rel : Union-management relationship 1= good 0 = Not good Column 6: Absent: Average number of days absent per employee
Regression Analysis:
The regression equation is
Absent = 10.3 - 0.000203 Wage - 0.107 Pct PT + 0.0599 Pct U + 1.56 Av Shift
- 2.64 UM Rel
Predictor
Coef
SE Coef
P
Constant
10.265
1.172
8.76 0.000
Wage
-0.00020330 0.00003573 -5.69 0.000
Pct PT
-0.10687
0.02949 -3.62 0.000
Pct U
0.05985
0.01240
4.83 0.000
Av Shift
1.5619
0.5027
3.11 0.002
UM Rel
-2.6366
0.4922 -5.36 0.000
S = 2.35589
R-Sq = 53.2%
R-Sq (adj) = 50.78
Analysis of Variance
Source
DF
MS
F
P
Regression
5
593.90 118.78 21.40 0.000
Residual Error 94
521.72
5.55
Total
99
1115.62
Transcribed Image Text:Regression Analysis: The regression equation is Absent = 10.3 - 0.000203 Wage - 0.107 Pct PT + 0.0599 Pct U + 1.56 Av Shift - 2.64 UM Rel Predictor Coef SE Coef P Constant 10.265 1.172 8.76 0.000 Wage -0.00020330 0.00003573 -5.69 0.000 Pct PT -0.10687 0.02949 -3.62 0.000 Pct U 0.05985 0.01240 4.83 0.000 Av Shift 1.5619 0.5027 3.11 0.002 UM Rel -2.6366 0.4922 -5.36 0.000 S = 2.35589 R-Sq = 53.2% R-Sq (adj) = 50.78 Analysis of Variance Source DF MS F P Regression 5 593.90 118.78 21.40 0.000 Residual Error 94 521.72 5.55 Total 99 1115.62
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