The U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year.  The analyst ran a simple linear regression in SPSS.  The results are shown below.   What proportion of variation in the number of complaints can be explained by hourly wages? From the results shown above, write the regression equation If wages were increased by $1.00, what is the expected effect on the number of complaints received per employee?

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The U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year.  The analyst ran a simple linear regression in SPSS.  The results are shown below.

 

What proportion of variation in the number of complaints can be explained by hourly wages?

From the results shown above, write the regression equation

If wages were increased by $1.00, what is the expected effect on the number of complaints received per employee?

Table 9: Coefficients
Coefficientsal
Unstandardized Coefficients Standardized Coefficients
Model
В
Std. Error
Beta
Sig.
1
(Constant)
20.2
4.357
4.636
.000
Hourly Wage
-1.20
.088
-.946
-13.636
.000
a. Dependent Variable: Number of Complaints
Transcribed Image Text:Table 9: Coefficients Coefficientsal Unstandardized Coefficients Standardized Coefficients Model В Std. Error Beta Sig. 1 (Constant) 20.2 4.357 4.636 .000 Hourly Wage -1.20 .088 -.946 -13.636 .000 a. Dependent Variable: Number of Complaints
Table 7: Model Summary
Model
R
R Square |Adjusted R Square Std. Error of the Estimate
1
.854a
.730
.695
6.6235
a. Predictors: (Constant), Hourly Wage
Table 8: ANOVA
ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
1918.458
1
1918.458
129.783
.000a
Residual
709.567
48
14.782
Total
2628.025
49
a. Predictors: (Constant), Hourly Wage
b. Dependent Variable: Number of Complaints
Transcribed Image Text:Table 7: Model Summary Model R R Square |Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints
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