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. 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 Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 20.2 4.357 4.636 .000 Hourly Wage -1.20 .088 -.946 -13.636 .000 a. Dependent Variable: Number of Complaints 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? The current minimum wage is $5.15. If an employee earns the minimum wage, how many complaints can that employee expect to receive? Is the regression coefficient statistically significant? How can you tell?

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Related questions

Question

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.

Table 7: Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.854^a	.730	.695	6.6235
a. Predictors: (Constant), Hourly Wage

Table 8: ANOVA

ANOVA^b
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	1918.458	1	1918.458	129.783	.000^a
Residual	709.567	48	14.782
Total	2628.025	49
a. Predictors: (Constant), Hourly Wage
b. Dependent Variable: Number of Complaints

Table 9: Coefficients

Coefficients^a
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
B	Std. Error	Beta
1	(Constant)	20.2	4.357		4.636	.000
Hourly Wage	-1.20	.088	-.946	-13.636	.000
a. Dependent Variable: Number of Complaints

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?
The current minimum wage is $5.15. If an employee earns the minimum wage, how many complaints can that employee expect to receive?
Is the regression coefficient statistically significant? How can you tell?

Expert Solution