Introductory Statistics (10th Edition)
10th Edition
ISBN: 9780321989178
Author: Neil A. Weiss
Publisher: PEARSON
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Textbook Question
Chapter A.6, Problem 108E
Custom-Homes Resales. Refer to Exercise A.46 on page A-16 regarding predicting the selling price of a home in the Equestrian Estates using the predictor variables square footage, number of bedrooms, number of bathrooms, and number of days on the market.
- a. Obtain output similar to that in Output A.14 on page A-50 and Fig. A.8 on page A-51.
- b. Perform a residual analysis to assess the assumptions of linearity of the regression equation, constancy of the conditional standard deviation, and normality of the conditional distributions. Check for outliers and influential observations.
- c. Does your analysis in part (b) reveal any violations of the assumptions for multiple regression inferences? Explain your answer.
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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.
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…
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…
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Chapter A Solutions
Introductory Statistics (10th Edition)
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