Question

The data referred to in this question were collected from several sales districts across the country. The data represent sales for a maker of asphalt roofing shingles. Information on the following variables is available. Sales Sales from last year in thousands of squares Expenditures Promotional expenditures in thousands of dollars Accounts Number of active accounts Competing brands Number of competing brands producing equivalent or similar products District potential A coded indicator of the potential of the district Partial SPSS regression output of a multiple regression model with sales as the response variable and the other four variables as predictor variables are given below. ANOVA Model Sum of Squares df Mean Square F Sig. 1 Regression 176777.1 4 44194.265 479.099 .000 Residual 1937.137 21 92.245 Total 178714.2 25 Coefficients Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 178.320 12.960 13.759 .00000 Promotional Expenditures 1.807 1.081 .039 1.672 .10944 Number of Active Accounts 3.318 .163 .558 20.368 .00000 Number of Competing Brands -21.185 .788 -.647 -26.887 .00000 District Potential .325 .468 .018 .694 .49544 What is the estimate for the standard error/deviation ?? 12.960 9.604 92.245 1937.137

          The data referred to in this question were collected from several sales districts across the country. The data represent sales for a maker of asphalt roofing shingles. Information on the following variables is available.
Sales Sales from last year in thousands of squares
Expenditures Promotional expenditures in thousands of dollars
Accounts Number of active accounts
Competing brands Number of competing brands producing equivalent or similar products
District potential A coded indicator of the potential of the district

Partial SPSS regression output of a multiple regression model with sales as the response variable and the other four variables as predictor variables are given below.

ANOVA
Model Sum of Squares df Mean Square F Sig.
1 Regression 176777.1 4 44194.265 479.099 .000
Residual 1937.137 21 92.245
Total 178714.2 25

Coefficients
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 178.320 12.960 13.759 .00000
Promotional Expenditures 1.807 1.081 .039 1.672 .10944
Number of Active Accounts 3.318 .163 .558 20.368 .00000
Number of Competing Brands -21.185 .788 -.647 -26.887 .00000
District Potential .325 .468 .018 .694 .49544

What is the estimate for the standard error/deviation ??
12.960
9.604
92.245
1937.137

The data referred to in this question were collected from several sales districts across the country. The data represent sales for a maker of asphalt roofing shingles. Information on the following variables is available.
Sales Sales from last year in thousands of squares
Expenditures Promotional expenditures in thousands of dollars
Accounts Number of active accounts
Competing brands Number of competing brands producing equivalent or similar products
District potential A coded indicator of the potential of the district

Partial SPSS regression output of a multiple regression model with sales as the response variable and the other four variables as predictor variables are given below.

ANOVA
Model Sum of Squares df Mean Square F Sig.
1 Regression 176777.1 4 44194.265 479.099 .000
Residual 1937.137 21 92.245
Total 178714.2 25

Coefficients
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 178.320 12.960 13.759 .00000
Promotional Expenditures 1.807 1.081 .039 1.672 .10944
Number of Active Accounts 3.318 .163 .558 20.368 .00000
Number of Competing Brands -21.185 .788 -.647 -26.887 .00000
District Potential .325 .468 .018 .694 .49544

What is the estimate for the standard error/deviation ??
12.960
9.604
92.245
1937.137

Added by Cindy B.

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