A sales manager is interested in determining the relationship between the amount spent on advertising and total sal The manager collects data for the past 24 months and runs a regression of sales on advertising expenditures. The results are presented below but, unfortunately, some values identified by asterisks are missing. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.492 0.242 0.208 40.975 24.000 ANOVA df MS F Significance F Regression Residual Total 1 11809.406 11809.406 7.034 Coefficients Standard Error t Stat P-value Intercept Advertising 26.239 4.021 0.001 2.015 2.652 0.015

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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Data is given

16) What is the value of residual sum of squares?
A) 11,759.9
B) 10,130.5
C) 10,945.2
D) 36,935.8
Transcribed Image Text:16) What is the value of residual sum of squares? A) 11,759.9 B) 10,130.5 C) 10,945.2 D) 36,935.8
A sales manager is interested in determining the relationship between the amount spent on advertising and total sales.
The manager collects data for the past 24 months and runs a regression of sales on advertising expenditures. The
results are presented below but, unfortunately, some values identified by asterisks are missing.
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
0.492
0.242
0.208
40.975
24.000
ANOVA
df
SS
MS
F
Significance F
Regression
Residual
Total
1
11809.406
11809.406
7.034
Coefficients
Standard Error
t Stat
P-value
Intercept
Advertising
26.239
4.021
0.001
2.015
2.652
0.015
Transcribed Image Text:A sales manager is interested in determining the relationship between the amount spent on advertising and total sales. The manager collects data for the past 24 months and runs a regression of sales on advertising expenditures. The results are presented below but, unfortunately, some values identified by asterisks are missing. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.492 0.242 0.208 40.975 24.000 ANOVA df SS MS F Significance F Regression Residual Total 1 11809.406 11809.406 7.034 Coefficients Standard Error t Stat P-value Intercept Advertising 26.239 4.021 0.001 2.015 2.652 0.015
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