Regression Residual Total Intercept Advertising Which of the following is true? Multiple Choice O O df 1 23 24 Coefficients 40.10 2.88 O SS 78.53 504.02 582.55 Standard Error 14.08 1.52 MS 78.53 21.91 t-stat 2.848 -1.895 If Advertising goes up by $100, then we predict Sales to go up by $2,880. If Sales go up by $100, then we predict Advertising to go up by $2,880. If Advertising goes up by $100, then we predict Sales to go up by $4,298. If Sales go up by $100, then we predict Advertising to go up by $4,298. F 3.58 p-value 0.0052 0.0608
Regression Residual Total Intercept Advertising Which of the following is true? Multiple Choice O O df 1 23 24 Coefficients 40.10 2.88 O SS 78.53 504.02 582.55 Standard Error 14.08 1.52 MS 78.53 21.91 t-stat 2.848 -1.895 If Advertising goes up by $100, then we predict Sales to go up by $2,880. If Sales go up by $100, then we predict Advertising to go up by $2,880. If Advertising goes up by $100, then we predict Sales to go up by $4,298. If Sales go up by $100, then we predict Advertising to go up by $4,298. F 3.58 p-value 0.0052 0.0608
Principles Of Marketing
17th Edition
ISBN:9780134492513
Author:Kotler, Philip, Armstrong, Gary (gary M.)
Publisher:Kotler, Philip, Armstrong, Gary (gary M.)
Chapter1: Marketing: Creating Customer Value And Engagement
Section: Chapter Questions
Problem 1.1DQ
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
Transcribed Image Text:A marketing analyst wants to examine the relationship between sales (in $1,000s) and advertising (in $100s) for firms in the food and beverage Industry and collects monthly data for 25 firms. He estimates the model
Sales-o Advertising. The following ANOVA table shows a portion of the regression results.
Regression
Residual
df
1
23
55
78.53
504.02
MS
78.53
21.91
T
3.58

Transcribed Image Text:Regression
Residual
Total
Intercept
Advertising
Which of the following is true?
Multiple Choice
O
O
df
1
23
24
Coefficients
40.10
2.88
O
SS
78.53
504.02
582.55
Standard Error
14.08
1.52
MS
78.53
21.91
t-stat
2.848
-1.895
If Advertising goes up by $100, then we predict Sales to go up by $2,880.
If Sales go up by $100, then we predict Advertising to go up by $2,880.
If Advertising goes up by $100, then we predict Sales to go up by $4,298.
If Sales go up by $100, then we predict Advertising to go up by $4,298.
F
3.58
p-value
0.0052
0.0608
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