Regression analysis
r 0.873
r squared 0.762
Standard error 11.547
n 7
ANOVA
SS
Regression 2.133.3333
Residual 666.6667
Total 2,800,000
Regression output p-value
Intercept 63.3333 .0005
Advertising 6.667 .0103
The local grocery store wants to predict the daily sales in dollars. The manager believes that the amount of newspaper advertising significantly affects the store sales. He randomly selects 7 days of data consisting of daily grocery store sales (in thousands of dollars) and advertising expenditures (in thousands of dollars). The Excel output given above summarizes the results of the regression model.
A. 83,333
B. 70,000
C. 68,333
D. 20,064,333
E. 20,063.33
Answer: A. 83,333
Be sure to use the number 3 instead of 3000.
r 0.873
r squared 0.762
Standard error 11.547
n 7
ANOVA
SS
Regression 2.133.3333
Residual 666.6667
Total 2,800,000
Regression output p-value
Intercept 63.3333 .0005
Advertising 6.667 .0103
The local grocery store wants to predict the daily sales in dollars. The manager believes that the amount of newspaper advertising significantly affects the store sales. He randomly selects 7 days of data consisting of daily grocery store sales (in thousands of dollars) and advertising expenditures (in thousands of dollars). The Excel output given above summarizes the results of the regression model.
If the manager decides to spend $3000 on advertising, based on the simple linear regression results given above, the estimated sales are:
A. 83,333
B. 70,000
C. 68,333
D. 20,064,333
E. 20,063.33
Answer: A. 83,333
Be sure to use the number 3 instead of 3000.