The following is an estimated demand function: Q = 875 + 6X +15Y − 5P Where Q is quantity sold, X is advertising expenditure (in thousands of dollars), Y is income (in thousands of dollars), and P is the good's price. The R2 was 0.92; the F-statistic was 57; the Standard Error of the Estimate (SEE) is 25. In the above regression: A. income has the highest importance in predicting sales. B. all of the options. C. the regression model is capable of explaining 92% of sales. D. if price increases 10 dollars, the sales will decrease 50.

See step by step explanation

Step-by-step explanation:

Q = 875  + 6 * Adv. expenditure + 15* Income - 5 * Product price

A.- As we can see each unitary change (in thousands of $) in Income, will mean an increase of  15 $ (thousands ) in the demand function, the highest impact in Q

C.- The regression model is capable of explaining  92 % of the variation in Q due to the predictors.

D.- If the price is increased by 10 $ demand will decrease in 50

192/15 = 12.8.

12.8

step-by-step explanation:

192/15 is 12.8.

however, it says to round to the nearest foot, which would equal 13.

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