Fill in the blanks. Aplia: Student Question ×YeChegg Study | Guided S/XV(2) Corn

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Aplia: Student Question ×YeChegg Study | Guided S/XV(2) Corny Jim's Ring S 4X C courses aplia.com af ser let/quiz?quiz actiontakeQuiz&quiz; probGuid-QNAPC0A801010000003b8d7620040000&c; =delil martinez 0003&ckzm; 151 1237912125 0AAA6B0. Graded Assignment | Read Chapter 15 | Back to Assignment Due Sunday 12.10.17 at 11:45 PM Attempts: Average: 7 1. Multiple regression model and the least-squares method The term marketing mix refers to the different components that can be controlled in a marketing strategy to increase sales or profit. The name comes from a cooking-mix analogy used by Neil Borden in his 1953 presidential address to the American Marketing Association. In 1960, E. Jerome McCarthy proposed the "four Ps" of marketing-product, price, place (or distribution), and promotion-as the most basic components of the marketing mix. Variables related to the four Ps are called marketing mix variables. A market researcher for a major manufacturer of computer printers is constructing a multiple regression model to predict monthly sales of printers using various marketing mix variables. The model uses historical data for various printer models and will be used to forecast sales for a newly introduced printer The dependent variable for the madel is: ysales in a given month (in thousands of dollars) The independent variables for the model are chosen from the following marketing mix variables: product feature index for the printer (a score based on its quantity and quality of features) average sale pnce (in dollars) number of retail stores selling the printer advertising spending for the given month (in thousands of dollars) amount of coupon rebate (in dollars) Xi = X2 = X4 = Xs = The market researcher decides to predict sales using only the product feature index for the printer, the average sale price, and the advertising spending for the given month. Session Timeout 59:34 8:18 PM 11/20/2017

Explanation / Answer

Here the marker researcher decides to predict sales only using the product feature index for printer (x1), average sale price (x2) and adverisiing spending for the month (x4)

so we will use only these three variables .

Question 1

The multiple regression model is

y = 0+ 1x1+ 2x2+ 4x4+ [Option A is the answer

Question 2.

Multiple regression equation

E(y) =  0+ 1x1+ 2x2+ 4x4 [Option C is the answer]

Question 3

Estimated multiple regression equation

y^ =  b0+ b1x1+ b2x2+ b4x4 { here we will not count the error term , as it is a estimated equation]

QUestion 4

The least square estimators are can be obtained by minimizing

LSE = [yi - (b0+ b1x1+ b2x2+ b4x4)]2 Option C is correct.

Question 5

y^ = 1278 + 107 x1  -225 x2  +21x4

The coefficient 107 in the  estimated multiple regression equation Just given is an  estimate of the change in average printer sales in a given month (in thousands of dollars) corresponding to a  107 unit average change in index score when other independent variables are held constant. If the index score increases by 9 units under this condition, you expect printer sales to increase on average by an estimated amount of 107 * 9 = 963 dollars.

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