1. Multiple regression model and the least-squares method Aa Aa The term marketi

Question

1. Multiple regression model and the least-squares method Aa Aa 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 model is: y sales in a given month (in thousands of dollars) The independent variables for the model are chosen from the following marketing mix variables: x1product feature index for the printer (a score based on its quantity and quality of features) X2 = average sale price (in dollars) x3number of retail stores selling the printer X4 = advertising spending for the given month (in thousands of dollars) amount of coupon rebate (in dollars) The market researcher decides to predict sales using only the product feature index for the printer, the advertising spending for the given month, and the amount of the coupon rebate.

Explanation / Answer

question 1

Multiple regression model is when we use only product feature index for the printer (x1), the advertising spending for the given month (x4 ), amount of coupon rebate (x5)

y = 0 + 1 x1 + 4 x4 + 5x5 +

Option C is correct.

Question 2

E(y) =  0 + 1 x1 + 4 x4 + 5x5

Option B is correct.

Question 3

y^ = b0 + b1 x1 + b4 x4 + b5x5 Option A is correct.

Question 4

Multiple regression equation = Option E is correct.

QUestion 5

y^ = 1179 + 87x1 + 65x4 + 18x5

Fill in the blanks

corresponding to a 1 unit changes in index score

When all of the other independent variables are held cosntant.

Index score increase by 12 points, expected increase in sales is 12 * 87 = 1044 units

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