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: X1 product feature index for the printer a score based on its quantity and quality of features) average sale price (in dollars X2 number of retail stores selling the printer X3 advertising spending for th ven month (in thousands of dollars e g amount of coupon rebate (in dollars X5 The market researcher decides to predict sales using only the product feature index for the printer, the average sale price, and the amount of the coupon rebate. The multiple regression model has the following form B. 2x2 B 1X1 5x5 1x1 B. 2x2 4x4 1X1 1x1 2x2 B 5x5 E The multiple regression equation has the following form O Ely) 1x1. 2x2 5X5 O Ely) 1X1 B 2x2 B 4X4 O Ely) 1x1. 2x2 5X5 E O Ely) 1X1 B 2x2 B 4X4 E

Explanation / Answer

1] The multiple regression model is ,

y = beta0 + beta1 x1 +beta2 x2 + beta5 x5 +epsilon

2] E(y) = beta0 + beta1 x1 + beta2 x2 + beta5 x5

3] yhat = b0 + b1x1 + b2x2 + b5x5

4] sum(yi-yihat)

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