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 predictor 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) x2 = average sale price (in dollars) x3 = number of retail stores selling the printer x4 = advertising spending for the given month (in thousands of dollars) X5 = amount of coupon rebate (in dollars) The market researcher decides to predict sales using only the product feature index for the printer, the number of retail stores selling the printer, and the amount of the coupon rebate. The multiple regression model has the following form: Oy Bo+B1x1 + B3x3 + B4x4 Oyẞo + B1x1 + B3×3 + B5×5 +8 Oy Bo+B1x1 + B3x3 + B5x5 Oy Bo+B1x1 + B3x3 + B4×4 + € According to the specified multiple regression model, the expected value of the dependent variable, given the values of the predictor variables, has the following form: E(y) = Bo+B1x1 + B3×3 + ẞ5x5 OE(y) =B0+ B1x1 + B3×3 + B4×4 + € E(y) Bo+B1x1 + B3x3 + B5x5 + Є E(y) =B0 + B1x1 + B3×3 + B4×4 The estimated multiple regression equation has the following form: The estimated multiple regression equation has the following form: Oŷbo+b1x1 + b3x3 + b5x5 +8 Oŷbo+b1x1 + b3x3 + b4x4 Oŷbo+b1x1 + b3x3 + b4x4 + € Oŷbo+b1x1 + b3x3 + b5x5 The least-squares estimates of the parameters ẞo, B1, B3, and ẞ5 in the multiple regression equation can be obtained by minimizing: Zi(yi - bo - bixli - b3x31-b5×5i) OΣi(yi - bo-b1x11 - b3x31-b4x4i) Zi(Yi-bo-b1x1i-b3x3 - b5x5i)2 ○ Zi(Yi - ŷi) Zi(yi-bo-b1x11 - b3x3 - b4x4i)2 Using the least-squares criterion, the researcher obtained the following estimated multiple regression equation: ŷ = 1,203 + 92x1 + 41x3 + 15×5 The coefficient 41 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 change in number of retail stores when of the other predictor variables are held constant. If the number of retail stores increases by 7 units under this condition, you expect printer sales to increase on average by an estimated amount of