Answer Happy

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 average sale price, and the number of retail stores selling the printer. The multiple regression model has the following form: O y = Bo + B3x3 + Bax4 + Boxs O y = Bo + B1x1 + B2x2 + B3x3 O y = Bo + B1x1 + B2x2 + B3x3 + 5 O y = Bo + B3<3 + B4x4 + Bsxs + E

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: O E(y) = Bo + B3x3 + B4x4 + Bsxs O E(y) = Bo + B1X1 + B2x2 + B3x3 O E(y) = Bo + B3x3 + B4x4 + B5x5 + 5 O E(y) = Bo + B1x1 + Byx2 + B3x3 + 3 The estimated multiple regression equation has the following form: O y = bo + b3x3 + 4x4 + box + 5 O y = bo + bıx1 + b9x2 + b3x3 0ý = + b3 + 54x4+ 555 O y = bo + bix1 + b 2 + b3x3 + 5 The least-squares estimates of the parameters Bo, B1, B2, and B3 in the multiple regression equation can be obtained by minimizing: Zi(yi-bo-bixli - b2x2 - 63x3) Zilvi -bo-b3x3i - baxdi - bxsi)2 Ο Σι(Yi - Vi) O Zilvi - bo-b3x3i - baxdi - bəxti) Zilvi -bo-b1x1i - baxa-b3x31)2 Using the least-squares criterion, the researcher obtained the following estimated multiple regression equation: ỹ = 1,304 + 113x1 - 237x2 + 30x3 The coefficient -237 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 sale price when of the other predictor variables are held constant. If the sale price increases by 5 units under this condition, you expect printer sales to increase on average by an estimated amount of