1. Multiple regression model and the least-squares methocd The term marketing mi

Question

1. Multiple regression model and the least-squares methocd 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: ysales 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) X3 number of retail stores selling the printer x4advertising 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 The multiple regression equation has the following form:

Explanation / Answer

Answer:- The multiple regression model has the following form:-

The correct option is:-

Y= 0+ 1x1+ 3x3+ 5x5+e

Reason:- the multiple regression model assumes that dependent variable Y is a linear function of selected independent variable x1,x3 and x5 and a error term demoted by e. That is Y= 0+ 1x1+ 3x3+ 5x5+e where 0, 1, 3,5 are unknown parameter.

Answer:- The multiple regression equation has the following form:-

Correct Answer:-

E(Y)= 0+ 1x1+ 3x3+ 5x5

Reason:- One of the assumption in this model is that mean of the errors term e is zero thus

E(Y)= E(0+ 1x1+ 3x3+ 5x5+e)

E(Y)= E(0+ 1x1+ 3x3+ 5x5)+E(e)

E(Y)= E(0+ 1x1+ 3x3+ 5x5)+0

E(Y)= E(0+ 1x1+ 3x3+ 5x5)

Answer:- the estimated multiple regression equation has the following form:-

The correct option is:-

Y^= b0+ b1x1+ b3x3+ b5x5

Reason:- In the multiple regression equation E(Y)= 0+ 1x1+ 3x3+ 5x5

The parameters 0, 1, 3,5 can be estimated by sample statistics which are denoted by b0, b1, b3,b5

Answer:-          The least square estimates of the parameters 0, 1, 3,5 in multiple regression equation can eb obtained by minimizing

Correct Answer:- i(yi- b0- b1xi- b3xi - b5xi)2

Answer:- y^= 1203+92x1+441x3+15x5

The coefficient 15 in the estimated multiple regression equation just give an estimate of the change in average Pinter sales in a given month corresponding to a ……………….. change in rebate amount when …………………of the other independent variable are held constant. If the rebate amount increases by 15 units under this condition , you expect the printer sales to increase on avg by an estimated amount of

Correct answer:-

One unit

All

Increase in printer sales = 15*15 = 225

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