You have a hunch that the product sales mentioned in Exercise 5 (Chap. 3) are not determined by price alone. So you perform a multivariate regression using Excel (or statistics software like SPSS). The results of the regression are listed in Fig. 10.18.

(a) Derive the regression function in algebraic form from the data in the table.

(b) Does the model serve to explain sales? Which metric plays a role in the explanation and what is its value?

(c) Assume you lower the price in every country by 1000 monetary units. How many more products would you sell?

(d) What is the effect of increasing advertising costs by 100,000 monetary units?

Explain the result and propose measures for improving the estimating equation.

Figure 10.18

Data From Exercise 5 In Chapter 3

You’re employed in the marketing department of an international car dealer. Your boss asks you to determine the most important factors influencing car sales. You receive the following data:

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Model Summary Model R .975a R Square .951 Adjusted R Square Std. Error of the Estimate .927 .510 a. Predictors: (Constant), Advertising budget [in 100,000s MUS], Number of dealerships, Unit price [in 1,000s of MUs] ANOVA Model Sum of Squares df Mean Square F Sig. Regression 30.439 3 10.146 39.008 .000b 1 Residual Total 1.561 6 0.260 32.000 a. Dependent Variable: Sales [in 1,000s of units] b. Predictors: (Constant), Advertising budget [in 100,000s MUS], Number of dealerships, Unit price [in 1,000s of MUs] Model Unstandardized Coefficients t Sig. B Std. Error (Constant) 24.346 3.107 7.84 .000 Number of dealerships .253 .101 2.50 .047 Unit price [in 1,000s of MUs] -.647 .080 -8.05 .000 Advertising budget [in 100,000s MUs] -.005 .023 -0.24 .817