Mark Price, the new productions manager for Speakers and Company, needs to find

Question

Mark Price, the new productions manager for Speakers and Company, needs to find out which variable most affects the demand for their line of stereo speakers. He is uncertain whether the unit price of the product or the effects of increased marketing are the main drivers in sales and wants to use regression analysis to figure out which factor drives more demand for their particular market. Pertinent information was collected by an extensive marketing project that lasted over the past 10 years and was reduced to the data that follow:

Perform a regression analysis based on these data using Excel. (Negative values should be indicated by a minus sign. Round your answers to 4 decimal places.)

Predict average yearly speaker sales for Speakers and Company based on the regression results if the price was $298 per unit and the amount spent on advertising (in thousands) was $855. (Enter your answer in thousands. Do not round intermediate calculations. Round your answer to the nearest whole number.)

Mark Price, the new productions manager for Speakers and Company, needs to find out which variable most affects the demand for their line of stereo speakers. He is uncertain whether the unit price of the product or the effects of increased marketing are the main drivers in sales and wants to use regression analysis to figure out which factor drives more demand for their particular market. Pertinent information was collected by an extensive marketing project that lasted over the past 10 years and was reduced to the data that follow:

Explanation / Answer

Multiple regression analysis in excel

(a)

Y = 1733.8392 - 5.3228.Price + 0.4347.Advertising

(c)

With Price = 298 and Adv = 855

Y = 1733.8392 - 5.3228 x 298 + 0.4347 x 855 = 519.36 or 519

SUMMARY OUTPUT Regression Statistics Multiple R 0.81 R Square 0.66 Adjusted R Square 0.58 Standard Error 170.66 Observations 12 ANOVA df SS MS F Significance F Regression 2 506382.1 253191.1 8.693 0.008 Residual 9 262134.8 29126.1 Total 11 768516.9 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1733.8392 800.33 2.17 0.06 -76.63 3544.31 -76.63 3544.31 Price -5.3228 2.90 -1.83 0.10 -11.89 1.24 -11.89 1.24 Adv 0.4347 0.23 1.89 0.09 -0.09 0.96 -0.09 0.96

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