Question

Observations are taken on sales of a certain mountain bike in 30 sporting goods stores. The regression model was Y = total sales (thousands of dollars), X1 = display floor space (square meters), X2 = competitors’ advertising expenditures (thousands of dollars), X3 = advertised price (dollars per unit). Predictor Coefficient Intercept 1,287.26 FloorSpace 11.52 CompetingAds −6.934 Price −0.1476 (a) Write the fitted regression equation. (Round your coefficient CompetingAds to 3 decimal places, coefficient Price to 4 decimal places, and other values to 2 decimal places. Negative values should be indicated by a minus sign.) yˆ y ^ = + * FloorSpace + * CompetingAds + * Price (b-1) The coefficient of FloorSpace says that each additional square foot of floor space adds about 11.52 to sales (in thousands of dollars). takes away 0.1496 from sales (in thousands of dollars). takes away 11.52 from sales (in thousands of dollars). adds about 6.934 to sales (in thousands of dollars). (b-2) The coefficient of CompetingAds says that each additional $1,000 of "competitors' advertising expenditures" takes away 0.1476 from sales (in thousands of dollars). reduces sales by about 6.934 from sales (in thousands of dollars). takes away11.52 from sales (in thousands of dollars). adds about 6.934 to sales (in thousands of dollars). (b-3) The coefficient of Price says that each additional $1 of advertised price adds about 6.934 to sales (in thousands of dollars). reduces sales by about 6.934 from sales (in thousands of dollars). takes away 11.52 from sales (in thousands of dollars). reduces sales by about 0.1476 from sales (in thousands of dollars). (c) The intercept is not meaningful, since a mountain bike cannot sell for zero, which will happen if all the variables are zero. False True (d) Make a prediction for Sales when FloorSpace = 53, CompetingAds = 109, and Price = 1,099. (Enter your answer in thousands. Round your answer to 2 decimal places.) Sales $ thousand

Answer 1