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). Coefficient 1,263.91 Predictor Intercept FloorSpace CompetingAds Price 11.29 -6.889 -0.1446 (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.) FloorSpace - |* CompetingAds +[ * Price (b-1) The coefficient of FloorSpace says that each additional square foot of floor space O takes away 11.29 from sales (in thousands of dollars). O adds about 11.29 to sales (in thousands of dollars). O takes away 0.1496 from sales (in thousands of dollars). O adds about 6.889 to sales (in thousands of dollars). (b-2) The coefficient of CompetingAds says that each additional $1,000 of "competitors' advertising expenditures" O takes away 0.1446 from sales (in thousands of dollars). O takes away11.29 from sales (in thousands of dollars). O reduces sales by about 6.889 from sales (in thousands of dollars). O adds about 6.889 to sales (in thousands of dollars). (b-3) The coefficient of Price says that each additional $1 of advertised price O adds about 6.889 to sales (in thousands of dollars). O reduces sales by about 0.1446 from sales (in thousands of dollars). O takes away 11.29 from sales (in thousands of dollars). O reduces sales by about 6.889 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. O False O True (d) Make a prediction for Sales when FloorSpace = 84, CompetingAds = 88, and Price = 1,138. (Enter your answer in thousands. Round your answer to 2 decimal places.) Sales |thousand