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 1225.44 FloorSpace 11.52 CompetingAds −6.935 Price −0.1496 (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.)
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 1225.44
FloorSpace 11.52
CompetingAds −6.935
Price −0.1496
(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.)
(b)
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Make a prediction for Sales when Floorspace = 80, Competing Ads = 100, and price = 1,200 (2 decimal places)
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