Observations are taken on sales of a certain mountain bike in 24 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). (a) Fill in the values in the table given here. (Negative values should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round your t-values to 3 decimal places and p-values to 4 decimal places.)   Predictor Coefficient SE tcalc p-value Intercept 1,272.5 361.9     FloorSpace 11,590 1.060     CompetingAds -6,697 3.938     Price -0.14802 0.08391

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Observations are taken on sales of a certain mountain bike in 24 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).

(a) Fill in the values in the table given here. (Negative values should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round your t-values to 3 decimal places and p-values to 4 decimal places.)
 

Predictor Coefficient SE tcalc p-value
Intercept 1,272.5 361.9    
FloorSpace 11,590 1.060    
CompetingAds -6,697 3.938    
Price -0.14802 0.08391    
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