The regression equation shows how each advertising variable is related to the big box store’s sales while holding the other variable constant. The coefficient for x1, the big box store’s advertising, is positive 92.30. This means that when the big box store increases its own advertising expenditure, predicted sales increase. The coefficient for x2, the specialty store’s advertising, is negative 26.89. This means that as the specialty store spends more on advertising, the big box store’s predicted sales decrease. Therefore, the relationship described in option A is correct. Options C and D incorrectly reverse the meaning of the positive coefficient on the big box store’s own advertising. Option B is directionally true in a general sense, but the clearest direct interpretation from the equation is the negative effect of the specialty store’s advertising on big box store sales, which is exactly stated in option A. Multiple regression allows analysts to isolate these effects and interpret how changes in each predictor influence the dependent variable. Thus, the correct answer is that if the specialty store increases its advertising expenditures, it will decrease the big box store’s sales.