The slope of price with OmniPower sales β 1 53217 indicates that for a given

# The slope of price with omnipower sales β 1 53217

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The slope of price with OmniPower sales ( β ̂ 1 = -53.217) indicates that, for a given amount of monthly promotional expenditures, the mean sales of OmniPower are estimated to decrease by 53.217 bars per month for each 1-cent increase in the price. The slope of monthly promotional expenditures with OmniPower sales ( β ̂ 2 = 3.613) indicates that, for a given price, the mean sales of OmniPower are estimated to increase by 3.613 bars for each additional \$1 spent on promotions. These estimates allow you to better understand the likely effect that price and promotion decisions will have in the marketplace. For example, a 10-cent decrease in price is estimated to increase mean sales by 532.17 bars, with a fixed amount of monthly promotional expenditures. A \$100 increase in promotional expenditures is estimated to increase mean sales by 361.3 bars, for a given price. Predicting the Dependent Variable Y You can use the multiple regression equation to predict values of the dependent variable. For example, what is the predicted sales for a store charging 79 cents during a month in which promotional expenditures are \$400? Using the multiple regression equation, ? ̂ = 5837.521 − 53.217(79) + 3.613(400) = 3078.57 Thus, your sales prediction for stores charging 79 cents and spending \$400 in promotional expenditures is 3,078.57 OmniPower bars per month

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