Chapter 5

# B using the forecasts from 2005 through 2009 compare

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b. Using the forecasts from 2005 through 2009, compare the accuracy of each of the forecasting methods based on the RMSE criterion. The lowest RMSE is for Exponential smoothing, using a weight of .9 on the past actual number and .1 on the forecast. That RMSE = 134.1, which is the lowest. RMSE = [(Y actual  Y forecast ) 2 /n] . c. Which forecast would you have used for 2010? Why? The one with the lowest RMSE, is a forecast of 1,268.85 for 2010. Based on past performance, this is the most accurate forecasting model of those examined. 6. The economic analysis division of Mapco Enterprises has estimated the demand function for its line of weed trimmers as QD = 18,000 + 0.4N − 350PM + 90PS where N = number of new homes completed in the primary market area PM = price of the Mapco trimmer PS = price of its competitor’s Surefire trimmer In 2010, 15,000 new homes are expected to be completed in the primary market area. Mapco plans to charge \$50 for its trimmer. The Surefire trimmer is expected to sell for \$55. Mapco Enterprises uses econometric forecasting What sales are forecast for 2010 under these conditions? Q D = 18,000 + 0.4(15,000)  350(50) + 90(55) = 11,450 expected to be sold in 2010. b If its competitor cuts the price of the Surefire trimmer to \$50, what effect will this have on Mapco’s sales?. Q D = 18,000 + 0.4(15,000)  350(50) + 90(50) = 11,000, expected sales decline when Surefire (the competitor) reduces prices. What effect would a 30 percent reduction in the number of new homes

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Myisha Coleman 1 completed have on Mapco’s sales (ignore the impact of the price cut of the Surefire trimmer)? c. A thirty percent reduction in new homes bring 15,000 down to .7(15,000) or 10,500. Q D = 18,000 + 0.4(10,500)  350(50) + 90(55) = 9,650 is the forecast. 9. Savings-Mart (a chain of discount department stores) sells patio and lawn furniture. Sales are seasonal, with higher sales during the spring and summer quarters and lower sales during the fall and winter quarters. The company developed the following quarterly sales forecasting model: Ŷt = 8.25 + 0.125t − 2.75D1t + 0.25D2t + 3.50D3t
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