A firm experienced the demand shown in the following table. Fill in the table by preparing forecasts based on a five-year moving average, a three-year moving average, and exponential smoothing (w = 0.9 and w=0.3). (Note: The exponential smoothing forecasts may be begun by assuming Ŷ+1=Y₁-) Moving Average Exponential Smoothing Year Actual Demand (5-year) (3-year) (W = 0.9) (W = 0.3) 2000 900 2001 885 2002 875 2003 870 2004 870 2005 875 2006 885 2007 900 2008 920 2009 945 44.4444 2010 The following table shows the square errors, (Y-1), for forecasts from 2005 through 2009. Fill the table by calculating the root mean square error (RMSE) for each of the methods. Square Error Moving Average Exponential Smoothing Year (5-year) (3-year) (W = 0.9) (W = 0.3) 2005 25 9 25 25 2006 100 169 100 36 2007 625 529 256 361 2008 1,600 1,089 484 1,089 2009 3,025 1,849 729 2,304 RMSE Based on the RMSE criterion, which of the forecasting methods is the most accurate? Exponential smoothing (w = 0.9) ○ Exponential smoothing (w = 0.3) ○ Three-year moving average O Five-year moving average

A firm experienced the demand shown in the following table. Fill in the table by preparing forecasts based on a five-year moving average, a three-year moving average, and exponential smoothing (w = 0.9 and w=0.3). (Note: The exponential smoothing forecasts may be begun by assuming Ŷ+1=Y₁-) Moving Average Exponential Smoothing Year Actual Demand (5-year) (3-year) (W = 0.9) (W = 0.3) 2000 900 2001 885 2002 875 2003 870 2004 870 2005 875 2006 885 2007 900 2008 920 2009 945 44.4444 2010 The following table shows the square errors, (Y-1), for forecasts from 2005 through 2009. Fill the table by calculating the root mean square error (RMSE) for each of the methods. Square Error Moving Average Exponential Smoothing Year (5-year) (3-year) (W = 0.9) (W = 0.3) 2005 25 9 25 25 2006 100 169 100 36 2007 625 529 256 361 2008 1,600 1,089 484 1,089 2009 3,025 1,849 729 2,304 RMSE Based on the RMSE criterion, which of the forecasting methods is the most accurate? Exponential smoothing (w = 0.9) ○ Exponential smoothing (w = 0.3) ○ Three-year moving average O Five-year moving average

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter5: Business And Economic Forecasting

Section: Chapter Questions

Problem 3E: Metropolitan Hospital has estimated its average monthly bed needs as N=1,000+9X where...

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All questions utilize the multivariate demand function for Smooth Sailing sailboats in C6 on text page 83. Compute to three decimal places. Initial values are: PX = $9500 PY = $10000 I = $15000 A = $170000 W = 160 This function is: Qs = 89830 -40PS +20PX +15PY +2I +.001A +10W 1.(a). Use the above to calculate the arc price elasticity of demand between PS = $9000 decreasing to PS = $8000. The arc elasticity formula is: 1.(b). Judging from the computation in (a), do you expect the revenue resulting from the decrease in Ps to $8000 to increase, remain the same, or decrease relative to the revenue at Ps = $9000. (Hint: see the table on page 65 of Truett). Explain your choice. 1.(c). Calculate the point elasticity of demand for Smooth Sailing sailboats at PS = $9000 (which should make Qs = 101600). The formula is: 1.(d). Does this elasticity value indicate that Smooth Sailing demand is relatively responsive to changes in the price of these sailboats? Explain…
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Bell Greenhouses has estimated its monthly demand for potting soil to be the following:N = 400 + 4Xwhere N = monthly demand for bags of potting soilX = time periods in months (March 2006 = 0)Assume this trend factor is expected to remain stable in the foreseeable future. The following table contains the monthly seasonal adjustment factors, which have been estimated using actual sales data from the past five years:MONTH ADJUSTMENT FACTOR (%)March +2June +15August +10December −12a. Forecast Bell Greenhouses’ demand for potting soil in March, June, August, and December 2007.b. If the following table shows the forecasted and actual potting soil sales by Bell Greenhouses for April in five different years, determine the seasonal adjustment factor to be used in making an April 2008 forecast.YEAR FORECAST ACTUAL 2007 500 515 2006 452…
A firm keeps a record of sales and prices over the past seven months, resulting in the following table: Price (ZMW/ton) Sales (tons) Nov. 1985 7.5 84.5 Dec. 8.0 82.0 Jan. 1986 8.0 84.0 Feb. 7.2 92.0 March 7.0 95.0 April 8.0 92.0 May 8.5 91.5 Use these observations to estimate demand as a linear function of both price and time. Further, utilise this function to estimate demand for the following month, on the assumption that: (a) price remains unchanged, (b) price increases to ZMW9/ton. Hence estimate the price elasticity of demand between these prices and find the price which would maximise sales revenue. Given the nature of the observations, comment on any difficulties in interpreting your results for decision-making purposes.
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