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 5E: A firm experienced the demand shown in the following table. *Unkown future value to be forecast Fill...

See similar textbooks

Similar questions

The Questor Corporation has experienced the following sales pattern over a 10-year period: Compute the equation of a trend line (similar to Equation 5.4) for these sales data to forecast sales for the next year. (Let 2004=0,2005=1, etc., for the time variable.) What does this equation forecast for sales in the year 2014? Use a first-order exponential smoothing model with a w of 0.9 to forecast sales for the year 2014.
Bell Greenhouses has estimated its monthly demand for potting soil to be the following: N=400+4X where N=monthlydemandforbagsofpottingsoil X=timeperiodsinmonths(March2006=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: Forecast Bell Greenhouses demand for potting soil in March, June, August, and December 2007. 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.
Question 3 The number of tons of brake assemblies received at an auto parts distribution center last month was 625. The forecast tonnage was 650 for last month. The company uses a simple exponential smoothing model with a smoothing constant of 0.46 to develop its forecasts. What will be the company's forecast for the next month? Add your answer
I- Demand for a product is estimated to be Q=960 - 1.2P + 1.4Y +.003A where, Q and P are the quantity and price of the product respectively, Y is income, and A is the advertising expenditures. All the variables are in the natural logarithmic form and all the estimated coefficients are statistically significant. The average annual sale and the average price of the product are 60000 units and $8000 respectively. A. Price elasticity of demand is -------------, income elasticity of demand is ---------, advertising elasticity of demand is B. The optimum level of advertising spending for the firm is
Director Very Busy needs to allocate time this week for office appointments, so he needs to forecast the number of employees who will seek appointments. The director has gathered the following time series data recently Period Employee Appointments 4 weeks ago 95 3 weeks ago 80 2 week ago 65 last week 50
You are using exponential smoothing to forecast demand. For September, suppose you forecasted 165 units and actually sold 178 units. If you use an alpha value of 0.2. what will be the exponential smoothing forecast for October?
Choose one of the following forecasting methods discussed in this chapter: last-value, averaging, moving-average, or exponential smoothing. Identify the conditions when the method is most appropriate to use and give an example of an application of this method.
4. Suppose we have a summary of forecasting techniques calculation results for Gundam Auto Sales Inc. (in the prior item) as follows: Mean Next Standard Period Error Forecasting Method Used Forecast (MSE) 1.3-period UnWMA 10.42 3.24 2.4-period UnWMA 10.38 3.51 3.3-period WMA 10.83 3.03 4. Simple Exponential Smoothing 5. Trend Projection 8.24 5.04 11.43 1.16 a. Which among the techniques is the most reliable? b. What does the result of the most reliable technique say?
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…
OA linear regression model is Units 3,414-0.839xWeek. For week 45, what is the forecast for the number of units? Round your answer to the nearest whole number. OO units
5. Exercise 5.5 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 forecasts may be begun by assuming Y t+ F Yt •) and w = 0.3 ). (Note: The exponential smoothing Moving Average Actual Demand (5-year) (3-year) Exponential Smoothing (W = 0.9) Year (W = 0.3) 2000 900 2001 885 900 900 2002 875 2003 870 887 ▼ 2004 870 877 ▼ 2005 875 880 872 Y 2006 885 875 872 2007 900 875 877 2008 920 880 887 - 2009 945 890 902 Y 2010 905 922 The following table shows the square errors, (Y; - T1-) , for forecasts from 2005 through 2009. Fill the table by calculating the root mean square error (RMSE) for each of the methods. Square Error Exponential Smoothing Moving Average 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,…
AD has estimated the following demand relationship for its product over the last four years, using monthly observations: ln Qt = 4.932- 1.238 ln Pt + 1.524 ln Yt-1 + 0.4865lnQt-1(2.54) (1.38) (3.65) (2.87)R2= 0.8738where Q = sales in units, P = price in Rs., Y is income in Rs,000, and the numbers in brackets are t-statistics.a. Interpret the above model.b. Make a sales forecast if price is Rs. 9, income last month was Rs. 25,000 and sales last month were 2,981 units.c. Make a sales forecast for the following month if there is no change in price or income.d. If price is increased by 5 per cent in general terms, estimate the effect on sales, stating any assumptions.