Consider the following actual (A) and forecast (F) demand levels for a commercial multiline telephone at Office Max: Time Period (t) Actual Demand (A) Forecast Demand (F) 1 60 60 42 60 58 54.60 4. 46 55.62 The first forecast, F1, was derived by observing A1. and setting F1, equal to A1. Subsequent forecasts were derived by exponential smoothing. What would be the smoothing constant (a) used to derive the subsequent forecasts? (round your response to two decimal

Consider the following actual (A) and forecast (F) demand levels for a commercial multiline telephone at Office Max: Time Period (t) Actual Demand (A) Forecast Demand (F) 1 60 60 42 60 58 54.60 4. 46 55.62 The first forecast, F1, was derived by observing A1. and setting F1, equal to A1. Subsequent forecasts were derived by exponential smoothing. What would be the smoothing constant (a) used to derive the subsequent forecasts? (round your response to two decimal

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 8E: Bell Greenhouses has estimated its monthly demand for potting soil to be the following: N=400+4X...

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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.
A firm experienced the demand shown in the following table. *Unkown future value to be forecast Fill in the table by preparing forecasts based on a five-year moving average, a three-year moving average, and exponential smoothing (with a w=0.9 and a w=0.3). Note The exponential smoothing forecasts may be begun by assuming Y t+1=Yt. Using the forecasts from 2005 through 2009, compare the accuracy of each of the forecasting methods based on the RMSE criterion. Which forecast would you have used for 2010? Why?
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.
Consider the following actual and forecast demandlevels for Big Mac hamburgers at a local McDonald’s restaurant:DAY ACTUAL DEMAND FORECAST DEMANDMonday 88 88Tuesday 72 88Wednesday 68 84Thursday 48 80FridayThe forecast for Monday was derived by observing Monday’sdemand level and setting Monday’s forecast level equal to this demand level. Subsequent forecasts were derived by using expo-nential smoothing with a smoothing constant of 0.25. Using this exponential smoothing method, what is the forecast for Big Macdemand for Friday?
Wharton Econometric Forecasting, LLC has been hired to analyze demand in 30 regional markets for Product Y, a major item. A statistical analysis of demand in these markets shows (standard errors in parentheses): QY = 26,950 − 450P + 220PX + 0.08A + 0.01I (11,000) (150) (180) (0.3) (0.05) R2 = 0.95 Standard Error of the Estimate = 10 Here, QY is market demand for Product Y, P is the price of Y in dollars, A is dollars of advertising expenditures, PX is the average price in dollars of another (unidentified) product, and I is dollars of household income. In a typical market, the price of Y is $100, PX is $70, advertising expenditures are $50,000, and the average family income is $60,000. 1. Which variables in this regression model are statistically significant at the 95 percent confidence level? Show your work.
Wharton Econometric Forecasting, LLC has been hired to analyze demand in 30 regional markets for Product Y, a major item. A statistical analysis of demand in these markets shows (standard errors in parentheses): QY = 26,950 − 450P + 220PX + 0.08A + 0.01I (11,000) (150) (180) (0.3) (0.05) R2 = 0.95 Standard Error of the Estimate = 10 Here, QY is market demand for Product Y, P is the price of Y in dollars, A is dollars of advertising expenditures, PX is the average price in dollars of another (unidentified) product, and I is dollars of household income. In a typical market, the price of Y is $100, PX is $70, advertising expenditures are $50,000, and average family income is $60,000. Use the estimated demand function to calculate the expected value of QY in a typical market. Calculate the 95% confidence interval within which you would expect to find actual values of sales.…
Tom Glass forecasts electrical demand for the Flatlands Public Power District (FPPD). The FPPD wants to take its Comstock power plant out of service for maintenance when demand is expected to be low. After shutdown, performing maintenance and getting the plant back on line takes two weeks. The utility has enough other generating capacity to satisfy 1,550 megawatts (MW) of demand while Comstock is out of service. Table shows weekly peak demands (in MW) for the past several autumns. When next in year 6 should the Comstock plant be scheduled for maintenance?
Demand for Orange Juice is given as Qd = 5000-2500 P + 1200 I +650E - 255 PS Suppose Income is I = Rs.500, Expectations E = 55, and Price of Ps= Rs 25. Find the Demand Equation. b. Using the demand function from part a., Calculate Elasticity of Demand for price range of Rs.125 and Rs. 155. What will be the 'Price Elasticity of Demand at P = Rs.125? d Interpret the Elasticity of Demand calculated in (C) above.
Demand for Orange Juice is given asQd = 5000 – 2500 P + 1200 I + 650 E – 255 PsSuppose Income is I = Rs.500, Expectations E = 55, and Price of Ps = Rs 25.a. Find the Demand Equation.b. Using the demand function from part a.,Calculate Elasticity of Demand for price range of Rs.125 and Rs.155.c. What will be the ‘Price Elasticity of Demand’ at P = Rs.125?d. Interpret the Elasticity of Demand calculated in (C) above.
Bell Greenhouses has estimated its monthly demand for potting soil to be the following: 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 +2 June +15 August +10 December −12 a. Forecast Bell Greenhouses’ demand for potting soil in March, June, August, and December 2007. Answer 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 452 438 2005 404 420 2004 356 380 2003 308 320
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.
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.