Based on the information in the following table, use the Exponential Smoothing Approach (a =0.5) to forecast the demand for periods 4-11 PERIOD MONTH Demand Forecast 1 Jan 89 2 Feb 151 89 Mar 60 120 4. Apr 107 May 131 Jun 140 7. Jul 71 8. Aug 94 Sep 102 10 Oct 119 11 Nov 3. 9,
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- Suppose that you work for a U.S. senator who is contemplating writing a bill that would put a national sales tax in place. Because the tax would be levied on the sales revenue of retail stores, the senator has asked you to prepare a forecast of retail store sales for year 8, based on data from year 1 through year 7. The data are: (c1p2) Year Retail Store Sales 1 $1,225 2 1,285 3 1,359 4 1,392 5 1,443 6 1,474 7 1,467 54 Chapter One a. Use the first naive forecasting model presented in this chapter to prepare a forecast of retail store sales for each year from 2 through 8. b. Prepare a time-series graph of the actual and forecast values of retail store sales for the entire period. (You will not have a forecast for year 1 or an actual value for year 8.) c. Calculate the root-mean-squared error for your forecast series using the values for year 2 through year 7. 3. Use the second naive forecasting model presented in this chapter to answer parts (a) through (c) of Exercise 2. Use P 0.2 in…A local moving company has collected data on the number of moves they have been asked to perform over the past two years. Moving is highly seasonal, so the owner/operator, who is both burly and highly educated, decides to apply the multiplicative seasonal method to forecast the number of customers for the coming year. The equation for the trend line of yearly sales is Ft = 16 + 60t. Please forecast demand for each quarter in Year 3. (Round the forecasts to whole numbers and show all calculations). Complete the table below and forecast the sales of Year 3 by quarter. Year 1 Year 2 Year 3 Quarter Demand Seasonal Index Quarter Demand Seasonal Index Average Seasonal Index Forecast 1 20 1 27 2 40 2 45 3 45 3 55 4 31 4 41 Total AverageAD 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.
- 21. Consider a firm subject to quarter-to-quarter variation in its sales. Suppose that the following equation was estimated using quarterly data for the period 2011–2018 (the time variable goes from 1 to 32). The variables D1, D2, and D3 are, respectively, dummy variables for the first, second, and third quarters (e.g., D1 is equal to 1 in the first quarter and 0 otherwise). Qt =a+bt+c1D1+c2D2+c3D3 The results of the estimation are presented here: a. Calculate the intercept in each of the four quarters. What do these values imply? b. Use this estimated equation to forecast sales in the fourth quarter of 2019.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?is the world heading for a recession? explain the conditons under which the world can be classified as being in a recession as COVID-19 persists. explain linking the pandemic to the various stages of a recession
- Consider the following model: yhat = 2.6+-0.9x² The prediction of y is yhat. What is the estimated marginal effect of x on y when x=2.7? PLZ MAKE SURE THIS IS RIGHT!!!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.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.Metropolitan Hospital has estimated its average monthly bed needs as N=1,000+9X where X=timeperiod(months);January2002=0 N=monthlybedneeds Assume that no new hospital additions are expected in the area in the foreseeable future. The following monthly seasonal adjustment factors have been estimated, using data from the past five years: Forecast Metropolitans bed demand for January, April, July, November, and December 2007. If the following actual and forecast values for June bed demands have been recorded, what seasonal adjustment factor would you recommend be used in making future June forecasts?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.