2. Using the same data above, compute for the WMA using the below weights. Compute for the MAD, MSE and MAPEH. Weights Period5 Last Month4 Two Months Ago3 Three Months Ago2 Four Months Ago1 Five Months Ago3. Compare the Percentage Errors for the Moving Average and Weighted MovingAverage as computed in nos 1 and 2. OXNJE1/a/NTA2Njg2Nzg5NDM2/detailsa pdf1013A0\9Open with Google Docs1. Develop a 5-month moving average for the below given. What wthe forecast month 15? Compute for the MAD, MSE and MAPE.MonthUnit Sales120212413041451551107.1148.1566.1211011811117 page3.5. JOXNJE1/a/NTA2Njg2Nzg5NDM2/details9 pdfE Öpen with Google Docs71141569.121101181111712118131191411315Page1 / 2Q +N8.

Forecasting can be done by many methods. Some of them are a simple moving average method, weighted…

sing the same data above, compute for the WMA using the below weights. Compute for the MAD, MSE and MAPEH. Weights Period 5 Last Month 4 Two Months Ago 3 Three Months Ago 2 Four Months Ago 1 Five Months Ago 3. Compare the Percentage Errors for the Moving Average and Weighted Moving Average as computed in nos 1 and 2.

sing the same data above, compute for the WMA using the below weights. Compute for the MAD, MSE and MAPEH. Weights Period 5 Last Month 4 Two Months Ago 3 Three Months Ago 2 Four Months Ago 1 Five Months Ago 3. Compare the Percentage Errors for the Moving Average and Weighted Moving Average as computed in nos 1 and 2.

Contemporary Marketing

18th Edition

ISBN:9780357033777

Author:Louis E. Boone, David L. Kurtz

Publisher:Louis E. Boone, David L. Kurtz

Chapter14: Pricing Strategies

Section14.2: Forecasting Demand

Problem 1LO

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The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?
The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.
Under what conditions might a firm use multiple forecasting methods?
The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?
The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?
The file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?
The file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
The following gives the number of pints of type B blood used at Woodlawn Hospital in the past 6 weeks: Pints Used Week Of August 31 360 September 7 389 September 14 410 September 21 381 September 28 368 October 5 374 a. Forecast the demand for the week of October 12 using a 4-week moving average. [Select] b. What is the MAD? [Select] c. Use a 4-week weighted moving average, with weights of .1, .1, .2, and .6 using .6 for the most recent week. Forecast demand for the week of October 12. [Select ] d. What is the MAD? [Select ] > e. Compute the forecast for the week of October 12 using exponential smoothing with a forecast for August 31 of 363 and a = [Select] .47 f. What is the MAD? [Select] g. Based on evaluating all three forecasting models, which forecast should be chosen? [Select]
Week Sales1 27502 31003 32504 28005 29006 30507 33008 31009 295010 300011 320012 3150Develop a three-week moving average. A.What is the forecast for week 5? (Make sure no decimal place and no comma. EX: 1234) B.What is the value of MSE (mean squared error) with regard to a three-week moving average? (Make sure no decimal place and no comma. EX: 1234) C.Develop the exponential smoothing with the alpha value of 0.20. What is the forecast for week 5? (Make sure no decimal place and no comma. EX: 1234) D.What is the value of MSE (mean squared error) with regard to the exponential smoothing? (Make sure no decimal place and no comma. EX: 1234) E.Which method is more accurate? (1 for Moving average, 2 for Exponential Smoothing)
The monthly demand for units manufactured by the Acme Rocket Company has been as follows:Month Units Month UnitsMay 100 September 105June 80 October 110July 110 November 125August 115 December 120a. Use the exponential smoothing method to forecast the number of units for June to January. Theinitial forecast for May was 105 units; a = 0.2.b. Calculate the absolute percentage error for each month from June through December and theMAD and MAPE of forecast error as of the end of December.c. Calculate the tracking signal as of the end of December. What can you say about the performanceof your forecasting method?
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