Problem 5You are trying to determine what forecast method to use. Based upon the followinghistorical data, calculate the following forecast and specify what procedure you wouldutilize.MAYJUNEMONTH JANUARY FEBRUARY MARCH APRILDEMAND 626567687173MONTH JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBERDEMAND 767878808485a. Calculate the simple three-month moving average for periods 4 to 12.b. Calculate the weighted three-month moving average for periods 4 to 12. Useweights of 0.50, 0.30, and 0.20 for the most recent, second most recent, andthird most recent periods, respectively.c. Calculate the single exponential smoothing forecast for periods 2 to 12 using aninitial forecast (F₁) of 61 and a of 0.30.d. Calculate the exponential smoothing with trend component forecast for periods 2to 12 using an initial trend forecast (T₁) of 1.8, and initial exponential smoothingforecast (F₁) of 60, an a of 0.30 and ō of 0.30.e. Calculate the MAD for the forecasts made by each technique in periods 4 to 12.Which forecasting method provides the best forecasts?

monthDemand Jan62Feb65Mar67Apr68May71Jun73Jul76Aug78Sep78Oct80Nov84Dec85

Answered: a. Calculate the simple three-month…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.7: Exponential Smoothing Models

Problem 26P: The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel...

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Under what conditions might a firm use multiple forecasting methods?
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.
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_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 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 owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?
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?
Use exponential smoothing with a=0.2 and a=0.5 to generate forecasts for periods 2 through 6. Use MAD and MSE to decide which of the two models produced a better forecast. Period Actual Forecast 1 15 17 2 18 3 14 4 16 5 13 6 16
Explain the difference between qualitative and quantitative approaches to forecasting. Describe three (3) qualitative methods used in forecasting. Given the following data of demand for shopping carts at a leading supermarket. Prepare a forecast for period 6 using each of the following approaches: Period 1 2 3 4 5 Demand 60 65 55 58 64 A three-period moving average. A weighted average using weights of .50 (most recent), .20 and .30. Exponential smoothing with a smoothing constant of .40. The manager of a large cement production factory in Road Town, Tortola has to choose between two alternative forecasting techniques. His production staff used both techniques in order to prepare forecasts for a six-month period (See table below). Using MAD as a criterion, which technique has the better performance record? FORECAST MONTH DEMAND TECHNIQUE 1 TECHNIQUE 2 1 492 488 495 2 470 484 482 3 485…
Please consider the following information and make forecasts for January, February, March, and April of Year 2. Use second-order polynomial regression to make forecasts. Adjusted seasonal index Actual sales Deseasonalized sales 836 825 Year 1 Year 2 January February March April May June July August September October November December January February March April 0.72 0.75 1:22 1.48 1 1.23 0.7 0.78 0.92 1.13 1.28 1.33 0.72 0.75 1.22 1.48 1220 1319 1020 1184 685 641 875 1008 1068 1141

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