Passenger miles flown on Northeast Airlines, a commuter firm serving the Boston hub, are shown for the past 12 weeks: Week 1 2 3 4 6. 7 8 9 10 11 12 Actual Passenger Miles (in thousands) 17 21 19 23 19 17 21 19 23 21 13 24 a) Assuming an initial forecast for week 1 of 17,000 miles, use exponential smoothing to compute miles for weeks 2 through 12. Use a = 0.25 (round your responses to two decimal places). Week 1 4 6 8 9 10 11 12 Forecasted Passenger Miles (in thousands) 17.00 17.00 18.00 18.25 19.44 19.33 18.75 19.31 19.23 20.17 b) The MAD for this model = thousand (round your response to two decimal places). c) Compute the Cumulative Forecast Errors, cumulative MAD in thousands, and tracking signals (round your responses to two decimal places). Cumulative Cumulative Tracking Signal Tracking Signal Forecast Forecast Week Errors MAD Week Errors MAD 1 0.00 0.00 7 9.23 2.11 4.37 - 2 4.00 2.00 2.00 8 8.92 1.89 4.73 3 5.00 1.67 3.00 12.69 2.09 6.06 4 9.75 2.44 4.00 10 13.52 1.97 6.87 5 9.31 2.04 4.57 11 6.98 2.09 3.35 12 Are the tracking signals within acceptable limits? The control limits for the tracking signal are set at ±3MADS. The tracking signals acceptable limits.

Passenger miles flown on Northeast Airlines, a commuter firm serving the Boston hub, are shown for the past 12 weeks: Week 1 2 3 4 6. 7 8 9 10 11 12 Actual Passenger Miles (in thousands) 17 21 19 23 19 17 21 19 23 21 13 24 a) Assuming an initial forecast for week 1 of 17,000 miles, use exponential smoothing to compute miles for weeks 2 through 12. Use a = 0.25 (round your responses to two decimal places). Week 1 4 6 8 9 10 11 12 Forecasted Passenger Miles (in thousands) 17.00 17.00 18.00 18.25 19.44 19.33 18.75 19.31 19.23 20.17 b) The MAD for this model = thousand (round your response to two decimal places). c) Compute the Cumulative Forecast Errors, cumulative MAD in thousands, and tracking signals (round your responses to two decimal places). Cumulative Cumulative Tracking Signal Tracking Signal Forecast Forecast Week Errors MAD Week Errors MAD 1 0.00 0.00 7 9.23 2.11 4.37 - 2 4.00 2.00 2.00 8 8.92 1.89 4.73 3 5.00 1.67 3.00 12.69 2.09 6.06 4 9.75 2.44 4.00 10 13.52 1.97 6.87 5 9.31 2.04 4.57 11 6.98 2.09 3.35 12 Are the tracking signals within acceptable limits? The control limits for the tracking signal are set at ±3MADS. The tracking signals acceptable limits.

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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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_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.
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?
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?
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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?
Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.
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