The actual values for a data set all range between 100 and 200, and a manager develops a forecast model for the data. If the mean squared error and the mean absolute deviation error measurements for forecast are identical, what can we conclude about the forecast? a. This is mathematically impossible. b. The forecast is accurate. c. The forecast tends to overpredict. d. The forecast tends to underpredict.

The actual values for a data set all range between 100 and 200, and a manager develops a forecast model for the data. If the mean squared error and the mean absolute deviation error measurements for forecast are identical, what can we conclude about the forecast? a. This is mathematically impossible. b. The forecast is accurate. c. The forecast tends to overpredict. d. The forecast tends to underpredict.

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 28P: The file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. a. Is seasonality present...

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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?
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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?
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