Two forecasting models were used to predict the future value of a time series. These are shown in the accompanying table, together with the actual values. Forecast Value Ft Model 1 Model 2 Actual Yt 7.5 6.3 6 6.3 6.7 6.6 5.4 7.1 7.3 8.2 7.5 9.4 Compute MAD and MPE for each model and determine which was more accurate.

Two forecasting models were used to predict the future value of a time series. These are shown in the accompanying table, together with the actual values. Forecast Value Ft Model 1 Model 2 Actual Yt 7.5 6.3 6 6.3 6.7 6.6 5.4 7.1 7.3 8.2 7.5 9.4 Compute MAD and MPE for each model and determine which was more accurate.

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.6: Moving Averages Models

Problem 22P: The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six...

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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 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_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 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_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 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_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_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?
Two different forecasting techniques (F1 and F2) were used to forecast demand for cases of bottled water. Actual demand and the two sets of forecasts are as follows: PREDICTED DEMAND Period Demand F1 F2 1 68 66 66 2 75 68 68 3 70 72 70 4 74 71 72 5 69 72 74 6 72 70 76 7 80 71 78 8 78 74 80 Click here for the Excel Data File Compute MAD for each set of forecasts. Given your results, which forecast appears to be more accurate? (Round your answers to 1 decimal place.) b. Compute the MSE for each set of forecasts. Given your results, which forecast appears to be more accurate? (Round your answers to 2 decimal places.) c. In practice, either MAD or MSE would be employed to compute forecast errors. What factors might lead a manager to choose one rather than the other? d. Compute MAPE for each data set. Which forecast appears to be more accurate? (Round your intermediate calculations and final answers to 2 decimal places.)
Two different forecasting techniques (F1 and F2) were used to forecast demand for cases of bottled water. Actual demand and the two sets of forecasts are as follows: PREDICTED DEMAND Period Demand F1 F2 1 68 61 60 2 75 67 66 3 70 75 70 4 74 69 67 5 69 74 72 6 72 67 78 7 80 74 78 8 78 76 80 Click here for the Excel Data File a. Compute MAD for each set of forecasts. Given your results, which forecast appears to be more accurate? (Round your answers to 2 decimal places.)
The forecasts generated by two forecasting methods and actual sales have been collected in the Microsoft Excel Online file below. Use the Microsoft Excel Online file below to develop forecast accuracy and answer the following question. Forecast Accuracy Measures Forecast 1 Period Sales Forecast Error Absolute Error Error^2 Abs. % Error 1 271 276 2 285 263 3 290 286 4 276 288 5 266 269 6 268 267 7 265 261 8 270 268 Total Average RSFE MAD MSE MAPE Tracking Signal 1. What is the MAD, the MSE, the MAPE, the RSFE, and the tracking signal for each forecasting method? Do not round intermediate calculations. Round your answers to two decimal places. Use the minus sign to enter negative values, if any. 2. What can be concluded about the quality of the forecasts 1? Assume that the control limit for the tracking signal…