A quality control manager wants to check the accuracy of the exponential smoothing with trend method. Using the smoothing constant alpha a =0.9 and delta õ = 0.3, find the forecast for quarters 2 and 3 using the exponential smoothing with trend method. Assume that the previous forecast (for quarter 1) including trend (FIT) was 220 units, and the previous trend component (T) was 20 units. Actual sales are given below: Period Actual Sales Quarter 1 200 Quarter 2 210 Quarter 3 190

A quality control manager wants to check the accuracy of the exponential smoothing with trend method. Using the smoothing constant alpha a =0.9 and delta õ = 0.3, find the forecast for quarters 2 and 3 using the exponential smoothing with trend method. Assume that the previous forecast (for quarter 1) including trend (FIT) was 220 units, and the previous trend component (T) was 20 units. Actual sales are given below: Period Actual Sales Quarter 1 200 Quarter 2 210 Quarter 3 190

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 25P: The file P13_25.xlsx contains the quarterly numbers of applications for home mortgage loans at a...

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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.
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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 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?
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