The following equation summarizes the trend portion of quarterly sales of condominiums over a long cycle. Sales also exhibit seasonal variations. Ft = 51 − 4.1t + 3.1t 2 where Ft = Unit sales t = 0 at the first quarter of last year Quarter Relative 1 0.95 2 0.95 3 0.45 4 1.65 Click here for the Excel Data File Using the information given, prepare a forecast of sales for each quarter of next year (not this year), and the first quarter of the year following that. (Round intermediate calculations and final answers to 2 decimal places.)

The following equation summarizes the trend portion of quarterly sales of condominiums over a long cycle. Sales also exhibit seasonal variations. Ft = 51 − 4.1t + 3.1t 2 where Ft = Unit sales t = 0 at the first quarter of last year Quarter Relative 1 0.95 2 0.95 3 0.45 4 1.65 Click here for the Excel Data File Using the information given, prepare a forecast of sales for each quarter of next year (not this year), and the first quarter of the year following that. (Round intermediate calculations and final answers to 2 decimal places.)

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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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_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?
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