The accompanying dataset provides data on the monthly usage of natural gas (in millions of cubic feet) for a certain region over two years. Implement the Holt-Winters multiplicative seasonalit model with no trend to find the forecast for periods 13-26, where x = 0.6 and y=0.9. Then find the MAD for periods 13-24. Click the icon to view the monthly use of natural gas data. Use the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-18, periods 19-24, and then for periods 25 and 26. (Type integers or decimals rounded to two decimal places as needed.) Period Forecast 13 Natural Gas Usage 14 15 16 17 18 ☐☐☐☐☐☐ Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Period Gas Usage 1 2 3 4 5 6 7 1891012345678192012223 24 251 227 157 138 53 33 30 29 28 41 87 196 231 252 240 135 34 34 27 27 29 39 86 188 - X

The accompanying dataset provides data on the monthly usage of natural gas (in millions of cubic feet) for a certain region over two years. Implement the Holt-Winters multiplicative seasonalit model with no trend to find the forecast for periods 13-26, where x = 0.6 and y=0.9. Then find the MAD for periods 13-24. Click the icon to view the monthly use of natural gas data. Use the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-18, periods 19-24, and then for periods 25 and 26. (Type integers or decimals rounded to two decimal places as needed.) Period Forecast 13 Natural Gas Usage 14 15 16 17 18 ☐☐☐☐☐☐ Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Period Gas Usage 1 2 3 4 5 6 7 1891012345678192012223 24 251 227 157 138 53 33 30 29 28 41 87 196 231 252 240 135 34 34 27 27 29 39 86 188 - X

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?
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_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?
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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?
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