A large Portland manufacturer wants to forecast demand for a piece of pollution-control equipment. A review of past sales (A,), as shown below, indicates that an increasing trend is present. Smoothing constants are assigned the values of a = 0.20 and ẞ= 0.4. The firm assumes the initial forecast for month 1 (F1) was 9.00 units and the trend over that period T, was 2.00 units. Using trend-adjusted exponential smoothing, Forecasts (Fr), Trend (T), and Forecasts Including Trend (FIT) for months 1 through 4 have already been developed and are provided below. Continue with the process and determine Fr, T, and FIT, for months 5 and 6 (round your responses to two decimal places): Month Actual Demand (t) (A₂) Forecast (F₂) Trend (T₂) Forecast Including Trend (FIT) 1 10.0 9.00 2.00 11.00 2 18.0 10.80 1.92 12.72 3 24.0 13.78 2.34 16.12 4 20.0 17.70 2.97 20.67 5 22.0 15.26 6 22.0 16.61 7 32.0 8 26.0 9 38.0 10

A large Portland manufacturer wants to forecast demand for a piece of pollution-control equipment. A review of past sales (A,), as shown below, indicates that an increasing trend is present. Smoothing constants are assigned the values of a = 0.20 and ẞ= 0.4. The firm assumes the initial forecast for month 1 (F1) was 9.00 units and the trend over that period T, was 2.00 units. Using trend-adjusted exponential smoothing, Forecasts (Fr), Trend (T), and Forecasts Including Trend (FIT) for months 1 through 4 have already been developed and are provided below. Continue with the process and determine Fr, T, and FIT, for months 5 and 6 (round your responses to two decimal places): Month Actual Demand (t) (A₂) Forecast (F₂) Trend (T₂) Forecast Including Trend (FIT) 1 10.0 9.00 2.00 11.00 2 18.0 10.80 1.92 12.72 3 24.0 13.78 2.34 16.12 4 20.0 17.70 2.97 20.67 5 22.0 15.26 6 22.0 16.61 7 32.0 8 26.0 9 38.0 10

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 26P: The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel...

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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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