Sales of PQR-Cool air conditioners have grown steadily during the past 5 years:The sales manager had predicted before the business started, that year 1's sales would be 410air conditioners.YEAR12 3 4 5 6conditioners.SALES450495518563584?a) Using exponential smoothing with a weight of a = 0.3, develop forecasts for years 2through 6.b) Using smoothing constants of 0.6 and 0.9, develop forecasts for the sales of PQR-Cool airc) What effect did the smoothing constant have on the forecast for PQR-Cool air conditioners?Refer to (a) and (b), which smoothing constant gives the more accurate forecast?d) Use a 3-year moving average forecasting model to forecast the sales of PQR-Coolair conditioners.e) Using the trend projection method, develop a forecasting model for the sales ofPQR-Cool air conditioners.f) Referring to answer in (a), (d), and (e), which will be a better forecaster inpredicting sales of PQR-Cool air conditioners? Justify your answer.

Ans) Explanation of how forecast will be calculated using exponential Smoothing and 3 period moving…

Answered: Sales of PQR-Cool air conditioners have…

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 27P: The file P13_27.xlsx contains yearly data on the proportion of Americans under the age of 18 living...

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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.
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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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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