↑Sales of tablet computers at Ted Glickman's electronics store in Washington, D.C., over the past 10 weeks are shown in the table below:Week 1Demand 205 6 7 8 9 1024 30 36 24 26 282423 28 38a) The forecast for weeks 2 through 10 using exponential smoothing with a 0.60 and a week 1 initial forecast of 20.0 are (round your responses to two decimal places):7Week1 2 3 4 5Demand 20 23 28 38 24Forecast 20.0366308249 1026 28b) For the forecast developed using exponential smoothing (a=0.60 and initial forecast 20.0), the MAD-sales (round your response to two decimal places).c) For the forecast developed using exponential smoothing (a=0.60 and initial forecast 20.0), the tracking signal =(round your response to two decimal places).

Given that:Initial Forecast = 20Remember the formula for calculating Forecast =Error = Demand -…

Sales of tablet computers at Ted Glickman's electronics store in Washington, D.C., over the past 10 weeks are shown in the table below: Week 1 Demand 20 2 3 4 5 6 7 8 9 10 23 28 38 24 30 36 24 26 28 a) The forecast for weeks 2 through 10 using exponential smoothing with a 0.60 and a week 1 initial forecast of 20.0 are (round your responses to two decimal places) Week 1 Demand 20 Forecast 20.0 6 7 8 9 10 30 36 24 26 28 2 3 4 23 28 38 5 24 b) For the forecast developed using exponential smoothing (a-0.60 and initial forecast 20.0), the MAD-sales c) For the forecast developed using exponential smoothing (a=0.60 and initial forecast 20.0), the tracking signal (round your response to two decimal places). round your response to two decimal places)

Sales of tablet computers at Ted Glickman's electronics store in Washington, D.C., over the past 10 weeks are shown in the table below: Week 1 Demand 20 2 3 4 5 6 7 8 9 10 23 28 38 24 30 36 24 26 28 a) The forecast for weeks 2 through 10 using exponential smoothing with a 0.60 and a week 1 initial forecast of 20.0 are (round your responses to two decimal places) Week 1 Demand 20 Forecast 20.0 6 7 8 9 10 30 36 24 26 28 2 3 4 23 28 38 5 24 b) For the forecast developed using exponential smoothing (a-0.60 and initial forecast 20.0), the MAD-sales c) For the forecast developed using exponential smoothing (a=0.60 and initial forecast 20.0), the tracking signal (round your response to two decimal places). round your response to two decimal places)

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 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 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_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?
The file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?
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The file P13_25.xlsx contains the quarterly numbers of applications for home mortgage loans at a branch office of Northern Central Bank. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b? Is it guaranteed to produce better forecasts for the future?
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