EBK OM
6th Edition
ISBN: 9781305888210
Author: Collier
Publisher: YUZU
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Question
Chapter 9, Problem 3PA
Summary Introduction
Interpretation: Exponential smoothing
Concept Introduction: Single exponential smoothing forecast values are calculated on the basis of mean average values of past time series.
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Forecasting with exponential smoothing has been compared to driving a car while gazing in the rearview mirror. What circumstances must exist for driving a car that are equivalent to the assumptions made while applying exponential smoothing.
The following table shows the actual demand observed over the last 11 years:
Year
1
2
3
4
5
6
7
8
9
10
11
Demand
7
9
6
10
12
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12
9
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Part 2
Using exponential smoothing with
α =
0.30 and a forecast for year 1 of
6.0, provide the forecast from periods 2 through 12 (round your responses to one decimal place).
Part 3
Provide the forecast from periods 2 through 12 using the naive approach (enter your responses as whole numbers).
in order to forecast a demand pattern that has a significant trend but no seasonality you can use both double exponential smoothing and regression. what is the advantage of using double exponential smoothing over regression?
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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?arrow_forwardThe 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?arrow_forwardThe 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?arrow_forward
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Single Exponential Smoothing & Weighted Moving Average Time Series Forecasting; Author: Matt Macarty;https://www.youtube.com/watch?v=IjETktmL4Kg;License: Standard YouTube License, CC-BY
Introduction to Forecasting - with Examples; Author: Dr. Bharatendra Rai;https://www.youtube.com/watch?v=98K7AG32qv8;License: Standard Youtube License