chapter4solutions

chapter4solutions - CHAPTER 4 DISCUSSION QUESTIONS 3. A...

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CHAPTER 4 DISCUSSION QUESTIONS 3. A time series model uses only historical values of the quantity of interest to predict future values of that quantity. The associative model, on the other hand, attempts to identify underlying causes or factors that control the variation of the quantity of interest, predict future values of these factors, and use these predictions in a model to predict future values of the specific quantity of interest. 4. Qualitative models incorporate subjective factors into the forecasting model. Qualitative models are useful when subjective factors are important. When quantitative data are difficult to obtain, qualitative models may be appropriate. 5. The term least squares refers to the holding of the sum of the square of the difference between the observed values and the regression line to a minimum. 6. The disadvantages of moving average forecasting models are that the averages always stay within past ranges, that they require extensive record keeping of past data, and that they cannot be used to develop a forecast several periods into the future. 7. When the smoothing constant, α , is large (close to 1.0), more weight is given to recent data; when α is low (close to 0.0), more weight is given to past data. 15. Measures of forecast accuracy : (a) MAD (mean absolute deviation). This is a sum of the absolute values of individual errors divided by the number of periods of data. (b) MSE (mean squared error). This is the average of the squared differences between the forecast and observed values. 16. Independent variable ( x ) is said to cause variations in the dependent variable ( y ). 17. Coefficient of determination is the percent of variation in the dependent variable ( y ) that is explained by a regression analysis. 18. Tracking signals alert the user of a forecasting tool to periods in which the forecast was in significant error. END-OF-CHAPTER PROBLEMS 4.2 (a) No, the data appear to have no consistent pattern. Year 1 2 3 4 5 6 7 8 9 10 11 Forecast Demand 7 9 5 9.0 13.0 8.0 12.0 13.0 9.0 11.0 7.0 (b) 3-year moving 7.0 7.7 9.0 10.0 11.0 11.0 11.3 11.0 9.0 (c) 3-year weighted 6.4 7.8 11.0 9.6 10.9 12.2 10.5 10.6 8.4 Chapter 4: Forecasting 1
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(d) The three-year moving average appears to give better results. 1 2 3 4 5 6 7 8 9 10 11 0 2 4 6 8 10 12 14 Demand 3-year moving 3-year weighted Forecast 4.3 Year 1 2 3 4 5 6 7 8 9 10 11 Forecast Demand 7 9.0 5.0 9.0 13.0 8.0 12.0 13.0 9.0 11.0 7.0 Naïve 7.0 9.0 5.0 9.0 13.0 8.0 12.0 13.0 9.0 11.0 7.0 Exp. Smoothing 6 6.5 7.8 6.4 7.7 10.3 9.2 10.6 11.8 10.4 10.7 8.8 1 2 3 4 5 6 7 8 9 10 11 0 2 4 6 8 10 12 14 Demand Exp. Smoothing Forecast Naive  Naïve tracks the ups and downs best, but lags the data by one period. Thus, it gives quite large errors. Exponential smoothing is much better because it smoothens the data and does not have as much variation. 4.4
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This note was uploaded on 02/15/2012 for the course BA 252 taught by Professor Jamescampbell during the Winter '03 term at UMSL.

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chapter4solutions - CHAPTER 4 DISCUSSION QUESTIONS 3. A...

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