Gerencia operacional
Páginas: 8 (1901 palabras)
Publicado: 12 de febrero de 2012
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 PREDICTFUTURE 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 theobserved 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, , is large (close to 1.0), more weight is given to recentdata; when 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 CONSISTENTPATTERN.
| | |Year |1 |
| |1 | 7 |5 |
| |2 | 9 |[pic] |
| |3 | 5 |[pic] |
| |4 | 9|[pic] |
| |5 |13 |[pic] |
| |6 | 8 |[pic] |
| |7 |Forecast |[pic] |
|4.6 | |Y Sales |X Period |[pic]|XY |
| |January | 20 | 1 | 1 | 20 |
| |February | 21 | 2 | 4 | 42 |
| |March | 15 | 3 | 9 | 45 |
| |April | 14 | 4 | 16 | 56|
| |May | 13 | 5 | 25 | 65 |
| |June | 16 | 6 | 36 | 96 |
| |July | 17 | 7 | 49 | 119 |
| |August | 18 | 8 | 64 | 144 |
||September | 20 | 9 | 81 | 180 |
| |October | 20 |10 |100 | 200 |
| |November | 21 |11 |121 | 231 |
| |December | 23 |12 |144 | 276 |
| |Sum...
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 PREDICTFUTURE 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 theobserved 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, , is large (close to 1.0), more weight is given to recentdata; when 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 CONSISTENTPATTERN.
| | |Year |1 |
| |1 | 7 |5 |
| |2 | 9 |[pic] |
| |3 | 5 |[pic] |
| |4 | 9|[pic] |
| |5 |13 |[pic] |
| |6 | 8 |[pic] |
| |7 |Forecast |[pic] |
|4.6 | |Y Sales |X Period |[pic]|XY |
| |January | 20 | 1 | 1 | 20 |
| |February | 21 | 2 | 4 | 42 |
| |March | 15 | 3 | 9 | 45 |
| |April | 14 | 4 | 16 | 56|
| |May | 13 | 5 | 25 | 65 |
| |June | 16 | 6 | 36 | 96 |
| |July | 17 | 7 | 49 | 119 |
| |August | 18 | 8 | 64 | 144 |
||September | 20 | 9 | 81 | 180 |
| |October | 20 |10 |100 | 200 |
| |November | 21 |11 |121 | 231 |
| |December | 23 |12 |144 | 276 |
| |Sum...
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