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| 1 | |
Good forecasts usually result by using choosing a/n |
| | A) | appropriate process involving several steps. |
| | B) | correct mathematical technique. |
| | C) | correct qualitative method. |
| | D) | experienced forecaster. |
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| 2 | |
Perfect forecasts can be obtained only by using |
| | A) | the right techniques. |
| | B) | personnel with the right experience. |
| | C) | a right blend of software and experience. |
| | D) | carefully controlled conditions, but never obtained in the real world. |
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| 3 | |
As the number of periods used in a moving average forecast increases, the forecast becomes |
| | A) | increasingly sensitive |
| | B) | much more responsive |
| | C) | very aggressive |
| | D) | less responsive |
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| 4 | |
If responsiveness to recent data is important to the forecaster, then the number of periods used in a moving average forecast should be |
| | A) | small. |
| | B) | large. |
| | C) | medium. |
| | D) | either small or large. |
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| 5 | |
As the number of periods used in a moving average forecast increases, the lag between the forecast and the actual event |
| | A) | increases. |
| | B) | decreases. |
| | C) | stays the same. |
| | D) | may increase or decrease. |
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| 6 | |
For the data (0,100), (1,200), (2,210), (3,220), (4,260), (5,330) where the first number in the parenthesis is the period and the second number is the actual demand, 3 period moving average forecast for period 6 will be equal to the |
| | A) | 270. |
| | B) | 220. |
| | C) | 275. |
| | D) | 170. |
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| 7 | |
For the data (0,100), (1,200), (2,210), (3,220), (4,260), (5,330) where the first number in the parenthesis is period and the second number is the actual demand, 4 period moving average forecast for period 6 will be equal to the |
| | A) | 250. |
| | B) | 255. |
| | C) | 270. |
| | D) | 182.5. |
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| 8 | |
For the data (0,100), (1,200), (2,210), (3,220), (4,260), (5,330) where the first number in the parenthesis is period and the second number is the actual demand, 4 period moving average forecast for period 7 will be equal to the |
| | A) | 250. |
| | B) | 255. |
| | C) | 270. |
| | D) | cannot find |
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| 9 | |
In exponential smoothing the actual weight assigned to previous data |
| | A) | decreases with the age of data. |
| | B) | increases with the age of data. |
| | C) | is independent of the age of data |
| | D) | stays the same regardless of age |
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| 10 | |
In exponential smoothing the sum of the weights assigned to previous data |
| | A) | tends to equal 1.0. |
| | B) | equals 1.0. |
| | C) | may be less than 1.0. |
| | D) | may be greater than 1.0. |
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| 11 | |
SUMPRODUCT function in excel can be used to |
| | A) | sum two columns/rows and then multiply the sum. |
| | B) | multiply numbers of one column/row with the corresponding numbers of the other column/row. |
| | C) | multiply corresponding numbers of two columns/rows and add the products. |
| | D) | either A or C since they will give the same result. |
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| 12 | |
For the data (0,100), (1,200), (2,220), (3,220), (4,266), (5,318) where the first number in the parenthesis is period and the second number is the actual demand, exponentially smoothed forecast for period 6 (F6) with initial forecast (F0) = 100 and α=0.2 is equal to |
| | A) | 206. |
| | B) | 306 |
| | C) | 318. |
| | D) | 305.7 |
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