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1 | |
Accurate forecasting can be done with inaccurate historical data, if the forecasting model is a good one. |
| | A) | True |
| | B) | False |
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2 | |
Aggregated (grouped) data frequently generate better forecasts than non-aggregated data. |
| | A) | True |
| | B) | False |
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3 | |
If a particular season of the year shows greater than average sales, the seasonal relative for that season is greater than 1.00. |
| | A) | True |
| | B) | False |
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4 | |
The Delphi technique is a forecasting model that incorporates the use of multiple regression. |
| | A) | True |
| | B) | False |
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5 | |
In a good forecast, about half of the errors, should be randomly scattered above zero and half below zero. |
| | A) | True |
| | B) | False |
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6 | |
Double exponential smoothing can be used if trend is present in data. |
| | A) | True |
| | B) | False |
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7 | |
Seasonality refers to data patterns that recur every year (or every week, or every month, etc.) at about the same time. |
| | A) | True |
| | B) | False |
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8 | |
Which of the following forecasting techniques generates trend forecasts? |
| | A) | Delphi method |
| | B) | Sales force composites |
| | C) | Moving averages |
| | D) | Single exponential smoothing |
| | E) | None of the above |
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9 | |
For this set of errors: - 1, + 4, 0, + 2, + 3, MAD is: |
| | A) | 1.0 |
| | B) | 1.6 |
| | C) | 2.0 |
| | D) | 2.5 |
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10 | |
Which probability distribution is used most extensively in dealing with forecasting errors? |
| | A) | Normal |
| | B) | Poisson |
| | C) | Exponential |
| | D) | Beta |
| | E) | Pareto |
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11 | |
The cumulative forecast error is important for determining the: |
| | A) | Mean squared error. |
| | B) | Bias in forecast error. |
| | C) | Mean absolute deviation. |
| | D) | Control limits |
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12 | |
Of these values, the value of that would track the data most closely is: |
| | A) | 0 |
| | B) | .01 |
| | C) | .10 |
| | D) | .20 |
| | E) | .30 |
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13 | |
Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast? |
| | A) | 0 |
| | B) | .01 |
| | C) | .1 |
| | D) | .5 |
| | E) | 1.0 |
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14 | |
Simple exponential smoothing is being used to forecast demand. The previous forecast of 66 turned out to be four units less than actual demand. The next forecast is 66.6, implying a smoothing constant, alpha, equal to: |
| | A) | .01 |
| | B) | .10 |
| | C) | .15 |
| | D) | .20 |
| | E) | .60 |
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