 |
1 | |
Accurate forecasting can be done with inaccurate historical data. If the forecasting model is a good one, it will improve the input used. |
|
| | A) | True |
| | B) | False |
|
2 | |
Aggregated (grouped) data frequently generate better forecasts than non-aggregated data used to forecast individual items. |
|
| | A) | True |
| | B) | False |
|
3 | |
If quantitative data is available on which to base a forecast, it is unnecessary to consider qualitative information. |
|
| | A) | True |
| | B) | False |
|
4 | |
The Delphi technique is a forecasting model, developed in India, which incorporates the use of multiple regression. |
|
| | A) | True |
| | B) | False |
|
5 | |
In a good forecast, about half of the forecast misses should be randomly scattered above the actual results and half below the actual results. |
|
| | A) | True |
| | B) | False |
|
6 | |
Double exponential smoothing can only be used if there is no observable trend in the data. |
|
| | A) | True |
| | B) | False |
|
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 |
|
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 |
|
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 |
| | E) | 10.0 |
|
10 | |
Which probability distribution is used most extensively in dealing with forecasting errors? |
|
| | A) | Normal |
| | B) | Poisson |
| | C) | Exponential |
| | D) | Beta |
| | E) | Pareto |
|
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 |
| | E) | Correlation coefficient |
|
12 | |
When we use exponential smoothing for forecasting, the alpha value (smoothing constant) that would give the greatest weight to the current actuals would be: |
|
| | A) | 0 |
| | B) | .01 |
| | C) | .10 |
| | D) | .20 |
| | E) | .30 |
|
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 |
|
14 | |
Simple exponential smoothing is being used to forecast demanThe previous forecast of 66 turned out to be six units less than actual demanThe next forecast is 66.9, implying a smoothing constant, alpha, equal to: |
|
| | A) | .01 |
| | B) | .10 |
| | C) | .15 |
| | D) | .20 |
| | E) | .60 |
|
15 | |
The temperature on Tuesday was 80 degrees, on Wednesday it was 82 degrees, on Thursday it was 78 degrees. A naive forecast for the temperature on Friday would be: |
|
| | A) | 78 |
| | B) | 80 |
| | C) | 82 |
| | D) | 84 |
| | E) | Can't tell from the data given |