Forecast that reflect very little happenstance fluctuation in the past data are said to exhibit
Question 1
- Forecast that reflect very little happenstance fluctuation in the past data are said to exhibit
[removed] | 1. | Seasonal effects |
[removed] | 2. | noise dampening response |
[removed] | 3. | impulse response |
[removed] | 4. | all of the above |
[removed] | 5. | none of the above |
5 points
Question 2
- A Winter's forecasting model that has zero values for the beta and gamma constants exhibit what type of behavior
[removed] | 1. | A simple exponential smoothing model |
[removed] | 2. | Impulse response |
[removed] | 3. | Noise dampening |
[removed] | 4. | all of the above |
[removed] | 5. | none of the above |
5 points
Question 3
- In measuring forecast accuracy, the average of the absolute difference between the forecast and the actual demand is called
[removed] | 1. | alpha |
[removed] | 2. | E-bar |
[removed] | 3. | MAD |
[removed] | 4. | all of the above |
[removed] | 5. | none of the above |
5 points
Question 4
- Choice the best type of forecasting methods for the type of data indicated
trend data that fits in a straight line |
| ||
| |||
short range forecast with no trends or seasonal effects | |
random data with no seasonal effects or trends | |
random data that illustrates a trend or seasonal pattern | |
|
|
20 points
Question 5
- In order to establish a forecast method that exhibits impulse response;
[removed] | 1. | an exponential smoothing forecast method should be used |
[removed] | 2. | the data must be linear |
[removed] | 3. | The alpha coefficient should be set close to 1 for exponential smoothing |
[removed] | 4. | The alpha coefficient should be set close to 0 for exponential smoothing |
[removed] | 5. | None of the above |
5 points
Question 6
- Refer to the data in table 1 posted in the discussion folder. Using the data, what is the forecast for November if a three month moving average model is used?
[removed] | 1. | 49.25 |
[removed] | 2. | 50.67 |
[removed] | 3. | 53.00 |
[removed] | 4. | none of the above |
5 points
Question 7
- Refer to the data in table 1 posted in the discussion folder. Using the data, which month has a demand forecast equal to 55 for a 3 month moving average approach
[removed] | 1. | April |
[removed] | 2. | June |
[removed] | 3. | August |
[removed] | 4. | October |
[removed] | 5. | None of the above |
5 points
Question 8
- Refer to the data in table 1 posted in the discussion folder. Using the data, what is the November forecast if exponential smoothing is used with an alpha value = .1
[removed] | 1. | 47.9 |
[removed] | 2. | 53.2 |
[removed] | 3. | 40.8 |
[removed] | 4. | 51.6 |
5 points
Question 9
- Refer to the data, table 1, from the discussion folder. Using this data, what is the forecast error % for an exponential smoothing model with a alpha of .6
[removed] | 1. | 10% |
[removed] | 2. | 12% |
[removed] | 3. | 14% |
[removed] | 4. | 16% |
[removed] | 5. | 18% |
5 points
Question 10
- Forecasting models are an integral part of business planning that requies input from
[removed] | 1. | marketing |
[removed] | 2. | demand estimates |
[removed] | 3. | sales forecast |
[removed] | 4. | all of the above |
[removed] | 5. | none of the above |
5 points
Question 11
- The alpha coefficient in exponential smothing
[removed] | 1. | is set equal to the actual value in period 1 |
[removed] | 2. | varies over a time series of data |
[removed] | 3. | is a value between 0 and 1 |
[removed] | 4. | all of the above |
[removed] | 5. | none of the above |
5 points
Question 12
- Quarterly data which reflect an increase every fourth quarter followed by a decrease every first quarter are said to be
[removed] | 1. | seasonal |
[removed] | 2. | cyclical |
[removed] | 3. | periodical |
[removed] | 4. | abnormal |
[removed] | 5. | following a trend |
5 points
Question 13
- To deseasonalize time series data
[removed] | 1. | divide each actual value by the trend line intercept |
[removed] | 2. | divide each actual value by its seasonal index factor |
[removed] | 3. | divide each actual value by total forecast error |
[removed] | 4. | divide each actual value by the alpha coefficient |
5 points
Question 14
- A linear trend for 12 months of data is y = 339.02 + 23.96x. What is the forecast for the next quarter (January, Feruary and March)?
[removed] | 1. | 1160.82 |
[removed] | 2. | 1807.74 |
[removed] | 3. | 2023.38 |
[removed] | 4. | 3641.59 |
5 points
Question 15
- Refer to the data in table 1 posted in the discussion folder. Using the data, what is the MAD for an exponential smoothing model with alpha = .1
[removed] | 1. | 6.2 |
[removed] | 2. | 7.7 |
[removed] | 3. | 8.3 |
[removed] | 4. | 8.8 |
5 points
Question 16
- The delphi method of forecasting is
[removed] | 1. | time series method for detecting seasonality |
[removed] | 2. | variation of exponential smoothing method |
[removed] | 3. | multiple regression method |
[removed] | 4. | qualitative method which solicits from experts |
[removed] | 5. | qualitative method for researching similar to data |
5 points
Question 17
- The ideal value of MAD is
[removed] | 1. | 0 |
[removed] | 2. | 100 |
[removed] | 3. | 10 |
[removed] | 4. | none of the above |
5 points
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