Forecast that reflect very little happenstance fluctuation in the past data are said to exhibit
Question 1
1. Forecast that reflect very little happenstance fluctuation in the past data are said to exhibit
|
|
1. |
Seasonal effects |
|
|
2. |
noise dampening response |
|
|
3. |
impulse response |
|
|
4. |
all of the above |
|
|
5. |
none of the above |
5 points
Question 2
1. A Winter's forecasting model that has zero values for the beta and gamma constants exhibit what type of behavior
|
|
1. |
A simple exponential smoothing model |
|
|
2. |
Impulse response |
|
|
3. |
Noise dampening |
|
|
4. |
all of the above |
|
|
5. |
none of the above |
5 points
Question 3
1. In measuring forecast accuracy, the average of the absolute difference between the forecast and the actual demand is called
|
|
1. |
alpha |
|
|
2. |
E-bar |
|
|
3. |
MAD |
|
|
4. |
all of the above |
|
|
5. |
none of the above |
5 points
Question 4
1. Choice the best type of forecasting methods for the type of data indicated
|
|
|
3. |
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 |
|
|
|
1.
2.
|
|
20 points
Question 5
1. In order to establish a forecast method that exhibits impulse response;
|
|
1. |
an exponential smoothing forecast method should be used |
|
|
2. |
the data must be linear |
|
|
3. |
The alpha coefficient should be set close to 1 for exponential smoothing |
|
|
4. |
The alpha coefficient should be set close to 0 for exponential smoothing |
|
|
5. |
None of the above |
5 points
Question 6
1. 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?
|
|
1. |
49.25 |
|
|
2. |
50.67 |
|
|
3. |
53.00 |
|
|
4. |
none of the above |
5 points
Question 7
1. 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
|
|
1. |
April |
|
|
2. |
June |
|
|
3. |
August |
|
|
4. |
October |
|
|
5. |
None of the above |
5 points
Question 8
1. 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
|
|
1. |
47.9 |
|
|
2. |
53.2 |
|
|
3. |
40.8 |
|
|
4. |
51.6 |
5 points
Question 9
1. 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
|
|
1. |
10% |
|
|
2. |
12% |
|
|
3. |
14% |
|
|
4. |
16% |
|
|
5. |
18% |
5 points
Question 10
1. Forecasting models are an integral part of business planning that requies input from
|
|
1. |
marketing |
|
|
2. |
demand estimates |
|
|
3. |
sales forecast |
|
|
4. |
all of the above |
|
|
5. |
none of the above |
5 points
Question 11
1. The alpha coefficient in exponential smothing
|
|
1. |
is set equal to the actual value in period 1 |
|
|
2. |
varies over a time series of data |
|
|
3. |
is a value between 0 and 1 |
|
|
4. |
all of the above |
|
|
5. |
none of the above |
5 points
Question 12
1. Quarterly data which reflect an increase every fourth quarter followed by a decrease every first quarter are said to be
|
|
1. |
seasonal |
|
|
2. |
cyclical |
|
|
3. |
periodical |
|
|
4. |
abnormal |
|
|
5. |
following a trend |
5 points
Question 13
1. To deseasonalize time series data
|
|
1. |
divide each actual value by the trend line intercept |
|
|
2. |
divide each actual value by its seasonal index factor |
|
|
3. |
divide each actual value by total forecast error |
|
|
4. |
divide each actual value by the alpha coefficient |
5 points
Question 14
1. 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)?
|
|
1. |
1160.82 |
|
|
2. |
1807.74 |
|
|
3. |
2023.38 |
|
|
4. |
3641.59 |
5 points
Question 15
1. 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
|
|
1. |
6.2 |
|
|
2. |
7.7 |
|
|
3. |
8.3 |
|
|
4. |
8.8 |
5 points
Question 16
1. The delphi method of forecasting is
|
|
1. |
time series method for detecting seasonality |
|
|
2. |
variation of exponential smoothing method |
|
|
3. |
multiple regression method |
|
|
4. |
qualitative method which solicits from experts |
|
|
5. |
qualitative method for researching similar to data |
5 points
Question 17
1. The ideal value of MAD is
|
|
1. |
0 |
|
|
2. |
100 |
|
|
3. |
10 |
|
|
4. |
none of the above |
5 points
Click Save and Submit to save and submit. Click Save All Answers to save all answers.