Chapter 18: Forecasting

The trend component must be linear.
1. True
2. False
If historical data are not available, one would forecast using a
1. qualitative approach
2. quantitative approach
The price of an item is graphed. Over time, there has been a general increase in price, possibly due to inflation. The time series component used to explain the long term increase is the
1. cyclical component
2. irregular component
3. seasonal component
4. trend component
The sales of appliance manufacturers are tied closely to the status of the economy. If the economy is doing well, in general, sales are better. The sales for an appliance manufacturer time series would show a significant
1. cyclical component
2. irregular component
3. seasonal component
4. trend component
The electricity use in Wisconsin time series peaks in July and August as the use of air conditioning increases. The increase at approximately the same time every summer is best explained by the
1. cyclical component
2. irregular component
3. seasonal component
4. trend component
The components that are usually considered predictable are the
1. cyclical and irregular components
2. trend, cyclical and irregular components
3. trend and seasonal components
4. trend, seasonal and irregular components
5. None of the above
The component that must be in every time series is the
1. cyclical component
2. irregular component
3. seasonal component
4. trend component
The moving average forecasting model presented in the text is appropriate for a time series with the following component(s)
1. Irregular
2. Trend and irregular
3. Trend, cyclical and irregular
4. Trend, seasonal, cyclical, and irregular
The exponential forecasting model presented in the text is appropriate for a time series with the following component(s)
1. Irregular
2. Trend and irregular
3. Trend, cyclical and irregular
4. Trend, seasonal, cyclical, and irregular
In forecasting, the purpose of the mean squared error is to
1. be an unbiased estimator of the within treatment variance
2. be the nemesis of the nice squared error
3. choose between two or more models
4. smooth a time series
Given the following time series

give the 3-period moving average forecast for period 7.
1. 53
2. 54.333
3. 54.667
4. 56
Given the following time series

give the 3-period weighted moving average forecast for period 7 giving a weight of 1/2 to the most recent period, a weight of 1/3 to the second most recent period, and a weight of 1/6 to the third most recent period.
1. 53
2. 54.333
3. 54.667
4. 56
Given the following time series

For the 3-period moving average forecasting model, the mean squared error is
1. 2.296
2. 4
3. 5.5
4. 7.778
Given the following time series

For the 3-period weighted moving average forecasting model with a weight of 1/2 to the most recent period, a weight of 1/3 to the second most recent period, and a weight of 1/6 to the third most recent period, the mean squared error is
1. 2.296
2. 4
3. 5.5
4. 7.778
Given the following time series

give the exponential smoothing forecast for period 7.
1. 53.811
2. 54.333
3. 54.667
4. 56.688