CHAPTER 11
FORECASTING MODELS
SOLUTIONS TO DISCUSSION QUESTIONS
1. Determine the use of the forecast.
3. Determine the time horizon of the forecast.
5. Gather the necessary data.
7. Make the forecast.
11-2. A time-series forecasting model uses historical data to predict future trends.
11-3. The only difference between causal models and time-series models is that causal models take into
11-4. Qualitative models incorporate subjective factors into the forecasting model. Judgmental models are
11-6. The disadvantages of the moving average forecasting model are that the averages always stay within
past levels, and the moving averages require extensive record keeping of past data.
11-8. The Delphi technique involves analyzing the predictions that a group of experts have made, and
11-9. MAPE is a measure for determining the accuracy of a forecasting model by taking the average of
11-10. We can draw line plots of the actual and forecast values for each observation (or time period).
Such line plots are automatically drawn by most forecasting software including ExcelModules. The line
11-11. The correlation coefficient is a measure of the strength of the linear relationship between two
variables. That is, it measures how one variable’s value is linearly related to changes in the value of the
11-12. In order to use Solver to determine the optimal weights in the weighted moving average model, we
set the weights as the decision variables (or Changing Cells). The objective (or Target Cell) is the