Running the Analysis

  1. From the menus choose:

    Analyze > Forecasting > Apply Traditional Models...

    Figure 1. Apply Time Series Models dialog box
    Apply Time Series Models dialog box
  2. Click Browse, then navigate to and select catalog_model.xml, or choose your own model file (saved from the previous example). See the topic Sample Files for more information.
  3. In the Forecast Period group, select First case after end of estimation period through a specified date.
  4. In the Date grid, enter 1999 for the year and 3 for the month.
  5. Click the Statistics tab.
    Figure 2. Apply Time Series Models, Statistics tab
    Apply Time Series Models, Statistics tab
  6. Select Display forecasts.

    This results in a table of forecasted values for the dependent variable.

  7. Click OK in the Apply Time Series Models dialog box.
    Figure 3. Forecast table
    Forecast table

    The forecast table contains the predicted values of the dependent series, taking into account the values of the two predictors mail and phone in the forecast period. The table also includes the upper confidence limit (UCL) and lower confidence limit (LCL) for the predictions.

    You've produced the sales forecast for the scenario of mailing 2000 more catalogs each month. You'll now want to prepare the data for the scenario of increasing the number of phone lines, which means resetting the variable mail to the original values and increasing the variable phone by 5. You can reset mail by copying the values of Predicted_mail_Model_1 in the forecast period and pasting them over the current values of mail in the forecast period. And you can increase the number of phone lines--by 5 for each month in the forecast period--either directly in the data editor or using the Compute Variable dialog box, like we did for the number of catalogs.

    To run the analysis, reopen the Apply Time Series Models dialog box as follows:

  8. Click the Dialog Recall toolbar button.
  9. Choose Apply Time Series Models.
  10. Click OK in the Apply Time Series Models dialog box.
Figure 4. Forecast tables for the two scenarios
Forecast tables for the two scenarios

Displaying the forecast tables for both scenarios shows that, in each of the three forecasted months, increasing the number of mailed catalogs is expected to generate approximately $1500 more in sales than increasing the number of phone lines that are open for ordering. Based on the analysis, it seems wise to allocate resources to the mailing of 2000 additional catalogs.