Using the Expert Modeler to Determine Significant Predictors

A catalog company, interested in developing a forecasting model, has collected data on monthly sales of men's clothing along with several series that might be used to explain some of the variation in sales. Possible predictors include the number of catalogs mailed, the number of pages in the catalog, the number of phone lines open for ordering, the amount spent on print advertising, and the number of customer service representatives. Are any of these predictors useful for forecasting?

To know more, go to Professional Edition>Forecasting >Specifying Options for the Expert Modeler

In this example, you will use the Expert Modeler with all of the candidate predictors to find the best model. Since the Expert Modeler only selects those predictors that have a statistically significant relationship with the dependent series, you will know which predictors are useful, and you will have a model for forecasting with them. Once you are finished, you might want to work through the next example, in, , Experimenting with Predictors by Applying Saved Models, which investigates the effect on sales of different predictor scenarios using the model built in this example.

The data for the current example is collected in catalog_seasfac.sav. See the topic Sample Files for more information.

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