Model Accuracy
A way to test for errors in models created by step-wise regression, is to not rely on the model's F-statistic, significance, or multiple-r, but instead assess the model against a set of data that was not used to create the model. This is often done by building a model based on a sample of the dataset available (e.g., 70%) and use the remaining 30% dataset to assess the accuracy of the model. Accuracy is then often measured as the actual standard error (Se), MAPE, or mean error between the predicted value and the actual value in the hold-out sample. This method is particularly valuable when data is collected in different settings (e.g., time, social) or when models are assumed to be generalizable.
Read more about this topic: Stepwise Regression
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