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
Famous quotes containing the words model and/or accuracy:
“Socrates, who was a perfect model in all great qualities, ... hit on a body and face so ugly and so incongruous with the beauty of his soul, he who was so madly in love with beauty.”
—Michel de Montaigne (1533–1592)
“In everything from athletic ability to popularity to looks, brains, and clothes, children rank themselves against others. At this age [7 and 8], children can tell you with amazing accuracy who has the coolest clothes, who tells the biggest lies, who is the best reader, who runs the fastest, and who is the most popular boy in the third grade.”
—Stanley I. Greenspan (20th century)