• RSquare Validation (also shown in the Step History report)
 • RMSE Validation
 • RSquare Test (if there is a test set)
 • RMSE Test (if there is a test set)
 • RSquare Validation (also shown in the Step History report)
 • Avg Log Error Validation
 • RSquare Test (if there is a test set)
 • Avg Log Error Test (if there is a test set)
Note: Max Validation RSquare considers only the models defined by p-value entry (Forward direction) or removal (Backward direction). It does not consider all possible models.
 – For each observation in the validation set, compute the prediction error. This is the difference between the actual response and the response predicted by the training set model.
 – Square and sum the prediction errors to obtain SSEValidation.
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 – RSquare Validation is:
 – For each observation in the validation set, compute the prediction error. This is the difference between the actual response and the response predicted by the training set model.
 – Square and sum the prediction errors to obtain the SSEValidation.
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 – RMSE Validation is:
An Entropy RSquare measure (also known as McFadden’s R2) for the validation set computed as follows:
 – A model is fit using the training set.
 – Predicted probabilities are obtained for all observations.
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 – RSquare Validation is:
 – For each observation in the validation set, compute the log of its predicted probability as determined by the model based on the training set.
 – Sum these logs, divide by the number of observations in the validation set, and take the negative of the resulting value.

Help created on 9/19/2017