Error sum of squares and power comparisons for model search, identification and discrimination

The model search, identification, and discrimination between models within a specified class of models are considered for finding the best model in describing the data. Two criterion functions, error sum of squares and power for testing the uncommon parameter in the models, are used for this purpose. Some general results with special reference to 2m fractional factorial plans are presented.