Abstract
Regression is one of the most widely used of all statistical methods. For univariate regression, the available data are one response variable and p predictor variables, all measured on each of n observations. We let Y denote the response variable and \(X_{1},\ldots,X_{p}\) be the predictor or explanatory variables.
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Notes
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When comparing models with the same number of parameters, all three criteria are optimized by the same model.
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Ruppert, D., Matteson, D.S. (2015). Regression: Basics. In: Statistics and Data Analysis for Financial Engineering. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2614-5_9
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