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| Statistical model validation ： ウィキペディア英語版 |
Regression validation
In statistics, regression validation is the process of deciding whether the numerical results quantifying hypothesized relationships between variables, obtained from regression analysis, are acceptable as descriptions of the data. The validation process can involve analyzing the goodness of fit of the regression, analyzing whether the regression residuals are random, and checking whether the model's predictive performance deteriorates substantially when applied to data that were not used in model estimation.
==Validation using ''R''2 ==
An ''R''2 (coefficient of determination) close to one does not guarantee that the model fits the data well, because as Anscombe's quartet shows, a high ''R''2 can occur in the presence of misspecification of the functional form of a relationship or in the presence of outliers that distort the true relationship.
One problem with the ''R''2 as a measure of model validity is that it can always be increased by adding more variables into the model, except in the unlikely event that the additional variables are exactly uncorrelated with the dependent variable in the data sample being used.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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