Cross validation of a regression equation using a different sample or a random subset of a large sample is especially valuable for which of the following?

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Quizplus · Accepted Answer

Cross validation is particularly important for stepwise regression because it involves selecting variables based on their statistical significance, which may lead to overfitting or underfitting the model to the data. Cross validation helps to assess the generalizability of the model to a new dataset or a subset of the original dataset. Cross validation is less important for standard regression or hierarchical regression, which involve specifying a fixed set of predictor variables in the model. Simultaneous regression is a less common approach to regression analysis and is not specifically targeted by cross validation.

Cross Validation of a Regression Equation Using a Different Sample