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The squared cross-validity coefficient is a measure of the predictive validity of a sample linear prediction equation. It provides a more realistic assessment of the usefulness of the equation than the squared multiple-correlation coefficient. The squared cross-validity coefficient cannot be larger than the squared multiple-correlation coefficient; its size is affected by the number of predictor variables and the size of the sample. Sample-size tables are presented that should result in very small discrepancies between the squared multiple correlation and the squared cross-validity correlation, thus facilitating the selection of sample size for predictive studies.
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Algina et al. (2000) studied this question.
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