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Kaiser’s Rule

A rule often used in principal components analysis for selecting the appropriate number of components. When the components are derived from the correlation matrix of the observed variables, the rule suggests retaining only those components with eigenvalues (variances) greater than one.

Kaiser’s Rule

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