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I comprehend from the above that a Euclidean distance utilizing all PCs will be comparable to the Mahalanobis length nevertheless it occasionally isn't crystal clear that utilizing the PCs with incredibly small eigenvalues is desirable. Many thanks
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In each of such programs, you utilize the Mahalanobis distance along with the chi-sq. distribution operate to attract conclusions.
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As a result in case you divide by k you get a "imply squared deviation." Even so, observe this differs from the standard MSD for regression residuals: in regression you'd probably divide by N, not k.