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What is Mahalanobis Distance for Outlier Detection about?

This document discusses methods for detecting outliers in univariate and multivariate data. It describes how to identify univariate outliers by converting values to standard scores and flagging cases beyond ±2.5 or ±3 standard deviations. It also explains that Mahalanobis D2 can identify multivariate outliers as cases beyond a chi-square probability of 0.001, measuring each case

Author

estoy.registrando5852

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EN