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1) you Examine Each and every info level from your sample set to mu and sigma matrices calculated out of your reference distribution (Whilst labeling a single cluster sample established and one other reference distribution could be arbitrary), therefore calculating the gap from Each individual stage to this so referred to as mahalanobis-centroid on the reference distribution.
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Reply Rick Wicklin on September 14, 2012 8:52 am They may be carefully relevant. Mahalanobis distance is actually a strategy for measuring distance that accounts for correlation in between variables. In multivariate speculation screening, the Mahalanobis length is used to construct test statistics. For instance, In case you have a random sample and you hypothesize that the multivariate mean of the population is mu0, it is normal to take into account the Mahalanobis length among xbar (the sample suggest) and mu0.
Lots of discriminant algorithms utilize the Mahalanobis distance, or you can use logistic regression, which might be my choice. Whilst you could potentially get it done "by hand," you'd probably be better off working with a conventional algorithm. If you want help, write-up your issue and sample details for the SAS Aid Communities.
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