It reduces on the familiar Euclidean distance for uncorrelated variables with device variance. For univariate typical facts, the univariate z-rating standardizes the distribution (making sure that it's suggest 0 and device variance) and gives a dimensionless quantity that specifies the gap from an observation towards the indicate in terms of the dimensions of the data.
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For many distributions, such look at here now as the normal distribution, this option of scale also can make a statement about probability. Especially, it is more possible to look at an observation which is about just one conventional deviation within the indicate than it can be to watch one that is many standard deviations absent.
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As a result for those who divide by k you will get a "indicate squared deviation." On the other hand, discover that this differs from the usual MSD for regression residuals: in regression you'd probably divide by N, not k.