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In statistics, we sometimes measure "nearness" or "farness" when it comes to the size of the info. Normally "scale" signifies "conventional deviation." For univariate details, we declare that an observation that's just one typical deviation with the signify is nearer into the necessarily mean than an observation that may be three normal deviations away.
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Perhaps you could locate it in the textbook that discusses Hotelling's T^2 statistic, which works by using a similar computation. If you can't locate it in print, you are able to normally investigate this site cite my blog site, which has been cited in several publications, papers, and perhaps by Wikipedia.
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For that reason if you divide by k you will get a "indicate squared deviation." Even so, discover that this differs from the usual MSD for regression residuals: in regression you'd divide by N, not k.