ZSCORE removes the mean and normalizes the data to a variance of 1. Can be used for Pre-Whitening of the data, too. [z,r,m] = zscore(x,DIM) z z-score of x along dimension DIM r is the inverse of the standard deviation m is the mean of x The data x can be reconstrated with x = z*diag(1./r) + repmat(m,size(z)./size(m)) z = x*diag(r) - repmat(m.*v,size(z)./size(m)) DIM dimension 1: STATS of columns 2: STATS of rows default or []: first DIMENSION, with more than 1 element see also: SUMSKIPNAN, MEAN, STD, DETREND REFERENCE(S): [1] http://mathworld.wolfram.com/z-Score.html

- mean MEAN calculates the mean of data elements.

- naninsttest NANINSTTEST checks whether the functions from NaN-toolbox have been

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