STD calculates the standard deviation. [y,v] = std(x [, opt[, DIM [, W]]]) opt option 0: normalizes with N-1 [default] provides the square root of best unbiased estimator of the variance 1: normalizes with N, this provides the square root of the second moment around the mean otherwise: best unbiased estimator of the standard deviation (see [1]) DIM dimension N STD of N-th dimension default or []: first DIMENSION, with more than 1 element W weights to compute weighted s.d. (default: []) if W=[], all weights are 1. number of elements in W must match size(x,DIM) y estimated standard deviation features: - provides an unbiased estimation of the S.D. - can deal with NaN's (missing values) - weighting of data - dimension argument also in Octave - compatible to Matlab and Octave see also: RMS, SUMSKIPNAN, MEAN, VAR, MEANSQ, References(s): [1] http://mathworld.wolfram.com/StandardDeviationDistribution.html

- center CENTER removes the mean
- sumskipnan SUMSKIPNAN adds all non-NaN values.

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

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