std

 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