STD    Standard deviation.
    For vectors, STD(X) returns the standard deviation. For matrices,
    STD(X) is a row vector containing the standard deviation of each
    column.  For N-D arrays, STD(X) is the standard deviation of the
    elements along the first non-singleton dimension of X.
 
    STD(X) normalizes by (N-1) where N is the sequence length.  This
    makes STD(X).^2 the best unbiased estimate of the variance if X
    is a sample from a normal distribution.
 
    STD(X,1) normalizes by N and produces the square root of the
    second moment of the sample about its mean.  STD(X,0) is the
    same as STD(X).
 
    STD(X,FLAG,DIM) takes the standard deviation along the dimension
    DIM of X.  When FLAG=0 STD normalizes by (N-1), otherwise STD
    normalizes by N.
 
    Example: If X = [4 -2 1
                     9  5 7]
      then std(X,0,1) is [ 3.5355 4.9497 4.2426] and std(X,0,2) is [3.0
                                                                    2.0]
    See also COV, MEAN, VAR, MEDIAN, CORRCOEF.