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Scilab Help >> Statistics > Descriptive Statistics > stdev


standard deviation (row orcolumn-wise) of vector/matrix entries

Calling Sequence

y = stdev(x)
y = stdev(x, '*')
y = stdev(x, 'r')
y = stdev(x, 'c')
y = stdev(x, orien, m)


x, y

real vector, matrix or hypermatrix


real scalar, vector or matrix


string scalar or positive integer, can be "*", "r" (or 1) or "c" (or 2)


real scalar, vector or hypermatrix, the a priori mean


stdev computes the "sample" standard deviation, that is, it is normalized by N-1, where N is the sequence length. If m is present, then stdev computes the mean squared deviation (normalized by N) using the a priori mean defined by m.

For a vector or a matrix x, y=stdev(x) returns in the scalar y the standard deviation of all the entries of x.

y=stdev(x,'r') (or, equivalently, y=stdev(x,1)) is the rowwise standard deviation. It returns in each entry of the row vector y the standard deviation of each column of x.

y=stdev(x,'c') (or, equivalently, y=stdev(x,2)) is the columnwise stdev. It returns in each entry of the column vector y the standard deviation of each row of x.

By extension, y=stdev(x,n) with n a positive integer returns the deviation along the n-th dimension, and if n>ndims(x), then stdev(x,n) returns zeros(x).

If m is a scalar, then it is expanded to match the size of mean(x) along the n-th dimension.


A = [1 2 10; 7 7.1 7.01];
stdev(A, 'r')
stdev(A, 'c')
stdev(A, 'c', mean(A,'c'))
stdev(A, 'c', 1)

See Also

  • nanstdev — standard deviation (ignoring the NANs).
  • stdevf — standard deviation
  • sum — sum of array elements
  • median — median (row median, column median,...) of vector/matrix/array entries
  • mean — mean (row mean, column mean) of vector/matrix entries


5.5.0 Can now compute the mean squared deviation using the a priori mean defined by m
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Last updated:
Fri Apr 11 14:06:53 CEST 2014