Please note that the recommended version of Scilab is 2023.1.0. This page might be outdated.
See the recommended documentation of this function
stdev
standard deviation (row or columnwise) of vector/matrix entries
Syntax
y = stdev(x) y = stdev(x, '*') y = stdev(x, 'r'1) y = stdev(x, 'c'2) y = stdev(x, orien, m)
Arguments
 x, y
real vector, matrix or hypermatrix
 y
real scalar, vector or matrix
 orien
"*"
(default),"r"
or 1,"c"
or 2, or 0<integer<=ndims(x): direction along which calculations are performed. m
real scalar, vector or hypermatrix, the a priori mean
Description
stdev computes the "sample" standard deviation, that
is, it is normalized by N1, 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.

Examples
A = [1 2 10; 7 7.1 7.01]; stdev(A) stdev(A, 'r') stdev(A, 'c') stdev(A, 2 ) // Deviation from a known (apriori, builtin) mean: A = grand(10, 10, "nor", 7.5, 3); stdev(A) / 3 // unknown mean => assessed from A before computing stdev stdev(A, '*', 7.5) / 3 // using the theoretical builtin mean // With an hypermatrix: A = grand(3, 5, 30, "nor", 4.1, 1.5); stdev(A) / 1.5 sd = stdev(A, 3, 4.1) / 1.5 mean(sd)
See also
History
Version  Description 
5.5.0  Can now compute the mean squared deviation using the a priori mean defined by m 
6.0.0  stdev(x, orien>ndims(x)) no longer returns zeros(x) but yields an error. 
6.0.1  stdev() is now overloadable. 
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