# 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)

## Arguments

x, y

  real vector, matrix or hypermatrix

y

  real scalar, vector or matrix

orien

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

m

  real scalar, vector or hypermatrix, the a priori mean

## Description

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.

## Examples

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

## See Also

- nanstdev
- stdevf
- sum
- median
- mean

## History

5.5.0
Can now compute the mean squared deviation using the a priori mean defined by `m`