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# stdev

standard deviation (row orcolumn-wise) 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 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.

### 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 (a-priori, built-in) 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 built-in 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 | stdev(x, orien>ndims(x)) no longer returns zeros(x) but yields an error. |

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