# var

var(v[, region])

Compute the sample variance of a vector or array `v`, optionally along dimensions in `region`. The algorithm will return an estimator of the generative distribution's variance under the assumption that each entry of `v` is an IID drawn from that generative distribution. This computation is equivalent to calculating `sumabs2(v - mean(v)) / (length(v) - 1)`. Note: Julia does not ignore `NaN` values in the computation. For applications requiring the handling of missing data, the `DataArray` package is recommended.

## Examples

1. Compute sample variance of a vector:

``````julia> v = [1, 2, 3, 4, 5];
julia> var(v)
2.5``````

This example calculates the sample variance of the vector `v`.

2. Compute sample variance along a specific dimension of an array:

``````julia> A = [1 2 3; 4 5 6; 7 8 9];
julia> var(A, 1)
6-element Array{Float64,1}:
9.0
9.0
9.0``````

Here, the `var` function calculates the sample variance along the first dimension (`1`) of the array `A`.

3. Compute sample variance along multiple dimensions of an array:
``````julia> B = [1 2 3; 4 5 6; 7 8 9];
julia> var(B, (1, 2))
1-element Array{Float64,1}:
6.666666666666667``````

In this example, the `var` function calculates the sample variance along both dimensions (`1` and `2`) of the array `B`.

Note: Julia does not ignore `NaN` values in the computation. If your data contains `NaN` values and you want to handle missing data, it is recommended to use the `DataArray` package.