# Function variance Compute the variance of a matrix or a list with values. In case of a (multi dimensional) array or matrix, the variance over all elements will be calculated. Additionally, it is possible to compute the variance along the rows or columns of a matrix by specifying the dimension as the second argument. Optionally, the type of normalization can be specified as the final parameter. The parameter `normalization` can be one of the following values: - 'unbiased' (default) The sum of squared errors is divided by (n - 1) - 'uncorrected' The sum of squared errors is divided by n - 'biased' The sum of squared errors is divided by (n + 1) Note that older browser may not like the variable name `var`. In that case, the function can be called as `math['var'](...)` instead of `math.var(...)`. ## Syntax ```js math.variance(a, b, c, ...) math.variance(A) math.variance(A, normalization) math.variance(A, dimension) math.variance(A, dimension, normalization) ``` ### Parameters Parameter | Type | Description --------- | ---- | ----------- `array` | Array | Matrix | A single matrix or or multiple scalar values `normalization` | string | Determines how to normalize the variance. Choose 'unbiased' (default), 'uncorrected', or 'biased'. Default value: 'unbiased'. ### Returns Type | Description ---- | ----------- * | The variance ## Examples ```js math.variance(2, 4, 6) // returns 4 math.variance([2, 4, 6, 8]) // returns 6.666666666666667 math.variance([2, 4, 6, 8], 'uncorrected') // returns 5 math.variance([2, 4, 6, 8], 'biased') // returns 4 math.variance([[1, 2, 3], [4, 5, 6]]) // returns 3.5 math.variance([[1, 2, 3], [4, 6, 8]], 0) // returns [4.5, 8, 12.5] math.variance([[1, 2, 3], [4, 6, 8]], 1) // returns [1, 4] math.variance([[1, 2, 3], [4, 6, 8]], 1, 'biased') // returns [0.5, 2] ``` ## See also [mean](mean.md), [median](median.md), [max](max.md), [min](min.md), [prod](prod.md), [std](std.md), [sum](sum.md)