# Function std Compute the standard deviation of a matrix or a list with values. The standard deviations is defined as the square root of the variance: `std(A) = sqrt(var(A))`. In case of a (multi dimensional) array or matrix, the standard deviation over all elements will be calculated. Optionally, the type of normalization can be specified as second 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) ## Syntax ```js math.std(a, b, c, ...) math.std(A) math.std(A, 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 standard deviation ## Examples ```js math.std(2, 4, 6) // returns 2 math.std([2, 4, 6, 8]) // returns 2.581988897471611 math.std([2, 4, 6, 8], 'uncorrected') // returns 2.23606797749979 math.std([2, 4, 6, 8], 'biased') // returns 2 math.std([[1, 2, 3], [4, 5, 6]]) // returns 1.8708286933869707 ``` ## See also [mean](mean.md), [median](median.md), [max](max.md), [min](min.md), [prod](prod.md), [sum](sum.md), [var](var.md)