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 #

``````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 #

``````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
``````