mathjs/docs/reference/functions/std.md

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

See also

mean, median, max, min, prod, sum, var