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62 lines
1.7 KiB
Markdown
62 lines
1.7 KiB
Markdown
<!-- Note: This file is automatically generated from source code comments. Changes made in this file will be overridden. -->
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# Function std
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Compute the standard deviation of a matrix or a list with values.
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The standard deviations is defined as the square root of the variance:
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`std(A) = sqrt(variance(A))`.
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In case of a (multi dimensional) array or matrix, the standard deviation
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over all elements will be calculated.
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Optionally, the type of normalization can be specified as second
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parameter. The parameter `normalization` can be one of the following values:
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- 'unbiased' (default) The sum of squared errors is divided by (n - 1)
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- 'uncorrected' The sum of squared errors is divided by n
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- 'biased' The sum of squared errors is divided by (n + 1)
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## Syntax
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```js
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math.std(a, b, c, ...)
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math.std(A)
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math.std(A, normalization)
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```
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### Parameters
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Parameter | Type | Description
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--------- | ---- | -----------
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`array` | Array | Matrix | A single matrix or or multiple scalar values
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`normalization` | string | Determines how to normalize the variance. Choose 'unbiased' (default), 'uncorrected', or 'biased'. Default value: 'unbiased'.
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### Returns
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Type | Description
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---- | -----------
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* | The standard deviation
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## Examples
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```js
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math.std(2, 4, 6) // returns 2
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math.std([2, 4, 6, 8]) // returns 2.581988897471611
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math.std([2, 4, 6, 8], 'uncorrected') // returns 2.23606797749979
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math.std([2, 4, 6, 8], 'biased') // returns 2
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math.std([[1, 2, 3], [4, 5, 6]]) // returns 1.8708286933869707
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```
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## See also
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[mean](mean.md),
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[median](median.md),
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[max](max.md),
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[min](min.md),
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[prod](prod.md),
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[sum](sum.md),
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[variance](variance.md)
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