About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

Standard Deviation

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]

> [Student's t][t-distribution] distribution [standard deviation][standard-deviation].

The [standard deviation][standard-deviation] for a [Student's t][t-distribution] random variable with degrees of freedom ν is

Standard deviation for a Student's t distribution.

For ν smaller than one, the standard deviation is not defined.

Installation

``bash npm install @stdlib/stats-base-dists-t-stdev`

`Usage`

`javascript var stdev = require( '@stdlib/stats-base-dists-t-stdev' );`

#### stdev( v )

Returns the [standard deviation][standard-deviation] of a [Student's t][t-distribution] distribution with degrees of freedom v.

`javascript var y = stdev( 9.0 ); // returns ~1.134

y = stdev( 3.5 ); // returns ~1.528`

If provided 1 < v <= 2, the function returns infinity.

`javascript var y = stdev( 1.5 ); // returns Infinity

y = stdev( 2.0 ); // returns Infinity`

If provided v <= 1, the function returns NaN.

`javascript var y = stdev( -1.0 ); // returns NaN

y = stdev( 0.8 ); // returns NaN`

`Examples`

`javascript var uniform = require( '@stdlib/random-array-uniform' ); var logEachMap = require( '@stdlib/console-log-each-map' ); var stdev = require( '@stdlib/stats-base-dists-t-stdev' );

var opts = { 'dtype': 'float64' }; var v = uniform( 10, 0.0, 20.0, opts );

logEachMap( 'v: %0.4f, SD(X;v): %0.4f', v, stdev );`

*

`C APIs`

`$3`

`c #include "stdlib/stats/base/dists/t/stdev.h"`

#### stdlib_base_dists_t_stdev( v )

Returns the standard deviation of a Student's t distribution.

`c double out = stdlib_base_dists_t_stdev( 9.0 ); // returns ~1.134`

The function accepts the following arguments:

- v: [in] double degrees of freedom.

`c double stdlib_base_dists_t_stdev( const double v );`

`$3`

`c #include "stdlib/stats/base/dists/t/stdev.h" #include #include

static double random_uniform( const double min, const double max ) { double v = (double)rand() / ( (double)RAND_MAX + 1.0 ); return min + ( v*(max-min) ); }

int main( void ) { double v; double y; int i;

for ( i = 0; i < 25; i++ ) { v = random_uniform( 0.0, 20.0 ); y = stdlib_base_dists_t_stdev( v ); printf( "v: %lf, SD(X;v): %lf\n", v, y ); } }`

*

`Notice`

This package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].

#### Community

[![Chat][chat-image]][chat-url]

---

`License`

See [LICENSE][stdlib-license].

`Copyright`

[npm-image]: http://img.shields.io/npm/v/@stdlib/stats-base-dists-t-stdev.svg [npm-url]: https://npmjs.org/package/@stdlib/stats-base-dists-t-stdev

[test-image]: https://github.com/stdlib-js/stats-base-dists-t-stdev/actions/workflows/test.yml/badge.svg?branch=v0.3.1 [test-url]: https://github.com/stdlib-js/stats-base-dists-t-stdev/actions/workflows/test.yml?query=branch:v0.3.1

[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-base-dists-t-stdev/main.svg [coverage-url]: https://codecov.io/github/stdlib-js/stats-base-dists-t-stdev?branch=main

[chat-image]: https://img.shields.io/badge/zulip-join_chat-brightgreen.svg [chat-url]: https://stdlib.zulipchat.com

[stdlib]: https://github.com/stdlib-js/stdlib

[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors

[umd]: https://github.com/umdjs/umd [es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules

[deno-url]: https://github.com/stdlib-js/stats-base-dists-t-stdev/tree/deno [deno-readme]: https://github.com/stdlib-js/stats-base-dists-t-stdev/blob/deno/README.md [umd-url]: https://github.com/stdlib-js/stats-base-dists-t-stdev/tree/umd [umd-readme]: https://github.com/stdlib-js/stats-base-dists-t-stdev/blob/umd/README.md [esm-url]: https://github.com/stdlib-js/stats-base-dists-t-stdev/tree/esm [esm-readme]: https://github.com/stdlib-js/stats-base-dists-t-stdev/blob/esm/README.md [branches-url]: https://github.com/stdlib-js/stats-base-dists-t-stdev/blob/main/branches.md

[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-base-dists-t-stdev/main/LICENSE

[t-distribution]: https://en.wikipedia.org/wiki/Student%27s_t-distribution

[standard-deviation]: https://en.wikipedia.org/wiki/Standard_deviation


  
    About stdlib...
  

  We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

  The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

  When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

  To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

`Standard Deviation`

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]

> [Student's t][t-distribution] distribution [standard deviation][standard-deviation].

The [standard deviation][standard-deviation] for a [Student's t][t-distribution] random variable with degrees of freedom ν is

For ν smaller than one, the standard deviation is not defined.

`Installation`

``bash npm install @stdlib/stats-base-dists-t-stdev `

`Usage`

`javascript var stdev = require( '@stdlib/stats-base-dists-t-stdev' ); `

#### stdev( v )

Returns the [standard deviation][standard-deviation] of a [Student's t][t-distribution] distribution with degrees of freedom v.

`javascript var y = stdev( 9.0 ); // returns ~1.134

y = stdev( 3.5 ); // returns ~1.528 `

If provided 1 < v <= 2, the function returns infinity.

`javascript var y = stdev( 1.5 ); // returns Infinity

y = stdev( 2.0 ); // returns Infinity `

If provided v <= 1, the function returns NaN.

`javascript var y = stdev( -1.0 ); // returns NaN

y = stdev( 0.8 ); // returns NaN `

`Examples`

`javascript var uniform = require( '@stdlib/random-array-uniform' ); var logEachMap = require( '@stdlib/console-log-each-map' ); var stdev = require( '@stdlib/stats-base-dists-t-stdev' );

var opts = { 'dtype': 'float64' }; var v = uniform( 10, 0.0, 20.0, opts );

logEachMap( 'v: %0.4f, SD(X;v): %0.4f', v, stdev ); `

*

`C APIs`

`$3`

`c #include "stdlib/stats/base/dists/t/stdev.h" `

#### stdlib_base_dists_t_stdev( v )

Returns the standard deviation of a Student's t distribution.

`c double out = stdlib_base_dists_t_stdev( 9.0 ); // returns ~1.134 `

The function accepts the following arguments:

- v: [in] double degrees of freedom.

`c double stdlib_base_dists_t_stdev( const double v ); `

`$3`

`c #include "stdlib/stats/base/dists/t/stdev.h" #include #include

static double random_uniform( const double min, const double max ) { double v = (double)rand() / ( (double)RAND_MAX + 1.0 ); return min + ( v*(max-min) ); }

int main( void ) { double v; double y; int i;

for ( i = 0; i < 25; i++ ) { v = random_uniform( 0.0, 20.0 ); y = stdlib_base_dists_t_stdev( v ); printf( "v: %lf, SD(X;v): %lf\n", v, y ); } } `

*

`Notice`

For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].

#### Community

[![Chat][chat-image]][chat-url]

---

`License`

See [LICENSE][stdlib-license].

`Copyright`

[npm-image]: http://img.shields.io/npm/v/@stdlib/stats-base-dists-t-stdev.svg [npm-url]: https://npmjs.org/package/@stdlib/stats-base-dists-t-stdev

[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-base-dists-t-stdev/main.svg [coverage-url]: https://codecov.io/github/stdlib-js/stats-base-dists-t-stdev?branch=main

[chat-image]: https://img.shields.io/badge/zulip-join_chat-brightgreen.svg [chat-url]: https://stdlib.zulipchat.com

[stdlib]: https://github.com/stdlib-js/stdlib

[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors

[umd]: https://github.com/umdjs/umd [es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules

[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-base-dists-t-stdev/main/LICENSE

[t-distribution]: https://en.wikipedia.org/wiki/Student%27s_t-distribution

[standard-deviation]: https://en.wikipedia.org/wiki/Standard_deviation