项目作者: stdlib-js

项目描述 :
Binomial distribution variance.
高级语言: Makefile
项目地址: git://github.com/stdlib-js/stats-base-dists-binomial-variance.git
创建时间: 2021-06-14T16:34:18Z
项目社区:https://github.com/stdlib-js/stats-base-dists-binomial-variance

开源协议:Apache License 2.0

下载




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!


Variance

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

[Binomial][binomial-distribution] distribution [variance][variance].



The [variance][variance] for a [binomial][binomial-distribution] random variable is



math \mathop{\mathrm{Var}}\left[ X \right] = n p (1-p)





where n is the number of trials and p is the success probability.



## Installation

bash npm install @stdlib/stats-base-dists-binomial-variance

Alternatively,

- To load the package in a website via a script tag without installation and bundlers, use the [ES Module][es-module] available on the [esm][esm-url] branch (see [README][esm-readme]).
- If you are using Deno, visit the [deno][deno-url] branch (see [README][deno-readme] for usage intructions).
- For use in Observable, or in browser/node environments, use the [Universal Module Definition (UMD)][umd] build available on the [umd][umd-url] branch (see [README][umd-readme]).

The [branches.md][branches-url] file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.



## Usage

javascript var variance = require( '@stdlib/stats-base-dists-binomial-variance' );

#### variance( n, p )

Returns the [variance][variance] of a [binomial][binomial-distribution] distribution with number of trials n and success probability p.

javascript var v = variance( 20, 0.1 ); // returns 1.8 v = variance( 50, 0.5 ); // returns 12.5

If provided NaN as any argument, the function returns NaN.

javascript var v = variance( NaN, 0.5 ); // returns NaN v = variance( 20, NaN ); // returns NaN

If provided a number of trials n which is not a nonnegative integer, the function returns NaN.

javascript var v = variance( 1.5, 0.5 ); // returns NaN v = variance( -2.0, 0.5 ); // returns NaN

If provided a success probability p outside of [0,1], the function returns NaN.

javascript var v = variance( 20, -1.0 ); // returns NaN v = variance( 20, 1.5 ); // returns NaN





## Examples



javascript var randu = require( '@stdlib/random-base-randu' ); var round = require( '@stdlib/math-base-special-round' ); var variance = require( '@stdlib/stats-base-dists-binomial-variance' ); var v; var i; var n; var p; for ( i = 0; i < 10; i++ ) { n = round( randu() * 100.0 ); p = randu(); v = variance( n, p ); console.log( 'n: %d, p: %d, Var(X;n,p): %d', n, p.toFixed( 4 ), v.toFixed( 4 ) ); }




## C APIs







### Usage

c #include "stdlib/stats/base/dists/binomial/variance.h"

#### stdlib_base_dists_binomial_variance( n, p )

Returns the [variance][variance] of a [binomial][binomial-distribution] distribution with number of trials n and success probability p.

c double out = stdlib_base_dists_binomial_variance( 100, 0.1 ); // returns 9.0

The function accepts the following arguments:

- n: [in] int32_t number of trials.
- p: [in] double success probability.

c double stdlib_base_dists_binomial_variance( const int32_t n, const double p );





### Examples

c #include "stdlib/stats/base/dists/binomial/variance.h" #include "stdlib/math/base/special/ceil.h" #include <stdlib.h> #include <stdint.h> #include <stdio.h> 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 ) { int32_t n; double p; double y; int i; for ( i = 0; i < 25; i++ ) { n = stdlib_base_ceil( random_uniform( 0.0, 100.0 ) ); p = random_uniform( 0.0, 1.0 ); y = stdlib_base_dists_binomial_variance( n, p ); printf( "n: %d, p: %lf, Var(X;n,p): %lf\n", n, p, y ); } return 0; }





*

## 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

Copyright © 2016-2025. The Stdlib [Authors][stdlib-authors].