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Discrete uniform distribution skewness.
The skewness for a discrete uniform random variable with minimum support a
and maximum support b
is
npm install @stdlib/stats-base-dists-discrete-uniform-skewness
Alternatively,
- To load the package in a website via a
script
tag without installation and bundlers, use the ES Module available on theesm
branch (see README). - If you are using Deno, visit the
deno
branch (see README for usage intructions). - For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the
umd
branch (see README).
The branches.md 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.
var skewness = require( '@stdlib/stats-base-dists-discrete-uniform-skewness' );
Returns the skewness of a discrete uniform distribution with parameters a
(minimum support) and b
(maximum support).
var v = skewness( 0, 1 );
// returns 0.0
v = skewness( 4, 12 );
// returns 0.0
v = skewness( 2, 8 );
// returns 0.0
If a
or b
is not an integer value, the function returns NaN
.
var v = skewness( 0.1, 2 );
// returns NaN
v = skewness( 0, 2.2 );
// returns NaN
v = skewness( NaN, 2 );
// returns NaN
v = skewness( 2, NaN );
// returns NaN
If provided a > b
, the function returns NaN
.
var y = skewness( 3, 2 );
// returns NaN
y = skewness( -1, -2 );
// returns NaN
var randint = require( '@stdlib/random-base-discrete-uniform' );
var skewness = require( '@stdlib/stats-base-dists-discrete-uniform-skewness' );
var randa = randint.factory( 0, 10 );
var randb = randint.factory();
var a;
var b;
var v;
var i;
for ( i = 0; i < 10; i++ ) {
a = randa();
b = randb( a, a+randa() );
v = skewness( a, b );
console.log( 'a: %d, b: %d, skew(X;a,b): %d', a.toFixed( 4 ), b.toFixed( 4 ), v.toFixed( 4 ) );
}
This package is part of 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, see the main project repository.
See LICENSE.
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