@stdlib/stats-base-dists-lognormal-skewness NPM

Skewness

Lognormal distribution skewness.

The skewness for a lognormal random variable with location parameter μ and scale parameter σ > 0 is

According to the definition, the natural logarithm of a random variable from a lognormal distribution follows a normal distribution.

Installation

npm install @stdlib/stats-base-dists-lognormal-skewness

Usage

var skewness = require( '@stdlib/stats-base-dists-lognormal-skewness' );

skewness( mu, sigma )

Returns the skewness for a lognormal distribution with location mu and scale sigma.

var y = skewness( 2.0, 1.0 );
// returns ~6.185

y = skewness( 0.0, 1.0 );
// returns ~6.185

y = skewness( -1.0, 2.0 );
// returns ~414.359

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

var y = skewness( NaN, 1.0 );
// returns NaN

y = skewness( 0.0, NaN );
// returns NaN

If provided sigma <= 0, the function returns NaN.

var y = skewness( 0.0, 0.0 );
// returns NaN

y = skewness( 0.0, -1.0 );
// returns NaN

Examples

var randu = require( '@stdlib/random-base-randu' );
var skewness = require( '@stdlib/stats-base-dists-lognormal-skewness' );

var sigma;
var mu;
var y;
var i;

for ( i = 0; i < 10; i++ ) {
    mu = ( randu()*10.0 ) - 5.0;
    sigma = randu() * 20.0;
    y = skewness( mu, sigma );
    console.log( 'µ: %d, σ: %d, skew(X;µ,σ): %d', mu.toFixed( 4 ), sigma.toFixed( 4 ), y.toFixed( 4 ) );
}

Notice

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.