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Variance

Student's t distribution variance.

The variance for a Student's t random variable with degrees of freedom ν is

$$\mathop{\mathrm{Var}}\left( X \right) = \begin{cases} \frac{\nu }{\nu-2} & \text{ for } \nu > 2 \\ \infty & \text{ for } 1 < \nu \le 2 \end{cases}$$

For ν smaller than one, the variance is not defined.

Installation

npm install @stdlib/stats-base-dists-t-variance

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm 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.

Usage

var variance = require( '@stdlib/stats-base-dists-t-variance' );

variance( v )

Returns the variance of a Student's t distribution with degrees of freedom v.

var y = variance( 9.0 );
// returns ~1.286

y = variance( 3.5 );
// returns ~2.333

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

var y = variance( 1.5 );
// returns Infinity

y = variance( 2.0 );
// returns Infinity

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

var y = variance( -1.0 );
// returns NaN

y = variance( 0.8 );
// returns NaN

Examples

var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var variance = require( '@stdlib/stats-base-dists-t-variance' );

var v;
var y;
var i;

for ( i = 0; i < 10; i++ ) {
    v = randu() * 20.0;
    y = variance( v );
    console.log( 'v: %d, Var(X,v): %d', v.toFixed( 4 ), y.toFixed( 4 ) );
}

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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.

Community

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License

See LICENSE.

Copyright

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