itervariance

Compute the unbiased sample variance over all iterated values.

The unbiased sample variance is defined as

Installation

npm install @stdlib/stats-iter-variance

Usage

var itervariance = require( '@stdlib/stats-iter-variance' );

itervariance( iterator[, mean] )

Computes the unbiased sample variance over all iterated values.

var array2iterator = require( '@stdlib/array-to-iterator' );

var arr = array2iterator( [ 2.0, 1.0, 3.0 ] );

var s2 = itervariance( arr );
// returns 1.0

If the mean is already known, provide a mean argument.

var array2iterator = require( '@stdlib/array-to-iterator' );

var arr = array2iterator( [ 2.0, 1.0, 3.0 ] );

var s2 = itervariance( arr, 2.0 );
// returns ~0.67

Notes

• If an iterated value is non-numeric (including NaN), the returned iterator returns NaN. If non-numeric iterated values are possible, you are advised to provide an iterator which type checks and handles non-numeric values accordingly.

Examples

var runif = require( '@stdlib/random-iter-uniform' );
var itervariance = require( '@stdlib/stats-iter-variance' );

// Create an iterator for generating uniformly distributed pseudorandom numbers:
var rand = runif( -10.0, 10.0, {
    'seed': 1234,
    'iter': 100
});

// Compute the unbiased sample variance:
var s2 = itervariance( rand );
// returns <number>

console.log( 'Variance: %d.', s2 );

See Also

• @stdlib/stats-iter/mean: compute the arithmetic mean over all iterated values.
• @stdlib/stats-iter/stdev: compute the corrected sample standard deviation over all iterated values.

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

License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.