Nearly Perfect Mediocracy

# npm

## @stdlib/stats-base-variancepn 0.0.10 • Public • Published

# variancepn

Calculate the variance of a strided array using a two-pass algorithm.

The population variance of a finite size population of size `N` is given by

where the population mean is given by

Often in the analysis of data, the true population variance is not known a priori and must be estimated from a sample drawn from the population distribution. If one attempts to use the formula for the population variance, the result is biased and yields a biased sample variance. To compute an unbiased sample variance for a sample of size `n`,

where the sample mean is given by

The use of the term `n-1` is commonly referred to as Bessel's correction. Note, however, that applying Bessel's correction can increase the mean squared error between the sample variance and population variance. Depending on the characteristics of the population distribution, other correction factors (e.g., `n-1.5`, `n+1`, etc) can yield better estimators.

## Installation

`npm install @stdlib/stats-base-variancepn`

## Usage

`var variancepn = require( '@stdlib/stats-base-variancepn' );`

#### variancepn( N, correction, x, stride )

Computes the variance of a strided array `x` using a two-pass algorithm.

```var x = [ 1.0, -2.0, 2.0 ];
var N = x.length;

var v = variancepn( N, 1, x, 1 );
// returns ~4.3333```

The function has the following parameters:

• N: number of indexed elements.
• correction: degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the variance according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the variance of a population, setting this parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the unbiased sample variance, setting this parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction).
• x: input `Array` or `typed array`.
• stride: index increment for `x`.

The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the variance of every other element in `x`,

```var floor = require( '@stdlib/math-base-special-floor' );

var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ];
var N = floor( x.length / 2 );

var v = variancepn( N, 1, x, 2 );
// returns 6.25```

Note that indexing is relative to the first index. To introduce an offset, use `typed array` views.

```var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );

var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var N = floor( x0.length / 2 );

var v = variancepn( N, 1, x1, 2 );
// returns 6.25```

#### variancepn.ndarray( N, correction, x, stride, offset )

Computes the variance of a strided array using a two-pass algorithm and alternative indexing semantics.

```var x = [ 1.0, -2.0, 2.0 ];
var N = x.length;

var v = variancepn.ndarray( N, 1, x, 1, 0 );
// returns ~4.33333```

The function has the following additional parameters:

• offset: starting index for `x`.

While `typed array` views mandate a view offset based on the underlying `buffer`, the `offset` parameter supports indexing semantics based on a starting index. For example, to calculate the variance for every other value in `x` starting from the second value

```var floor = require( '@stdlib/math-base-special-floor' );

var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
var N = floor( x.length / 2 );

var v = variancepn.ndarray( N, 1, x, 2, 1 );
// returns 6.25```

## Notes

• If `N <= 0`, both functions return `NaN`.
• If `N - c` is less than or equal to `0` (where `c` corresponds to the provided degrees of freedom adjustment), both functions return `NaN`.
• Depending on the environment, the typed versions (`dvariancepn`, `svariancepn`, etc.) are likely to be significantly more performant.

## Examples

```var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float64Array = require( '@stdlib/array-float64' );
var variancepn = require( '@stdlib/stats-base-variancepn' );

var x;
var i;

x = new Float64Array( 10 );
for ( i = 0; i < x.length; i++ ) {
x[ i ] = round( (randu()*100.0) - 50.0 );
}
console.log( x );

var v = variancepn( x.length, 1, x, 1 );
console.log( v );```

## References

• Neely, Peter M. 1966. "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients." Communications of the ACM 9 (7). Association for Computing Machinery: 496–99. doi:10.1145/365719.365958.
• Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In Proceedings of the 30th International Conference on Scientific and Statistical Database Management. New York, NY, USA: Association for Computing Machinery. doi:10.1145/3221269.3223036.

## 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 ### Install

`npm i @stdlib/stats-base-variancepn`

### Repository

github.com/stdlib-js/stats-base-variancepn

stdlib.io

805

0.0.10