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@stdlib/stats-base-ndarray-dstdevyc
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@stdlib/stats-base-ndarray-dstdevyc/dist/index.js
@stdlib/stats-base-ndarray-dstdevyc/lib/index.js

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dstdevyc

Calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using a one-pass algorithm proposed by Youngs and Cramer.

The population standard deviation of a finite size population of size N is given by

Equation for the population standard deviation.

-->

where the population mean is given by

Often in the analysis of data, the true population standard deviation 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 standard deviation, the result is biased and yields an uncorrected sample standard deviation. To compute a corrected sample standard deviation 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 standard deviation and population standard deviation. 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-ndarray-dstdevyc

Usage

var dstdevyc = require( '@stdlib/stats-base-ndarray-dstdevyc' );

dstdevyc( arrays )

Computes the standard deviation of a one-dimensional double-precision floating-point ndarray using a one-pass algorithm proposed by Youngs and Cramer.

var Float64Array = require( '@stdlib/array-float64' );
var ndarray = require( '@stdlib/ndarray-base-ctor' );
var scalar2ndarray = require( '@stdlib/ndarray-from-scalar' );

var opts = {
    'dtype': 'float64'
};

var xbuf = new Float64Array( [ 1.0, -2.0, 2.0 ] );
var x = new ndarray( opts.dtype, xbuf, [ 3 ], [ 1 ], 0, 'row-major' );
var correction = scalar2ndarray( 1.0, opts );

var v = dstdevyc( [ x, correction ] );
// returns ~2.0817

The function has the following parameters:

arrays: array-like object containing two elements: a one-dimensional input ndarray and a zero-dimensional ndarray specifying the degrees of freedom adjustment. Providing a non-zero degrees of freedom adjustment has the effect of adjusting the divisor during the calculation of the standard deviation according to N-c where N is the number of elements in the input ndarray and c corresponds to the provided degrees of freedom adjustment. When computing the standard deviation 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 corrected sample standard deviation, 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).

Notes

If provided an empty one-dimensional ndarray, the function returns NaN.
If N - c is less than or equal to 0 (where N corresponds to the number of elements in the input ndarray and c corresponds to the provided degrees of freedom adjustment), the function returns NaN.

Examples

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var ndarray = require( '@stdlib/ndarray-base-ctor' );
var scalar2ndarray = require( '@stdlib/ndarray-from-scalar' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var dstdevyc = require( '@stdlib/stats-base-ndarray-dstdevyc' );

var opts = {
    'dtype': 'float64'
};

var xbuf = discreteUniform( 10, -50, 50, opts );
var x = new ndarray( opts.dtype, xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
console.log( ndarray2array( x ) );

var correction = scalar2ndarray( 1.0, opts );
var v = dstdevyc( [ x, correction ] );
console.log( v );

References

Youngs, Edward A., and Elliot M. Cramer. 1971. "Some Results Relevant to Choice of Sum and Sum-of-Product Algorithms." Technometrics 13 (3): 657–65. doi:10.1080/00401706.1971.10488826.

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.

@stdlib/stats-base-ndarray-dstdevyc