About stdlib...
We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.
The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.
When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.
To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!
Compute a one-sample Z-test for a one-dimensional double-precision floating-point ndarray.
A Z-test commonly refers to a one-sample location test which compares the mean of a set of measurements X to a given constant when the standard deviation is known. A Z-test supports testing three different null hypotheses H0:
H0: μ ≥ μ0versus the alternative hypothesisH1: μ < μ0.H0: μ ≤ μ0versus the alternative hypothesisH1: μ > μ0.H0: μ = μ0versus the alternative hypothesisH1: μ ≠ μ0.
To use in Observable,
dztest = require( 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-ndarray-dztest@umd/browser.js' )To vendor stdlib functionality and avoid installing dependency trees for Node.js, you can use the UMD server build:
var dztest = require( 'path/to/vendor/umd/stats-base-ndarray-dztest/index.js' )To include the bundle in a webpage,
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-ndarray-dztest@umd/browser.js"></script>If no recognized module system is present, access bundle contents via the global scope:
<script type="text/javascript">
(function () {
window.dztest;
})();
</script>Computes a one-sample Z-test for a one-dimensional double-precision floating-point ndarray.
var Float64Results = require( '@stdlib/stats-base-ztest-one-sample-results-float64' );
var resolveEnum = require( '@stdlib/stats-base-ztest-alternative-resolve-enum' );
var structFactory = require( '@stdlib/array-struct-factory' );
var Float64Array = require( '@stdlib/array-float64' );
var scalar2ndarray = require( '@stdlib/ndarray-from-scalar' );
var ndarray = require( '@stdlib/ndarray-ctor' );
var opts = {
'dtype': 'float64'
};
var xbuf = new Float64Array( [ 1.0, 3.0, 4.0, 2.0 ] );
var x = new ndarray( opts.dtype, xbuf, [ 4 ], [ 1 ], 0, 'row-major' );
var alt = scalar2ndarray( resolveEnum( 'two-sided' ), {
'dtype': 'int8'
});
var alpha = scalar2ndarray( 0.05, opts );
var mu = scalar2ndarray( 0.0, opts );
var sigma = scalar2ndarray( 1.0, opts );
var ResultsArray = structFactory( Float64Results );
var out = new ndarray( Float64Results, new ResultsArray( 1 ), [], [ 0 ], 0, 'row-major' );
var v = dztest( [ x, out, alt, alpha, mu, sigma ] );
var bool = ( v === out );
// returns trueThe function has the following parameters:
-
arrays: array-like object containing the following ndarrays in order:
- a one-dimensional input ndarray.
- a zero-dimensional output ndarray containing a results object.
- a zero-dimensional ndarray specifying the alternative hypothesis.
- a zero-dimensional ndarray specifying the significance level.
- a zero-dimensional ndarray specifying the mean under the null hypothesis.
- a zero-dimensional ndarray specifying the known standard deviation.
- As a general rule of thumb, a Z-test is most reliable for sample sizes greater than
50. For smaller sample sizes or when the standard deviation is unknown, prefer a t-test.
<!DOCTYPE html>
<html lang="en">
<body>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-ztest-one-sample-results-float64@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-ztest-alternative-resolve-enum@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/array-struct-factory@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/random-array-normal@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-ctor@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-from-scalar@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-to-array@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-ndarray-dztest@umd/browser.js"></script>
<script type="text/javascript">
(function () {
var opts = {
'dtype': 'float64'
};
// Create a one-dimensional ndarray containing pseudorandom numbers drawn from a normal distribution:
var xbuf = normal( 100, 0.0, 1.0, opts );
var x = new ndarray( opts.dtype, xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
console.log( ndarray2array( x ) );
// Specify the alternative hypothesis:
var alt = scalar2ndarray( resolveEnum( 'two-sided' ), {
'dtype': 'int8'
});
// Specify the significance level:
var alpha = scalar2ndarray( 0.05, opts );
// Specify the mean under the null hypothesis:
var mu = scalar2ndarray( 0.0, opts );
// Specify the known standard deviation:
var sigma = scalar2ndarray( 1.0, opts );
// Create a zero-dimensional results ndarray:
var ResultsArray = structFactory( Float64Results );
var out = new ndarray( Float64Results, new ResultsArray( 1 ), [], [ 0 ], 0, 'row-major' );
// Perform a Z-test:
var v = dztest( [ x, out, alt, alpha, mu, sigma ] );
console.log( v.get().toString() );
})();
</script>
</body>
</html>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.
See LICENSE.
Copyright © 2016-2026. The Stdlib Authors.
