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Float64Results

Create a two-sample Z-test double-precision floating-point results object.

Installation

npm install @stdlib/stats-base-ztest-two-sample-results-float64

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 Float64Results = require( '@stdlib/stats-base-ztest-two-sample-results-float64' );

Float64Results( [arg[, byteOffset[, byteLength]]] )

Returns a two-sample Z-test double-precision floating-point results object.

var results = new Float64Results();
// returns {...}

The function supports the following parameters:

• arg: an ArrayBuffer or a data object (optional).
• byteOffset: byte offset (optional).
• byteLength: maximum byte length (optional).

A data object argument is an object having one or more of the following properties:

• rejected: boolean indicating whether the null hypothesis was rejected.
• alternative: the alternative hypothesis (e.g., 'two-sided', 'less', or 'greater').
• alpha: significance level.
• pValue: p-value.
• statistic: test statistic.
• ci: confidence interval as a Float64Array.
• nullValue: difference in means under the null hypothesis.
• xmean: sample mean of x.
• ymean: sample mean of y.

Float64Results.prototype.rejected

Boolean indicating whether the null hypothesis was rejected.

var results = new Float64Results();
// returns {...}

// ...

var v = results.rejected;
// returns <boolean>

Float64Results.prototype.alternative

The alternative hypothesis.

var results = new Float64Results();
// returns {...}

// ...

var v = results.alternative;
// returns <string>

Float64Results.prototype.alpha

Significance level.

var results = new Float64Results();
// returns {...}

// ...

var v = results.alpha;
// returns <number>

Float64Results.prototype.pValue

The test p-value.

var results = new Float64Results();
// returns {...}

// ...

var v = results.pValue;
// returns <number>

Float64Results.prototype.statistic

The test statistic.

var results = new Float64Results();
// returns {...}

// ...

var v = results.statistic;
// returns <number>

Float64Results.prototype.ci

Confidence interval.

var results = new Float64Results();
// returns {...}

// ...

var v = results.ci;
// returns <Float64Array>

Float64Results.prototype.nullValue

Difference in means under the null hypothesis.

var results = new Float64Results();
// returns {...}

// ...

var v = results.nullValue;
// returns <number>

Float64Results.prototype.xmean

Sample mean of x.

var results = new Float64Results();
// returns {...}

// ...

var v = results.xmean;
// returns <number>

Float64Results.prototype.ymean

Sample mean of y.

var results = new Float64Results();
// returns {...}

// ...

var v = results.ymean;
// returns <number>

Float64Results.prototype.toString( [options] )

Serializes a results object to a formatted string.

var results = new Float64Results();
// returns {...}

// ...

var v = results.toString();
// returns <string>

The method supports the following options:

• digits: number of digits to display after decimal points. Default: 4.
• decision: boolean indicating whether to show the test decision. Default: true.

Example output:

Two-sample Z-test

Alternative hypothesis: True difference in means is less than 1.0

    pValue: 0.0406
    statistic: 9.9901
    95% confidence interval: [9.7821, 10.4451]

Test Decision: Reject null in favor of alternative at 5% significance level

Float64Results.prototype.toJSON( [options] )

Serializes a results object as a JSON object.

var results = new Float64Results();
// returns {...}

// ...

var v = results.toJSON();
// returns {...}

JSON.stringify() implicitly calls this method when stringifying a results instance.

Float64Results.prototype.toDataView()

Returns a DataView of a results object.

var results = new Float64Results();
// returns {...}

// ...

var v = results.toDataView();
// returns <DataView>

Notes

• A results object is a struct providing a fixed-width composite data structure for storing two-sample Z-test results and providing an ABI-stable data layout for JavaScript-C interoperation.

Examples

var Float64Array = require( '@stdlib/array-float64' );
var Results = require( '@stdlib/stats-base-ztest-two-sample-results-float64' );

var results = new Results({
    'rejected': true,
    'alpha': 0.05,
    'pValue': 0.0132,
    'statistic': 2.4773,
    'nullValue': 0.0,
    'xmean': 3.7561,
    'ymean': 3.0129,
    'ci': new Float64Array( [ 9.9983, 11.4123 ] ),
    'alternative': 'two-sided'
});

var str = results.toString({
    'format': 'linear'
});
console.log( str );

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C APIs

Usage

#include "stdlib/stats/base/ztest/two-sample/results/float64.h"

stdlib_stats_ztest_two_sample_float64_results

Structure for holding double-precision floating-point test results.

#include <stdbool.h>
#include <stdint.h>

struct stdlib_stats_ztest_two_sample_float64_results {
    // Boolean indicating whether the null hypothesis was rejected:
    bool rejected;

    // Alternative hypothesis:
    int8_t alternative;

    // Significance level:
    double alpha;

    // p-value:
    double pValue;

    // Test statistic:
    double statistic;

    // Confidence interval:
    double ci[ 2 ];

    // Difference in means under the null hypothesis:
    double nullValue;

    // Sample mean of `x`:
    double xmean;

    // Sample mean of `y`:
    double ymean;
};

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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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Copyright

Copyright © 2016-2025. The Stdlib Authors.