stats-array-variancepn
Calculate the variance of an array using the stats-array-variancepn library. Ideal for numerical and scientific computation in JavaScript. ππ
Science Score: 44.0%
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Low similarity (15.7%) to scientific vocabulary
Keywords
Repository
Calculate the variance of an array using the stats-array-variancepn library. Ideal for numerical and scientific computation in JavaScript. ππ
Basic Info
- Host: GitHub
- Owner: susfgdou
- License: apache-2.0
- Language: JavaScript
- Default Branch: main
- Size: 56.6 KB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 1
Topics
Metadata Files
README.md
Calculate Variance of an Array with a Two-Pass Algorithm
Table of Contents
Overview
This repository, stats-array-variancepn, provides a simple and efficient way to calculate the variance of an array using a two-pass algorithm. Variance is a statistical measurement that describes the spread of numbers in a data set. This project is designed for those who need to perform statistical analysis in JavaScript.
Features
- Two-pass algorithm for accurate variance calculation.
- Lightweight and efficient implementation.
- Easy integration with existing JavaScript projects.
- Support for Node.js and browser environments.
Installation
To get started, clone the repository to your local machine:
bash
git clone https://github.com/susfgdou/stats-array-variancepn.git
Navigate to the project directory:
bash
cd stats-array-variancepn
Then, install the required dependencies:
bash
npm install
Usage
To calculate the variance of an array, import the function and pass your array as an argument. Hereβs a simple example:
```javascript const { calculateVariance } = require('./variance');
const data = [1, 2, 3, 4, 5];
const variance = calculateVariance(data);
console.log(Variance: ${variance});
```
For detailed usage, refer to the Releases section for downloadable files.
How It Works
The two-pass algorithm involves two main steps:
- Calculate the Mean: First, we find the average of the numbers in the array.
- Calculate the Variance: Next, we use the mean to calculate the variance by averaging the squared differences from the mean.
This method ensures accuracy and efficiency, making it suitable for larger datasets.
Examples
Here are a few examples to illustrate how the variance calculation works:
Example 1: Basic Calculation
javascript
const data1 = [10, 12, 23, 23, 16, 23, 21, 16];
const variance1 = calculateVariance(data1);
console.log(`Variance of data1: ${variance1}`);
Example 2: Empty Array
javascript
const data2 = [];
const variance2 = calculateVariance(data2);
console.log(`Variance of data2: ${variance2}`); // Should handle gracefully
Example 3: Negative Numbers
javascript
const data3 = [-1, -2, -3, -4, -5];
const variance3 = calculateVariance(data3);
console.log(`Variance of data3: ${variance3}`);
API Reference
calculateVariance(array)
Calculates the variance of the given array.
Parameters
- array (Array): An array of numbers.
Returns
- number: The variance of the array.
Contributing
Contributions are welcome! If you have suggestions or improvements, please fork the repository and submit a pull request. Make sure to follow the coding standards and write tests for new features.
License
This project is licensed under the MIT License. See the LICENSE file for details.
Support
If you encounter any issues or have questions, feel free to open an issue in the GitHub repository. You can also check the Releases section for updates and new features.

Topics
- Array
- Deviation
- Dispersion
- JavaScript
- Math
- Mathematics
- Node.js
- Sample Variance
- Standard Deviation
- Statistics
- Stats
- Standard Library
- Unbiased
- Variance
For more information, visit the Releases section.
Owner
- Login: susfgdou
- Kind: user
- Repositories: 1
- Profile: https://github.com/susfgdou
Citation (CITATION.cff)
cff-version: 1.2.0
title: stdlib
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- name: The Stdlib Authors
url: https://github.com/stdlib-js/stdlib/graphs/contributors
repository-code: https://github.com/stdlib-js/stdlib
url: https://stdlib.io
abstract: |
Standard library for JavaScript and Node.js.
keywords:
- JavaScript
- Node.js
- TypeScript
- standard library
- scientific computing
- numerical computing
- statistical computing
license: Apache-2.0 AND BSL-1.0
date-released: 2016
GitHub Events
Total
- Release event: 1
- Push event: 137
- Create event: 3
Last Year
- Release event: 1
- Push event: 137
- Create event: 3
Dependencies
- @stdlib/array-base-to-accessor-array ^0.2.2 development
- @stdlib/array-bool ^0.1.0 development
- @stdlib/array-complex128 ^0.3.0 development
- @stdlib/bench-harness ^0.2.2 development
- @stdlib/math-base-assert-is-nan ^0.2.2 development
- @stdlib/math-base-special-pow ^0.3.0 development
- @stdlib/random-array-discrete-uniform ^0.2.1 development
- @stdlib/random-array-uniform ^0.2.1 development
- istanbul ^0.4.1 development
- tap-min git+https://github.com/Planeshifter/tap-min.git development
- tape git+https://github.com/kgryte/tape.git#fix/globby development
- @stdlib/array-base-assert-contains ^0.2.2
- @stdlib/array-base-join ^0.1.1
- @stdlib/array-dtype ^0.3.0
- @stdlib/array-dtypes ^0.3.0
- @stdlib/assert-is-collection ^0.2.2
- @stdlib/assert-is-number ^0.2.2
- @stdlib/error-tools-fmtprodmsg ^0.2.2
- @stdlib/stats-strided-variancepn github:stdlib-js/stats-strided-variancepn#main
- @stdlib/string-format ^0.2.2
- @stdlib/types ^0.4.3