stats-analysis/README.md

Engineering statistics and data analysis

# Statistics and Data Analysis

[![Build Status](https://travis-ci.org/alyssaq/stats-analysis.png?branch=master)](https://travis-ci.org/alyssaq/stats-analysis)
[![codecov.io](https://codecov.io/github/alyssaq/stats-analysis/coverage.svg?branch=master)](https://codecov.io/github/alyssaq/stats-analysis?branch=master)

Mini javascript statistics library for nodejs or the browser.   
No production dependencies.   

## Current Library Coverage

 - Standard Deviation
 - Mean
 - Median (sorts before calculating)
 - Median Absolute Deviation (MAD)
 - Outlier Detection & Filtering using Iglewicz and Hoaglin's method (MAD) - Use this if the order of your data does not matter.
 - Outlier Detection & Filtering using Median Differencing (Default method) - Use this if the order of your data matters. This looks at the difference between adjacent points best for time series data.

## Node.js / Browserify / ES6 module

```js
$ npm install stats-analysis

var stats = require("./stats-analysis") // include statistics library
```

## Browser
```html
<script src="https://npmcdn.com/stats-analysis"></script>
```
```js
window.stats
```

## Usage
```js
var arr = [-2, 1, 2, 3, 3, 4, 15]

//standard deviation
stats.stdev(arr).toFixed(2) * 1 // Round to 2dp and convert to number
> 4.98

//mean
stats.mean(arr).toFixed(2) * 1
> 3.57

//median
stats.median(arr)
> 2

//median absolute deviation
stats.MAD(arr)
> 1

// Outlier detection. Returns indexes of outliers
stats.indexOfOutliers(arr)  // Default theshold of 3
> [6]

stats.indexOfOutliers(arr, 6) // Supply higher threshold to allow more outliers.

// Outlier filtering. Returns array with outliers removed.
stats.filterOutliers(arr)
> [-2, 1, 2, 3, 3, 4]
```

To use different outlier methods:
```
stats.filterOutliers(arr, stats.outlierMethod.medianDiff)
stats.filterOutliers(arr, stats.outlierMethod.medianDiff, 6) // Different threshold
stats.filterOutliers(arr, stats.outlierMethod.MAD) // Default

stats.indexOfOutliers(arr, stats.outlierMethod.medianDiff)
stats.indexOfOutliers(arr, stats.outlierMethod.medianDiff, 6) // Different threshold
stats.indexOfOutliers(arr, stats.outlierMethod.MAD) // Default
```

## Development

[Mocha](http://visionmedia.github.io/mocha/) is used as the testing framework.  
Istanbul and [codecov](https://github.com/cainus/codecov.io) used for code coverage.

Commands:

```js
$ npm install   // Grab mocha
$ npm run lint  // Ensure code consistency with standard
$ npm test      // Run tests
$ npm run cov   // Run code coverage. (Ensure 100%)
```

## Resources

**Engineering statistics handbook:**   
http://www.itl.nist.gov/div898/handbook/index.htm

## Contribute to the library
1. Fork it!
2. Create your feature branch: `git checkout -b my-new-feature`
3. Make changes and ensure tests and code coverage all pass.
4. Commit your changes: `git commit -m 'Add some feature'`
5. Push to the branch: `git push origin my-new-feature`
6. Submit a pull request :D

## License
MIT