I wrote to understand statistics better. It's useful as a node
module as well as in the browser with visualization frameworks like
Here are some of the kinds of things you can do:
// Find the mean (average) of a set of numbers. This
// takes an array of numbers
var mean = ss.mean([1, 2, 3]);
// The variance of numbers is the sum of the squared
// differences between numbers and the mean of the list.
var variance = ss.variance([1, 2, 3]);
// Create a linear regression based on a dataset of
// two-dimensional arrays. This returns a new function
// that you can call for the value of the line at
// each X value.
var linear_regression_line = ss.linear_regression()
.data([[0, 1], [2, 2], [3, 3]]).line();
// The r-squared function can be given a dataset just
// like linear regressions, and it'll tell you roughly how
// close the linear regression comes to actually estimating
// the data.
var r_squared = ss.r_squared([[1, 2]], linear_regression_line);
As you can see, it does a bit of [descriptive statistics](http://en.wikipedia.org/wiki/Descriptive_statistics)
as well as [statistical inference](http://en.wikipedia.org/wiki/Statistical_inference),
and there's even some code for a bayesian classifier in there.
Libraries like [science.js](https://github.com/jasondavies/science.js/),
are written by smarter people and are probably more performant and shiny.
The point of `simple-statistics` is that it's simple, accessible,
and aims to be as low on concept as possible.
Install `simple-statistics` with [npm](http://npmjs.org/) or download
`simple_statistics.js` from GitHub to use it in the browser.