regression.js is a javascript library containing a collection of least squares fitting methods for finding a trend in a set of data. It currently contains methods for linear, exponential, logarithmic, power and polynomial trends.

Most regressions require only two parameters - the regression method (linear, exponential, logarithmic, power or polynomial) and a data source. A third parameter can be used to define the degree of a polynomial when a polynomial regression is required.

regression.js will return an object containing an equation array and a points array.

equation: [gradient, y-intercept] in the form y = mx + c

var data = [[0,1],[32, 67] .... [12, 79]];
var result = regression('linear', data);

equation: [a, b] in the form y = ae^bx

equation: [a, b] in the form y = a + b ln x

equation: [a, b] in the form y = ax^b

equation: [a0, .... , an] in the form a0x^0 ... + anx^n

var data = [[0,1],[32, 67] .... [12, 79]];
var result = regression('polynomial', data, 4);