spm-regression

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.

Usage
=====
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.

Linear regression
-----------------

equation: ``[gradient, y-intercept]` in the form y = mx + c
`
var data = [[0,1],[32, 67] .... [12, 79]];
var result = regression('linear', data);
`

Linear regression through the origin
-----------------

equation: `[gradient]` in the form y = mx
`
var data = [[0,1],[32, 67] .... [12, 79]];
var result = regression('linearThroughOrigin', data);
`

Exponential regression
----------------------

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

Logarithmic regression
----------------------

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

Power law regression
--------------------

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

Polynomial regression
---------------------

equation: `[a0, .... , an]` in the form a0x^0 ... + anx^n
`
var data = [[0,1],[32, 67] .... [12, 79]];
var result = regression('polynomial', data, 4);
`

Lastvalue
---------

Not exactly a regression. Uses the last value to fill the blanks when forecasting.

Filling the blanks and forecasting
==================================

`
var data = [[0,1], [32, null] .... [12, 79]];
`

If you use a `null`` value for data, regressionjs will fill it using the trend.