# Online regression calculator

This is a javascript based regression calculatr based on Tom Alexander's regression.js library (https://github.com/Tom-Alexander/regression-js) which uses the linear least-squares fitting method to calculate an equation that fits best a given set of data points.

Enter or copy/paste your data in the box below in semicolon separated format. A #-sign is a comment.
After that entering the data you select the curve type that you expect to fit best your data and you press calculate.

` `

## Linear regression

Calculate the equation of the line best approximating the above data points.
Equation: f(x)=a * x + b

a:
b:

Equation:

Calculate the equation of the parabolic curve best approximating the above data points.
Equation: f(x)=a * x^2 + b * x + c

a:
b:
c:

Equation:

## Polynomial regression

Calculate a polynom of degree n to best approximate the above data points.
Equation: f(x)=an * x^n + ... + a1 * x + a0

Select degree of the polynom:

Equation:

## Exponential regression

Fits the input data to an exponential curve.
Equation: f(x)=a * e^(b*x)

a:
b:

Equation:

## Logarithmic regression

Fits the input data to a logarithmic curve (natural logarithm).
Equation: f(x)=a + b * ln(x)

a:
b:

Equation:

## Power law regression

Fits the input data to a power law curve.
Equation: f(x)=a * x^b

a:
b:

Equation: