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:

Quadratic regression

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:





This page requires Javascript to be enabled!

© Guido Socher, version 2018-11-25 , MIT License.