= Existing entry value |
= Missing entry value |
= Calculated value for missing entry

Linear regression was developed in the field of statistics as a model for understanding
the relationships among numerical variables, but has been borrowed by machine learning.

It is both a statistical algorithm and a machine learning algorithm and
can be used to model and analyze variables, when the focus is on the relationship between a
dependent variable and one or more independent variables (or 'predictors').
Most importantly it can be utilized to answer questions such as:
How does gas consumption depend on external temperature? Or, how much gas is needed for a given temperature?

This experiment visualizes several different regression functions. It is built on top the of Regression.JS library.