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

Visualizing Regression Functions

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.