Linear Regression is one of the most important models in machine learning, it is also a very useful statistical method to understand the relation between two variables (X and Y).

Despite the apparent simplicity of Linear regression, it relies on several assumptions that should be validated before conducting a linear regression model.

  1. Linear Relationship: The response variable (Y) should be in a linear relation with the explanatory variables (X).
  2. Residuals Homoscedasticity: The residual errors should be of a constant variance at any value of X.
  3. Independence of Residuals: After fitting the linear regression model, residuals should be independent random variables.

Kholoud Ibrahim

Data Engineer @Dell Technologies , Passionate about Data analytics and Machine Learning ,Aiming to explain hard concepts in a simple manner.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store