Linear regression

Linear regression is a fundamental statistical method used for modeling the relationship between a dependent variable (usually denoted as “Y”) and one or more independent variables (often denoted as “X”). It aims to find the best-fitting linear equation that describes how changes in the independent variables affect the dependent variable. Many of you may know this as the ordinary least squares (OLS) method.

In simpler terms, linear regression helps us predict a continuous numeric outcome based on one or more input features. For this to work, if you are unaware, many assumptions must be held true. If you would like to understand these more, then a simple search will bring you a lot of good information on them. In this tutorial, we will delve into both simple linear regression (one independent variable) and multiple linear regression (multiple independent variables).