Mastering Linear Regression with Statsmodels




Linear Regression is one of the most essential techniques used in Data Science and Machine Learning to predict the value of a certain variable based on the value of another variable.

The goal of this article is to explore in-depth how Linear Regression works and the math behind it, explaining the relationship between dependent variables and independent variables and the main conditions for a successful Linear Regression.

For this notebook, we are going to use Sklearn’s diabetes dataset, which is particularly useful for regression tasks.

Linear Regression is a statistical method widely used in various fields, including economics, biology, engineering, and many more for predictive modeling and hypothesis testing.

It is based on modeling the relationship between and dependent variable and one or more independent variables by fitting a linear equation to the observed data. The primary objective of a linear regression is finding the best-fitting line that can accurately predict the output of the dependent variable given the values of the independent variable(s).