Welcome to the Linear Regression Repository! This repository is dedicated to providing a comprehensive collection of resources and code examples for two types of linear regression: Simple Linear Regression and Multiple Linear Regression. Whether you are new to linear regression or an experienced practitioner, this repository has everything you need to understand and implement these regression techniques effectively.
Simple Linear Regression is the basic form of linear regression that involves predicting a dependent variable using a single independent variable. This section of the repository provides an introduction to the theory, assumptions, and implementation of Simple Linear Regression. You will find resources and code examples that demonstrate how to fit a line to a dataset and make predictions based on the linear relationship between variables.
Multiple Linear Regression extends Simple Linear Regression to accommodate multiple independent variables. In this section, you will learn about the concepts, assumptions, and implementation of Multiple Linear Regression. The resources and code examples provided will guide you in building models that consider multiple factors influencing the dependent variable, allowing for more accurate predictions and deeper insights.