Linear Regression Online Courses & Certifications
Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It is a fundamental and widely used technique in statistics and machine learning for predicting continuous outcomes based on input features.
In a simple linear regression, there is only one independent variable, while in multiple linear regression, there are multiple independent variables. The goal of linear regression is to find the best-fitting straight line (linear equation) that describes the relationship between the independent variable(s) and the dependent variable.
It’s important to note that linear regression assumes a linear relationship between the dependent and independent variables. If the relationship is not linear, other regression techniques, such as polynomial regression or non-linear regression, may be more appropriate. Additionally, linear regression may require the assumption of certain statistical properties, such as the normality of residuals and homoscedasticity, for accurate interpretation and inference.
This course builds on simple linear regression by working with multiple predictor variables rather than just one. Multiple linear regression is a powerful tool for data scientists looking to analyze how multiple factors are related to an outcome. Python is used by professionals in the Data Analysis and Data Science fields as part of their daily work.
Learn how to fit and interpret linear regression with a single predictor variable.
Get started with machine learning and learn how to build, implement, and evaluate linear regression models.
Learn about the difference between simple linear regression and multiple linear regression in R