In this post, I cover the more common types of regressionanalyses and how to decide which one is right for your data. I’ll provide an overview along with information to help you choose.
Widely used in fields like business, economics, healthcare, and social sciences, regressionanalysis provides a robust framework for data-driven decision-making. This article explores the methods, types, and practical applications of regressionanalysis, offering a comprehensive guide for researchers and practitioners.
TypesofRegressionAnalysis: Have you ever wondered how businesses predict next quarter’s sales, or how economists forecast inflation trends? The answer often lies in regressionanalysis.
The two main types of regression are linear regression and logistic regression. Linear regression is used to predict a continuous numerical outcome, while logistic regression is used to predict a binary categorical outcome (e.g., yes or no, pass or fail).
Explore the diverse landscape of Regression Analysis in this blog, covering fundamental types such as Simple Linear and Multiple Linear Regression, advanced models like Polynomial, Ridge, and Lasso Regression, along with specialised applications like Logistic and Time Series Regression.
In machine learning, there are many types of regression models, each with strengths for specific data scenarios and prediction needs. These examples highlight the diversity and versatility of regression techniques across diverse domains, including how they’re applied in real-world contexts.
In this post, I cover the more common types of regression analyses and how to decide which one is right for your data. I’ll provide an overview along with information to help you choose.