where Y is the response, or dependent, variable, the Xs represent the p explanatory variables, and the bs are the regression coefficients. For example, suppose that you would like to model a person's ...

Troy Segal is an editor and writer. She has 20+ years of experience covering personal finance, wealth management, and business news. Catherine Falls Commercial/Getty Images Linear regression is a type ...

A behind-the-scenes blog about research methods at Pew Research Center. For our latest findings, visit pewresearch.org. Many of Pew Research Center’s survey analyses show relationships between two ...

The purpose of this tutorial is to continue our exploration of regression by constructing linear models with two or more explanatory variables. This is an extension of Lesson 9. I will start with a ...

When you perform regression analysis in Microsoft Excel, you are engaging in a statistical process that helps you understand the relationship between variables. This technique is particularly useful ...

In this article, a Bayesian model for a constrained linear regression problem is studied. The constraints arise naturally in the context of predicting the new crop of apples for the year ahead. We ...

In order to improve the prediction accuracy of deep seafloor manganese nodule resource content, a prediction method of deep seafloor manganese nodule resource content based on multiple regression ...

Linear regression is a powerful and long-established statistical tool that is commonly used across applied sciences, economics and many other fields. Linear regression considers the relationship ...