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 ...

Linear regression is one of the simplest and most useful tools for analyzing data. It helps you find the relationship between variables so you can make predictions and understand patterns. In this ...

Model building via linear regression models. Method of least squares, theory and practice. Checking for adequacy of a model, examination of residuals, checking outliers. Practical hand on experience ...

Goodness-of-fit statistics for general multiple-linear-regression equations are reviewed for the case of replicated responses. A modification of the coefficient of determination is recommended. This ...

During the course of operation, businesses accumulate all kinds of data such as numbers related to sales performance and profit, and information about clients. Companies often seek out employees with ...

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 ...

Regression models to relate a scalar Y to a functional predictor X (t) are becoming increasingly common. Work in this area has concentrated on estimating a coefficient function, β(t), with Y related ...

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 ...

Not all regression is bad. Logan Webb allowed more damage in high-leverage situations than low-leverage situations last season. Either he’s uniquely bad when the pressure is on, or it was just a ...