Regression output can be interpreted for both the size and strength of a correlation among one or more variables on the dependent variable.

Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is ...

Linear forecasting models can be used in both types of forecasting methods. In the case of causal methods, the causal model may consist of a linear regression with several explanatory variables.

Nonlinear regression modeling is similar to linear regression modeling in that both seek to track a particular response from a set of variables graphically.

One common problem in the use of multiple linear or logistic regression when analysing clinical data is the occurrence of explanatory variables (covariates) which are not independent, ie ...

Correlated response data often arise in longitudinal and familial studies. The marginal regression model and its associated generalized estimating equation (GEE) method are becoming more and more ...

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 ...

Added-variable plots are useful for a variety of data-analytic purposes but can be seriously misleading when used to investigate curvature and predictor transformations in linear regression, unless ...