Involving multiple explanatory variables adds complexity to the method, but the overall principles remain the same. - Multiple linear regression formula. The equation for multiple linear regression ...

Learn how to classify your variables into continuous, categorical, ordinal, and binary, and how to handle them for effective linear regression projects.

In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables).

Lesson 9 Simple Linear Regression. The purpose of this tutorial is to continue our exploration of multivariate statistics by conducting a simple (one explanatory variable) linear regression analysis.

In the case of causal methods, the causal model may consist of a linear regression with several explanatory variables. This method is useful when there is no time component. Advertisement ...

In the multivariate linear regression model, it is desirable to include the important explanatory variables to achieve maximal prediction. In this context, the present paper is an attempt to extend ...

This paper presents a fuzzy clusterwise regression method aiming to provide linear regression models that are based on homogeneous clusters of observations with respect to the explanatory variables ...