Multiple linear regression (MLR) is a statistical technique that uses several explanatory variables to predict the outcome of a response variable.

Learn the difference between linear regression and multiple regression and how investors can use these types of statistical analysis.

Lesson 10 Multiple Linear Regression 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 ...

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 ...

Moderated multiple regression models allow the simple relationship between the dependent variable and an independent variable to depend on the level of another independent variable. The moderated ...

The purpose of this paper is to introduce a multiple regression model that allows for stochastic changes of the trading-day coefficients used to calculate trading-day variations. Estimation and ...