Multiple regression

A multiple regression is a linear regression with more than one covariate (predictor variable). It can be viewed as a simple case of canonical correlation.

An equation used to predict a dependent variable, y from two independents, u and v is:

 $y=\beta _{0}+\beta _{1}u+\beta _{2}v+\beta _{3}u^{2}+\beta _{4}uv+\beta _{5}v^{2}$

Multiple Regression Correlation Coefficient

The multiple regression correlation coefficient ($R^{2}$) is a measure of the proportion of variability explained by, or due to the regression (linear relationship) in a sample of paired data. It is a number between zero and one and a value close to zero suggests a poor model. [1]

External links

• Multiple Regression Analysis using Excel and Minitab- Free Tutorial at Six Sigma First.com
• Statistics Solutions: Multiple Regression - Basic description of multiple regression