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:

$y\; =\; \backslash beta\_0\; +\; \backslash beta\_1u\; +\; \backslash beta\_2v\; +\; \backslash beta\_3u^2\; +\; \backslash beta\_4uv\; +\; \backslash beta\_5v^2$

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. ^*

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
• Statistically Significant Consulting, LLC- Multiple Regression Explained in Plain English

Estimation theory