Webinar: Improving Multiple Linear Regression Models with the Box-Tidwell Transformation
One of the assumptions for multiple linear regression is that the predictor X’s are linearly related to the response Y. When the relationship is nonlinear, common tools to deal with this include a Box-Cox transformation of the response, and adding interaction/quadratic terms to the predictors, but these may not suffice. This webinar introduces the lesser known but very powerful Box-Tidwell test and power transformation of continuous predictors, that can be used to dramatically improve your multiple linear regression model. This will be demonstrated with examples using SigmaXL software.
About John Noguera
John Noguera is Co-founder and Chief Technology Officer of SigmaXL, Inc., a leading provider of user-friendly Excel add-ins for Lean Six Sigma tools, statistical & graphical analysis and Monte Carlo simulation. He leads the development of SigmaXL and DiscoverSim with a passion for ease of use, practical & powerful features, and statistical accuracy. John is a Six Sigma master black belt and was an instructor at Motorola University. He has authored conference papers on Statistical Process Control and Six-Sigma Quality and is a contributing author in the Encyclopedia of Statistics in Quality and Reliability (Wiley).