Upcoming Webinar

Webinar: Problems with R-squared in Model Building and Decision Making

Kevin Keller
Kevin Keller

Webinar: Problems with R-squared in Model Building and Decision Making

In generalized terms, there are two types of statistical models we attempt to create. One is some form of a regression model, where we take historical data and attempt to model the independent variables (inputs) against a dependent variable (output) - however they might have behaved. The other is a model created from an experimental design where we carefully control the variation of the inputs to model the output. Both of these analyses utilize Analysis of Variance (ANOVA) and one of the statistics generated is R-Squared (as well as R-squared adjusted). The interpretation of what R-squared actually means in these two types of models is radically different. Furthermore, we will explore in the regression setting how businesses can make mistakes in decision making due to the fascination of “higher R-squared is better” in model building.

About Kevin Keller

Kevin Keller has over 35 years of industrial experience. He spent the first 20 years in the electronic materials world making high-tech circuit boards for military equipment and silicon wafers for the semiconductor industry. Kevin retired a few years ago from Anheuser-Busch InBev, where he served as a Master Black Belt. Kevin was responsible for Quality Systems in manufacturing cans and lids for the beverage market. He is now an independent consultant for global and local companies in Lean Six Sigma, Quality, and Quality Management. He has a BS degree in Chemical Engineering from the University of Missouri-Rolla and a Master's in Applied Statistics from The Ohio State University.

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