Live Webinar

Why You Should Consider Partial Least Squares For Your Next Statistical Modeling

Tuesday, October 18 | 12:00 PM - 1:00 PM EDT | Cost: Complimentary

About the Webinar

Partial Least Squares (PROC PLS in SAS) can be useful in modelling situations where you have many predictor variables which are (highly) correlated with each other. This talk will cover some of the benefits of using Partial Least Squares for modelling, and also some of the disadvantages. An example will be discussed where a model needs to be fit and there are 1000 predictor variables. This talk will not cover the mathematics of PLS.

You will learn about:

  • Benefits of PLS
    • PLS is robust against multicollinearity
    • Variable selection step can be skipped
    • Yields more stable models

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About the Expert


Paige Miller
Credit Risk Analyst
M&T Bank

Paige Miller received a bachelor’s degree in statistics and master’s degree in statistics from the University at Buffalo. He has worked as a statistician for the US Department of Labor, Eastman Kodak, Truesense Imaging, ON Semiconductor and currently works for M&T Bank in Buffalo, NY as a Credit Risk Analyst.

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