johnmcgregor

Dr. John F. McGregor

About Dr. John F. McGregor

John has dedicated the last 40 years to helping manufacturers improve and optimize their processes using multivariate data analysis. During his tenure at McMaster University, John authored over 200 peer reviewed journal publications in the areas of mathematical modeling of processes, optimization and control, rapid product development and image analysis.John has received many awards for his pioneering work in developing and applying multivariate analysis to solve complex manufacturing problems, including the Shewhart Medal and W.G. Hunter Award from the American Society for Quality, the Herman Wold Medal from the Swedish Chemical Society and the R.S. Jane Award and Century of Achievement Award from the Canadian Society for Chemical Engineering. In 2004, John founded ProSensus to help manufacturers learn more from their big data to increase yield, reduce operating costs, and improve product quality.

Empirical Models for Analyzing “BIG” Data – What’s the Difference?

By |2019-06-19T10:17:11+00:00October 2nd, 2017|Multivariate News|

"BIG Data" is a current buzzword across almost all businesses and plays a key part in Industry 4.0. The term "BIG Data" is most appropriately used to characterize data that is not just large but complex in nature. Furthermore, there is not just one issue with BIG data – there are different purposes or objectives [...]

The Fallacy of the Golden Batch

By |2019-06-19T11:55:23+00:00January 29th, 2015|Multivariate News|

Over the years, batch automation has promoted the concept of a Golden Batch: a batch that progresses ideally and gives both excellent yields and final product quality. Once such a dream batch has been made, replication of the "sweet spot" quickly becomes the focus. But replicating the golden batch's outcome is much more complicated than [...]

Misconceptions about Latent Variable Models

By |2019-06-19T12:58:25+00:00August 14th, 2014|Multivariate News|

While giving courses over the years, I've noticed that certain misconceptions about latent variable models persist among statisticians and chemometricians. At the 14th annual ENBIS conference (European Network for Business & Industrial Statistics) in September, I have decided to discuss a number of these during my plenary George Box Medal presentation. Some of the key [...]