Director, Pre-Sales Support, JMP
Ian Cox currently works in the JMP Division of SAS. Before joining SAS in 1999, he worked for Digital Equipment Corporation, Motorola, and BBN Software Solutions Ltd. and has been a consultant for many companies on data analysis, process control, and experimental design. A Six Sigma Black Belt, he was a Visiting Fellow at Cranfield University and is a Fellow of the Royal Statistical Society in the United Kingdom. Cox holds a PhD in theoretical physics.
By This Author
Discovering Partial Least Squares with JMP®
Using JMP statistical discovery software from SAS, Discovering Partial Least Squares with JMP explores Partial Least Squares and positions it within the more general context of multivariate analysis. This book motivates current and potential users of JMP to extend their analytical repertoire by embracing PLS. Dynamically interacting with JMP, you will develop confidence as you explore underlying concepts and work through the examples. The authors provide background and guidance to support and empower you on this journey.
Visual Six Sigma: Making Data Analysis Lean, Second Edition
Visual Six Sigma: Making Data Analysis Lean, Second Edition allows you to make sound business decisions faster by understanding the patterns of variation in your data and separating it into useful signal and random noise. Leading with a visual approach to analysis, it presents underlying principles, concepts, and a detailed roadmap that enable you to take an active role in data-driven process improvement and decision making. It empowers you to use your contextual knowledge to pose relevant questions, get good answers, and make sound decisions.
- Marie Gaudard is a consultant specializing in statistical training with the use of JMP. She is currently a statistical writer with the JMP documentation team
- Satish Garla is a former Analytical Consultant in Risk Practice at SAS.
- Sam Gardner is a Senior Research Scientist at Eli Lilly and Company where he is focusing on business analytics and using statistical modeling.