Overview

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.

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9781612908229

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Reviews

The authors have written a text which is an excellent supplement to the manuals supplied with JMP. The techniques of multiple linear regression (MLR) and principal components analysis are reviewed in the context of application within JMP before the principles of PLS are described. Instructions for performing PLS within JMP are provided together with examples of model specification, fit, and diagnostic reports. Detailed case studies are provided from a range of disciplines, such as predicting octane value from NIR spectra; predictive models for consumer preference, and taste panel data for bread. A number of JSL scripts are provided so that the reader can perform the operations described within the text with simulations used to illustrate key points; for example, the effect of multiple colinearity on parameter estimates in MLR. The scripts and simulations bring the text to life making it a valuable addition to the JMP multivariate modeller's bookshelf. -- Alan Brown, Principal Technical Expert (Statistics) Syngenta UK Ltd

The authors have written a text which is an excellent supplement to the manuals supplied with JMP. The techniques of multiple linear regression (MLR) and principal components analysis are reviewed in the context of application within JMP before the principles of PLS are described. Instructions for performing PLS within JMP are provided together with examples of model specification, fit, and diagnostic reports. Detailed case studies are provided from a range of disciplines, such as predicting octane value from NIR spectra; predictive models for consumer preference, and taste panel data for bread. A number of JSL scripts are provided so that the reader can perform the operations described within the text with simulations used to illustrate key points; for example, the effect of multiple colinearity on parameter estimates in MLR. The scripts and simulations bring the text to life making it a valuable addition to the JMP multivariate modeller's bookshelf. -- Alan Brown, Principal Technical Expert (Statistics) Syngenta UK Ltd As nicely stated by the authors, Partial Least Squares (PLS) can deal effectively with wide data, tall data, square data, collinear variables, and noisy data. These different characterizations of uncertainty often make standard analysis difficult, if not impossible. PLS can handle these situations and, with the combination of JMP applications, the book positions PLS within reach of practitioners and researchers in various domains of applications. This book is not about the theory of PLS but about its applications in real-life problems. It does include, however, a historical perspective and the mathematical foundations of the PLS algorithms. Combining this theoretical foundation with practical implementations provides unique insights that make this an important contribution to the statistical literature. -- Professor Ron S. Kenett Research Professor, University of Turin, Italy and International Professor, NYU Center for Risk Engineering, USA

As nicely stated by the authors, Partial Least Squares (PLS) can deal effectively with wide data, tall data, square data, collinear variables, and noisy data. These different characterizations of uncertainty often make standard analysis difficult, if not impossible. PLS can handle these situations and, with the combination of JMP applications, the book positions PLS within reach of practitioners and researchers in various domains of applications. This book is not about the theory of PLS but about its applications in real-life problems. It does include, however, a historical perspective and the mathematical foundations of the PLS algorithms. Combining this theoretical foundation with practical implementations provides unique insights that make this an important contribution to the statistical literature. -- Professor Ron S. Kenett Research Professor, University of Turin, Italy and International Professor, NYU Center for Risk Engineering, USA The authors have written a text which is an excellent supplement to the manuals supplied with JMP. The techniques of multiple linear regression (MLR) and principal components analysis are reviewed in the context of application within JMP before the principles of PLS are described. Instructions for performing PLS within JMP are provided together with examples of model specification, fit, and diagnostic reports. Detailed case studies are provided from a range of disciplines, such as predicting octane value from NIR spectra; predictive models for consumer preference, and taste panel data for bread. A number of JSL scripts are provided so that the reader can perform the operations described within the text with simulations used to illustrate key points; for example, the effect of multiple colinearity on parameter estimates in MLR. The scripts and simulations bring the text to life making it a valuable addition to the JMP multivariate modeller's bookshelf. -- Alan Brown, Principal Technical Expert (Statistics) Syngenta UK Ltd

The authors have written a text which is an excellent supplement to the manuals supplied with JMP. The techniques of multiple linear regression (MLR) and principal components analysis are reviewed in the context of application within JMP before the principles of PLS are described. Instructions for performing PLS within JMP are provided together with examples of model specification, fit, and diagnostic reports. Detailed case studies are provided from a range of disciplines, such as predicting octane value from NIR spectra; predictive models for consumer preference, and taste panel data for bread. Alan Brown, Principal Technical Expert (Statistics), Syngenta UK Ltd

Author Information

Ian Cox currently works in the JMP Division of SAS. Before joining SAS in 1999, he worked for Digital, 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 Ph.D. in theoretical physics. Marie Gaudard is a consultant in the North Haven Group, a small consulting firm specializing in statistical training and consulting using JMP. She earned her Ph.D. in statistics in 1977 and was a Professor of Statistics at the University of New Hampshire from 1977 until 2004. She has been heavily involved in statistical consulting since 1981. Gaudard has worked with a variety of clients in transactional areas, including government agencies and financial departments, as well as with manufacturers, including automotive, printing, paper, plastics, precision steel, and paving, as well as shipyards. She has also been involved in the analysis of medical data. Gaudard has extensive experience in providing consulting and training courses for business and industry in the areas of Six Sigma, Design for Six Sigma, forecasting and demand planning, and data mining.

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