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Discovering Partial Least Squares with JMP, by Ian Cox, Marie Gaudard
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Partial Least Squares (PLS) is a flexible statistical modeling technique that applies to data of any shape. It models relationships between inputs and outputs even when there are more predictors than observations. Using JMP statistical discovery software from SAS, Discovering Partial Least Squares with JMP explores PLS and positions it within the more general context of multivariate analysis.
Ian Cox and Marie Gaudard use a “learning through doing” style. This approach, coupled with the interactivity that JMP itself provides, allows you to actively engage with the content. Four complete case studies are presented, accompanied by data tables that are available for download. The detailed “how to” steps, together with the interpretation of the results, help to make this book unique.
Discovering Partial Least Squares with JMP is of interest to professionals engaged in continuing development, as well as to students and instructors in a formal academic setting. The content aligns well with topics covered in introductory courses on: psychometrics, customer relationship management, market research, consumer research, environmental studies, and chemometrics. The book can also function as a supplement to courses in multivariate statistics and to courses on statistical methods in biology, ecology, chemistry, and genomics.
While the book is helpful and instructive to those who are using JMP, a knowledge of JMP is not required, and little or no prior statistical knowledge is necessary. By working through the introductory chapters and the case studies, you gain a deeper understanding of PLS and learn how to use JMP to perform PLS analyses in real-world situations.
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.
- Sales Rank: #1950281 in Books
- Published on: 2013-10-18
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .70" w x 7.50" l, 1.18 pounds
- Binding: Perfect Paperback
- 306 pages
Review
"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 , 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
" --Alan Brown, Principal Technical Expert (Statistics), Syngenta UK Ltd
"After recalling the essence of what partial least squares (PLS) is doing and how it relates to other widely used multivariate techniques such as PCA or MLR, Discovering Partial Least Squares with JMP provides a series of case-studies. Each case-study is self-contained in its own chapter and the reader can focus independently on areas of his own interest.
As a French reader, I obviously jumped to the last case-study, which addresses the problem of "baking bread that people like." The analysis is conducted in a multi-stage manner, starting with a visual inspection of histograms and bi-plots. A PLS model is then built to relate consumer-rating items with Overall Liking. Similarly, another PLS model is built to relate expert sensory ratings with consumer ratings. Both resulting models are finally combined to provide a useful model involving a limited number of sensory ratings that explain Overall Liking. All of this is done with detailed explanations, numerous figures, and even JSL scripts that appear on the left panel of the data table so the user can reproduce each single step of the analysis.
While the analysis is performed, the authors do not neglect potential pitfalls that the reader should be aware of, in particular with respect to variable selection. They also illustrate with JMP� a unique interactive profiler that can be used to how selected predictors relate effectively to the response of interest. Technical details for Singular Value Decomposition (SVD) and PLS are given in the appendix, along with useful explanations of widely used indicators such as VIPs. The only slightly negative note is that little is said on the lack of interpretability of latent variables, even if this lack of interpretability does not hinder the predictive power of PLS. Readers might want to consider reading Shmueli's 2010 article, "To Explain or to Predict?", that appeared in Statistical Science.
Written by Dr. Ian Cox and Marie Gaudard, this book will be extremely useful for statistics practitioners who want to apply effectively predictive models, such as PLS, and fully benefit from JMP graphical and interactive functionalities." --Dr. Paul Fogel, Consultant
About the Author
Ian Cox 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. In addition to Discovering Partial Least Squares with JMP, Ian co-authored the book Visual Six Sigma: Making Data Analysis Lean.
Marie Gaudard is a consultant in the North Haven Group, a firm specializing in statistical training and consulting using JMP. She was a Professor of Statistics at the University of New Hampshire from 1977 until 2004. She has worked with a variety of clients both in manufacturing and transactional areas, providing consulting and training courses in the areas of Six Sigma, Design for Six Sigma, predictive modeling, and forecasting and demand planning. Gaudard holds a Ph.D. in statistics. In addition to Discovering Partial Least Squares with JMP, Marie co-authored the book Visual Six Sigma: Making Data Analysis Lean.
Most helpful customer reviews
1 of 1 people found the following review helpful.
This is the way a JMP book should be written!
By S. Whites
I'm a JMP Pro user with 2 years of experience; I thoroughly enjoyed this book. Not only is it an excellent, hands-on overview of PLS, it includes many JMP tips and tricks. Like many JMP users, I'm an enthusiast who enjoys learning more about JMP's capabilities. The authors have done a wonderful job describing how the PLS platform can be used to solve high-dimensional problems. Highly recommended!
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