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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.
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