Discovering Partial Least Squares with JMP®
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Sample Chapter: Predicting Biological Activity
Partial Least Squares (PLS) is a flexible statistical technique that applies to data of any shape. It models relationships between inputs and outputs even when the inputs are correlated and noisy, there are multiple outputs, and there are more inputs than observations.
The book and e-book Discovering Partial Least Squares with JMP®, by Ian Cox and Marie Gaudard uses JMP to explore PLS and position it within the context of statistical modeling and multivariate analysis.
While the book is helpful and instructive to those who are already using JMP, 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 will gain a deeper understanding of PLS and learn how to confidently use JMP to perform PLS analyses in real-world situations.
In reviewing the book, Professor Ron S. Kenett, CEO of KPA, commented that
“ . . . combining this theoretical foundation with practical implementations provides unique insights that make this an important contribution to the statistical literature.”
Sample chapter: Predicting Biological Activity
New drugs are developed from chemicals that are biologically active, and because testing a compound for activity is expensive, it’s useful to try to predict this from other, cheaper, chemical measurements. This example studies the relationship between the size, hydrophobicity, and polarity of key chemical groups at various sites on a molecule, and the activity of the compound itself.
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