Transcription of An Introduction to Partial Least Squares Regression
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An Introduction to Partial Least Squares Regression
An Introduction toPartial Least Squares RegressionRandall D. Tobias, SAS Institute Inc., Cary, NCAbstractPartial Least Squares is a popular method for softmodelling in industrial applications. This paper intro-duces the basic concepts and illustrates them witha chemometric example. An appendix describes theexperimental PLS procedure of SAS/STAT in science and engineering often involvesusing controllable and/or easy-to-measure variables(factors) to explain, regulate, or predict the behavior ofother variables (responses). When the factors are fewin number, are not significantly redundant (collinear),and have a well-understood relationship to the re-sponses, then multiple linear Regression (MLR) canbe a good way to turn data into information.
called ‘‘latent variables’’ in formal structural equation modelling (Dykstra 1983, 1985). Figure 3 gives a schematic outline of the method. The overall goal (shown in the lower box) is to use Factors Responses Population Sample Factors Responses TU Figure 3: Indirect modeling the factors to predict the responses in the population.
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