The algorithm reduces the number of predictors using a technique similar to principal components analysis to extract a set of components that describes maximum correlation between the predictors and response variables. To perform PLS, Minitab uses the nonlinear iterative partial least squares (NIPALS) algorithm developed by Herman Wold. Therefore, it is not usually used to screen out variables that are not useful in explaining the response. In PLS regression, the emphasis is on developing predictive models. A common application is to model the relationship between spectral measurements (NIR, IR, UV), which include many variables that are often correlated with each other, and chemical composition or other physio-chemical properties.
PLS regression is primarily used in the chemical, drug, food, and plastic industries. This means that the predictors can be measured with error, making PLS more robust to measurement uncertainty. PLS does not assume that the predictors are fixed, unlike multiple regression. PLS regression is especially useful when your predictors are highly collinear, or when you have more predictors than observations and ordinary least-squares regression either produces coefficients with high standard errors or fails completely. Partial least squares (PLS) regression is a technique that reduces the predictors to a smaller set of uncorrelated components and performs least squares regression on these components, instead of on the original data.
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