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Partial least squares (PLS) is a method for constructing predictive models when there are many factors and they are highly collinear. It is useful for variable selection and dimension reduction
There are two primary reason for using PLS
- Prediction
PLS is most commonly used for constructing predictive model when the the information contained in a large number of original variables and they are highly collinear.
- Interpretation
PLS can be used to discover important features of a large data set. It often reveals relationships that were previously unsuspected, thereby allowing interpretations of the data that may not ordinarily result from examination of the data.
To perform partial least squares in Origin, select Statistics: Multivariate Analysis: Partial Least Square
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