Partial Least Squares Regression Modeling Analysis

This document discusses the Partial Least Squares Regression modeling analysis program. This program implements a modern statistical analysis method that operates within the MATLAB environment. Compared to conventional analytical approaches, it offers several distinct advantages. Most notably, it effectively handles high-dimensional data by reducing variable dimensions while preserving most of the original information through latent variable extraction. The algorithm employs covariance maximization between predictor and response variables to identify optimal components. Additionally, it proficiently addresses multicollinearity issues and small sample size scenarios, which pose significant challenges for other methods. The implementation typically utilizes MATLAB's plsregress function, which calculates regression coefficients, variance explanations, and latent components. In summary, Partial Least Squares Regression modeling serves as a powerful computational tool that enhances our ability to understand and interpret complex datasets through its robust mathematical framework.