Partial Least Squares: A Comprehensive Guide to Overcoming Data Challenges

Multicollinearity and Multivariate Analysis with PLS

Keywords: Partial Least Squares (PLS), Multicollinearity, Multivariate Analysis, Predictive Modeling, PLS Regression, PLS Discriminant Analysis, High-dimensional Data, Variance vs. Covariance, Machine Learning, Data Science, Statistical Analysis, R Programming, Python Programming, Dimensionality Reduction, Canonical Correlation Analysis, Principal Components Regression, Model Benchmarking, Chemometrics, Sensory Analysis.

Introduction to Partial Least Squares

Overview of Linear Regression and Its Limitations

Linear regression stands as one of the most fundamental and widely used statistical methods for understanding the relationship between a dependent variable and one or more independent variables. By fitting a linear equation to observed data, linear regression models can predict the outcome of a dependent variable based on the values of independent variables. However, despite its ubiquity, linear regression comes with its own set of limitations, particularly when dealing with multicollinearity among predictors and high-dimensional data.