Regression Analysis
In this comprehensive tutorial, we delve into the intricacies of Partial Least Squares Regression (PLSR) using the PAST 4.13 software. PLSR is a powerful multivariate statistical technique used for modeling relationships between predictor variables and response variables, especially in situations where there are high levels of multicollinearity or when the number of predictors exceeds the number of observations.
In this video, we provide a step-by-step guide on how to perform PLSR analysis in PAST 4.13, from data preparation to model interpretation. We begin by explaining the fundamentals of PLSR and its applications in various fields, including chemistry, biology, and economics.
Next, we demonstrate how to import data into PAST 4.13 and preprocess it for PLSR analysis, including handling missing values, scaling variables, and checking for outliers. We then guide you through the process of building a PLSR model, selecting the optimal number of components, and assessing model performance using cross-validation techniques.
Key Topics Covered:
- Introduction to Partial Least Squares Regression (PLSR) and its Applications
- Data preparation and preprocessing for PLSR analysis in PAST 4.13
- Building PLSR models and selecting the optimal number of components
- Assessing model performance using cross-validation techniques
- Interpreting PLSR results and extracting meaningful insights
- Practical applications and case studies demonstrating the use of PLSR in research and industry