Multiple Linear Regression Model with Principal Component Analysis

In this article, the author presents a method combining Multiple Linear Regression models with Principal Component Analysis principles to solve problems. The author provides detailed explanations on implementing this approach using MATLAB programming, including key functions like pca() for dimensionality reduction and regress() or fitlm() for regression modeling. The implementation covers data preprocessing, principal component extraction, regression coefficient calculation, and model validation steps. Furthermore, the author compares this method with alternative approaches and discusses both the advantages and limitations of the proposed technique. The article includes practical code examples demonstrating how to handle multicollinearity issues through PCA before performing regression analysis. Overall, this article offers an in-depth solution that helps readers better understand and apply Multiple Linear Regression models combined with Principal Component Analysis principles in real-world scenarios.