Implementing Regression Analysis in MATLAB

In this article, we provide a comprehensive guide on performing regression analysis using MATLAB. Regression analysis enables us to identify relationships between two or more variables and utilize these relationships to predict future outcomes. MATLAB offers multiple approaches for implementing regression analysis, including linear regression, polynomial regression, and logistic regression. We will explore the underlying principles and practical implementation of these methods, demonstrating how to apply them to analyze datasets in MATLAB. The article will cover key functions such as fitlm() for linear regression, polyfit() for polynomial curve fitting, and fitglm() for generalized linear models including logistic regression. Additionally, we will discuss techniques for evaluating regression models through residual analysis, R-squared values, and cross-validation methods using functions like plotResiduals() and crossval(). The content will also explain how to interpret regression results and extract meaningful insights from coefficients, confidence intervals, and prediction bounds. By studying this material, you will gain proficiency in conducting regression analysis with MATLAB and develop a deeper understanding of your data through practical implementation examples and statistical validation approaches.