Linear Regression and Parameter Estimation in MATLAB

In engineering and scientific research, translating raw data into predictive models is a critical skill. Understanding how to construct linear models and estimate parameters numerically allows you to make sense of complex experimental results. This text-based course guides you through the fundamental mathematics and practical programming steps to implement linear regression and parameter estimation. You will start with core statistical terminology and build up to writing clean, efficient MATLAB code to calculate predicted values, evaluate model fit, and understand Bayesian estimation basics. What you'll learn: Understand the foundational concepts of linear regression, residuals, and parameter estimation; Write MATLAB code to calculate predicted values and evaluate model accuracy; Apply ordinary least squares to fit linear models to experimental data; Explore the basics of Bayesian parameter estimation and uncertainty quantification; Structure numerical scripts cleanly using modern programming practices like modular functions and vectorization; Analyze model diagnostics to identify outliers and assess goodness of fit. You will begin by learning key definitions and the mathematical foundation of linear models. From there, you will progress to hands-on numerical implementation in MATLAB, translating formulas into executable scripts and analyzing engineering scenarios. This course is designed for engineering students, researchers, and beginners to numerical methods who want to master regression analysis. No prior advanced statistics experience is required, though a basic familiarity with MATLAB is helpful. Start reading today to unlock the power of numerical modeling and data analysis in your engineering workflow.