Least Squares Regression in MATLAB for Chemical Engineers

Interpreting experimental data is a core skill for chemical engineers, but manual calculation of model fits and confidence intervals is slow and error-prone. This text-based course shows you how to automate least-squares analysis using MATLAB. You will transition from basic data entry to writing robust computational routines that perform linear regression, construct design matrices, and evaluate statistical significance. What you'll learn: - Understand the mathematical foundations of linear least-squares regression and design matrices. - Write MATLAB code to solve normal equations and fit linear models to experimental data. - Calculate confidence intervals and degrees of freedom to evaluate the reliability of your model fits. - Apply modern MATLAB practices, including table-based data structures and vectorized operations. - Analyze chemical engineering datasets to extract physical parameters and reaction constants. The course starts with fundamental statistical and matrix definitions before guiding you through step-by-step code construction. You will read structured explanations, study clear code snippets, and complete written exercises to reinforce your learning. This course is designed for engineering students and practicing professionals who want to master numerical data fitting in MATLAB, with no advanced programming experience required. Start building your data-fitting skills today.