Data Fitting and Regression in Chemical Engineering with MATLAB

Chemical engineering relies heavily on interpreting experimental data, from reaction kinetics to thermodynamic properties. Mastering the mathematical tools to fit this data is essential for modeling, analyzing, and optimizing chemical systems. In this introductory text-only course, you will learn how to perform linear regression and multiple least squares data fitting using MATLAB. You will start with the foundational mathematical concepts of regression, transition to constructing design matrices, and learn how to extract statistical confidence intervals to evaluate the reliability of your chemical engineering models. What you'll learn: - Understand the core mathematical principles of linear least squares and multiple regression. - Build design matrices to represent complex chemical engineering and thermodynamic systems. - Compute confidence intervals using statistical toolkits to assess model uncertainty and parameter significance. - Apply regression techniques to real-world chemical data, such as reaction rates and concentration profiles. - Practice modern MATLAB scripting conventions, including vectorized operations and structured data handling for cleaner code. The course begins with foundational definitions and statistical terminology before guiding you through step-by-step code implementations. You will read clear explanations, analyze structured MATLAB code blocks, and complete practical written exercises to solidify your data-fitting skills. This course is designed for beginner chemical engineering students and researchers looking to build solid data analysis skills. No prior advanced statistics experience is required, though a basic familiarity with MATLAB is helpful. Start mastering chemical data regression and build more reliable engineering models today.