Linear Regression for Engineers: Simple and Multiple Regression Models

Engineering projects generate vast amounts of data, but raw numbers are only useful if you can identify the underlying relationships between variables. This text-only course introduces you to linear regression, the fundamental statistical tool used to model, analyze, and predict engineering system behaviors. You will transition from understanding basic statistical concepts to confidently formulating, fitting, and evaluating regression models. By reading through practical engineering scenarios, you will learn how to predict physical outcomes, assess model accuracy, and make informed design and operational decisions based on data. What you'll learn: Understand the foundational principles of simple and multiple linear regression; Calculate and interpret regression coefficients, residuals, and the coefficient of determination; Apply hypothesis testing and confidence intervals to evaluate model significance; Diagnose model assumptions and identify common issues like multicollinearity and heteroscedasticity; Evaluate model performance using modern validation techniques and metrics. The course begins with essential statistical definitions and the mechanics of simple linear regression before advancing to multi-variable systems. You will progress through step-by-step written explanations, mathematical breakdowns, and practical engineering case studies. This course is designed for engineering students, practicing engineers, and technical professionals who are new to statistical modeling, with no prior background in advanced statistics required. Start reading today to master the core statistical models that drive modern engineering analysis.