Linear regression is one of the most widely used statistical tools in clinical research, offering a straightforward way to examine relationships between variables, adjust for confounding, and even predict outcomes. In orthopaedics, it has been applied to questions ranging from prosthesis alignment in knee arthroplasty to the impact of physical therapy on cost of care. At its core, regression fits a line that best describes the relationship between dependent and independent variables, using techniques like Ordinary Least Squares. While assumptions such as linearity, independence, homoscedasticity, normality of residuals, and low collinearity must be met for valid results, the model’s simplicity, interpretability, and computational efficiency make it invaluable. Beyond its role in hypothesis testing, regression supports risk factor identification, prognostic modeling, and evidence synthesis. Yet, limitations remain: outliers, overfitting, and the inability to establish causality without experimental design. Despite these, linear regression continues to serve as a foundation for modern orthopaedic analytics and beyond.