<p>Regression analysis is a powerful tool for cardiothoracic surgeons, enabling the prediction of patient outcomes and identification of key risk factors. This article explores the fundamentals of regression, including simple, multiple, and logistic regression models, and their applications in predicting surgical success, recovery times, and complication risks. Ensuring model assumptions are valid, selecting appropriate variables, and interpreting coefficients allows clinicians to make evidence-based decisions. Advanced techniques, such as handling non-linear relationships through polynomial regression and log transformations, are essential when dealing with more complex data. These techniques are critical in clinical research for addressing key research questions.</p>