Healthcare Data Analysis with Regression Models in R

Healthcare decisions increasingly rely on data, but turning raw clinical numbers into actionable insights requires robust statistical methods. Understanding how different variables interact is key to predicting patient outcomes and improving healthcare delivery. This text-only course guides you from foundational statistical concepts to building and interpreting regression models using R. You will learn how to analyze healthcare datasets, evaluate model performance, and draw valid conclusions to support clinical and administrative decision-making. What you'll learn: Understand foundational regression concepts and key terminology used in healthcare research; Build and interpret linear regression models to analyze continuous healthcare outcomes; Implement logistic regression to predict binary clinical events and patient risks; Identify and manage non-linear trends, interacting variables, and outliers in medical data; Apply modern R packages and clean coding practices to prepare healthcare datasets; Evaluate model fit and validate statistical assumptions using diagnostics. The course starts with essential definitions and basic statistical concepts before introducing linear and logistic regression workflows. You will progress through written explanations and practical data scenarios using R, learning to interpret statistical outputs in a healthcare context. Designed for healthcare professionals, clinical researchers, and aspiring data analysts who are new to regression modeling, this course requires no advanced programming or statistical background. Start reading today to unlock the power of regression analysis in healthcare.