Analyzing Healthcare Data with R: Linear Regression and Correlation

Healthcare organizations generate vast amounts of data, but translating that data into actionable insights requires robust statistical techniques. Understanding how different variables interact—such as patient age and recovery times—is essential for improving clinical and operational outcomes. This text-based course guides you through the foundational concepts of linear relationships and regression analysis specifically within a healthcare context. You will learn to use R statistical programming to clean, visualize, and model clinical data, turning raw numbers into meaningful statistical evidence. What you'll learn: - Understand the core principles of correlation and linear relationships in healthcare datasets. - Build and interpret ordinary least squares (OLS) linear regression models using R. - Evaluate model performance using key diagnostic tests and residual analysis. - Incorporate categorical clinical data into your models using dummy variables. - Apply modern tidyverse workflows in R for efficient data preparation and visualization. - Communicate statistical findings clearly to healthcare stakeholders and clinical teams. The course begins with essential terminology, establishing a solid foundation in statistical concepts before moving to hands-on analysis. You will progress from simple correlation to multi-variable linear regression, practicing diagnostic testing and interpretation through written scenarios. This course is designed for healthcare professionals, clinical researchers, and aspiring data analysts who are new to R and statistical modeling. No prior programming or advanced statistics experience is required. Start reading today to unlock the power of predictive analysis in healthcare.