Introduction to Simple Regression in Public Health

Understanding how different factors impact community health is the foundation of effective public health policy and research. Simple linear regression is the essential statistical tool that allows you to quantify these relationships and draw meaningful conclusions from health data. This course guides you through the core principles of biostatistics, showing you how to model the relationship between a single predictor and a health outcome. You will gain the confidence to read, interpret, and critique regression analyses found in modern scientific literature, and apply these methods to real-world public health scenarios. What you'll learn: * Understand foundational biostatistics terminology and the core concepts of simple linear regression. * Formulate linear equations to model relationships between predictors and public health outcomes. * Interpret regression coefficients, confidence intervals, and p-values in the context of health studies. * Evaluate model assumptions and identify potential confounding variables in epidemiological data. * Apply reproducible research practices when documenting and reporting statistical findings. The journey begins with basic statistical definitions before moving step-by-step through model building, parameter interpretation, and validation. You will progress through clear written explanations, practical health-focused examples, and self-paced exercises. This course is designed for beginners, including aspiring public health professionals, clinical researchers, and students with no prior background in advanced statistics. Start building your quantitative skills today and unlock the stories hidden within public health data.