Multiple Regression Analysis for Public Health Research

Understanding how multiple factors simultaneously influence health outcomes is essential for any modern public health professional or researcher. Moving beyond simple correlations allows you to uncover the deeper drivers of community health and disease patterns. This course guides you through the transition from basic statistical concepts to building and interpreting sophisticated regression models that reflect the complexity of real-world health data. What you'll learn: - Understand foundational biostatistics terminology and the core principles of linear modeling - Master the transition from simple linear regression to multivariable models for complex data - Interpret regression coefficients and statistical significance within a public health context - Analyze the impact of confounding variables and interaction effects on health outcomes - Apply model diagnostic techniques to ensure the validity and reliability of your findings - Practice data cleaning and preparation steps essential for modern statistical analysis The course begins with essential definitions and foundational biostatistics theory before moving into the mechanics of building models and interpreting results through written explanations and practical exercises. You will learn to navigate the nuances of public health data and communicate findings effectively. This course is designed for beginners in biostatistics, health science students, and researchers who have a basic grasp of statistics but no prior experience with advanced modeling. No specialized software knowledge is required to start. Begin your journey toward mastering the statistical tools used in professional public health literature.