Linear Regression in R for Public Health Data

Understanding the factors that influence public health outcomes is crucial for effective interventions and policy-making. This course equips you with the foundational skills to apply linear regression, a powerful statistical tool, to real-world public health datasets using the R programming language. You will transform from a beginner in statistical modeling to someone capable of building and interpreting predictive models for health-related phenomena. What you'll learn: * Learn core concepts of linear regression, including assumptions and interpretation * Apply R programming fundamentals for data loading, cleaning, and manipulation relevant to public health studies * Construct simple and multiple linear regression models using R's statistical capabilities * Evaluate model performance and diagnose common issues like heteroscedasticity or multicollinearity * Interpret regression coefficients and statistical significance in the context of public health research * Practice modern R data wrangling techniques using the `tidyverse` package for efficient data preparation * Communicate findings effectively through clear, reproducible reports using R code The course progresses from fundamental statistical concepts and R basics, through building and validating linear regression models, to interpreting results for actionable public health insights. You will read explanations and practice through structured exercises. This course is designed for absolute beginners in statistical modeling and R programming, including public health students, researchers, and professionals who wish to analyze health data. No prior knowledge of statistics or programming is required. Begin your journey into quantitative public health analysis today.