Analysis of Variance in R: Comparing Group Means and Interactions

When comparing three or more groups, simple statistical tests are no longer sufficient and increase your risk of error. Analysis of Variance (ANOVA) provides the robust framework you need to identify true differences across multiple categories. This written course guides you through the foundational concepts of ANOVA and shows you how to implement them step-by-step using R. By reading through clear explanations and structured code examples, you will transition from understanding basic statistical variance to confidently interpreting complex interaction effects in your data. You will also learn to prepare your data using modern tidy conventions to ensure clean and reproducible analysis. What you'll learn: - Understand the fundamental statistical concepts of variance, hypothesis testing, and group means. - Perform one-way and two-way ANOVA using modern R syntax and tidy data principles. - Evaluate critical statistical assumptions, including normality and homogeneity of variance, using diagnostic workflows. - Interpret interaction effects to see how multiple factors combine to influence your results. - Conduct post-hoc pairwise comparisons to pinpoint exactly where significant differences lie. You will begin by exploring core statistical definitions and data structure requirements. From there, you will progress through practical R code examples, learning how to run ANOVA models, check mathematical assumptions, and write clear summaries of your findings. This course is designed for beginners in statistics and R programming who want to expand their data analysis toolkit. No advanced mathematical background is required. Start mastering statistical group comparisons in R today.