Advanced Statistical Modeling in R

Moving beyond basic t-tests and simple linear regressions is essential for unlocking the true power of your data. This text-based course guides you through sophisticated statistical modeling and multivariate techniques in R without unnecessary complexity. You will transition from basic data summaries to executing advanced predictive and grouping models. By learning to structure your analyses using modern tidy statistics workflows, you will gain the confidence to interpret complex data relationships, evaluate group differences, and perform dimensional reduction. What you'll learn: - Understand foundational concepts of multivariate statistics and advanced hypothesis testing. - Apply analysis of covariance (ANCOVA) and mixed-design analysis of variance (ANOVA) to compare group means. - Build and interpret binomial, ordinal, and multinomial logistic regression models for categorical outcomes. - Group complex datasets using k-means and hierarchical clustering techniques. - Reduce data dimensionality using principal component analysis (PCA) and factor analysis. - Implement modern R workflows using tidyverse principles to keep your statistical code clean and reproducible. The course begins with essential terminology and foundational theory before guiding you step-by-step through advanced testing, predictive modeling, and unsupervised classification techniques. You will read clear explanations, study structured code examples, and practice interpreting real-world statistical outputs. This course is designed for learners who understand basic R syntax and want to expand their statistical toolkit. No prior advanced math background is required. Start writing elegant, advanced statistical code in R today.