Advanced Statistical Inference and Modelling with R

Traditional linear regression is a powerful tool, but real-world data often breaks its core assumptions by presenting binary outcomes, count data, or nested groupings. To extract reliable insights from complex datasets, you need to expand your analytical toolkit to modern statistical modeling techniques. This text-based course equips you with the theoretical foundation and practical skills to confidently model diverse data structures using R. You will transition from basic linear models to advanced frameworks, learning how to select, fit, and evaluate the right model for your specific research or business question. Through clear explanations and structured code walkthroughs, you will develop a rigorous approach to statistical inference. What you'll learn: - Understand the core mathematical foundations of generalized linear models (GLMs) and statistical inference - Model binary and categorical outcomes using logistic and multinomial regression techniques in R - Analyze count data effectively using Poisson regression and address overdispersion challenges - Configure mixed-effects and hierarchical models to account for grouped or nested experimental designs - Apply modern diagnostics and model selection criteria to validate your statistical assumptions - Implement tidyverse-aligned workflows for clean, reproducible data modeling and analysis The course begins with a thorough grounding in statistical theory, probability distributions, and parameter estimation, ensuring you understand the 'why' behind the models. From there, you will systematically explore GLMs, hierarchical structures, and diagnostic workflows through detailed, step-by-step text explanations and R code examples. This course is designed for data analysts, researchers, and students who already understand basic linear regression and want to master advanced modeling techniques using R. No advanced mathematical background is required to start. Start expanding your statistical capabilities and write sophisticated modeling workflows in R today.