Comprehensive Linear Modeling with R

Explore Comprehensive Linear Modeling with R and master a wide array of statistical techniques for data analysis. This course offers a deep dive into both linear and non-linear modeling approaches, perfect for graduate students and data-analytic professionals seeking to enhance their skills.

You'll learn basic, conditional, and simultaneous inference techniques, building a strong foundation in statistical reasoning. The course covers Analysis of Variance (ANOVA), a cornerstone of statistical analysis, and delves into linear regression, including methods for validating linear models using graphical displays and model comparison.

Discover the power of Generalized Linear Models (GLMs) with numerous practical examples. Gain expertise in survival analysis using both linear and non-linear models, and explore advanced techniques like smoothers and Generalized Additive Models (GAMs).

This course also covers complex experimental designs, including longitudinal models with and without Generalized Estimating Equations (GEE), mixed-effects models, split-plot designs, and nested models. You'll gain hands-on experience using R Commander, a user-friendly GUI for R, offering an SPSS-like interface to implement a vast range of statistical and graphical techniques. Note: R Commander's GUI has known compatibility issues on macOS.

The curriculum progresses systematically, starting with graphical plotting techniques and culminating in simultaneous inference methods. Each technique is explained with clear concepts and demonstrated through real-world examples using actual research data. Whether you're a beginner, intermediate, or advanced R user, this course provides the knowledge and skills to confidently perform sophisticated linear modeling tasks. It's ideal for anyone performing quantitative analysis or data modeling in their professional role.