Comprehensive Linear Modeling with R

🚀 Course Title: Mastering Linear Modeling with R 📊

Course Headline: Learn to Model with R: ANOVA, Regression, GLMs, Survival Analysis, GAMs, Mixed-Effects, Split-Plot & Nested Designs

Comprehensive Linear Modeling with R is your ultimate guide to mastering a wide array of statistical modeling techniques using everyone's favorite language for data analysis—R! This course, led by the esteemed Dr. Geoffrey Hubona, offers an in-depth exploration of various contemporary approaches to linear and non-linear data modeling. From the basics of ANOVA and regression to advanced topics like survival analysis and mixed-effects models, this course covers it all.

Course Highlights:

• R Commander Mastery: Learn to harness the power of R Commander for a seamless GUI experience in data analysis.
• Diverse Modeling Techniques: Delve into ANOVA, linear regression, GLMs, survival analysis, GAMs, and more with real-world examples.
• Advanced Topics: Explore mixed-effects, split-plot, and nested designs to tackle complex data structures.
• Validating Models: Understand how to validate your models using various graphical tools for model comparison.
• Simultaneous Inference: Discover the techniques necessary for simultaneous inference within the linear modeling framework.

What You'll Learn:

📈 Graphical Techniques: Get started with a range of plotting methods to visualize your data effectively. 🔢 Inference and Conditional Inference: Master the foundational concepts for establishing statistical inferences. 🔄 Linear Regression & Validation: Learn how to perform linear regression and validate your models using robust methodologies. 🌱 Generalized Linear Models (GLMs): Explore beyond regular linear models to understand the broader scope of GLMs. 🏥 Survival Analysis: Handle survival data with confidence, understanding the implications for longitudinal studies. 📋 Smoothing & GAMs: Discover the role of smoothers and GAMs in modeling complex relationships in your data. 🔧 Longitudinal Data Models: Learn to model data from studies that measure the same variables at multiple time points. 🔄 Mixed-Effects, Split-Plot & Nested Designs: Tackle multilevel and nested data structures with ease. ✅ Model Selection & Comparison: Gain insights into comparing and choosing the best model for your dataset.

Who Should Take This Course:

This course is designed for graduate students, researchers, and professionals across disciplines who are looking to enhance their data analysis skills using R. Whether you're a beginner with basic knowledge of R or an experienced user seeking to refine your techniques, this course will provide valuable insights and practical experience to improve your linear modeling abilities.

Prerequisites:

• Basic knowledge of R is required.
• Familiarity with statistical concepts is helpful but not mandatory.

Course Format:

The course is structured to ensure a comprehensive understanding of the topics covered through theoretical explanations and practical demonstrations using real data sets. Each concept is first introduced theoretically before being applied in practice, ensuring a solid grasp of both the principles and their practical implementation.

Special Note:

Please be aware that R Commander, which is used extensively in this course, may encounter issues on Mac computers due to compatibility concerns with the underlying GTK+ libraries. It is recommended to use R Commander on Windows or Linux environments for a smoother learning experience.

Embark on your journey to becoming an expert in linear modeling with R. Sign up now and transform your data into meaningful insights with Comprehensive Linear Modeling with R! 🌟