Advanced Linear Models with R
Classical regression diagnostics, Generalized Linear Models (GLMs) for count and categorical data, and mixed-effects models for correlated observations. Practical data analysis in R is presented alongside non-parametric smoothing, decision trees, and neural networks.
What This Course Covers
Advanced Linear Models with R is structured into 15 chapters that build on each other progressively:
Each chapter combines interactive AI tutoring with hands-on examples. After you learn the material, Lambdio's spaced repetition algorithm schedules review sessions at optimal intervals — so you retain concepts and techniques long-term.
How to Study Advanced Linear Models with R on Lambdio
Lambdio's AI-powered platform adapts to how Computer Science courses are best learned. Here's our recommended approach:
Advanced Linear Models with R is a mathematically rigorous, code-intensive subject that bridges classical regression theory with modern statistical learning. Standard Mode is the correct choice because the course involves matrix algebra for least squares, likelihood theory for GLMs, variance component estimation for mixed models, and R code demonstrations for every method — from IRWLS algorithms to tree pruning and neural network weight decay. Socratic Mode is less suitable since discovering the Gauss-Markov theorem, canonical links, or REML estimation through questions alone would be inefficient without direct mathematical exposition and practical R implementations. The Medium difficulty of this course, combined with its heavy reliance on linear algebra, probability theory, and R programming, makes High priority the ideal default — the spaced repetition algorithm will schedule frequent reviews so that core concepts (the exponential family, deviance, BLUPs, the bias-variance tradeoff) and R modeling patterns remain locked in long-term memory. For quick reinforcement of key definitions, diagnostics, and R function syntax, pair Standard Mode with Quiz Mode during review sessions.
Interactive Quiz
Test your knowledge with these sample questions from the course. Tap an answer to see if you're right:
What You'll Be Able to Do After This Course
- ✓Specify, estimate, and diagnose classical linear regression models using R and interpret model diagnostics including leverage, Cook's distance, and VIF
- ✓Apply Generalized Linear Models (GLMs) for binomial, count, and multinomial response data using appropriate link functions and R
- ✓Construct and interpret log-linear models for contingency table analysis, including tests for independence and association structures
- ✓Fit Gamma and Inverse Gaussian GLMs for specialized continuous response data with non-constant variance
- ✓Build linear mixed-effects models for grouped and longitudinal data, estimating variance components via REML and interpreting BLUPs
- ✓Implement nonparametric regression methods including kernel smoothing, splines, and local polynomial regression in R
- ✓Construct generalized additive models (GAMs), recursive partitioning trees, and neural networks for flexible predictive modeling
- ✓Perform maximum likelihood estimation, conduct likelihood ratio, Wald, and score tests, and interpret Fisher Information
Frequently Asked Questions
Related Courses
Continue your learning journey with these related courses:
Start Studying Advanced Linear Models with R
Create your free account and start learning with Lambdio's AI tutoring and spaced repetition.