Regression Analysis with R
Practical linear regression, parameter estimation, and hypothesis testing in R — diagnostic checks, transformations, variable selection, regularization, and experimental design.
What This Course Covers
Regression Analysis with R is structured into 17 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 Regression Analysis with R on Lambdio
Lambdio's AI-powered platform adapts to how Data Science courses are best learned. Here's our recommended approach:
Regression Analysis with R is a procedural, formula- and code-driven subject: fitting least squares models, reading diagnostic plots, choosing transformations, applying shrinkage penalties, and specifying ANOVA and blocked designs in R. Standard Mode is the right fit because each method has a precise structure — a defined estimation procedure, a set of assumptions to check, and a specific way to interpret output — that is best learned through direct explanation followed by guided practice. Socratic Mode would be inefficient here, since guiding a learner to rediscover the normal equations, the meaning of a leverage value, or the difference between ridge and lasso through questioning alone would slow down skill acquisition that depends on clear exposition and repetition. Because the course is quantitatively dense and its methods build on one another (least squares feeds inference, inference feeds diagnostics, diagnostics feed model repair and selection), High priority is the sensible default: the spaced repetition algorithm will schedule frequent reviews so that core ideas — the Gauss-Markov conditions, the bias-variance trade-off, missingness mechanisms, and blocking logic — plus R workflow patterns remain locked in long-term memory. Quiz Mode is an ideal review companion for quickly testing recall of definitions, diagnostic thresholds, and the behavior of each modeling technique.
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
- ✓Formulate empirical research questions, distinguish observational from experimental data, and perform structured initial data analysis before modeling
- ✓Fit and interpret least squares regression models in R, understand the Gauss-Markov theorem, and recognize identifiability and collinearity problems
- ✓Conduct hypothesis tests and construct confidence intervals for parameters, including permutation tests and bootstrap alternatives
- ✓Produce and interpret predictions, confidence intervals, and prediction intervals, and use autoregression for time-ordered data
- ✓Encode categorical predictors with dummy variables and model interactions between categorical and quantitative variables
- ✓Analyze group differences using one-way ANOVA, factorial designs, and multiple comparison procedures with proper error-rate control
- ✓Diagnose regression assumptions using residual plots, Q-Q plots, leverage, and influence measures, and repair models with transformations, polynomials, and splines
- ✓Apply generalized and weighted least squares, robust regression, and handle measurement error, multicollinearity, and missing data
- ✓Perform subset selection and regularization using AIC, BIC, ridge regression, lasso, principal components regression, and partial least squares
- ✓Design and analyze blocked, Latin square, and balanced incomplete block experiments and interpret their linear model representations
Frequently Asked Questions
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