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Categorical Data Analysis with R

HardComputer ScienceData Science12 chapters

This course introduces practical statistical methods for analyzing categorical and count data, focusing on models like logistic and Poisson regression. You will learn how to build, test, and diagnose these models using the R programming language.

What This Course Covers

Categorical Data Analysis with R is structured into 12 chapters that build on each other progressively:

Chapter 1: Contingency Tables and Binary Responses▼
Chapter 2: Logistic Regression Model and Link Functions▼
Chapter 3: Inference and Hypothesis Testing▼
Chapter 4: Odds Ratios and Predicted Probabilities▼
Chapter 5: Interactions, Factors, and GLMs▼
Chapter 6: Multinomial Logit Models▼
Chapter 7: Proportional Odds Models▼
Chapter 8: Poisson Regression Foundations▼
Chapter 9: Loglinear Models and Rate Regression▼
Chapter 10: Residual Diagnostics and Goodness-of-Fit▼
Chapter 11: Model Selection and Regularization▼
Chapter 12: Overdispersion and Mixed-Effects Models▼

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 Categorical Data Analysis with R on Lambdio

Lambdio's AI-powered platform adapts to how Computer Science courses are best learned. Here's our recommended approach:

Learning Mode
Standard Mode — for first-time learning of each chapter
Review Modes
Standard, Quiz — for spaced repetition reviews
Learning Priority
High Priority — controls how often the algorithm schedules reviews

Categorical Data Analysis with R is a mathematically demanding, code-intensive subject that covers logistic regression, multinomial models, Poisson regression, and GLMMs — all requiring structured explanations and hands-on R implementation. Standard Mode is the right fit because learning the mathematical derivations, model syntax, and diagnostic procedures requires step-by-step tutorials with code examples and comprehension checks; Socratic Mode would be inefficient for acquiring these technical skills. The Hard difficulty rating means the material is dense and conceptually challenging, making High priority the appropriate default so that the spaced repetition algorithm schedules frequent reviews to reinforce both the statistical theory and the R coding patterns. For quick reinforcement of key concepts like odds ratio interpretation, deviance cutoffs, and model selection criteria, use Quiz Mode to keep essential knowledge sharp between study sessions.

Interactive Quiz

Test your knowledge with these sample questions from the course. Tap an answer to see if you're right:

Q1: What are the three primary metrics for comparing binary responses between two groups?
Q2: Why can ordinary linear regression not be used for binary response variables?
Q3: Which test is generally preferred over the Wald test for hypothesis testing in logistic regression, especially for small-to-moderate sample sizes?
Q4: How is the odds ratio interpreted for a c-unit increase in a continuous predictor x in a logistic regression model?
Q5: What are the three components of a Generalized Linear Model (GLM)?
Q6: In a baseline-category logit model for nominal responses with J categories, how many equations are constructed?
Q7: What assumption does the proportional odds model make about slope parameters across cumulative logits?

What You'll Be Able to Do After This Course

  • ✓Construct and interpret 2x2 contingency tables, odds ratios, and confidence intervals for binary response comparisons
  • ✓Fit logistic regression models in R using glm() and interpret log-odds, odds ratios, and predicted probabilities
  • ✓Conduct likelihood ratio tests and Wald tests for parameter inference and model comparison in logistic regression
  • ✓Build and interpret multinomial logit models for nominal response variables with three or more categories
  • ✓Fit proportional odds models for ordinal response variables and test the proportional odds assumption
  • ✓Apply Poisson regression for count data and use offsets for rate modeling with unequal exposure windows
  • ✓Diagnose model fit using standardized Pearson residuals, deviance, and goodness-of-fit statistics
  • ✓Perform model selection using AIC, BIC, and stepwise procedures, and address overdispersion with negative binomial or quasi-likelihood models

Frequently Asked Questions

Do I need prior experience with R to take this course?▼
What statistical background is required?▼
How long does it take to complete this course?▼
What software and R packages do I need?▼
How does Lambdio's AI tutor help me master categorical data analysis?▼

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