STEM / applied
Categorical data analysis · Undergraduate · Math
Learning objectives from the Categorical data analysis syllabus, grouped by unit. Click an objective for study materials.
Topics typically covered
Click a topic for the full text and related unit practice.
Undergraduate Categorical data analysis — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
Contingency tables
- Two-way tables: independence — Two-way tables: independence
- Association — Association
- Chi-square tests — Chi-square tests
- Expected counts — Expected counts
- Odds ratios — Odds ratios
- Relative risk for 2×2 tables — Relative risk for 2×2 tables
- Exact tests for small samples — Exact tests for small samples
- Stratified tables — Stratified tables
- Mantel–Haenszel methods — Mantel–Haenszel methods
Logistic and log-linear models
- Logistic regression for binary outcomes — Logistic regression for binary outcomes
- Multinomial — Multinomial
- Ordinal logistic models (intro) — Ordinal logistic models (intro)
- Log-linear models for count data — Log-linear models for count data
- Model selection and deviance — Model selection and deviance
- Overdispersion — Overdispersion
- Quasi-likelihood (intro) — Quasi-likelihood (intro)
Advanced categorical methods
- Repeated categorical data: GEE preview — Repeated categorical data: GEE preview
- Matched-pair designs — Matched-pair designs
- McNemar's test — McNemar's test
- Agresti-style interpretation of odds ratios — Agresti-style interpretation of odds ratios
- Residual analysis for GLMs — Residual analysis for GLMs
Learning objectives
Click an objective for study materials.
Contingency tables
- Two-way tables: independence — Two-way tables: independence
- Association — Association
- Chi-square tests — Chi-square tests
- Expected counts — Expected counts
- Odds ratios — Odds ratios
- Relative risk for 2×2 tables — Relative risk for 2×2 tables
- Exact tests for small samples — Exact tests for small samples
- Stratified tables — Stratified tables
Logistic and log-linear models
- Logistic regression for binary outcomes — Logistic regression for binary outcomes
- Multinomial — Multinomial
- Ordinal logistic models (intro) — Ordinal logistic models (intro)
- Log-linear models for count data — Log-linear models for count data
- Model selection and deviance — Model selection and deviance
- Overdispersion — Overdispersion
- Quasi-likelihood (intro) — Quasi-likelihood (intro)
Advanced categorical methods
- Repeated categorical data: GEE preview — Repeated categorical data: GEE preview
- Matched-pair designs — Matched-pair designs
- McNemar's test — McNemar's test
- Agresti-style interpretation of odds ratios — Agresti-style interpretation of odds ratios
- Residual analysis for GLMs — Residual analysis for GLMs
Multi-Unit Problems
Course-level sets that combine skills across study units (coming soon).
Browse Multi-Unit ProblemsWhat each unit includes
Open a unit below for full materials. Typical resources:
- Study guide
- Exam Strategy
- Common Mistakes
- Worksheets
- Word problems
- Mixed Practice
- Multi-Unit Problems
- Review
- Practice test
- Answer key
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Contingency tables
Two-way tables: independence
Coming soon - Logistic and log-linear models
Logistic regression for binary outcomes
Coming soon - Advanced categorical methods
Repeated categorical data: GEE preview
Coming soon
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$1,162 · Categorical data analysis · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.