Generalized linear models
Graduate · Math
Syllabus focus
Topics typically covered
Click a topic for the full text and related unit practice.
Graduate Generalized linear models — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
GLM framework
- Exponential family distributions — Exponential family distributions
- Link functions — Link functions
- Linear predictors — Linear predictors
- Iteratively reweighted least squares — Iteratively reweighted least squares
- Deviance and analysis of deviance — Deviance and analysis of deviance
- Quasi-likelihood for overdispersion — Quasi-likelihood for overdispersion
Common models
- Logistic regression for binomial data — Logistic regression for binomial data
- Poisson — Poisson
- Negative binomial regression — Negative binomial regression
- Gamma regression for continuous positive data — Gamma regression for continuous positive data
- Ordinal — Ordinal
- Multinomial models (introduction) — Multinomial models (introduction)
- Zero-inflated — Zero-inflated
- Hurdle models (overview) — Hurdle models (overview)
Diagnostics and extensions
- Residuals for GLMs: Pearson — Residuals for GLMs: Pearson
- Deviance — Deviance
- Influence — Influence
- Separation in logistic regression — Separation in logistic regression
- Generalized additive models (introduction) — Generalized additive models (introduction)
- GEE for correlated data (preview) — GEE for correlated data (preview)
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- GLM framework
Exponential family distributions
Coming soon - Common models
Logistic regression for binomial data
Coming soon - Diagnostics and extensions
Residuals for GLMs: Pearson
Coming soon
Notes
Graduate Generalized linear models — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable). Topic outline: `content/topics/graduate/glm.json`.