Standard syllabus
Generalized linear models · Graduate · Math
Learning objectives from the Generalized linear models syllabus, grouped by unit. Click an objective for study materials.
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)
Learning objectives
Click an objective for study materials.
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)
Definitions and structure
- 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
Proofs and reasoning
- Quasi-likelihood for overdispersion — Quasi-likelihood for overdispersion
- 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
Abstraction and generalization
- Ordinal — Ordinal
- Multinomial models (introduction) — Multinomial models (introduction)
- Zero-inflated — Zero-inflated
- Hurdle models (overview) — Hurdle models (overview)
- Residuals for GLMs: Pearson — Residuals for GLMs: Pearson
Modeling and computation
- Apply exponential family distributions in engineering contexts — Apply exponential family distributions in engineering contexts
- Apply link functions in engineering contexts — Apply link functions in engineering contexts
- Apply linear predictors in engineering contexts — Apply linear predictors in engineering contexts
- Apply iteratively reweighted least squares in engineering contexts — Apply iteratively reweighted least squares in engineering contexts
- Apply deviance and analysis of deviance in engineering contexts — Apply deviance and analysis of deviance in engineering contexts
Data and technology
- Use software to explore generalized linear models problems numerically — Use software to explore generalized linear models problems numerically
- Interpret computational results against analytic predictions — Interpret computational results against analytic predictions
- Build spreadsheets or scripts for routine calculations — Build spreadsheets or scripts for routine calculations
- Visualize functions, fields, or datasets tied to course topics — Visualize functions, fields, or datasets tied to course topics
- Connect course methods to lab, industry, or research workflows — Connect course methods to lab, industry, or research workflows
Problem-solving practice
- Quasi-likelihood for overdispersion — Quasi-likelihood for overdispersion
- 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
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.
- 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
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$1,162 · Generalized linear models · 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.