Applied Multivariate Statistical Analysis (6th edition, Pearson, 2007) by Johnson and Wichern — the classic applied treatment of multivariate methods: the multivariate normal, inference about mean vectors, MANOVA, covariance structure, principal components, factor analysis, canonical correlation, discrimination and classification, and clustering, grounded throughout in worked data examples.
Statistics
2026-09-27
Worked solutions to all 164 end-of-chapter exercises and 57 worksheet practicals (353 numbered worksheet questions) in The R Software: Fundamentals of Programming and Statistical Analysis (Springer, 2013) — data structures, import and export, data manipulation, plots, programming, sessions, matrix algebra and optimisation, descriptive statistics, simulation, confidence intervals and tests, linear regression and ANOVA — with executed R code and committed output throughout.
Worked solutions to all 624 exercises in Casella & Berger's Statistical Inference (2nd edition) — probability theory, transformations and expectations, common families of distributions, multiple random variables, properties of a random sample, principles of data reduction, point estimation, hypothesis testing, interval estimation, asymptotic evaluations, and the analysis of variance and regression.
Worked solutions to all 215 exercises in Bayesian Data Analysis (3rd edition) by Gelman, Carlin, Stern, Dunson, Vehtari and Rubin — probability and inference, single- and multi-parameter models, hierarchical models, model checking and comparison, MCMC and Hamiltonian Monte Carlo, variational inference, regression and generalized linear models, and nonparametric Bayesian models.
Bayesian Data Analysis (3rd edition, CRC Press, 2013) by Gelman, Carlin, Stern, Dunson, Vehtari and Rubin — the standard graduate reference for Bayesian modelling: single- and multi-parameter models, hierarchical models, model checking and comparison, posterior computation from MCMC to HMC and variational inference, regression, and nonparametric models.
Worked solutions to all 215 exercises live at Solutions to Gelman et al.’s Bayesian Data Analysis.
Statistical Inference (2nd edition, Duxbury, 2002) by George Casella and Roger L. Berger — the classical graduate text on the theory of statistics: probability, transformations and expectations, families of distributions, multiple random variables, properties of random samples, principles of data reduction, point estimation, hypothesis testing, interval estimation, asymptotics, ANOVA and regression.
Worked solutions to all 624 exercises live at Solutions to Casella & Berger’s Statistical Inference.
A student's guide to MATH5806 at UNSW: generalised linear models in R, the Dobson & Barnett textbook, assessment breakdown (quiz, R assignment, mid-session, final), plus free notes and fully worked GLM solutions.
Annette J. Dobson and Adrian G. Barnett, An Introduction to Generalized
Linear Models, 4th edition, Chapman & Hall/CRC, 2018. The MATH5806 text:
exponential family, estimation and inference, normal linear models,
binomial and Poisson regression, contingency tables, survival analysis,
clustered and longitudinal data, Bayesian methods and MCMC. The book’s
datasets ship in the dobson R package.
Solutions to every exercise live at Solutions to Dobson & Barnett’s An Introduction to Generalized Linear Models.
how regression models are estimated and judged, from MATH5806: score and fisher information, newton-raphson and the method of scoring, the wald/score/likelihood-ratio trio, and exact t and F inference in the linear gaussian model.
exponential-family GLM theory from MATH5806: the canonical form, link functions, fisher scoring as iteratively reweighted least squares, deviance, poisson and log-linear regression, and quasi-likelihood.