Graduate Course Descriptions

STAT 535: Applied Linear Models. (3:3:0)
WHEN TAUGHT: Fall
PREREQUISITE: Departmental consent.
DESCRIPTION: Theory of estimation and testing in linear models. Analysis of full-rank model, over-parameterized model, cell-means model, unequal subclass frequencies, and missing and fused cells. Estimability issues, diagnostics.

STAT 536: Modern Regression Methods. (3:3:0)
WHEN TAUGHT: Winter
PREREQUISITE: STAT 535 & STAT 624; or departmental consent.
DESCRIPTION: Weighted least squares, Bayesian linear models, robust regression, nonlinear regression, local regression, generalized additive models, tree-structured regression.

STAT 537: Generalized Linear Methods
WHEN TAUGHT: Winter On Demand
PREREQUISITE: STAT 535; STAT 624
DESCRIPTION: Generalized linear models framework binary data, polytomous data, log-linear models.

STAT 538: Survival Analysis. (3:3:0)
WHEN TAUGHT: Winter
PREREQUISITE: STAT 340
DESCRIPTION: Basic concepts of survival analysis; hazard functions; types of censoring; Kaplan-Meier estimates; log-rank tests; proportional hazard models; examples drawn from clinical and epidemiological literature.

STAT 590R: Statistical Consulting. (1-3:Arr:0)
WHEN TAUGHT: Winter
PREREQUISITE: Departmental consent.
DESCRIPTION: Introduction to statistical consulting, oral presentations, presentation packages, written reports. Extensive applied experience in the Center for Collaborative Research and Statistical Consulting.

STAT 591R: Graduate Seminar in Statistics. (0:1:0)
WHEN TAUGHT: Fall; Winter

STAT 595R: Special Topics in Statistics. (1-3:ARR:0)
PREREQUISITE:
Instructor's consent
Statistical computations; theory of risk; expert systems in statistics; biostatistical methods; quality methods; sampling practicum.

STAT 599R: Academic Internship: Statistics. (1-9:0:0)
PREREQUISITE: Departmental consent.
DESCRIPTION: On-the-job experience. Report required.

STAT 624: Statistical Computation (3:3:0)
WHEN TAUGHT: Fall
PREREQUISITE: Departmental consent
DESCRIPTION: Fundamental numerical methods used by statisticians; programming concepts; efficient use of software available for statisticians; simulation studies.

STAT 631: Adv. Experimental Design (3:3:0)
PREREQUISITE: Departmental consent
DESCRIPTION: Response surface methods, mixture designs and optimal designs; fractions of two-level, three-level, and mixed-level factorials; analysis of experiments with complex aliasing; robust parameter designs.

STAT 635: Mixed Model Methods (3:3:0)
WHEN TAUGHT: Winter
PREREQUISITE: STAT 535, STAT 624, STAT 642
DESCRIPTION: Fixed effects, random effects, repeated measures, nonindependent data, general covariance structures, estimation methods.

STAT 637: Generalized Linear Models (3:3:0)
PREREQUISITE: STAT 535, STAT 642
DESCRIPTION: Generalized linear models framework, binary data, polytomous data, log-linear models.

STAT 641: Prob Theory & Math Stat 1 (3:3:0)
WHEN TAUGHT: Fall
PREREQUISITE: Departmental consent
DESCRIPTION: Axioms of probability; combinatorics; random variables, densities and distributions; expectation; independence; joint distributions; conditional probability; inequalities; derived random variables; generating functions; limit theorems; convergence results.

STAT 642: Prob & Theory & Math Stat 2 (3:3:0)
WHEN TAUGHT: Winter
PREREQUISITE: STAT 641
DESCRITPTION: Introduction to statistical theory; principles of sufficiency and likelihood; point and interval estimation; maximum likelihood; Bayesian inference; hypothesis testing; Neyman-Pearson lemma; likelihood ratio tests; asymptotic results, including data method; exponential family.

STAT 643: Theory of Linear Models (3:3:0)
PREREQUISITE: STAT 642
DESCRIPTION: Random vectors; multivariate normal distribution; quadratic forms distribution; quadratic forms distribution; full rank and non-full rank linear models hypothesis testing; random predictors; estimability; Bayesian topics; mixed and/or generalized linear models.

STAT 651: Bayesian Methods (3:3:0)
WHEN TAUGHT: Fall Contact Dept.
PREREQUISITE: STAT 356 & STAT 642
DESCRIPTION: Basic Bayesian inference; conjugate and nonconjugate analyses; Markov Chain Monte Carlo methods; hierarchical modeling; convergence diagnostics

STAT 666: Multivariate Statistical Methods (3:3:0)
WHEN TAUGHT: Fall Contact Dept.
PREREQUISITE: STAT 535, & STAT 624, & STAT 642
DESCRIPTION: Inference about mean vectors and covariance matrices; multivariate analysis of variance and regression; canonical correlation; discriminant, cluster, principal component, and factor analysis