Statistics Courses

Required Courses

Stat 535 - Applied 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
Weighted least squares, measurement error models, robust regression, nonlinear regression, local regression, generalized additive models, tree-structured regression.

STAT 624    Statistical Computation
Fundamental numerical methods used by statisticians; programming concepts; efficient use of software available for statisticians; simulation studies.

STAT 641    Probability Theory & Mathematical Statistics 1
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    Probability Theory & Mathematical Statistics 2
Introduction to statistical theory; principles of sufficiency and likelihood; point and interval estimation; maximum likelihood; Bayesian inference; hypothesis testing; Neyman-Pearson dilemma; likelihood ratio tests; asymptotic results including delta method; exponential family.

Graduate Electives

STAT 537    Generalized Linear Models
Generalized linear models framework, binary data, polytomous data, log-linear models.

STAT 538    Survival Analysis
Basic concepts of survival analysis, hazard functions, types of censoring, Kaplan-Meier estimates, Logrank tests, proportional hazard models, examples drawn from clinical and epidemiological literature.

STAT 545    Stochastic Processes
Conditional expectation and probabilities, Markov chains, solutions using time reversible chains, modeling using hidden Markov chains, exponential waiting times, Poisson processes, Brownian motion with approximations.

STAT 631    Advanced Experimental Design
Response surface methods, mixture designs, 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
Fixed effects, random effects, repeated measures, non-independent data, general covariance structures, estimation methods.

STAT 643    Theory of Linear Models
Random vectors, multivariate normal 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
Basic Bayesian inference, conjugate and non-conjugate analysises, Markov Chain Monte Carlo Methods, hierarchical modeling, convergence diagnostics.

STAT 666    Multivariate Statistical Methods
Inference about mean vectors and covariance matrices, multivariate analysis of variance and regression, canonical correlation, discriminant analysis, cluster analysis, principal component analysis, factor analysis.

While not required, we recommend these undergraduate
courses to expand your career opportunities.

STAT 424    Statistical Computing 2
STAT 431    Experimental Design
STAT 435    Nonparametric Statistical Methods
STAT 462    Quality Control & Industrial Statistics
STAT 466    Introduction to Reliability
STAT 469    Applied Time Series & Forecasting