600
Advanced computational techniques for data management, statistical computing and simulation, including SAS Macro programming language, R, and SAS SQL. Prerequisite:
STA 575, 584.
Credits
3(3-0)
Contingency tables, logistic and Poisson regression models, log-linear models, nonparametric methods of survival analysis, Cox proportional hazard models and accelerated failure time models. Prerequisites:
STA 580, 584.
Credits
3(3-0)
Theory and application of least squares method and hypothesis testing for the linear regression models. Prerequisites:
MTH 525;
STA 584.
Credits
3(3-0)
Stochastic convergence and limiting theorems, sampling distributions, theory of point estimation and hypothesis testing, general linear hypotheses, sequential probability ratio test. Prerequisites:
MTH 532 and
STA 584.
Credits
3(3-0)
Multivariate normal distributions, multivariate methods including multivariate analysis of variance, multivariate regression, principal component analysis, factor analysis, canonical correlation, discriminant analysis and cluster analysis. Prerequisites:
STA 580,
STA 584.
Credits
3(3-0)
Data mining techniques for analyzing high dimensional data: include cluster and sequence analysis, self-organizing maps, support vector machine, path mining, and Bayesian network. Recommended:
STA 580 or equivalent.
Credits
3(3-0)
Topics include single and multiple parameter models, Bayesian computation, Markov Chain Monte Carlo methods, hierarchical models, model comparisons and regression models. Prerequisite:
STA 684.
Credits
3(3-0)
In-depth capstone practicum project supervised by a faculty member or advanced internship experience in external agency supervised by a faculty member and a professional supervisor. CR/NC only. Prerequisite: Permission of the program advisor.
Credits
3(Spec)
Subject matter not included in regular courses. Course may be taken for credit more than once, total credit not to exceed six hours. Prerequisites: Graduate student status and permission of instructor.
Credits
1-6(Spec)
The in-depth study of a topic in statistics under the direction of a faculty member. May be taken for credit more than once, total credit not to exceed nine hours. Prerequisites: Permission of instructor.
Credits
1-9(Spec)