600
A non-credit course intended for students who have completed all program credits but still need to use university resources to complete their degree requirements. Prerequisite: Permission of graduate advisor or department chairperson.
Credits
1(1-0)
Prerequisites
Permission of graduate advisor or department chairperson.
Corequisites
None.
Advanced computational techniques for data management, statistical computing and simulation, including SAS Macro programming language, R, and SAS SQL. Prerequisite:
STA 575.
Credits
3(3-0)
Prerequisites
STA 575
Corequisites
None.
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;
STA 581 or 584.
Credits
3(3-0)
Prerequisites
STA 580; STA 581 or STA 584
Corequisites
None.
Theory and application of least squares method and hypothesis testing for the linear regression models. Prerequisites:
MTH 525;
STA 584.
Credits
3(3-0)
Prerequisites
MTH 525; STA 584
Corequisites
None.
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)
Prerequisites
MTH 532 and STA 584
Corequisites
None.
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 581 or 584.
Credits
3(3-0)
Prerequisites
STA 580; STA 581 or STA 584
Corequisites
None.
Data mining techniques for analyzing big data: include advanced topics in linear and nonlinear regression, and tree modeling, resampling methods, support vector machine, rare-event modeling. Prerequisite:
STA 591.
Credits
3(3-0)
Prerequisites
STA 591
Corequisites
None.
Topics include single and multiple parameter models, Bayesian computation, Markov Chain Monte Carlo methods, hierarchical models, model comparisons and regression models. Prerequisite:
STA 581 or 584.
Credits
3(3-0)
Prerequisites
STA 684
Corequisites
None.
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)
Prerequisites
Permission of the program advisor
Corequisites
None.
Topics that are 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; permission of instructor.
Credits
1-6(Spec)
Prerequisites
Graduate student status; permission of instructor
Corequisites
None.
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)
Prerequisites
Permission of instructor
Corequisites
None.
A project in an area of statistics or analytics related to, but extending beyond, material covered in required coursework. CR/NC only. Prerequisite: Permission of advisor.
Credits
3(Spec)
Prerequisites
Permission of advisor
Corequisites
None.