Course Descriptions

 
5180   Statistical Packages
Prerequisite(s): STA 3381 or equivalent.

Emphasis on use of the computer to perform statistical data analysis through use of integrated statistical packages. Instruction includes use of SAS and Splus.

5300   Statistical Methods

Introduction to descriptive and inferential statistics. Topics may be selected from the following: descriptive statistics and graphs, probability, regression, correlation, tests of hypotheses, interval estimation, measurement, reliability, experimental design, analysis of variance, nonparametric methods, and multivariate methods.

5301   Introduction to Experimental Design
Prerequisite(s): Graduate standing.

Simple and complex analysis of variance and analysis of covariance designs. The general linear model approach, including full-rank and less than full-rank models, will be emphasized.

5305   Advanced Experimental Design
Prerequisite(s): STA 5381.

The course examines a variety of complex experimental designs that are available to researchers including split-plot factorial designs, confounded factorial designs, fractional factorial designs, incomplete block designs, and analysis of covariance. The designs are examined within the framework of the general linear model. Extensive use is made of computer software.

5351 Introduction to Theory of Statistics
Pre-requisite(s): MTH 2321 or equivalent or consent of instructor.

Introduction to mathematics of statistics. Fundamentals of probability theory, convergence concepts, sampling distributions, and matrix algebra.

5352 Theory of Statistics I
Pre-requisite(s): MTH 2321 or STA 5351 or consent of instructor.  Co-requisite(s): STA 5380.

Theory of random variables, distribution and density functions, statistical estimation, and hypothesis testing. Topics include probability, probability distributions, expectation, point and interval estimation, and sufficiency.

5353   Theory of Statistics II
Prerequisite(s): STA 5352.

Topics include sampling distributions, likelihood and sufficiency principles, point and interval estimation, loss functions, Bayesian analysis, asymptotic convergence, and test of hypothesis.

5362   Time Series Analysis
Prerequisite(s): STA 5352.

Statistical methods of analyzing time series. Topics include autocorrelation function and spectrum, stationary and non-stationary time series, linear filtering, trend elimination, forecasting, general models and auto regressive integrated moving average models with applications in economics and engineering.

5364   Survival and Reliability Theory
Prerequisite(s): STA 5352.

Basic concepts of lifetime distributions. Topics include types of censoring, inference procedures for exponential, Weibull, extreme value distributions, parametric and nonparametric estimation of survival function and accelerated life testing.

5365   Design of Experiments and Clinical Trials
Prerequisite(s): Graduate standing.

Traditional designs of experiments are presented within the framework of the general linear model. Also included are the latest designs and analyses for clinical trials and longitudinal studies.

5367   Managerial Epidemiology (Cross-listed as HPA 5367)

This course presents the basic principles of epidemiology with particular emphasis on applications in healthcare management. Topics include specific tools of epidemiology used for purposes of planning, monitoring, and evaluating population health. These include identification and of disease, measures of incidence and prevalence, study designs, confidence intervals, p-values, statistical interaction, causal inference, and survival analysis. Methods for managing the health of populations using an understanding of the factors that influence population health are discussed. Strategies that health care organizations and systems can use to control these factors are also considered.

5377   Spatial Statistics
Prerequisite(s): STA 5353; or consent of instructor.

Exploratory spatial data analysis using both graphical and quantitative descriptions of spatial data including the empirical variogram. Topics include several theoretical isotropic and anisotropic variogram models and various methods for fitting variogram models such as maximum likelihood, restricted maximum likelihood, and weighted least squares. Techniques for prediction of spatial processes will include simple, ordinary, universal, and Bayesian kriging. Spatial sampling procedures, lattice data, and spatial point processes will also be considered. Existing software and case studies involving data from the environment, geological, and social sciences will be discussed.

5380   Methods in Statistics I
Prerequisite(s): MTH 2311 and MTH 2321.    Co-requisite(s): STA 5352.

Descriptive parametric and nonparametric inferential methods for qualitative and quantitative data from a single population.  Parametric and nonparametric inferential methods for qualitative and quantitative data from two populations. Linear regression using matrix notation, including topics in multiple regression, modeling diagnostic procedures, and model selection.

5381   Methods in Statistics II
Prerequisite(s): STA 5380 or consent of instructor.  Co-requisite(s): STA 5353.

A continuation of STA 5380 with robust regression, quantile regression, and regression trees. K population descriptive and inferential methods. A matrix approach to one-way analysis of variance and least squares in balanced designs with fixed and random effects. Multiple comparison procedures, power, and sample size. A brief introduction to generalized linear models.

5383   Introduction to Multivariate Analysis
Prerequisite(s): STA 5381 or equivalent.

Statistical models and procedures for describing and analyzing random vector response data. Supporting theoretical topics include matrix algebra, vector geometry, the multivariate normal distribution and inference on multivariate parameters. Various procedures are used to analyze multivariate data sets.

5384   Multivariate Statistical Methods

Discriminant analysis, canonical correlation analysis, and multivariate analysis of variance.

5387   Stochastic Processes
Prerequisite(s): STA 5353.

The study of probability theory as motivated by applications from a variety of subject matters. Topics include: Markov chains, branching processes, Poisson processes, continuous time Markov chains with applications to queuing systems, and renewal theory.

5388   Seminar in Statistics
Prerequisite(s): Consent of instructor.

Selected topics in Statistics. May be repeated once with change of topic.

5V85   Practice in Statistics                                 (1 to 3 sem. hrs.)

Consulting, research, and teaching in statistics.

5V95   Topics in Statistics                                   (1 to 3 sem. hrs.)
Prerequisite(s): Consent of instructor.

Selected topics in statistics. May involve texts, current literature, or an applied data model analysis. This course may be repeated with change of topic.

5V99   Thesis                                                          (1 to 3 sem. hrs.)

Supervised research for the master’s thesis. A maximum of three semester hours to count for the degree.

6351   Large Sample Theory
Prerequisite(s): STA 5353.

Large sample theory, including convergence concepts, laws of large numbers, central limit theorems, and asymptotic concepts in inference.

6352   Bayesian Theory
Prerequisite(s): STA 5353 or equivalent.

Bayesian statistical inference, including foundations, decision theory, prior construction, Bayesian point and interval estimation, and other inference topics. Comparisons between Bayesian and non-Bayesian methods are emphasized throughout.

6353   Semiparametric Regression Models
Prerequisite(s): STA 5353.

Semiparametric inference, with an emphasis on regression models applicable to a wider class of problems than can be addressed with parametric regression models. Topics include scatterplot smoothing, mixed models, additive models, interaction models, and generalized regression. Models are implemented using various statistical computing packages.

6360   Bayesian Methods for Data Analysis
Prerequisite(s): STA 5353 or equivalent.

Bayesian methods for data analysis. Includes an overview of the Bayesian approach to statistical inference, performance of Bayesian procedures, Bayesian computational issues, model criticism, and model selection. Case studies from a variety of fields are incorporated into the study. Implementation of models using Markov chain Monte Carlo methods is emphasized.

6366   Statistical Bioinformatics
Prerequisite(s): STA 5353 and STA 5383; or consent of instructor.

Critical evaluation of current statistical methodology used for the analysis of genomic and proteomic data.

6375   Computational Statistics I
Prerequisite(s): MTH 2311 and MTH 2321.  Co-requisite(s): STA 5352

A comprehensive introduction to computing for statisticians. Topics range from information technology and fundamentals of scientific computing to computing environments and workflows, statistical document preparation for reproducible research, and programming languages.

6376   Computational Statistics II
Prerequisite: STA 6375.

A continuation of 6375 Computational Statistics I with an emphasis on computational and applied mathematics, pseudo-random variate generation, and Monte Carlo methods.

6382   Theory of Linear Models
Prerequisite(s): STA 5353 and STA 5381; and knowledge of matrix theory.

Theory of general linear models including regression models, experimental design models, and variance component models. Least squares estimation. Gauss-Markov theorem and less than full rank hypotheses.

6383   Advanced Multivariate Analysis
Prerequisite(s): STA 5383.

Multivariate normal and related distributions. Topics include generalizations of classical test statistics including Wilk's Lambda and Hotelling's T2, discriminant analysis, canonical variate analysis, and principal component analysis.

6384   Analysis of Categorical Data
Prerequisite(s): STA 5353 and STA 5381 or equivalent.

Theory of generalized linear models including logistic, probit, and log linear models with special application to categorical and ordinal categorical data analysis.

6V99   Dissertation                                    (1 to 6 sem. hrs.)

Supervised research for the doctoral dissertation. Maximum of nine semester hours will count for the degree. A student may register for one to six semester hours in one semester.