Prerequisite(s): STA 3381 or equivalent.Emphasis on use of the computer to perform statistical data analysis through use of integratedstatistical packages. Instruction includes use of SAS and Splus.

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Graduate Courses (Graduate Catalog page 393)

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.

Prerequisite(s): Graduate standing. Simple and complex analysis of variance and analysis of covariance designs. The general linear model appraoch, including full-rank and less than full-rank models, will be emphasized.

Prerequisite(s): STA 5381 or consent of instructor. 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.

Introduction to probability theory. Fundamentals of probability theory, random variables, distribution and density functions, expectations, transformations of random variables, moment generating functions, convergence concepts, sampling distributions, and order statistics.

Prerequisite(s): STA 5351. Theory of statistical estimation and hypothesis testing. Topics include point and interval estimation, suffi ciency, properties of estimators, and Bayes techniques.

Prerequisite(s): STA 5352. Topics include sampling distributions, likelihood and suffi ciency principles, point and interval estimation, loss functions, Bayesian analysis, asymptotic convergence, and test of hypothesis.

Prerequisite(s): STA 5352. Statistical methods of analyzing time series. Topics include autocorrelation function and spectrum, stationary and non stationary time series, linear fi ltering, trend elimination, forecasting, general models and auto regressive integrated moving average models with applications in economics and engineering.

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.

Prerequisite(s): STA 5352. The study of current parametric and nonparametric methods in biostatistics. Topics include the latest design and analysis procedures for clinical trials, longitudinal studies, and case-controlled studies.

Prerequisite(s): STA 5383 and STA 5353; or consent of instructor. Critical evaluation of current statistical methodology used for the analysis of genomic and proteomic data.

See HPA 5367 for course information.

Prerequisite(s): STA 5352. Development of statistical concepts and theory underlying procedures used in statistical process control applications. Topics include sampling inspection procedures, continuous sampling procedures, theory of process control procedures, and experimental design and response surface analysis to design and analyze process experiments.

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.

Introduction to the more common statistical concepts and methods. Interval estimation, tests of hypotheses, non-parametric methods, linear regression and correlation, categorical data analysis, design of experiments and analysis of variance, and the use of computer packages.

Prerequisite(s): STA 3381 and MTH 2311. Statistical methods and linear algebra. Theory and applications of simple and multiple regression models. Topics include review of statistical theory inference in regression, model selection, residual analysis, general linear regression model, multicollinearity, partial correlation coeffi cients, logistic regression, and other appropriate topics.

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.

Prerequisite: STA 5300 Discriminant analysis, canonical correlation analysis, and multivariate analysis of variance.

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.

Prerequisite(s): Consent of instructor. Selected topics in Statistics. May be repeated once with change of topic.

Consulting, research, and teaching in statistics.

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.

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

Prerequisite(s): STA 5353. Large sample theory, including convergence concepts, laws of large numbers, central limit theorems, and asymptotic concepts in inference.

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.

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 scatterplotsmoothing, mixed models, additive models, interaction models, and generalized regression. Models are implemented using various statistical computing packages.

Prerequisite(s): STA 5352 and MTH 5380; 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 fi elds are incorporated into the study. Implementation of models using Markov chain Monte Carlo methods is emphasized.

Prerequisite(s): STA 5353. Topics in statistical simulation and computation including pseudo-random variate generation, optimization, Monte Carlo simulation, Bootstrap and Jackknife methods.

Prerequisite(s): STA 5353, 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.

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.

Prerequisite(s): STA 5353 or consent of instructor. Theory and methods for the analysis of cross-classifi ed categorical data. A modern treatment, including extensions of classical probit analysis, multivariate logistic models, GSK model, loglinear models in analysis of multi-way contingency tables, and specialized methods for ordinal categorical data.

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.