**STA ****5066. ****Data Management and Analysis with SAS (3)**. Prerequisite: Previous background in statistics at least through linear regression or instructor permission. This course introduces SAS software in lab-based format. SAS is the world’s most widely used statistical package for managing and analyzing data. The objective of this course is for students to develop the skills necessary to address data management and analysis issues using SAS. This course includes a complete introduction to data management for scientific and industrial data and an overview of SAS statistical procedures.

**STA ****5067. ****Advanced Data Management and Analysis with SAS (3)**. Prerequisite: STA 5066. This course presents additional methods for managing and analyzing data with the SAS system. It covers as many of the following topics as time permits: Advanced Data step Topics, Manipulation of Data with Proc SQL, the SAS Macro Facility, Simulation with the data step and Analyses with Proc IML.

**STA ****5106. ****Computational Methods in Statistics I (3)**. Prerequisites: At least one previous course in statistics above STA 1013 and some previous programming experience; or instructor permission. This course utilizes Matlab and a programming language (C/Fortran). Floating point arithmetic, numerical matrix analysis, multiple regression analysis, nonlinear optimization, root finding, numerical integration, and Monte Carlo sampling.

**STA ****5107. ****Computational Methods in Statistics II (3)**. Prerequisite: STA 5106 or instructor permission. This course utilizes Matlab and a programming language (C/Fortran). The course is a continuation of STA 5106 in computational techniques for linear and nonlinear statistics. The course also covers statistical image understanding, elements of pattern theory, simulated annealing, Metropolis-Hastings algorithm, and Gibbs sampling.

**STA ****5126. ****Introduction to Applied Statistics. (3)**. Prerequisite: MAC 1105. This course offers graduate credit for non-statistics majors. Topics include data collection, sample variation, basic probability, confidence intervals, hypothesis testing, analysis of variance, contingency tables, correlation, regression, and nonparametric statistics. No credit is given for STA 5126 if a “C-” or better is earned in STA 2023, STA 2122, STA 2171, STA 3014, STA 3032, or QMB 3200.

**STA ****5166. ****Statistics in Applications I (3)**. Prerequisite: MAC 2313. This course introduces topics such as comparison of two treatments, random sampling, randomization and blocking with two comparisons, statistical inference for means, variances, proportions and frequencies, and analysis of variance.

**STA ****5167. ****Statistics in Applications II (3)**. Prerequisite: STA 5166. This course focuses on topics such as special designs in analysis of variance, linear and nonlinear regression, least squares and weighted least squares, case analysis, model building, nonleast squares estimation.

**STA ****5168. ****Statistics in Applications III (3)**. Prerequisite: STA 5167. This course focuses on topics such as response surface methods, repeated measures and split-plot designs, basic log-linear and logit models for two-way and multiway tables, and multinomial response models.

**STA ****5172. ****Fundamentals of Biostatistics (3)**. Prerequisite: A previous course in statistics or instructor permission. This course introduces students to the statistical methods used in studying the prevention of disease in human populations.

**STA ****5176. ****Statistical Modeling with Application to Biology (3)**. Prerequisite: STA 4442 or STA 5440. This course covers maximum likelihood principle, missing data and EM algorithm; assessment tools such as bootstrap and cross-validation; Markov chain and hidden Markov models; classification and regression trees (CART); Bayesian models and Markov Chain Monte Carlo algorithms.

**STA ****5179. ****Applied Survival Analysis (3)**. Prerequisite: STA 2171. This course is an applied introduction to survival analysis, one of the most commonly used analytic tools in biomedical studies. Topics to be covered include censoring and time scale, descriptive methods, parametric methods, and regression methods, which stress the proportional hazards model.

**STA ****5197. ****Longitudinal Data Analysis (3)**. This course explores modeling longitudinal data through analysis and interpretation of the data using standard statistical software (SAS/R and WinBugs/JAGS).

**STA ****5198. ****Epidemiology for Statisticians (3)**. Prerequisites: STA 5167 and STA 5327 or instructor permission. This course covers fundamental methods of epidemiology for statisticians. With a focus on identification of risk factors for disease, topics include exposure-disease association, design of cohort, matched and randomized studies; cross-sectional and longitudinal studies; statistical analysis of data arising from such studies, confounding, adjustment and causality; and evaluation of diagnostic and screening tests.

**STA ****5206. ****Analysis of Variance and Design of Experiments (3)**. Prerequisite: One of STA 2122, STA 4322, or STA 5126. This course expounds on topics such as one and two-way classifications, nesting, blocking, multiple comparisons, incomplete designs, variance components, factorial designs, confounding. Graduate credit for non-statistics majors only.

**STA ****5207. ****Applied Regression Methods (3)**. Prerequisite: One of STA 2122, STA 4322, or STA 5126. This course discusses topics such as general linear hypothesis, analysis of covariance, multiple correlation and regression, response surface methods. Graduate credit for non-statistics majors only.

**STA ****5208. ****Linear Statistical Models (3)**. Prerequisite: STA 5327.

**STA ****5225. ****Sample Surveys (3)**. Prerequisite: A course in statistics above STA 1013 or instructor permission. This course introduces topics such as simple, stratified, systematic, and cluster random sampling, ratio and regression estimation and multistage sampling.

**STA ****5238. ****Applied Logistic Regression (3)**. Prerequisite: STA 3032 or an equivalent upper division course that covers basic statistics at least through linear regression. This course is an applied introduction to logistic regression, one of the most commonly used analytic tools in statistical studies. Topics include fitting the model, interpretation of the model, model building, assessing model fit, model validation, and model uncertainty.

**STA ****5244. ****Clinical Trials (3)**. Prerequisite: STA 2171. This course offers an introduction to clinical trials. Topics to be covered include defining the research question, basic study designs, randomization, blinding, sample size, baseline assessment, data collection and quality control, monitoring, issues in data analysis, closing out a trial, reporting and interpreting results, and issues in multicenter trials.

**STA ****5323. ****Introduction to Mathematical Statistics (3)**. Prerequisite: MAC 2313 or equivalent. This course discusses topics such as distributions of random variables, conditional probability and independence, multivariate distributions, sampling distributions, Bayes’ rule, counting problems, expectations.

**STA ****5325. ****Mathematical Statistics (3)**. Prerequisites: STA 4442 or STA 5440 and either MAC 2313 or STA 5326. This course explores topics such as sufficiency, point estimation, confidence intervals, hypothesis testing, regression, linear models, Bayesian models.

**STA ****5326. ****Distribution Theory and Inference (3)**. Prerequisites: MAC 2313; at least one previous course in statistics or probability. This course is an introduction to probability, random variables, distributions, limit laws, conditional distributions, and expectations.

**STA ****5327. ****Statistical Inference (3)**. Prerequisites: STA 5166 and STA 5326. This course introduces students to the basics of statistical inference and its applications. The overarching goal is to introduce statistical techniques to estimate and provide uncertainty measures of the estimates themselves of key quantities of a population e.g. mean, median, location shift, variance, etc. using the observed sample.

**STA ****5334. ****Limit Theory of Statistics (3)**. Prerequisite: STA 5327. This course focuses on topics such as convergence of distribution and random variables, laws of large numbers, central limit theorems, asymptotic distributions, asymptotic efficiency, rates of convergence, the weak invariance principle.

**STA ****5363. ****Fundamental Algorithms for Statistical Data (3)**. Prerequisite: MAC 2313, MAS 3105, STA 2122, or instructor permission. Familiarity with the python programming language is encouraged. This course provides an introduction to the fundamental elements necessary for conducting research in Machine Learning, Data Science, and Computer Vision. Students learn fundamental data structures, algorithms and numerical methods for successful research and develop the skills to confidently write efficient and manageable experimental/research code in Python.

**STA ****5440. ****Introductory Probability I (3)**. Prerequisite: MAC 2311. This course discusses topics such as random variables, probability of random variables, generating functions, central limit theorem, laws of large numbers.

**STA ****5446. ****Probability and Measure (3)**. Prerequisites: MAA 4227, MAA 5307, or the equivalent. This course explores classes of sets, probability measures, construction of probability measures, random variables, expectation and integration, independence and product measures.

**STA ****5447. ****Probability Theory (3)**. Prerequisites: STA 5326 and STA 5446.

**STA ****5507. ****Applied Nonparametric Statistics (3)**. Prerequisite: A course in statistics above STA 1013 or instructor permission. This course focuses on applications of nonparametric tests, estimates, confidence intervals, multiple comparison procedures, multivariate nonparametric methods, and nonparametric methods for censored data.

**STA ****5635. ****Applied Machine Learning (3)**. Prerequisite: STA 3032 or instructor permission. This course is a hands-on introduction to statistical methods for supervised, unsupervised, and semi-supervised learning. It explores fundamental techniques including but not limited to Support Vector Machines, Decision Trees, Linear Discriminant Analysis, Random Forests, Neural Networks, and different flavors of Boosting.

**STA ****5666. ****Statistics for Quality and Productivity (3)**. Prerequisites: STA 5167 or instructor permission, and either STA 4322 or STA 5126. This course discusses statistics for quality control and productivity; graphical methods; control charts; design and experiment for product and process improvement.

**STA ****5707. ****Applied Multivariate Analysis (3)**. Prerequisite: One of STA 5167, STA 5207, or STA 5327. This course discusses inference about mean vectors and covariance matrices, canonical correlation, principal components, discriminant analysis, cluster analysis, and computer techniques.

**STA ****5721. ****High-Dimensional Statistics (3)**. Prerequisites: STA 5167 and STA 5326. Recommended prerequisite: STA 5168. This course covers a range of modern statistical topics in high dimensional modeling and analysis. The course teaches methods, theory and computation with rich high-dimensional data applications from signal processing, machine learning, bioinformatics and econometrics.

**STA ****5807r. ****Topics in Stochastic Processes (3)**. Prerequisite: STA 5326. May be repeated to a maximum of twelve semester hours.

**STA ****5856. ****Time Series and Forecasting Methods (3)**. Prerequisite: STA 5126, QMB 3200, or equivalent. This course explores autoregressive, moving average and mixed models, autocovariance and autocorrelation functions, model identification, forecasting techniques, seasonal model identification estimation and forecasting, intervention and transfer function model identification, estimation and forecasting.

**STA ****5906r. ****Directed Individual Study (1–12)**. (S/U grade only). May be repeated to a maximum of twelve semester hours.

**STA ****5910r. ****Supervised Research (0–5)**. (S/U grade only). May be repeated to a maximum of five semester hours. A maximum of three semester hours may apply to the master’s degree.

**STA ****5920r. ****Statistics Colloquium (1)**. (S/U grade only). May be repeated to a maximum of twelve semester hours.

**STA ****5934r. ****Selected Topics in Statistics, Probability, or Operations Research (2–3)**. May be repeated to a maximum of twelve semester hours.

**STA ****5939. ****Introduction to Statistical Consulting (3)**. Prerequisite: STA 5167, or STA 5327, or instructor permission. This course consists of the formulation of statistical problems from client information, the analysis of complex data sets by computer, and practical consulting experience.

**STA ****5940r. ****Supervised Consulting (1–3)**. (S/U grade only). May be repeated to a maximum of twelve semester hours.

**STA ****5941r. ****Supervised Teaching (1–5)**. (S/U grade only). May be repeated to a maximum of five semester hours. A maximum of three semester hours may apply to the master’s degree.

**STA 5945. Internship in Statistics (0-6)**. In this course, students gain practical experience in the application of statistical methods working as an intern at an appropriate company or government agency performing statistical analysis under supervision of a corporate, or government. This course may also be taken by students working on an approved data-based grant project in another department on campus or on an interdisciplinary grant project involving statistics and another department on campus. In this case, the affiliate faculty member will be the student’s supervisor on the project.

**STA ****5971Cr. ****Thesis (3–6)**. (S/U grade only). Six semester hours required.

**STA ****6174r. ****Advanced Methods in Epidemiology (3)**. Prerequisites: STA 5167 and STA 5325. This course presents advanced methods for describing, analyzing, and modeling data from observational studies. The initial offering includes introductions to meta-analytic methods, bootstrap methods, and randomization tests. Topics vary with each offering. May be repeated up to a maximum of six semester hours.

**STA ****6246r. ****Advanced Probability in Applied Statistics (2–3)**. Prerequisite: STA 5167. May be repeated to a maximum of twelve semester hours.

**STA ****6346. ****Advanced Probability and Inference I (3)**. Prerequisites: STA 5326 and STA 5327. This course covers the basics of the probability theory, random elements, and stochastic processes; characteristic functions and probability inequalities; central limit theorems; elements of Markov dependence and martingale theory; common scholastic processes arising in biostatistics; advanced treatment of sufficient statistics, exponential families, estimation, and testing; as well as elements of asymptotic theory of statistical inference.

**STA ****6448. ****Advanced Probability and Inference II (3)**. Prerequisites: STA 5326 and STA 5327. This course covers unbiased and locally most powerful tests (including the multiparameter case); envelope power function; best average power test; Bayes and empirical Bayes procedures; likelihood, quasi likelihood, and profile likelihood; order statistics and empirical distributions; general central limit theorems; variance stabilizing transformations; U-statistics; least squares, weighted least squares, and generalized least squares estimation; generalized estimating equations; asymptotic theory for BAN estimators; asymptotic theory for likelihood ratio, Wald, and score tests; log-linear models; asymptotics for linear inference; as well as robust statistical inference.

**STA ****6468r. ****Advanced Topics in Probability and Statistics (2–3)**. May be repeated to a maximum of twelve semester hours.

**STA ****6557. ****Object Data Analysis (3)**. Prerequisite: One of STA 5707, STA 5327, or STA 5746. This course covers the most inclusive type of data analysis known in statistics; examples of such data in astronomy, biology, digital imagery, medical imaging, computer vision, pattern recognition, astrophysics, learning, Earth sciences including meteorology and geology; introduction to abstract manifolds, tangent bundles, embedding, Riemannian structures; sample spaces with a manifold structure; foundations of nonparametric statistics on manifolds: location and spread parameters for distributions on manifolds; large sample theory on manifolds, density, and function estimation on manifolds; nonparametric inference on manifolds; statistical analysis on special manifolds arising in statistics: directional and axial data analysis, projective, affine, and similarity shape data analyses, size-and-shape data analysis, diffusion tensor image analysis; concrete case studies in astronomy, image analysis, medical imaging: MRI, CT, Confocal Laser Tomography, eye imaging, brain imaging, bioinformatics, computer vision, and 3D scene recognition.

**STA ****6709. ****Spatial Statistics (3)**. Prerequisites: STA 5167 and STA 5327; or instructor permission. This course examines methods for the analysis of spatial data, including geostatistical data, lattice data, and point patterns. The course also includes theory and applications of basic principles and techniques.

**STA 6723. Statistical Optimization (3).** Prerequisite: STA 5167. The course teaches matrix algebra and optimization which are at the core of modern statistics. The course also involves a wide range of real world applications in statistics, biostatistics, machine learning, finance, signal processing, and related research areas.

**STA ****6906r. ****Directed Individual Study (1–12)**. (S/U grade only). May be repeated.

**STA ****6980r. ****Dissertation (1–12)**. (S/U grade only).

**STA ****8964. ****Preliminary Doctoral Examination (0)**. (P/F grade only.)

**STA ****8976. ****Master’s Thesis Defense (0)**. (P/F grade only.)

**STA ****8985. ****Defense of Dissertation (0)**. (P/F grade only.)