STAT 100  Data, Models, and Reality: An Introduction to the Scientific Method 

The formation of models of reality and the ways models are tested by their analysis in the light of data are considered. We cover a variety of examples from antiquity to the present time. 
STAT 280  Elemenatary Applied Statistics, Sections I & II 

Topics include basic probability, descriptive statistics, probability distributions, confidence inervals, significance testing, simple linear regression and correlation, association between categorized variables. 
STAT 281  History of Numbers and Games of Chance 

Starting with the colorful history of numbers, we discover their use to characterize chance or luck through probability; students will participate in one major project and submit a reportapplication areas include physics, computer science, sports, finance etc. The course is accessible to sophomores/juniors in science, engineering or business.
crosslisted with COMP 281 and ELEC 281. 
STAT 300  Model Building 

* DISTRIBUTION COURSE: GROUP III Examples to illustrate mathematical formulation (modeling) of scientific problems, their solution and interpretation. Problems from engineering, epidemiology, economics, and other areas are covered. Realworld situations are emphasized. Satisfies statistics design criteria.
Prerequisite: Math 211 or permission of instructor. 
STAT 305  Introduction to Statistics for the Biosciences 

An introduction to statistics for Biosciences with emphasis on statistical models and data analysis techniques. Computerassisted data analysis, including biological examples, is explored in laboratory sessions.
Prerequisites: MATH 101 and MATH 102 
STAT 310  Probability and Statistics 

Probability theory and the central concepts and methods of statistics including probability distributions, expectation, estimation, hypothesis testing, sampling distributions, linear models. Section 1 presents the general use in multiple disciplines; section 2 focuses on problem sets and examples in civil and environmental engineering.
Prerequisite: MATH 102 Recommended prerequisite: MATH 212
Crosslisted with: ECON 382 
STAT 312  Probability & Statistics for Civil & Environmental Engineers (replaced section 2 of STAT 310) 

Basic Methodologies and problem solving of probability and statistics for civial and environmental engineering. Calculus is required. Prerequisite: Math 102 
STAT 313  Uncertainty & Risk in Urban Infrastructures 

Practical applications and relevance of infrastructure risks are developed in the context of real engineering problems and phenomena, including unique systems and challenges of the gulf coast area. The course starts with a survey of the roles of probability in engineering and the focuses on computerbased methods, the Bayesian approach, risk analysis tools, and infrastructure safety.
Prerequisite: STAT 312 OR STAT 310 OR ECON 307 OR ECON 382 OR STAT 331 OR ELEC 331
Crosslisted with: CEVE 313 
STAT 331  Applied Probability 

Applied probability with applications from reliability, operations research, and population biology. Topics include: axioms of probability; conditional probability; in dependence; random variables; probability distribution functions; parametric families of distributions; expectation and conditional expectation; generation functions; law of large numbers; central limit theorem; discretetime Markov chains; branching and Poisson processes.
Prerequisites: MATH 211 and 212
Crosslisted with: ELEC 331 
STAT 339  Statistical Methods  Psychology (no longer jointed listed with PSYC) 

Introduction to quantitative and computer methods applicable to the analysis of experimental and correlational data. 
STAT 340  Statistical Inference 

This one hour course covers application and development of methods in statistical inference using R.
Prequisites: STAT 331 OR STAT 312 OR ELEC 303 
STAT 385  Methods for Data Analysis and System Optimization 

The three general topic areas covered in this methodology oriented course are statistical methods including regression, sampling and experimental design; simulation based methods in statistics, queueing and inventory problems; and an introduction to optimization methods. Excel will serve as the basic computing software.
Prerequisite(s): STAT 280 OR STAT 305 OR STAT 310 OR ECON 307 OR STAT 312 
STAT 400  Econometrics 

Survey of estimation and forecasting models. Includes multiple regression time series analysis. A good understanding of linear algebra is highly desirable.
Prerequisities: (ECON 307 OR STAT 310 OR ECON 382 OR STAT 381) AND (MATH 211 OR MATH 355 OR CAAM 335) or permission of instructor
Crosslisted with: ECON 400 
STAT 405  Statistical Computing and Graphics 

Programming techniques and tools useful and advanced statistical studies. Higher level graphical methods and exploratory data analysis. 
STAT 410  Introduction to Regression and Statistical Computing 

A survey of regression, linear models, and experimental design. Topics include simple and multiple linear regression, single and multifactor studies, analysis of variance, analysis of covariance, model selection, diagnostics. Data analysis using statistical software is emphasized. Graduate/Undergraduate Equivalency: STAT 615. Recommended Prerequisite(s): STAT 431. Prerequisities: (STAT 310 OR ECON 307 OR ECON 382 OR STAT 312) AND (MATH 355 OR CAAM 335) or permission of instructor. 
STAT 411  Advanced Statistical Method (previously STAT 420) 

Advanced topics in statistical applications such as sampling, experimental design and statistical process control.
Prerequisites: STAT 310 OR STAT 312 OR ECON 307, STAT 410 or STAT 615 
STAT 413 (now STAT 405)  Statistical Computing in Practice 

Practical aspects of statistical computing, graphics and code development with exposure to multiple software packages, editing environments and hardware platforms. 
STAT 421  Applied Time Series and Forecasting 

Applied time series modeling and forecasting, with applications to financial markets. UG/GR version: STAT 621. This is the undergraduate version of STAT 621.
Prerequisite: STAT 310 OR ECON 307 OR STAT 312 or permission of instructor
Recommended prerequisite: STAT 410 
STAT 422  BAYESIAN DATA ANALYSIS 

This course will cover Bayesian methods for analyzing data. The emphasis will be on applied data analysis rather than theoretical development. We will consider a variety of models, including linear regression, hierarchical models, and models for categorical data. Computational methods will be emphasized.
Equivalent course: STAT 622
Prerequisite: STAT 410 
STAT 423  Probability in Bioinformatics & Genetics 

Course introduces the student to modern biotechnology and genomic data. Statistical methods to analyze genomic data are covered, including probability models, basic stochastic processes, and statistical modeling. Biological topics include DNA sequence analysis, phylogenetic inference, gene finding, and molecular evolution. Graduate/Undergraduate Equivalency: STAT 623.
Prerequisite(s): STAT 305 or STAT 310 or STAT 331 or permission of instructor 
STAT 431  Overview of Mathematical Statistics 

Topics include random variables, distributions, transformations, moment generating functions, common families of distributions, independence, sampling distributions, the basics of estimation theory, hypothesis testing and Bayesian inference.
Prerequisites: STAT 310 OR STAT 312 OR STAT 331 OR STAT 431 or permission of instructor 
STAT 440  Statistics for Bioengineering 

Course covers application of statistics to bioengineering. Topics include descriptive statistics, estimation, hypothesis testing, ANOVA, and regression. Offered first five weeks of the semester. See BIOE 440.
Prerequisite: BioE 252
Crosslisted: BIOE 440 
STAT 449/649  Quantitative Financial Risk Management 

This course covers the use of financial securities and derivatives to take or hedge financial risk positions. Most commonly used instruments, from simple forwards and futures to exotic options and swaptions are covered. The pricing of derivative securities will also be studied, but the emphasis will be on the mechanics and uses of financial engineering methods. Students receiving graduate credit in STAT 649 will be expected to address additional homework and test questions targeting a graduate level understanding of the material.
Prerequisities: MATH 211 AND MATH 212 AND (ECON 400 OR STAT 410) AND STAT 310 OR ECON 307 OR STAT 312 OR STAT 331 
STAT 450  Practicum in Statistical Modeling (not offered every year) 

This course introduces current theoretical and applied problems encountered in statistical practice. The content changes each semester in response to contemporary topics.
Equivalent course: STAT 540
Restrictions: seniors only, STAT majors only 
STAT 453  Biostatistics 

An overview of statistical methodologies useful in the practice of Biostatistics. Topics include epidemiology, rates, and proportions, categorical data analysis, regression, and logistic regression, retrospective studies, casecontrol studies, survival analysis. Real biomedical applications serve as context for evaluating assumptions of statistical methods and models. R serves as the computing software. Graduate/Undergraduate Equivalency: STAT 553.
Equivalent course: STAT 553
Prerequisite: STAT 410 or permission of instructor 
STAT 470  From Sequence to Structure: An Introduction to Computational Biology 

Contemporary introduction to problems in computational biology spanning sequence to structure. The course has three modules: the first introduces students to the design and statistical analysis of gene expression studies; the second covers statistical machine learning techniques for understanding experimental data generated in computational biology; and the third introduces problems in the modeling of protein structure using computational methods from robotics. The course is project oriented with an emphasis on computation and problemsolving.
Prerequisite: COMP 280 AND COMP 212 AND STAT 310 or STAT 331
Crosslisted with: BIOE 470, COMP 470 
STAT 482/682  Quantitative Financial Analytics 

A modern approach to fundamental analytics of securities, the classic works of Graham and Dedd. Deconstructing the Efficient Market Hypotheses, Financial Statement Analysis, Capital Market Theory, CAPM, APT, FamaFrench, Empirical Financial Forecasting. 
STAT 484  Environmental Risk Assessment & Human Health 

This course is a series of group projects. Student assessment is performed through quantification of the role and contributions of each student in the group and the overall strength of the project. Undergraduates will be members of the teams led by graduate students. The grading scale for each group will be separate. Graduate students will be graded on leadership as well as the other aspects of the project. Undergraduates will be graded on their ability to contribute to the group effort. Recommended prerequisites: STAT 305 
STAT 485  Environmental Statistics and Decision Making 

A project oriented computer intensive course focusing on statistical and mathematical solutions and investigations for the purpose of environmental decisions. This course is the undergraduate version of STAT 685 with reduced requirements.
Equivalent course: STAT 685
Prerequisite: STAT 305 or STAT 385 or permission of instructor 
STAT 486  Market Models 

This course takes the classical efficient market models and superimposes upon it models for other stochastic phenomena not generally accounted for in efficient market theory, showing how risk is lessened by portfolios and other mechanisms. The course uses computer simulations as an alternative to closed form solutions with advanced problem sets.
Equivalent course: STAT 686
Prerequisities: STAT 310 OR ECON 307 OR ECON 382 OR STAT 312 
STAT 490  Independent Study 

Independent Study 
STAT 491  Independent Study 

Independent Study 
STAT 495  Introduction to Statistics (no longer offered) 

This course is taught through the Political Science Department as POLI 495. 
STAT 499  Mathematical Sciences VIGRE Seminar 

This course prepares a student for research in the mathematical sciences. Each section is dedicated to a different topic. Current topics include bioinformatics, biomathematics, computational finance, simulation driven optimization, and data simulation. Each semester may introduce new topics.
Equivalent course: STAT 699 
STAT 502  Neural Machine Learning 

Review of major Artificial Neural Network paradigms. Analytical discussion of supervised and unsupervised learning. Emphasis on stateoftheart Hebbian (biologically most plausible) learning paradigms and their relation to information theoretical methods. Applications to data analysis such as pattern recognition, clustering, classification, blind source separation, nonlinear PCA. 
STAT 503  Topics in Methods and Data Analysis 

Applications of least squares and general linear mode.
Crosslisted with: POLI 503 
STAT 509  Advanced Psychological Statistics I 

Introduction to inferential statistics with emphasis on analysis of variance. Students who do not meet registrations requirements as Graduate and Psychology Majors, must receive Instructor permission to register.
Restriction(s): Must be enrolled in one of the following Major(s): Psychology. Must be in one of the following Classification(s):Graduate. Must be enrolled in one of the following Level(s):Graduate.
xlisted with Psyc 502. 
STAT 510  Advanced Psychological Statistics II 

A continuation of PSYC 502, focusing on multiple regression. Other multivariate techniques and distributionfree statistics are also covered.
Crosslisted with: PSYC 503 
STAT 522  Advanced Bayesian Statistics 

Modern topics in Bayesian statistics.
Prerequisites: Stat 422 or 622 
STAT 532  Mathematical Statistics I 

The first semester in a twosemester sequence in mathematical statistics: random variables, distributions, small and large sample theorems of hypothesis testing, point estimation, and confidence intervals; topics such as exponential families, univariate and multivariate linear models, and nonparametric inference will also be discussed. Required for graduate students in statistics. Prerequisities: STAT 431 AND STAT 615 or permission of instructor. 
STAT 533  Advanced Statistical Inference (previously Mathematical Statistics II ) 

A continuation of STAT 532. Required for Ph.D. students in statistics.
Corequisite: STAT 581
Prerequisities: STAT 532 
STAT 540  Practicum in Statistical Modeling 

This course introduces current theoretical and applied problems encountered in statistical practice. The content changes each semester in response to contemporary topics. Designed for graduate students in statistics. Prerequisite  STAT 431 or consent of instructor.
Restriction: GR students only 
STAT 541  Multivariate Analysis 

Study of multivariate data analysis and theory. Topics include normal theory, principal components, factor analysis, discrimination, estimation and hypothesis testing, multivariate analysis of variance and regression clustering.
Prerequisities: STAT 431 AND (STAT 410 OR STAT 615) 
STAT 542  Simulation 

Topics in stochastic simulation including; random number generators; Monte Carlo methods, resampling methods, Markov Chain Monte Carlo, importance sampling and simulation based estimation for stochastic processes.
Prerequisities: STAT 431 AND STAT 615 
STAT 545  Generalized Linear Models & Categorical Analysis  this course is taught every other year 

Contingency tables, association parameters, chisquared tests, general theory of generalized linear models, logistics regression, loglinear models, poisson regression.
This course is taught on an every other year basis.
Prerequisities: STAT 431 AND STAT 615 
STAT 546  Design and Analysis of Experiments and Sampling Theory 

Graduate Level Course
Prerequisities: STAT 431 AND STAT 615 
STAT 547  Survival Analysis 

Lifetime tables, cumulative distribution theory, censored data, KaplanMeier survival curves, logrank tests, Cox proportional hazards models, parametric and non parametric estimation, hypothesis testing. 
STAT 549  Functional Data Analysis 

Statistical methods for functional data; spaces of functions; preprocessing of functional data; probability models for functional data; basis representations including spline functions, orthogonal bases such as wavelets, and functional principal components; methods of inference for functional data including both frequentist and Bayesian methods.
STAT 533 AND STAT 581 
STAT 550  Nonparametric Function Estimation  this course is taught every other year 

Survey of topics in data analysis including data visualization, multivariate density estimation, and nonparametric regression. Advanced applications will include clustering, discrimination, dimension reduction, and bumphunting using nonparametric density procedures.
This course is offered on an every other year basis. 
STAT 551  Advanced Topics in Time Series  this course is taught every other year 

The course will cover current topics in both modeling and forecasting discrete and continuous time series. A brief coverage will also be given to spatial and spatialtemporal processes. Emphasis will be placed on applications in the area of computational finance.
Prerequisities: STAT 532 AND STAT 621 or permission of instructor
This course is offered on an every other year basis. 
STAT 552  Applied Stochastic Processes 

This course covers the theory of some of the most frequently used stochastic processes in application; discrete and continuous time, Markov chains, Poisson and renewal processes, and Brownian motion.
Prerequisites: STAT 431 
STAT 553  Biostatistics 

This course covers the design of biomedical and epidemiological studies and the analysis of the resulting data. The applied methods will be related to theory whenever practical. Emphasis will be placed on the similarity between various forms of analysis and reporting results in terms of measures of effect or association. Emphasis will also be given to identifying statistical assumptions and performing analyses to verify these assumptions. SPlus (R) will serve as the basic computing software. Same as STAT 453 with advanced problem sets.
Prerequisities: STAT 615 or permission of instructor 
STAT 581  Mathematical Probability I 

Measuretheoretic foundations of probability. Open to qualified undergraduates. Required for PhD students in Statistics.
Also offered as CAAM 581. 
STAT 582  Mathematical Probability II 

Measuretheoretic foundations of probability, A continuation of STAT 581. Prerequisite(s): STAT 581 
STAT 583  Introduction to Random Processes and Applications 

Review of basic probability; Sequence of random variables; Random vectors and estimation; Basic concepts of random processes; Random processes in linear systems, expansion of random processes; Wiener filtering; Spectral representation of random processes; Whitenoise integrals.
This course is offered through the Electrical and Computer Engineering Department as ELEC 533. 
STAT 586  Wavelets and Spectral Analysis 


STAT 590  Independent Study 


STAT 591  Independent Study 

Independent study for graduate level research topics in statistics. 
STAT 600  Graduate Seminar in Statistics 

Students participate in the process of researching professional literature (journal articles, book chapters, dissertations), preparing, delivering and critiquing talks. Literature topics change each semester. Restriction(s): Must be enrolled in one of the following Major(s): Statistics. Must be enrolled in one of the following Level(s):Graduate. 
STAT 601  Statistics Colloquium 

with repeatable credit: 10 
STAT 604  Advanced Economic Statistics 

Statistical inference and the testing of hypotheses multiple and partial correlation analysis; analysis of variance and regression.
This course is offered through the Economics Department as ECON 504. 
STAT 606  SAS Statistical Programming 

This course will cover the following areas: 1) DATA step including arrays, merging, doloop processing, if then else statements, SET statements, importing and exporting, space optimization 2) PROC TABULATE and PROC REPORT 3) Brief functions survey, e.g. random number generators, character and mathematical functions, time and date functions etc. 4) Formats 5) Brief survey of statistical PROCï¿½s 6) SAS ODS (Output Delivery System) from statistical procedures 7) Output datasets from statistical procedures 8) PROC GRAPH and Statistical Graphics Procedures (SGPLOT, SGPANEL, SGSCATTER) 9) PROC SQL (includes builtin short course on basic SQL) 10) PROC IML including functions, subroutines and optimization etc. 11) Macro programming facility 
STAT 610  Econometrics I 

Estimation and inference in single equation regression models, multicollinearity, autocorrelated and heteroskedastic disturbances, distributed lags, asymptotic theory, and maximum likelihood techniques. Emphasis is placed on the ability to analyze critically the literature. Also offered as ECON 510. Prerequisite(s): ECON 504 
STAT 611  Econometrics II 

Topics in linear and nonlinear simultaneous equations estimation, including qualitative and categorical dependent variables models and duration analysis. Applied exercises use SAS and the Wharton Quarterly Econometric Model.
This course is offered through the Department of Economics as ECON 511. 
STAT 615  Introduction to Regression and Statistical Computing 

A survey of regression, linear models, and experimental design. Topics include simple and multiple linear regression, single and multifactor studies, analysis of variance, analysis of covariance, model selection, diagnostics. Data analysis using statistical software is emphasized. This is a graduate version of STAT 410 with advanced assignments. Graduate/Undergraduate Equivalency: STAT 410.
Restrictions: Must be enrolled in one of the following Level(s): Graduate Must be enrolled in one of the following Class(es): Graduate 
STAT 620  Special Topics 

Seminar on advanced topics in Statistics. 
STAT 621  Applied Time Series and Forecasting 

Applied time series modeling and forecasting, with applications to financial markets with advanced problem sets. UG/GR version: STAT 421. This is the graduate version of STAT 421 with advanced assignments.
Pre or corequisite(s): STAT 615 or permission of instructor. 
STAT 622  Bayesian Data Analysis 

This course will cover Bayesian methods for analyzing data. The emphasis will be on applied data analysis rather than theoretical development. We will consider a variety of models, including linear regression, hierarchical models, and models for categorical data.
Prerequisities: STAT 431 AND (STAT 615 OR STAT 410) 
STAT 623  Probability in Bioinformatics and Genetics 

Course introduces the student to modern biotechnology and genomic data. Statistical methods to analyze genomic data are covered, including probability models, basic stochastic processes, and statistical modeling. Biological topics include DNA sequence analysis, phylogenetic inference, gene finding, and molecular evolution. Graduate/Undergraduate Equivalency: STAT 423.
Prerequisites: STAT 305 OR STAT 310 OR STAT 331 or permission of instructor. 
STAT 630  Topics in Clinical Trials 

This course deals with fundamental concepts in the design of clinical stuides, ranging from early dosefinding studies (phase I) to screening studies (phase II) to randomized comparative studies (phase III). The goal is to prepare the student to read the clinical trial literature critically and to design cilinical studies. Additionally, the faculty will introduce newer designs for clinical studies that incorporate prior knowledge and/or satisfy optimality considerations. Topics include protocol writing; randomization; sample size calculation; study design options; interim monitoring; adaptive designs; multiple end points; and writing up the results of a clinical trial for publication. Prerequisites: STAT 410 and STAT 431
This course is not offered every year. 
STAT 631  Graphical Models 

This course will focus on providing diverse mathematical tools for graduate students from statistical inference and learning; graph theory, signal processing and systems; coding theory and communications, and information theory. We will discuss exact and approximate statistical inference over large number of interacting variables, and develop probilistics and optimizationbased computational methods. We will cover hidden Markov models, belief propagation, Monte Carlo sampling algorithms, and variational Bayesian methods.
Prerequisite: STAT 552 Crosslisted with: ELEC 633 
STAT 639  Extreme Value Theory 

Extreme Value Theory is used in many areas such as financial markets, risk management, environmental studies, as well as network design. In this course we will study the theory and practice of extreme value theory. Prerequisite(s): STAT 532 
STAT 640  Data Mining and Statistical Learning 

Survey of ideas, methods, and tools for analyzing large data sets; techniques for searching for unexpected relationships in data. Topics from supervised and unsupervised learning include regression, discriminant analysis, kernels, model selection, bootstrapping, trees, MARS, boosting, classification, clustering, neural networks, SVM, association rules, principal curves, multidimensional scaling, and projection pursuit.
Prerequisites: STAT 431 AND (STAT 410 OR STAT 615) 
STAT 645  Data Visualisation 

This advanced graduate course will address critical evaluation of data through visualisation.The focus is on statistical graphics, graphics that display "statistical" data (observations are in rows and variables in columns), with some forays into the field of information visualisation.
Recommended prerequisite: STAT 541
Prerequisites: STAT 405 AND STAT 431 AND STAT 615 
STAT 647  Advanced Survival Analysis 

Prerequisites: STAT 547 
STAT 649  Quantitative Financial Risk Management 

This course covers the use of financial securities and derivatives to take or hedge financial risk positions. Most commonly used instruments, from simple forwards and futures to exotic options and swaptions are covered. The pricing of derivatives securities will also be studied, but the emphasis will be on the mechanics and uses of financial engineering methods. Students receiving graduate credit in STAT 649 will be expected to address additional homework and test questions targeting a graduate level understanding of the material. Graduate/Undergraduate Equivalency: STAT 449.
Prerequisities: STAT 431 OR STAT 615 
STAT 650  Stochastic Differential Equations 

This course will cover both theory and applications of stochastic differential equations. Topics include: the Langevin equation from physics, the Wiener process, white noise, the martingale theory, numerical methods and simulation, the Ito and Stratonovitch theories, applications in finance, signal processing, materials science, biology, and other fields.
Prerequisite(s): STAT 581 
STAT 655  Nonparametric Bayesian Data Analysis  this course is taught every other year 

The course reviews the current state of nonparametric Bayesian inference problems, including density estimation, regression, survival analysis, hierarchical models and model validation.
Prereq STAT 531, 420, or permission of instructor.
This course is offered on an every other year basis. 
STAT 670  Statistical Genetics 

This course centers on applications of statistics in genetic problems, especially as they pertain to genotypephenotype association. Various data structures will be the centerpiece of the course, including genotype, allelesharing, and geneexpression. Topics include family and populationbased study design, linkage, association, differential gene expression. Genetic analysis software will also be discussed and used.
Prerequisites: STAT 431 and STAT 615 
STAT 673  Probability and Statistics for Systems Biology 

Stochastic modeling of signaling pathways; Models of cancer growth and spread (stochastic and spatial); Stem cell differentiation dynamics; Viral infection and the immune system. Plus background topics (dispersed) such as Markov processes, diffusion equations, branching processes and other. 
STAT 675  Advanced Methods Genomics & Proteomics 

We propose to discuss development & application of statistical methods in the analysis of highthorughput boinformatics data that arise from problems in medical research, in particular cancer research, molecular and sturctural biology. We present a broad overview of statistical inference problems related to three main high throughput platforms: microarray gene expression, serial anaysis gene expression (SAGE), and mass spectrometry proteomic profiles. Our main focus is on the design, statistical inference and data analysis, from a statistician's perspective, of data sets arising from such high throughput experiments. 
STAT 678  Microarray Data Analysis 

This course is an introduction to the statistical and bioinformatic analysis of microarray data. The course covers both Affymetrix oligonucleotide arrays and two color fluorescence cDNA microarrays. The course introduces students to the full range of processing microarray experiments, from experimental design, through image processing, background correction, normalization, and quality control to the downstream statistical analysis of differential expression. The course includes coverage of the key statistical concept of multiple testing. The covers common methods of pattern identification and pattern recognition in the context of microarrays. It also includes the bioinformatic interpretation of the results through tools to interact with public genome databases. All concepts will be illustrated through handson interaction with publicly available microarray data sets. Homework assignments will require some knowledge of R, a statistical programming language. The course will include a brief introduction to R. This class meets in the GSBS library (BSRB 53.8351). 
STAT 684  Environmental Risk Assessment & Human Health 

Learn and apply quantitative risk assessment methodology to estimate human health risk from environmental exposure to contamination in air, soil and water. Students will conduct a series of team projects focused on toxicology, risk based screening levels, exposure concentration estimation and risk characterization. xlisted with CEVE 684. Prerequisite: STAT 305 
STAT 685  Environmental Statistics and Decision Making 

A project oriented computer intensive course focusing on statistical and mathematical solutions and investigations for the purpose of environmental decisions. This course is required for EADM students. Prerequisite(s): STAT 305 or STAT 385 
STAT 686  Market Models 

This course takes the classical efficient market models and superimposes upon it models for other stochastic phenomena not generally accounted for in efficient market theory, showing how risk is lessened by portfolios and other mechanisms. The course uses computer simulations as an alternative to closed form solutions with advanced problem sets. UG version: STAT 486.
Prerequisite(s): STAT 431 AND STAT 615 
STAT 688  Decision Theory with Medical Application 

Statistical inference, decision theory, and simulation as applied to assist in making individual clinical decisions, policy recommendations, and as a guide to study design and research; topics include statistical decision theory, decision analysis, decision trees, markvo models and simulation, costeffectiveness analysis, metaanalysis, and sensitivity analysis. Grading will be based on regularly assigned homework exercises and term projects.
Prerequisites: STAT 422 AND STAT 615 or permission of instructor 
STAT 699  Mathematical Sciences VIGRE Seminar 

This course prepares a student for research in the mathematical sciences on a specific topic. Each section is dedicated to a different topic. Current topics include bioinformatics, biomathematics, computational finance, simulation driven optimization, and data simulation. The topics change each semester. 
STAT 800  Thesis 

Thesis for Graduate Students 