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Statistics
 

Course Overviews

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 report-application areas include physics, computer science, sports, finance etc. The course is accessible to sophomores/juniors in science, engineering or business.

cross-listed 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. Real-world 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. Computer-assisted data analysis, including biological examples, is explored in laboratory sessions.

Pre-requisites: 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

Cross-listed 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 computer-based 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

Cross-listed 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; discrete-time Markov chains; branching and Poisson processes.

Pre-requisites: MATH 211 and 212

Cross-listed 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.

Pre-requisities: (ECON 307 OR STAT 310 OR ECON 382 OR STAT 381) AND (MATH 211 OR MATH 355 OR CAAM 335) or permission of instructor

Cross-listed 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 multi-factor 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. Pre-requisities: (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.

Pre-requisites: 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.

Pre-requisite(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

Cross-listed: 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.

Pre-requisities: 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, case-control 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 problem-solving.

Prerequisite: COMP 280 AND COMP 212 AND STAT 310 or STAT 331

Cross-listed 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, Fama-French, 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

Pre-requisities: 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 state-of-the-art 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, non-linear PCA.

STAT 503 - Topics in Methods and Data Analysis
  Applications of least squares and general linear mode.

Cross-listed 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 distribution-free statistics are also covered.

Cross-listed 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 two-semester 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. Pre-requisities: 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

Pre-requisities: 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.

Pre-requisities: 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.

Pre-requisities: STAT 431 AND STAT 615

STAT 545 - Generalized Linear Models & Categorical Analysis - this course is taught every other year
  Contingency tables, association parameters, chi-squared tests, general theory of generalized linear models, logistics regression, loglinear models, poisson regression.

This course is taught on an every other year basis.

Pre-requisities: STAT 431 AND STAT 615

STAT 546 - Design and Analysis of Experiments and Sampling Theory
  Graduate Level Course

Pre-requisities: STAT 431 AND STAT 615

STAT 547 - Survival Analysis
  Lifetime tables, cumulative distribution theory, censored data, Kaplan-Meier survival curves, log-rank 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; pre-processing 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 bump-hunting 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 spatial-temporal processes. Emphasis will be placed on applications in the area of computational finance.

Pre-requisities: 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.

Pre-requisites: 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. S-Plus (R) will serve as the basic computing software. Same as STAT 453 with advanced problem sets.

Pre-requisities: STAT 615 or permission of instructor

STAT 581 - Mathematical Probability I
  Measure-theoretic foundations of probability. Open to qualified undergraduates. Required for PhD students in Statistics.

Also offered as CAAM 581.

STAT 582 - Mathematical Probability II
  Measure-theoretic foundations of probability, A continuation of STAT 581. Pre-requisite(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; White-noise 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, do-loop 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 PROC�s 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 built-in 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. Pre-requisite(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 multi-factor 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 co-requisite(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.

Pre-requisities: 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 dose-finding 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 optimization-based computational methods. We will cover hidden Markov models, belief propagation, Monte Carlo sampling algorithms, and variational Bayesian methods.

Prerequisite: STAT 552 Cross-listed 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
  Pre-requisites: 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.

Pre-requisities: 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 genotype-phenotype association. Various data structures will be the centerpiece of the course, including genotype, allele-sharing, and gene-expression. Topics include family and population-based 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 high-thorughput 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 hands-on 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. Pre-requisite(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.

Pre-requisite(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, cost-effectiveness analysis, meta-analysis, and sensitivity analysis. Grading will be based on regularly assigned homework exercises and term projects.

Pre-requisites: 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