UVa Course Catalog (Unofficial, Lou's List)
Catalog of Courses for Statistics    
Class Schedules Index Course Catalogs Index Class Search Page
These pages present data mined from the University of Virginia's student information system (SIS). I hope that you will find them useful. — Lou Bloomfield, Department of Physics
Statistics
STAT 1100Chance: An Introduction to Statistics (3.00)
Offered
Spring 2017
Studies introductory statistics and probability, visual methods for summarizing quantitative information, basic experimental design and sampling methods, ethics and experimentation, causation, and interpretation of statistical analyzes. Applications use data drawn from current scientific and medical journals, newspaper articles, and the Internet. Students will not receive credit for both STAT 1100 and STAT 1120.
STAT 1120Introduction to Statistics (3.00)
Includes graphical displays of data, relationships in data, design of experiments, causation, random sampling, probability distributions, inference, confidence intervals, tests of hypotheses, and regression and correlation. Students will not receive credit for both STAT 1100 and STAT 1120.
STAT 2020Statistics for Biologists (4.00)
Offered
Spring 2017
This course includes a basic treatment of probability, and covers inference for one and two populations, including both hypothesis testing and confidence intervals. Analysis of variance and linear regression are also covered. Applications are drawn from biology and medicine.
STAT 2120Introduction to Statistical Analysis (4.00)
Offered
Spring 2017
Introduction to the probability and statistical theory underlying the estimation of parameters and testing of statistical hypotheses, including those arising in the context of simple and multiple regression models. Students will use computers and statistical programs to analyze data. Examples and applications are drawn from economics, business, and other fields. Students will not receive credit for both STAT 2120 and ECON 3710. Prerequisite: MATH 1210 or equivalent; co-requisite: Concurrent enrollment in a discussion section of STAT 2120.
STAT 2559New Course in Statistics (1.00 - 4.00)
This course provides the opportunity to offer a new topic in teh subject area of statistics.
Course was offered Fall 2014
STAT 2720Introduction to Mathematical Probability and Statistics (3.00)
An introduction to the mathematical foundations of probability and statistics. Topics include discrete and continuous random variables; discrete, continuous, and joint probability distributions; sampling distributions, point estimation; confidence intervals and hypothesis testing for one and two samples. The software Stata will be incorporated. Prerequisite: One of MATH 1220, MATH 1320, or APMA 1110.
STAT 3010Statistical Computing and Graphics (3.00)
Introduces statistical computing using S-PLUS. Topics include descriptive statistics for continuous and categorical variables, methods for handling missing data, basics of graphical perception, graphical displays, exploratory data analysis, and the simultaneous display of multiple variables. Students should be experienced with basic text-editing and file manipulation on either a PC or a UNIX system, and with either a programming language (e.g. BASIC) or a spreadsheet program (e.g. MINITAB or EXCEL). Credit earned in this course cannot be applied toward a graduate degree in statistics. Prerequisite: STAT 1100 or 1120 or instructor permission.
STAT 3080From Data to Knowledge (3.00)
Offered
Spring 2017
Most elementary statistics courses start with a technique & present various surface level examples. This course will use relatively complicated data sets and approach them from multiple angles with elementary statistical techniques. Simulation techniques such as the bootstrap will also be used. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using R statistical software. Prerequisite: An introductory statistics course.
STAT 3110Foundations of Statistics (3.00)
This course provides an overview of basic probability and matrix algebra required for statistics. Topics include sample spaces and events, properties of probability, conditional probability, discrete and continuous random variables, expected values, joint distributions, matrix arithmetic, matrix inverses, systems of linear equations, eigenspaces, and covariance and correlation matrices.
STAT 3118Probability for Statistics (1.50)
This course provides an overview of basic probability required for statistics. Topics include sample spaces and events, properties of probability, conditional probability, discrete and continuous random variables, expected values, and joint distributions. Credit for this course cannot be received after receiving credit for MATH 3100 or APMA 3100.
STAT 3119Matrix Algebra for Statistics (1.50)
This course provides a basic introduction to matrix algebra required for statistics. Topics include matrix arithmetic, matrix inverses, systems of linear equations, eigenspaces, and covariance and correlation matrices. Credit for this course cannot be received after receiving credit for MATH 3350, MATH 3351, or APMA 3080.
STAT 3120Introduction to Mathematical Statistics (3.00)
Offered
Spring 2017
This course provides a calculus-based introduction to mathematical statistics with some applications. Topics include: sampling theory, point estimation, interval estimation, testing hypotheses, linear regression, correlation, analysis of variance, and categorical data. Prerequisite: MATH 3100 or APMA 3100.
STAT 3130Design and Analysis of Sample Surveys (3.00)
Offered
Spring 2017
Main designs & estimation techniques used in sample surveys; including simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation; non-response problems, measurement errors. Properties of sample surveys are developed through simulation procedures. Uses SUDAAN software package for analyzing sample surveys.
STAT 3150Theory of Interest (3.00)
Topics include growth and time value of money, equations of value and yield rates, annuities (including contingent payments), loan amortization schedules, bonds. Additional topics are options and derivatives, as time permits. Prerequisites: MATH 1220 or MATH 1320
STAT 3220Introduction to Regression Analysis (3.00)
Offered
Spring 2017
This course provides a survey of regression analysis techniques, covering topics from simple regression, multiple regression, logistic regression, and analysis of variance. The primary focus is on model development and applications. Prerequisite: STAT 1100 or STAT 1120 or STAT 2120.
STAT 3240Coding in Matlab/Mathematica with Applications (3.00)
This course focuses on an introduction to programming and data manipulation, with an emphasis on applications. Students have the choice of using Matlab or Mathematica as their programming language, with course instruction spanning both languages. Topics include loops, data structures, functions and functional programming, randomness, matrices, and string manipulation, plus applications selected from chemistry, statistics, or image processing. Prerequisite: One semester of calculus is recommended but not required.
Course was offered Fall 2016, Fall 2015
STAT 3250Data Analysis with Python (3.00)
This course provides an introduction to data analysis using the Python programming language. Topics include using the IPython development environment; data analysis packages NumPy and pandas; data loading, storage, cleaning, merging, transformation, and aggregation; data plotting and visualization and time series data.
Course was offered Fall 2016
STAT 3430Statistical Computing with SAS and R (4.00)
The course covers database management, programming, elementary statistical analysis, and report generation in SAS. Topics include: managing SAS Data Sets; DATA-step programming; data summarization and reporting using PROCs PRINT, MEANS, FREQ, UNIVARIATE, CORR, and REG; elementary graphics; introductions to the Output Delivery System, the SAS Macro language, PROC IML, and PROC SQL. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Introductory statistics course
STAT 3480Nonparametric and Rank-Based Statistics (3.00)
This course includes an overview of parametric vs. nonparametric methods including one-sample, two-sample, and k-sample methods; pair comparison and block designs; tests for trends and association; multivariate tests; analysis of censored data; bootstrap methods; multifactor experiments; and smoothing methods. Prerequisite: STAT 1120 or STAT 2120
Course was offered Spring 2016, Fall 2015
STAT 3559New Course in Statistics (1.00 - 4.00)
This course provides the opportunity to offer a new topic in the subject area of Statistics.
STAT 3980Applied Statistics Laboratory (1.00)
Enrollment in STAT LAB (3980) is required for all students in the department's 3000-level appled statistics courses (STAT 3080, 3220, 3430, 3130). STAT 3980 may be repeated for credit provided that a student is enrolled in at least one of these 3000-level applied courses; however, no more than one unit of STAT 3980 may be taken in any semester.
STAT 4160Experimental Design (3.00)
Introduces various topics in experimental design, including simple comparative experiments, single factor analysis of variance, randomized blocks, Latin squares, factorial designs, blocking and confounding, and two-level factorial designs. The statistical software R is used throughout this course.
STAT 4170Financial Time Series and Forecasting (3.00)
This course introduces topics in time series analysis as they relate to financial data. Topics include properties of financial data, moving average and ARMA models, exponential smoothing, ARCH and GARCH models, volatility models, case studies in linear time series, high frequency financial data, and value at risk.
Course was offered Fall 2016
STAT 4210Big Data Tools (3.00)
This course provides an introduction to tools use for the management and analysis of big data, including Hadoop (MapReduce), parallel computing, cloud computing, and web scraping for data acquisition. Several projects are incorporated into the course.
STAT 4220Applied Analytics for Business (3.00)
This course focuses on applying data analytic techniques to business, including customer analytics, business analytics, and web analytics through mining of social media and other online data. Several projects are incorporated into the course.
STAT 4260Databases (3.00)
This course provides an introduction to databases. Topics include traditional relational databases and SQL (Structured Query Language) for retrieving information from them, and several noSQL databases built on different organizational structures, such as PostgreSQL (an open source relational database), MongoDB and CouchDB (key-document), Redis (key-value), HBase (column family), and Neo4J (graphs).
STAT 4310Data Visualization and Presentation (3.00)
Introduces methods for effectively presenting data both visually and in table form. Software used will include the open-source R and Tableau visualization software. Students will work together on team projects developing reports and presentations to be presented to the class.
STAT 4630Statistical Machine Learning (3.00)
Offered
Spring 2017
Introduces various topics in machine learning, including regression, classification, resampling methods, linear model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. The statistical software R is incorporated throughout. Prerequisite: STAT 3220, STAT 5120, or ECON 3720, and previous experience with R.
Course was offered Spring 2017
STAT 4993Independent Study (1.00 - 4.00)
Offered
Spring 2017
Reading and study programs in areas of interest to individual students. For students interested in topics not covered in regular courses. Students must obtain a faculty advisor to approve and direct the program.
STAT 4995Statistical Consulting (1.00 - 3.00)
Offered
Spring 2017
Introduces the practice of statistical consultation. A combination of formal lectures, meetings with clients of the statistical consulting service, and sessions in the statistical computing laboratory. Students will work together with a graduate student consultant. Prerequisite: instructor permission.
Course was offered Spring 2017, Fall 2011
STAT 4996Capstone (3.00)
Students will work in teams on a capstone project. The project will involve significant data preparation and analysis of data, preparation of a comprehensive project report, and presentation of results. Many projects will come from external clients who have data analysis challenges.
STAT 5000Introduction to Applied Statistics (3.00)
Introduces estimation and hypothesis testing in applied statistics, especially the medical sciences. Measurement issues, measures of central tendency and dispersion, probability, discrete probability distributions (binomial and Poisson), continuous probability distributions (normal, t, chi-square, and F), and one- and two-sample inference, power and sample size calculations, introduction to non-parametric methods, one-way ANOVA and multiple comparisons. Prerequisite: Instructor permission; corequisite: STAT 5980.
STAT 5020Mathematical Statistics (3.00)
A calculus based introduction to the principles of statistical inference. Topics include sampling theory, point estimation, confidence intervals, hypothesis testing. Additional topics such as nonparametric methods or Bayesian statistics. May not be used for graduate degrees in Statistics. May not be taken if credit has been received for STAT 3120. Prerequisites: MATH 3100 or 5100 or consent of instructor.
STAT 5120Applied Linear Models (3.00)
Offered
Spring 2017
Linear regression models, inferences in regression analysis, model validation, selection of independent variables, multicollinearity, influential observations, autocorrelation in time series data, polynomial regression, and nonlinear regression. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite:STAT 3120, and either MATH 3351 or APMA 3080
STAT 5140Survival Analysis and Reliability Theory (3.00)
Topics include lifetime distributions, hazard functions, competing-risks, proportional hazards, censored data, accelerated-life models, Kaplan-Meier estimator, stochastic models, renewal processes, and Bayesian methods for lifetime and reliability data analysis. Prerequisite: MATH 3120 or 5100, or instructor permission; corequisite: STAT 5980.
Course was offered Fall 2016, Spring 2011
STAT 5150Actuarial Statistics (3.00)
Covers the main topics required by students preparing for the examinations in Actuarial Statistics, set by the American Society of Actuaries. Topics include life tables, life insurance and annuities, survival distributions, net premiums and premium reserves, multiple life functions and decrement models, valuation of pension plans, insurance models, and benefits and dividends. Prerequisite: MATH 3120 or 5100, or instructor permission.
Course was offered Fall 2015, Spring 2013
STAT 5170Applied Time Series (3.00)
Offered
Spring 2017
Studies the basic time series models in both the time domain (ARMA models) and the frequency domain (spectral models), emphasizing application to real data sets. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: STAT 3120
STAT 5180Design and Analysis of Sample Surveys (3.00)
This course covers the main designs and estimation techniques used in sample surveys: simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation, and non response and other non sampling errors. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using R statistical software. Prerequisites: STAT 3120.
Course was offered Spring 2013, Fall 2010
STAT 5265Investment Science I (3.00)
The course will cover a broad range of topics, with the overall theme being the quantitative modeling of asset allocation and portfolio theory. It begins with deterministic cash flows (interest theory, fixed-income securities), the modeling of interest rates (term structure of interest rates), stochastic cash flows, mean-variance portfolio theory, capital asset pricing model, and the utility theory basis for financial modeling. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using Matlab or R statistical software. Prerequisite: MATH 3100.
Course was offered Fall 2013
STAT 5266Investment Science II (3.00)
This course is a follow-up to Investment Science I (Stat 5265). It begins with models for derivative securities, including asset dynamics, options and interest rate derivatives. The remaining portion of the course then combines all of the ideas from the two courses to formulate strategies of optimal portfolio growth and a general theory of investment evaluation. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using Matlab or R statistical software. Prerequisite: MATH 3100, STAT 5265.
Course was offered Spring 2014, Spring 2010
STAT 5310Clinical Trials Methodology (3.00)
Studies experimental designs for randomized clinical trials, sources of bias in clinical studies, informed consent, logistics, and interim monitoring procedures (group sequential and Bayesian methods). Prerequisite: A basic statistics course (MATH 3120/5100) or instructor permission.
Course was offered Fall 2011
STAT 5330Data Mining (3.00)
This course introduces a plethora of methods in data mining through the statistical point of view. Topics include linear regression and classification, nonparametric smoothing, decision tree, support vector machine, cluster analysis and principal components analysis. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisites: Previous or concurrent enrollment in STAT 5120 or STAT 6120.
Course was offered Fall 2016, Spring 2012, Fall 2010
STAT 5340Bootstrap and Other Resampling Methods (3.00)
This course introduces the basic ideas of resampling methods, from jackknife and the classic bootstrap due to Efron to advanced bootstrap techniques such as the estimating function bootstrap and the Markov chain marginal bootstrap.
STAT 5350Applied Causal Inference (1.00 - 3.00)
Introduces statistical methods used for causal inference, particularly for quasi-experimental data. Focus is on the potential outcomes framework as an organizing principle and examining the estimation of treatment effects under various assumptions. Topics include matching, instrumental variables, difference-in-difference, regression discontinuity, synthetic control, and sensitivity analysis. Examples come from various fields.
Course was offered Spring 2016
STAT 5390Exploratory Data Analysis (3.00)
Introduces philosophy and methods of exploratory (vs confirmatory) data analysis: QQ plots; letter values; re-expression; median polish; robust regression/anova; smoothers; fitting discrete, skewed, long-tailed distributions; diagnostic plots; standardization. Emphasis on real data, computation (R), reports, presentations. Prerequisite: A previous statistics course; previous exposure to calculus and linear algebra recommended.
Course was offered Fall 2016, Fall 2015, Spring 2015
STAT 5410Introduction to Statistical Software (1.00)
This course develops basic data skills in SAS and R, focusing on data-set management and the production of elementary statistics. Topics include data input, cleaning and reshaping data, producing basic statistics, and simple graphics. The student is prepared for the development of advanced data-analysis techniques in applied statistics courses.
Course was offered Fall 2014, Fall 2013
STAT 5430Statistical Computing with SAS and R (3.00)
Topics include importing data from various sources into R/SAS, manipulating and combining datasets, transform variables, "clean" data so that it is ready for further analysis, manipulating character strings, export datasets, and produce basic graphical and tabular summaries of data. More advanced topics will include how to write, de-bug, and tune functions and macros. Approximately equal time will be spent using SAS and R. Prerequisites: Introductory statistics course.
Course was offered Fall 2016, Fall 2015, Fall 2014, Fall 2010
STAT 5510Contemporary Topics in Statistics (1.00)
This course exposes students to new data types and emerging topics in statistical methodology and computation, emphasizing literacy and applied data-analysis. Topics vary by instructor.
Course was offered Spring 2016, Spring 2015, Spring 2014
STAT 5559New Course in Statistics (1.00 - 4.00)
This course provides the opportunity to offer a new topic in the subject area of statistics.
STAT 5630Statistical Machine Learning (3.00)
Offered
Spring 2017
Introduces various topics in machine learning, including regression, classification, resampling methods, linear model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. The statistical software R is incorporated throughout. Prerequisite: STAT 5120, STAT 6120, or ECON 3720, and previous experience with R Prerequisite: STAT 5120, STAT 6120, or ECON 3720, and previous experience with R
Course was offered Spring 2017, Spring 2016, Spring 2015
STAT 5980Applied Statistics Laboratory (1.00)
This course, the laboratory component of the department's applied statistics program, deals with the use of computer packages in data analysis. Enrollment in STAT 5980 is required for all students in the department's 5000-level applied statistics courses (STAT 5010, 5120, 5130, 5140, 5160, 5170, 5200). STAT 5980 may be repeated for credit provided that a student is enrolled in at least one of these 5000-level applied courses; however, no more than one unit of STAT 5980 may be taken in any semester. Corequisite: 5000-level STAT applied statistics course.
STAT 5993Directed Reading (3.00)
Research into current statistical problems under faculty supervision.
STAT 5999Topics in Statistics (3.00)
Studies topics in statistics that are not part of the regular course offerings. Prerequisite: Instructor permission.
STAT 6021Linear Models for Data Science (3.00)
An introduction to linear statistical models in the context of data science. Topics include simple and multiple linear regression, generalized linear models, time series, analysis of covariance, tree-based classification, and principal components. The primary software is R. Prerequisite: A previous statistics course, a previous linear algebra course, and permission of instructor.
Course was offered Fall 2016, Fall 2015, Fall 2014
STAT 6120Linear Models (3.00)
Course develops fundamental methodology to regression and linear-models analysis in general. Topics include model fitting and inference, partial and sequential testing, variable selection, transformations, diagnostics for influential observations, multicollinearity, and regression in nonstandard settings. Conceptual discussion in lectures is supplemented withhands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics, or instructor permission.
STAT 6130Applied Multivariate Statistics (3.00)
Offered
Spring 2017
This course develops fundamental methodology to the analysis of multivariate data. Topics include the multivariate normal distributions, multivariate regression, multivariate analysis of variance (MANOVA), principal components analysis, factor analysis, and discriminant analysis. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics, or instructor permission.
Course was offered Spring 2017, Fall 2015, Fall 2014
STAT 6160Experimental Design (3.00)
This course develops fundamental concepts and methodology in the design and analysis of experiments. Topics include analysis of variance, multiple comparison tests, completely randomized designs, the general linear model approach to ANOVA, randomized block designs, Latin square and related designs, completely randomized factorial designs with two or more treatments, hierarchical designs, split-plot and confounded factorial designs, and analysis of covariance. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software.
Course was offered Spring 2016
STAT 6190Introduction to Mathematical Statistics (3.00)
This course introduces fundamental concepts in probability that underlie statistical thinking and methodology. Topics include the probability framework, canonical probability distributions, transformations, expectation, moments and momentgenerating functions, parametric families, elementary inequalities, multivariate distributions, and convergence concepts for sequences of random variables. Prerequisite:Graduate standing in Statistics, or instructor permission.
Course was offered Fall 2016, Fall 2015, Fall 2014, Fall 2013
STAT 6250Longitudinal Data Analysis (3.00)
This course develops fundamental methodology to the analysis of longitudinal data. Topics include data structures, modeling the mean and covariance, estimation and inference with respect to the marginal models, linear mixed-effects models, and generalized linear mixed-effects models. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: STAT 6120 and graduate standing in Statistics.
Course was offered Spring 2016
STAT 6260Categorical Data Analysis (3.00)
Offered
Spring 2017
This course develops fundamental methodology to the analysis of categorical data. Topics include contingency tables, generalized linear models, logistic regression, and logit and loglinear models. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics, or instructor permission.
Course was offered Spring 2017, Spring 2015
STAT 6430Statistical Computing for Data Science (3.00)
An introduction to statistical programming, including data manipulation and cleaning, importing and exporting data, managing missing values, data frames, functions, lists, matrices, writing functions, and the use of packages. Efficient programming practices and methods of summarizing and visualizing data are emphasized throughout. SAS and R are the primary computational tools. Prerequisite: A previous statistics course and permission of instructor.
Course was offered Summer 2016, Summer 2015, Summer 2014
STAT 6440Introduction to Bayesian Methods (3.00)
Course provides an introduction to Bayesian methods with an emphasis on modeling and applications. Topics include the elicitation of prior distributions, deriving posterior and predictive distributions and their moments, Bayesian linear and generalized linear regression, and Bayesian hierarchical models. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: STAT 6120, STAT 6190, and graduate standing in Statistics.
Course was offered Fall 2015, Fall 2014
STAT 6510Advanced Data Experience (1.00)
This course develops skills in using data analysis to contribute to research. Each student completes a data-analysis project using data from an interdisciplinary research effort. Topics will vary, and are tailored to the objectives of the projects, and may include discussion of computationally intensive statistical methods that are commonly applied in research.
Course was offered Fall 2016, Fall 2015, Fall 2014
STAT 6520Statistical Literature (1.00)
Offered
Spring 2017
This course develops skills in reading the statistical research literature and prepares the student for contributing to it. Each student completes a well written and properly formatted paper that would be suitable for publication. The paper reviews literature relevant to a specialized research area, and possibly suggests an original research problem. Topics will vary from term to term.
Course was offered Spring 2017, Spring 2016, Spring 2015
STAT 6559New Course in Statistics (1.00 - 4.00)
This course provides the opportunity to offer a new topic in the subject area of statistics.
STAT 7100Introduction to Advanced Statistical Inference (3.00)
Offered
Spring 2017
This course introduces fundamental concepts in the classical theory of statistical inference. Topics include sufficiency and related statistical principles, elementary decision theory, point estimation, hypothesis testing, likelihood-ratio tests, interval estimation, large-sample analysis, and elementary modeling applications. Prerequisite: STAT 6190 and graduate standing in Statistics
Course was offered Spring 2017, Spring 2016
STAT 7130Generalized Linear Models (3.00)
Course develops fundamental data-analysis methodology based on generalized linear models.Topics include the origins of generalized linear models, binary and polytomous data, probit analysis, logit models for proportions, log-linear models for counts, inverse polynomial models, quasi-likelihood models, & survival data models. Conceptual disc. is supplemented w/hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: STAT 6120, STAT 6190, and graduate standing in Statistics
Course was offered Spring 2011
STAT 7150Non-Parametric Statistical Analysis (3.00)
Includes order statistics, distribution-free statistics, U-statistics, rank tests and estimates, asymtotic efficiency, Bahadur efficiency, M-estimates, one- and two-way layouts, multivariate location models, rank correlation, and linear models. Prerequisite: STAT 5190 and one of STAT 5120, 5130, 5140, 5160, 5170; or instructor permission.
STAT 7180Sample Surveys (3.00)
This course develops fundamental methodology related to the main designs and estimation techniques used in sample surveys. Topics include simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation, and non-response and other non-sampling errors. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics, or instructor permission.
Course was offered Fall 2010
STAT 7200Introduction to Advanced Probability (3.00)
This course introduces fundamental concepts in probability from a measure-theoretic perspective. Topics include sigma fields, general measures, integration and expectation, the Radon-Nikodym derivative, product measure and conditioning, convergence concepts, and important limit theorems. The student is prepared for advanced study of statistical theory and stochastic processes. Prerequisite: STAT 6190 and graduate standing in Statistics
STAT 7510Advanced Topics in Statistical Inference (3.00)
Offered
Spring 2017
This course covers advanced theory and methodology in statistical inference. It includes, but is not limited to, substantial, in-depth coverage of topics in asymptotic inference. Context and additional topics vary by instructor.
Course was offered Spring 2017
STAT 7520Advanced Topics in Probability (3.00)
This course covers advanced theory and methodology in probability. It includes, but is not limited to, substantial, in-depth coverage of topics in stochastic processes. Context and additional topics vary by instructor. Prerequisite: STAT 7200
STAT 7559Applied Biostatistical Data Analysis (1.00 - 4.00)
The objective is to help students integrate and apply statistical methods learned in other courses to real data from medial research. Students will learn to identifiy the scientific objectives of a study, and develop and implement appropriate strategies. They will present their intermediate and final results in both oral and written forms. This course will prepare the students for a future career as applied statisticians.
Course was offered Spring 2013, Fall 2010
STAT 7950Statistical Bioinformatics in Medicine (3.00)
Provides an introduction to bioinformatics and discusses important topics in computational biology in medicine, particularly based on modern statistical computing approaches. Reviews state-of-the-art high-throughput biotechnologies, their applications in medicine, and analysis techniques. Requires active student participation in various discussions on the current topics in biotechnology and bioinformatics.
Course was offered Fall 2011
STAT 7995Statistical Consulting (3.00)
Offered
Spring 2017
This course develops skills related to the practice of statistical consulting. It covers conceptual topics and provides experience with data analysis projects found in or resembling those in statistical practice. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics
STAT 8120Topics in Statistics (3.00)
Study of topics in statistics that are currently the subject of active research.
STAT 8170Advanced Time Series (3.00)
Introduces stationary stochastic processes, related limit theorems, and spectral representations. Includes an asymtotic theory for estimation in both the time and frequency domains. Prerequisite: MATH 7360, STAT 5170, or instructor permission.
STAT 9120Statistics Seminar (3.00)
Offered
Spring 2017
Advanced graduate seminar in current research topics. Offerings in each semester are determined by student and faculty research interests.
STAT 9993Directed Reading (1.00 - 9.00)
Offered
Spring 2017
Research into current statistical problems under faculty supervision.
STAT 9998Non-Topical Research, Preparation for Doctoral Research (1.00 - 12.00)
Offered
Spring 2017
For doctoral research, taken before a dissertation director has been selected.
STAT 9999Non-Topical Research (1.00 - 12.00)
Offered
Spring 2017
For doctoral research, taken under the supervision of a dissertation director.