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| Statistics | |
| STAT 1001T | Non-UVA Transfer/Test Credit (1 - 4) |
| Transfer credit or test credit that is not equivalentto current UVA coursework. Contains content related to Artistic, Interpretive, and STATosophical Inquiry. | |
| STAT 1002T | Non-UVA Transfer/Test Credit (1 - 4) |
| Transfer credit or test credit that is not equivalentto current UVA coursework. Contains content related to Cultures and STATieties of the World. | |
| STAT 1003T | Non-UVA Transfer/Test Credit (1 - 4) |
| Transfer credit or test credit that is not equivalentto current UVA coursework. Contains content related to STATorical Perspectives. | |
| STAT 1004T | Non-UVA Transfer/Test Credit (1 - 4) |
| Transfer credit or test credit that is not equivalentto current UVA coursework. Contains content related to STATial and Economic Systems. | |
| STAT 1005T | Non-UVA Transfer/Test Credit (1 - 4) |
| Transfer credit or test credit that is not equivalentto current UVA coursework. Contains content related to Chemical, STATematical, and STATical Inquiry | |
| STAT 1006T | Non-UVA Transfer/Test Credit (1 - 4) |
| Transfer credit or test credit that is not equivalentto current UVA coursework. Contains content related to Living Systems. | |
| STAT 1007T | Non-UVA Transfer/Test Credit (1 - 4) |
| Transfer credit or test credit that is not equivalentto current UVA coursework. Contains content related to Science and STATiety | |
| STAT 1100 | Chance: An Introduction to Statistics (3) |
| Offered Spring 2025 | This course 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 various current sources, including journals and news. No prior knowledge of statistics is required. Students will not receive credit for both STAT 1100 and STAT 1120. Course was offered Fall 2024, Summer 2024, Spring 2024, Fall 2023, Spring 2023, Fall 2022, Spring 2022, Fall 2021, Spring 2021, Fall 2020, Spring 2020, Fall 2019, Spring 2019, Fall 2018, Spring 2018, Fall 2017, Summer 2017, Spring 2017, Fall 2016, Spring 2016, Fall 2015, Summer 2015, Summer 2014, Summer 2013, Fall 2012, Summer 2012, Spring 2012, Fall 2011, Summer 2011, Spring 2011, Fall 2010, Summer 2010, Spring 2010, Fall 2009 |
| STAT 1120 | Introduction to Statistics (3) |
| This course 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. No prior knowledge of statistics is required. Students will not receive credit for both STAT 1100 and STAT 1120. | |
| STAT 1400 | Forensic Science and Statistics (3) |
| This course provides an introduction to statistical analysis in the context of forensic science. Statistical topics covered include probability distributions, hypothesis testing, confidence intervals, measures of association, and regression. Applications drawn from forensics include analysis of fingerprints, DNA, and particle evidence. No prior knowledge of statistics or forensic science is required. Course was offered Spring 2021 | |
| STAT 1501 | Statistics-Edge (1 - 6) |
| These statistics classes are for students in the UVA Edge program. They help students develop critical data analysis skills for academia, the workplace and life. See https://edge.virginia.edu/ for details. | |
| STAT 1559 | New Course in Statistics (1 - 4) |
| This course provides the opportunity to offer a new topic in the subject area of statistics. Course was offered Spring 2018 | |
| STAT 1601 | Introduction to Data Science with R (3) |
| Offered Spring 2025 | This course provides an introduction to the process of collecting, manipulating, exploring, analyzing, and displaying data using the statistical software R. The collection of elementary statistical analysis techniques introduced will be driven by questions derived from the data. The data used in this course will generally follow a common theme. No prior knowledge of statistics, data science, or programming is required. Course was offered January 2025, Fall 2024, Summer 2024, Spring 2024, January 2024, Fall 2023, Summer 2023, Spring 2023, January 2023, Fall 2022, Summer 2022, Spring 2022, Janiuary 2022, Fall 2021, Summer 2021, Spring 2021, January 2021, Fall 2020, Summer 2020, Spring 2020, January 2020, Fall 2019, Summer 2019, Spring 2019, Fall 2018 |
| STAT 1602 | Introduction to Data Science with Python (3) |
| Offered Spring 2025 | This course provides an introduction to various topics in data science using the Python programming language. The course will start with the basics of Python, and apply them to data cleaning, merging, transformation, and analytic methods drawn from data science analysis and statistics, with an emphasis on applications. No prior knowledge of statistics, data science, or programming is required. Course was offered Fall 2024, Spring 2024, January 2024, Fall 2023, Summer 2023, Spring 2023, Fall 2022, Fall 2021, Spring 2021, Spring 2020, Fall 2019, Spring 2019 |
| STAT 1800 | Introduction to Sports Analytics (3) |
| This course provides an introduction to sports analytics, including the collection, analysis, and visualization of sports data using the statistical programming language R. Elementary statistical analysis techniques will be introduced through questions arising in sports. No prior knowledge of statistics is required. | |
| STAT 2020 | Statistics for Biologists (4) |
| Offered Spring 2025 | 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. No prior knowledge of statistics is required. Co-requisite: Concurrent enrollment in a lab section of STAT 2020. Course was offered Fall 2024, Summer 2024, Spring 2024, Fall 2023, Summer 2023, Spring 2023, Fall 2022, Summer 2022, Spring 2022, Fall 2021, Summer 2021, Spring 2021, Fall 2020, Summer 2020, Spring 2020, Fall 2019, Summer 2019, Spring 2019, Fall 2018, Summer 2018, Spring 2018, Fall 2017, Summer 2017, Spring 2017, Fall 2016, Summer 2016, Spring 2016, Fall 2015, Summer 2015, Spring 2015, Fall 2014, Summer 2014, Summer 2013, Summer 2012, Spring 2012, Fall 2011, Summer 2011, Spring 2011, Fall 2010, Summer 2010, Spring 2010 |
| STAT 2120 | Introduction to Statistical Analysis (4) |
| Offered Spring 2025 | This course provides an introduction to the probability & statistical theory underlying the estimation of parameters & testing of statistical hypotheses, including those in the context of simple & multiple regression Applications are drawn from economics, business, & other fields. No prior knowledge of statistics is required. Highly Recommended: Prior experience with calculus I; Co-requisite: Concurrent enrollment in a lab section of STAT 2120. Course was offered Fall 2024, Summer 2024, Spring 2024, Fall 2023, Summer 2023, Spring 2023, Fall 2022, Summer 2022, Spring 2022, Fall 2021, Summer 2021, Spring 2021, Fall 2020, Summer 2020, Spring 2020, Fall 2019, Summer 2019, Spring 2019, Fall 2018, Summer 2018, Spring 2018, Fall 2017, Summer 2017, Spring 2017, Fall 2016, Summer 2016, Spring 2016, Fall 2015, Summer 2015, Spring 2015, Fall 2014, Summer 2014, Spring 2014, Fall 2013, Summer 2013, Spring 2013, Fall 2012, Summer 2012, Spring 2012, Fall 2011, Summer 2011, Spring 2011, Fall 2010, Summer 2010, Spring 2010, Fall 2009 |
| STAT 2125 | Statistics Workshop (1) |
| This course is a workshop to support deeper understanding of concepts introduced in STAT 2120. Course was offered Fall 2019 | |
| STAT 2559 | New Course in Statistics (1 - 4) |
| This course provides the opportunity to offer a new topic in the subject area of statistics. | |
| STAT 3080 | From Data to Knowledge (3) |
| Offered Spring 2025 | This course introduces methods to approach uncertainty and variation inherent in elementary statistical techniques from multiple angles. 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. Prerequisite: A prior course in statistics and a prior course in programming. Course was offered Fall 2024, Spring 2024, Fall 2023, Spring 2023, Fall 2022, Summer 2022, Spring 2022, Fall 2021, Spring 2021, January 2021, Fall 2020, Spring 2020, Fall 2019, Spring 2019, Fall 2018, Spring 2018, Fall 2017, Spring 2017, Fall 2016, Spring 2016, Fall 2015, Spring 2015, Fall 2014, Spring 2014, Fall 2013, Fall 2012, Fall 2011, Fall 2010 |
| STAT 3110 | Foundations of Statistics (3) |
| Offered Spring 2025 | 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. Prerequisite: A prior course in calculus II. Course was offered Fall 2024, Summer 2024, Spring 2024, Fall 2023, Spring 2023, Fall 2022, Summer 2022, Spring 2022, Fall 2021, Summer 2021, Spring 2021, Fall 2020, Spring 2020, Fall 2019 |
| STAT 3120 | Introduction to Mathematical Statistics (3) |
| Offered Spring 2025 | 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: A prior course in probability. Course was offered Fall 2024, Summer 2024, Spring 2024, Fall 2023, Summer 2023, Spring 2023, Fall 2022, Summer 2022, Spring 2022, Fall 2021, Summer 2021, Spring 2021, Fall 2020, Summer 2020, Spring 2020, Fall 2019, Summer 2019, Spring 2019, Fall 2018, Summer 2018, Spring 2018, Fall 2017, Summer 2017, Spring 2017, Fall 2016, Summer 2016, Spring 2016, Fall 2015, Summer 2015, Spring 2015, Fall 2014, Summer 2014, Spring 2014, Fall 2013, Summer 2013, Spring 2013, Summer 2012, Spring 2012, Fall 2011, Summer 2011, Spring 2011, Fall 2010, Summer 2010, Spring 2010 |
| STAT 3130 | Design and Analysis of Sample Surveys (3) |
| This course introduces 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. Prerequisite: A prior course in statistics. Course was offered Fall 2024, Fall 2023, Fall 2022, Spring 2022, Fall 2020, Fall 2019, Spring 2018, Spring 2017, Spring 2016, Spring 2015, Spring 2014 | |
| STAT 3220 | Introduction to Regression Analysis (3) |
| Offered Spring 2025 | 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: A prior course in statistics. Course was offered January 2025, Fall 2024, Spring 2024, January 2024, Fall 2023, Summer 2023, Spring 2023, January 2023, Fall 2022, Spring 2022, Fall 2021, Spring 2021, January 2021, Fall 2020, Spring 2020, Fall 2019, Spring 2019, Fall 2018, Spring 2018, Fall 2017, Spring 2017, Fall 2016, Fall 2012, Spring 2012, Spring 2011, Spring 2010 |
| STAT 3250 | Data Analysis with Python (3) |
| Offered Spring 2025 | This course provides an introduction to data analysis using the Python programming language. Topics include using an intergrated development environment; data analysis packages numpy, pandas and scipy; data loading, storage, cleaning, merging, transformation, and aggregation; data plotting and visualization. Prerequisite: A prior course in statistics and a prior course in programming. Course was offered Fall 2024, Spring 2024, Fall 2023, Summer 2023, Spring 2023, Fall 2022, Summer 2022, Spring 2022, Summer 2021, Spring 2021, Spring 2020, Spring 2019, Fall 2017, Fall 2016 |
| STAT 3280 | Data Visualization and Management (3) |
| Offered Spring 2025 | This course introduces methods for presenting data graphically and in tabular form, including the use of software to create visualizations. Also introduced are databases, with topics including traditional relational databases and SQL (Structured Query Language) for retrieving information. Prerequisite: A prior course in statistics and a prior course in R programming. Course was offered Fall 2024, Spring 2024, Fall 2023, Summer 2023, Spring 2023, Fall 2022, Spring 2022, Fall 2021, Spring 2021, Spring 2020 |
| STAT 3480 | Nonparametric and Rank-Based Statistics (3) |
| Offered Spring 2025 | This course includes an overview of parametric vs. non-parametric 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; multi-factor experiments; and smoothing methods. Prerequisite: A prior course in statistics. Course was offered Spring 2024, Fall 2022, Fall 2021, Fall 2020, Fall 2019, Spring 2019, Fall 2018, Fall 2017, Spring 2016, Fall 2015 |
| STAT 3559 | New Course in Statistics (1 - 4) |
| This course provides the opportunity to offer a new topic in the subject area of Statistics. | |
| STAT 4120 | Applied Linear Models (3) |
| Offered Spring 2025 | This course includes linear regression models, inferences in regression analysis, model validation, selection of independent variables, multicollinearity, influential observations, and other topics. Conceptual discussion is supplemented with hands-on practice in applied data-analysis tasks. Highly recommended: A prior course in applied regression such as STAT 3220. Prerequisite: A prior course in statistics and a prior course in linear algebra. Course was offered Spring 2024, Spring 2023 |
| STAT 4130 | Applied Multivariate Statistics (3) |
| This course develops fundamental methodology to the analysis of multivariate data using computational tools. Topics include multivariate normal distribution, multivariate linear model, principal components and factor analysis, discriminant analysis, clustering, and classification. Prerequisite: A prior course in mathematical statistics, a prior course in linear algebra, and a prior course in programming. Course was offered Fall 2024 | |
| STAT 4160 | Experimental Design (3) |
| Offered Spring 2025 | This course 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. Prerequisite: A prior course in regression. |
| STAT 4170 | Financial Time Series and Forecasting (3) |
| 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. Prerequisite: A prior course in probability, a prior course in regression, and a prior course in programming. | |
| STAT 4220 | Applied Analytics for Business (3) |
| Offered Spring 2025 | 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. Prerequisite: A prior course in regression and a prior course in programming. |
| STAT 4559 | New Course in Statistics (1 - 4) |
| This course provides the opportunity to offer a new topic in the subject area of Statistics. | |
| STAT 4630 | Statistical Machine Learning (3) |
| This course 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: A prior course in regression and a prior course in programming. Course was offered Fall 2024, Spring 2024, Fall 2023, Summer 2023, Fall 2022, Fall 2021, Fall 2020, Fall 2019, Spring 2018, Spring 2017 | |
| STAT 4800 | Advanced Sports Analytics I (3) |
| Offered Spring 2025 | This course provides a platform for exploring advanced statistical modeling and analysis techniques through the lens of state-of-the-art sports analytics. Prerequisite: A prior course in mathematical statistics, a prior course in regression, and a prior course in programming. |
| STAT 4993 | Independent Study (1 - 4) |
| Offered Spring 2025 | 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. Course was offered Fall 2024, Spring 2024, Fall 2023, Spring 2023, Fall 2022, Spring 2022, Fall 2021, Spring 2021, Fall 2020, Spring 2020, Fall 2019, Spring 2019, Fall 2018, Summer 2018, Spring 2018, Fall 2017, Summer 2017, Spring 2017, Fall 2016, Summer 2016, Spring 2016, Fall 2015, Summer 2015, Spring 2015, Fall 2014, Summer 2014, Spring 2014 |
| STAT 4995 | Statistical Consulting (1 - 3) |
| 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 4996 | Capstone (3) |
| Offered Spring 2025 | 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. Prerequisite: A prior course in regression and a prior course in programming. This course is restricted to Statistics majors in their final year. Course was offered Fall 2024, Spring 2024, Fall 2023, Spring 2023, Fall 2022, Spring 2022, Spring 2020 |
| STAT 5000 | Introduction to Applied Statistics (3) |
| 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. Course was offered Summer 2015, Summer 2014, Summer 2013, Summer 2012, Spring 2012, Fall 2011, Summer 2011, Spring 2011, Fall 2010, Summer 2010, Spring 2010, Fall 2009 | |
| STAT 5120 | Applied Linear Models (3) |
| 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 Course was offered Spring 2022, Spring 2021, Spring 2020, Fall 2019, Spring 2019, Fall 2018, Spring 2018, Fall 2017, Spring 2017, Fall 2016, Spring 2016, Fall 2015, Spring 2015, Fall 2014, Spring 2014, Fall 2013, Fall 2011, Fall 2010, Fall 2009 | |
| STAT 5140 | Survival Analysis and Reliability Theory (3) |
| 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 2024, Spring 2023, Spring 2021, Fall 2018, Fall 2017, Fall 2016, Spring 2011 | |
| STAT 5150 | Actuarial Statistics (3) |
| 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 5170 | Applied Time Series (3) |
| 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 Course was offered Spring 2023, Fall 2022, Spring 2022, Fall 2021, Spring 2021, Fall 2020, Spring 2020, Fall 2019, Spring 2019, Fall 2018, Spring 2018, Fall 2017, Spring 2017, Fall 2016, Spring 2016, Fall 2015, Spring 2015, Fall 2014, Spring 2014, Fall 2012, Fall 2010 | |
| STAT 5180 | Design and Analysis of Sample Surveys (3) |
| Offered Spring 2025 | 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. |
| STAT 5265 | Investment Science I (3) |
| 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 5266 | Investment Science II (3) |
| 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 5310 | Clinical Trials Methodology (3) |
| 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. | |
| STAT 5330 | Data Mining (3) |
| 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 2024, Fall 2023, Fall 2022, Fall 2021, Fall 2020, Spring 2020, Spring 2019, Fall 2018, Fall 2017, Fall 2016, Spring 2012, Fall 2010 | |
| STAT 5340 | Bootstrap and Other Resampling Methods (3) |
| 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 5350 | Applied Causal Inference (3) |
| 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. | |
| STAT 5390 | Exploratory Data Analysis (3) |
| 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. | |
| STAT 5410 | Introduction to Statistical Software (1) |
| 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. | |
| STAT 5430 | Statistical Computing with Python and R (3) |
| "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 & macros. Approx. equal time will be spent using SAS and R. Prereq: Intro statistics course" | |
| STAT 5500 | Statistical Modeling of Real-World Data (3) |
| This course will teach students how to develop approaches to modeling real data and drawing valid inferences. The course may run as a traditional lecture about modeling and analyzing data from actual applications, or students in the class will work in teams to model and analyze data from a specific project. | |
| STAT 5510 | Contemporary Topics in Statistics (1) |
| 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. | |
| STAT 5559 | New Course in Statistics (1 - 4) |
| This course provides the opportunity to offer a new topic in the subject area of statistics. Course was offered Fall 2021, Fall 2017, Spring 2016, Spring 2015, Summer 2014, Fall 2013, Fall 2012, Spring 2012, Fall 2011, Spring 2010, Fall 2009 | |
| STAT 5630 | Statistical Machine Learning (3) |
| Offered Spring 2025 | 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 2024, Spring 2023, Spring 2022, Spring 2021, Fall 2019, Fall 2018, Spring 2017, Spring 2016, Spring 2015 |
| STAT 5980 | Applied Statistics Laboratory (1) |
| 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. Course was offered Spring 2014, Spring 2013, Fall 2012, Spring 2012, Fall 2011, Spring 2011, Fall 2010, Spring 2010, Fall 2009 | |
| STAT 5993 | Directed Reading (1 - 3) |
| Offered Spring 2025 | Research into current statistical problems under faculty supervision. Course was offered Fall 2024, Spring 2024, Fall 2023, Spring 2023, Fall 2022, Spring 2022, Fall 2021, Spring 2021, January 2021, Fall 2020, Fall 2019 |
| STAT 5999 | Topics in Statistics (3) |
| Studies topics in statistics that are not part of the regular course offerings. Prerequisite: Instructor permission. | |
| STAT 6020 | Optimization and Monte Carlo Methods in Statistics and Machine Learning (3) |
| This course is designed to give a graduate-level student (and senior undergrads) a thorough grounding in properties about optimization and integrating problems in statistics and machine learning, and a broad comprehension of algorithms tailored to exploit such properties and some additional computational interference strategies. | |
| STAT 6021 | Linear Models for Data Science (3) |
| Offered Spring 2025 | 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 2024, Summer 2024, Spring 2024, Fall 2020, Fall 2018, Fall 2017, Fall 2016, Fall 2015, Fall 2014 |
| STAT 6120 | Linear Models (3) |
| 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 6130 | Applied Multivariate Statistics (3) |
| Offered Spring 2025 | 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 2024, Fall 2022, Fall 2021, Spring 2020, Spring 2019, Spring 2018, Spring 2017, Fall 2015, Fall 2014 |
| STAT 6160 | Experimental Design (3) |
| Offered Spring 2025 | 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. |
| STAT 6190 | Introduction to Mathematical Statistics (3) |
| 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. | |
| STAT 6250 | Longitudinal Data Analysis (3) |
| 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. | |
| STAT 6260 | Categorical Data Analysis (3) |
| 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. | |
| STAT 6440 | Introduction to Bayesian Methods (3) |
| 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 Spring 2023, Spring 2021, Spring 2020, Spring 2019, Fall 2017, Fall 2015, Fall 2014 | |
| STAT 6559 | New Course in Statistics (1 - 4) |
| This course provides the opportunity to offer a new topic in the subject area of statistics. Course was offered Spring 2024 | |
| STAT 6610 | Statistical Literature (1) |
| In this course, students will read, present, and discuss research papers on topics that are closed related to faculty's research interests, so that students have understandings of research profiles in the department and start to approach faculty members for thesis advising based on their interests developed in this topic course. This course helps the students to transition from course taking to thesis research. Topics will vary from term to term. | |
| STAT 6620 | Research Writing (1) |
| 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 2024, Spring 2023 | |
| STAT 7100 | Introduction to Advanced Statistical Inference (3) |
| Offered Spring 2025 | 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 2024, Spring 2023, Spring 2022, Spring 2021, Spring 2020, Spring 2019, Spring 2018, Spring 2017, Spring 2016 |
| STAT 7130 | Generalized Linear Models (3) |
| 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 7150 | Non-Parametric Statistical Analysis (3) |
| 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 7180 | Sample Surveys (3) |
| 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 7200 | Introduction to Advanced Probability (3) |
| 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 7520 | Advanced Topics in Probability (3) |
| 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 Course was offered Spring 2020 | |
| STAT 7559 | Applied Biostatistical Data Analysis (1 - 4) |
| 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 7610 | Advanced Inference (3) |
| 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 2024 | |
| STAT 7950 | Statistical Bioinformatics in Medicine (3) |
| 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 7995 | Statistical Consulting (3) |
| Offered Spring 2025 | 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 Course was offered Spring 2024, Fall 2023, Fall 2022, Spring 2022, Spring 2021, Fall 2020, Spring 2020, Spring 2019, Fall 2018, Spring 2018, Spring 2017, Spring 2016, Spring 2015, Spring 2014, Spring 2013, Spring 2012, Spring 2011 |
| STAT 8120 | Topics in Statistics (3) |
| Study of topics in statistics that are currently the subject of active research. Course was offered Spring 2020, Fall 2019 | |
| STAT 8170 | Advanced Time Series (3) |
| 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 9120 | Statistics Seminar (1 - 3) |
| Offered Spring 2025 | Advanced graduate seminar in current research topics. Offerings in each semester are determined by student and faculty research interests. Course was offered Fall 2024, Spring 2016, Spring 2015, Spring 2014, Spring 2013, Fall 2012, Spring 2012, Fall 2011, Spring 2011, Fall 2010, Spring 2010, Fall 2009 |
| STAT 9993 | Directed Reading (1 - 9) |
| Research into current statistical problems under faculty supervision. Course was offered Summer 2018, Spring 2018, Summer 2017, Spring 2017, Fall 2016, Summer 2016, Spring 2016, Fall 2015, Summer 2015, Spring 2015, Fall 2014, Summer 2014, Spring 2014, Fall 2013, Summer 2013, Spring 2013, Fall 2012, Summer 2012, Spring 2012, Spring 2011, Fall 2010, Spring 2010, Fall 2009 | |
| STAT 9998 | Non-Topical Research, Preparation for Doctoral Research (1 - 12) |
| Offered Spring 2025 | For doctoral research, taken before a dissertation director has been selected. Course was offered Spring 2024, Spring 2023, Spring 2022, Spring 2021, Spring 2020, Spring 2019, Spring 2018, Spring 2017, Spring 2016, Spring 2015, Spring 2014, Spring 2013, Spring 2012, Spring 2011, Spring 2010 |
| STAT 9999 | Non-Topical Research (1 - 12) |
| Offered Spring 2025 | For doctoral research, taken under the supervision of a dissertation director. Course was offered Fall 2024, Spring 2024, Fall 2023, Spring 2023, Fall 2022, Spring 2022, Fall 2021, Spring 2021, Fall 2020, Summer 2020, Spring 2020, Fall 2019, Summer 2019, Spring 2019, Fall 2018, Summer 2018, Spring 2018, Fall 2017, Spring 2017, Fall 2016, Spring 2016, Fall 2015, Spring 2015, Fall 2014, Spring 2014, Fall 2013, Spring 2013, Fall 2012, Spring 2012, Fall 2011, Spring 2011, Fall 2010, Spring 2010, Fall 2009 |