Courses

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.

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.

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.. 

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.

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.

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.

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.

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.

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 distribution-based statistics and a prior course in programming.

SIS enrollment requirements: Students must have completed ONE of STAT 1100, STAT 1120, STAT 2020, STAT 2120, STAT 3120, APMA 3110, APMA 3120 & ONE of STAT 1601, STAT 1602, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113

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.

SIS enrollment requirements: Students must have completed ONE of MATH 1220, MATH 1320, APMA 1110

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.

SIS enrollment requirements: Students must have completed ONE of STAT 3110, MATH 3100, APMA 3100

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.

SIS enrollment requirements: Students must have completed ONE of STAT 1601, STAT 1602, STAT 1100, STAT 1120, STAT 2020, STAT 2120, STAT 3120, APMA 3110, APMA 3120

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.

SIS enrollment requirements: Students must have completed ONE of STAT 1601, STAT 1602, STAT 1100, STAT 1120, STAT 2020, STAT 2120, STAT 3120, APMA 3110, APMA 3120

This course provides an introduction to data analysis using the Python programming language. Topics include using an integrated 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.

SIS enrollment requirements: Students must have completed ONE of STAT 1601, STAT 1602, STAT 1100, STAT 1120, STAT 2020, STAT 2120, STAT 3120, APMA 3110, APMA 3120 & ONE of STAT 1601, STAT 1602, STAT 3080, CS 1110, CS 1111, CS 1112, CS 1113

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 programming.

SIS enrollment requirements: Students must have completed ONE of STAT 1601, STAT 1602, STAT 1100, STAT 1120, STAT 2020, STAT 2120, STAT 3120, APMA 3110, APMA 3120 & ONE of STAT 1601, STAT 1602, STAT 3080, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113

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.

SIS enrollment requirements: Students must have completed ONE of STAT 1601, STAT 1602, STAT 1100, STAT 1120, STAT 2020, STAT 2120, STAT 3120, APMA 3110, APMA 3120

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.

SIS enrollment requirements: Students must have completed ONE of STAT 1601, STAT 1602, STAT 1100, STAT 1120, STAT 2020, STAT 2120, STAT 3120, APMA 3110, APMA 3120 & ONE of STAT 3110, MATH 3350, MATH 3351, APMA 3080

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.

SIS enrollment requirements: Students must have completed STAT 3120 & ONE of STAT 3110, MATH 3350, MATH 3351, APMA 3080 & ONE of STAT 1601, STAT 1602, STAT 3080, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113

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.

SIS enrollment requirements: Students must have completed ONE of STAT 3220, STAT 4120, STAT 5120, ECON 3720, ECON 4720, SYS 4021

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.

SIS enrollment requirements: Students must have completed ONE of STAT 3110, MATH 3100, APMA 3100 & ONE of STAT 3220, STAT 4120, STAT 5120, ECON 3720, ECON 4720, SYS 4021 & ONE of STAT 1601, STAT 1602, STAT 3080, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113

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.

SIS enrollment requirements: Students must have completed ONE of STAT 3220, STAT 4120, STAT 5120, ECON 3720, ECON 4720, SYS 4021 & ONE of STAT 1601, STAT 1602, STAT 3080, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113

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.

SIS enrollment requirements: Students must have completed ONE of STAT 3220, STAT 4120, STAT 5120, ECON 3720, ECON 4720, SYS 4021 & ONE of STAT 1601, STAT 1602, STAT 3080, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113

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.

SIS enrollment requirements: Students must have completed STAT 3120 & ONE of STAT 3220, STAT 4120, STAT 5120, ECON 3720, ECON 4720, SYS 4021 & ONE of STAT 1601, STAT 1602, STAT 3080, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113

This course is for students interested in topics that are not covered in other courses and involves reading and study in areas of interest to the individual student. Students must obtain a faculty advisor to approve and direct the program.  

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.

SIS enrollment requirements: Students must have completed ONE of STAT 3220, STAT 4120, STAT 5120, ECON 3720, ECON 4720, SYS 4021 & ONE of STAT 1601, STAT 1602, STAT 3080, STAT 3250, CS 1110, CS 1111, CS 1112, CS 1113 & 4th Year Statistics major

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.

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

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 or5100, or instructor permission; corequisite: STAT5980.

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 or5100, or instructor permission.

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

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.

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.

This course is a follow-up to Investment Science I (Stat5265). 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, STAT5265.

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

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 STAT5120 or STAT 6120.

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.

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.

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.

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.

Topics include importing data from various sources into R/SAS, manipulate and combine datasets, transform variables, “clean” data so that it is ready for further analysis, manipulate 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.

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.

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: STAT5120, STAT 6120, or ECON 3720, and previous experience with R Prerequisite: STAT5120, STAT 6120, or ECON 3720, and previous experience with R

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.

Research into current statistical problems under faculty supervision.

Studies topics in statistics that are not part of the regular course offerings. Prerequisite: Instructor permission.

This course is designed to give a graduate-level student a thorough grounding in a wide range of problems in statistics and machine learning that can be formulated as optimization or integration problems, and a broad comprehension of algorithms to solve these problems. The focus of the first 3/4 of the course will be on optimization methods (start from convex optimization, but also touch upon some nonconvex optimization methods widely used in statistics and machine learning, such as EM algorithm, variational inference). In the last 1/4 of the course, we will introduce some of the most important ideas of Monte Carlo methods. Important applications in machine learning and statistics will be discussed as motivating examples.

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 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.

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 dataanalysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics, or instructor permission.

This course develops fundamental concepts and methodology in the design and analysis of experiments. Topics include analysis of variance, multiple comparison tests, randomized block designs, Latin square and related designs, factorial designs, split-plot and related 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. Prerequisite: Graduate standing in Statistics, or instructor permission.

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.

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.

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.

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 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.

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.

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.

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

A rigorous mathematical development of the principles of statistics. Covers point and interval estimation, hypothesis testing, asymtotic theory, Bayesian statistics, and decision theory from a unified perspective. Prerequisite: STAT 7110 or instructor permission.

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

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: STAT5190 and one of STAT5120,5130,5140,5160,5170; or instructor permission.

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.

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

This course covers important mathematical and probabilistic ideas used in statistical modeling. Topics include L2 estimation, stationarity in time series, spatial and time point processes, Brownian motion, and modeling with stochastic differential equations. Prerequisite: STAT 7200

An introduction to martingale theory and stochastic differential equations with applications to survival analysis and sequential clinical trials. Prerequisites: STAT 7200 or MATH 7360

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.

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

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.

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

Study of topics in statistics that are currently the subject of active research.

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, STAT5170, or instructor permission.

Advanced graduate seminar in current research topics. Offerings in each semester are determined by student and faculty research interests.

Research into current statistical problems under faculty supervision.

For doctoral research, taken before a dissertation director has been selected.

For doctoral research, taken under the supervision of a dissertation director.