Course Listing for 2023-24Note: This is a TENTATIVE schedule. The course listings shown here are neither guaranteed, nor considered "final". Department Chairs may provide updated information regarding course offerings or faculty assignments throughout the year. Be sure to check this list regularly for new or revised information.
Course Title description Fall 2023 Winter 2024 Spring 2024 Summer 2024 STATS 200 
STATS 200ABasics of probability theory, random variables and basic transformations, univariate distributions - discrete and continuous, multivariate distributions. Prerequisites: Statistics 120A-B-C or equivalent or consent of instructor. Zhaoxia Yu
STATS 200 Random samples, transformations, limit laws, normal distribution theory, introduction to stochastic processes, data reduction, point estimation (maximum likelihood). Prerequisites: Statistics 120A-B-C or equivalent or consent of instructor. Yaming Yu
STATS 200 Interval estimation, hypothesis testing, decision theory and Bayesian inference, basic linear model theory. Prerequisites: Statistics 120A-B-C or equivalent or consent of instructor. Zhaoxia Yu
STATS 201 Introduction to statistical methods for analyzing data from experiments and surveys. Methods covered include two-sample procedures, analysis of variance, simple and multiple linear regression. May not be taken for graduate credit by Statistics graduate students. Prerequisite: knowledge of basic statistics (at level of Statistics 7). Concurrent with Statistics 110. Sevan Gregory Gulesserian (2)
STATS 202 Introduction to statistical methods for analyzing data from surveys or experiments. Emphasizes application and understanding of methods for categorical data including contingency tables, logistic and Poisson regression, loglinear models. May not be taken for graduate credit by Statistics graduate students. Prerequisite: Statistics 201 or equivalent. Concurrent with Statistics 111. Ana Kenney
STATS 203 Introduction to statistical methods for analyzing longitudinal data from experiments and cohort studies. Topics covered include survival methods for censored time-to-event data, linear mixed models, non-linear mixed effects models, and generalized estimating equations. May not be taken for graduate credit by Statistics graduate students. Prerequisite: Statistics 202 or equivalent. Concurrent with Statistics 112. Sevan Gregory Gulesserian
STATS 205 Basic Bayesian concepts and methods with emphasis on data analysis. Special emphasis on specification of prior distributions. Development for one-two samples and on to binary, Poisson, and linear regression. Analyses performed using free OpenBugs software. Veronica Berrocal
STATS 210 Statistical methods for analyzing data from surveys and experiments. Topics include randomization and model-based inference, two-sample methods, analysis of variance, linear regression and model diagnostics. Prerequisite: knowledge of basic statistics (at the level of Statistics 7), calculus, linear algebra. Veronica Berrocal
STATS 210 Introduction to statistical methods for analyzing discrete and non-normal outcomes. Emphasizes the development and application of methods for categorical data, including contingency tables, logistic and Poisson regression, loglinear models. Sevan Gregory Gulesserian
STATS 210 Introduction to statistical methods for analyzing longitudinal outcomes. Emphasizes the development and application of regression methods for correlated and censored outcomes. Methods for continuous and discrete correlated outcomes, as well as censored outcomes, are covered. Sevan Gregory Gulesserian
STATS 211 Development of the theory and application of generalized linear models. Topics include likelihood estimation and asymptotic distributional theory for exponential families, quasi-likelihood and mixed model development. Emphasizes methodological development and application to real scientific problems. Daniel Gillen
STATS 212 Development and application of statistical methods for analyzing corrected data. Topics covered include repeated measures ANOVA, linear mixed models, non-linear mixed effects models, and generalized estimating equations. Emphasizes both theoretical development and application of the presented methodology. Bin Nan
STATS 220 Advanced topics in probability and statistical inference including measure theoretic probability, large sample theory, decision theory, resampling and Monte Carlo methods, nonparametric methods. Prerequisites: Statistics 200A-B-C. Yaming Yu
STATS 220 Advanced topics in probability and statistical inference including measure theoretic probability, large sample theory, decision theory, resampling and Monte Carlo methods, nonparametric methods. Prerequisites: Statistics 200A-B-C. Weining Shen
STATS 225 Introduction to the Bayesian approach to statistical inference. Topics include univariate and multivariate models, choice of prior distributions, hierarchical models, computation including Markov chain Monte Carlo, model checking, and model selection. Prerequisites: two quarters of upper-division or graduate training in probability and statistics, or consent of instructor. Weining Shen
STATS 230 Numerical computations and algorithms with applications in statistics. Topics include optimization methods including the EM algorithm, random number generation and simulation, Markov chain simulation tools, and numerical integration. Prerequisites: two quarters of upper-division or graduate training in probability and statistics. Statistics 230 and CS 206 may not both be taken for credit. Volodymyr Minin
STATS 265 Various approaches to causal inference focusing on the Rubin causal model and propensity-score methods. Topics include randomized experiments, observational studies, non-compliance, ignorable and non-ignorable treatment assignment, instrumental variables, and sensitivity analysis. Applications from economics, politics, education, and medicine are discussed. Prerequisites: Statistics 200A-B-C and 210. Tianchen Qian
STATS 270 Introduction to the theory and application of stochastic processes. Topics include Markov chains, continuous-time Markov processes, Poisson processes, and Brownian motion. Applications include Markov chain Monte Carlo methods and financial modeling (for example, option pricing). Prerequisites: Statistics 120A-B-C or consent of instructor. Statistics 270 and Mathematics 271A-B-C may not both be taken for credit. Veronica Berrocal
STATS 275 Training in collaborative research and practical application of statistics. Emphasis on effective communication as it relates to identifying scientific objectives, formulating a statistical analysis plan, choice of statistical methods, and interpretation of results and their limitations to non-statisticians. Joni Ricks-Oddie
STATS 280 Periodic seminar series covering topics of current research in statistics and its application. Prerequisites: graduate standing and consent of instructor. Satisfactory/Unsatisfactory only. May be repeated for credit as topics vary. Hengrui Cai
Veronica Berrocal
Bin Nan
STATS 281 Introduction to basic principles of probability and statistical inference. Axiomatic definition of probability, random variables, probability distributions, expectation. Weining Shen (2)
STATS 281 Introduction to basic principles of probability and statistical inference. Point estimation, interval estimating, and testing hypotheses, Bayesian approaches to inference. Tianchen Qian (2)
STATS 281 Introduction to basic principles of probability and statistical inference. Contingency table analysis, linear regression, analysis of variance, model checking. Hengrui Cai (2)
STATS 295 May be repeated for credit as topics vary. Peiyong Qu
Weining Shen
