Graduate Statistics Courses

200A. Introduction to Probability Theory (4)

Lecture—3 hours; discussion—1 hour. Prerequisite: Mathematics 21A, 21B, 21C, and 22A; consent of instructor. Fundamental concepts of probability theory, discrete and continuous random variables, standard distributions, moments and moment-generating functions, laws of large numbers and the central limit theorem. No credit to students who have taken course 131A. GE credit: SciEng | QL, SE.—F, W, S. (F, W, S.)

200B. Introduction to Mathematical Statistics I (4)

Lecture—3 hours; discussion—1 hour. Prerequisite: course 200A or the consent of the instructor. Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. No credit to students who have taken course 131B. GE credit: SciEng | SE.—W, S. (W, S.)

200C. Introduction to Mathematical Statistics II (4)

Lecture—3 hours; discussion—1 hour. Prerequisite: course 200B or consent of the instructor. Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. GE credit: No credit to students who have taken course 131C. SciEng | SE.—S. (S.)

201. SAS Programming for Statistical Analysis

Introductory SAS language, data management, statistical applications, methods. Includes basics, graphics, summary statistics, data sets, variables and functions, linear models, repetitive code, simple macros, GLIM and GAM, formatting output, correspondence analysis, bootstrap. Prepare SAS base programmer certification exam.

205. Statistical Methods for Research with SAS (4)

Lecture--3 hours; laboratory--1 hour. Prerequisite: An introductory upper division statistics course and same knowledge of vectors and matrices; suggested courses are 100, 102, or 103, or the equivalent. Focus on linear statistical models widely used in scientific research. Emphasis on concepts, methods, and data analysis using SAS. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, variable transformation, factorial designs and ANCOVA. Offered in alternate years.—III. (III.)

206. Statistical Methods for Research I

Lecture—3 hours; laboratory/discussion—1 hour. Prerequisite: introductory statistics course; some knowledge of vectors and matrices. Focus on linear statistical models. Emphasis on concepts, method and data analysis; formal mathematics kept to minimum. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, factorial designs and analysis of covariance. Use of professional level software

207. Statistical Methods for Research II

Lecture—3 hours; laboratory/discussion—1 hour. Prerequisite: course 206; knowledge of vectors and matrices. Linear and nonlinear statistical models emphasis on concepts, methods/data analysis using professional level software; formal mathematics kept to minimum. Topics include linear mixed models, repeated measures, generalized linear models, model selection, analysis of missing data, and multiple testing procedures.

208. Statistical Methods in Machine Learning

Lecture—3 hours; laboratory/discussion—1 hour. Prerequisite: course 206, 207 and 135, or their equivalents. Focus on linear and nonlinear statistical models. Emphasis on concepts, methods, and data analysis; formal mathematics kept to minimum. Topics include resampling methods, regularization techniques in regression and modern classification, cluster analysis and dimension reduction techniques. Use professional level software.

222. Biostatistics: Survival Analysis (4)

Lecture--3 hours; discussion/laboratory--1 hour. Prerequisite: course 131C. Incomplete data; life tables; nonparametric methods; parametric methods; accelerated failure time models; proportional hazards models; partial likelihood; advanced topics. (Same course as Biostatistics 222.)--I. (I)

223. Biostatistics: Generalized Linear Models (4)

Lecture--3 hours; discussion/laboratory--1 hour. Prerequisite: course 131C. Likelihood and linear regression; generalized linear model; Binomial regression; case-control studies; dose-response and bioassay; Poisson regression; Gamma regression; quasi-likelihood models; estimating equations; multivariate GLMs. (Same course as Biostatistics 223.)--II. (II)

224. Analysis of Longitudinal Data (4)

Lecture--3 hours; discussion/laboratory--1 hour. Prerequisite: course/Biostatistics 222, 223 and course 232B or consent of instructor. Standard and advanced methodology, theory, algorithms, and applications relevant for analysis of repeated measurements and longitudinal data in biostatistical and statistical settings. (Same course as Biostatistics 224.)--III. (III.)

225. Clinical Trials (4)

Lecture--3 hours; discussion/laboratory--1 hour. Prerequisite: course/Biosatistics 223 or consent of instructor. Basic statistical principles of clinical designs, including bias, randomization, blocking, and masking. Practical applications of widely-used designs, including dose-finding, comparative and cluster randomization designs. Advanced statistical procedures for analysis of data collected in clinical trials. (Same course as Biostatistics 225.) Offered in alternate years.--III.

226. Statistical Methods for Bioinformatics (4)

Lecture--3 hours; discussion--1 hour. Prerequisite: course 131C or consent of instructor; data analysis experience recommended. Standard and advanced statistical methodology, theory, algorithms, and applications relevant to the analysis of -omics data. (Same course as Biostatistics 226.) Offered in alternate years.--(II.)

231A. Mathematical Statistics I (4)

Lecture--3 hours; discussion--1 hour. Prerequisite: course 131A, B, and C, Mathematics 25 and Mathematics 125A or the equivalent. First part of three-quarter sequence on mathematical statistics. Emphasizes foundations. Topics include basic concepts in asymptotic theory, decision theory, and an overview of methods of point estimation.--I. (I.)

231B. Mathematical Statistics II (4)

Lecture--3 hours; discussion--1 hour. Prerequisite: course 231A. Second part of a three-quarter sequence on mathematical statistics. Emphasizes: hyposthesis testing (including multiple testing) as well as theory for linear models.--II. (II.)

231C. Mathematical Statistics III (4)

Lecture--3 hours; discussion--1 hour. Prerequisite: course 231A, 231B. Third part of three-quarter sequence on mathematical statistics. Emphasizes large sample theory and their applications. Topics include statistical functionals, smoothing methods and optimization techniques relevant for statistics.--III. (III.)

232A. Applied Statistics I (4)

Lecture--3 hours; laboratory--1 hour. Prerequisite: course 106, 108, 131A, 131B, 131C, Mathematics 167. Estimation and testing for the general linear model, regression, analysis of designed experiments, and missing data techniques.--I. (I.)

232B. Applied Statistics II (4)

Lecture--3 hours; laboratory--1 hour. Prerequisite: course 106, 108, 131A, 131B, 131C, 232A and Mathematics 167. Alternative approaches to regression, model selection, nonparametric methods amenable to linear model framework and their applications.--II. (II.)

232C. Applied Statistics III (4)

Lecture--3 hours; laboratory--1 hour. Prerequisite: course 106,108, 131A, 131B, 131C, 232B and Mathematics 167. Multivariate analysis: multivariate distributions, multivariate linear models, data analytic methods including principal component, factor, discriminant, cluster, and canonical correlation and cluster analysis.--III. (III.)

233. Design of Experiments (3)

Lecture--3 hours. Prerequisite: course 131C. Topics from balanced and partially balanced incomplete block designs, fractional factorials, and response surfaces. Offered in alternate years.--(III.)

235A-235B-235C. Probability Theory
(4-4-4)

Lecture--3 hours; term paper or discussion--1 hour. Prerequisite: Mathematics 127C and 131 or course 131A or consent of instructor. Measure-theoretic foundations, abstract integration, independence, laws of large numbers, characteristic functions, central limit theorems. Weak convergence in metric spaces, Brownian motion, invariance principle. Conditional expectation. Topics selected from martingales, Markov chains, ergodic theory. (Same course as Mathematics 235A-235B-235C.)--I-II-III. (I-II-III.)

237A-237B. Time Series Analysis (4-4)

Lecture--3 hours; term paper. Prerequisite: course 131B or the equivalent; course 237A is a prerequisite for course 237B. Advanced topics in time series analysis and applications. Models for experimental data, measures of dependence, large-sample theory, statistical estimation and inference. Univariate and multivariate spectral analysis, regression, ARIMA models, state-space models, Kalman filtering. Offered in alternate years.--(I-II.)

238. Theory of Multivariate Analysis (4)

Lecture--3 hours; term paper. Prerequisite: courses 131B and 135. Multivariate normal and Wishart distributions, Hotelling's T-Squared, simultaneous inference, likelihood ratio and union intersection tests, Bayesian methods, discriminant analysis, principal component and factor analysis, multivariate clustering, multivariate regression and analysis of variance, application to data. Offered in alternate years.--II.

240A-240B. Nonparametric Inference (4-4)

Lecture--3 hours; term paper. Prerequisite: course 231C; courses 235A-235B-235C recommended. Comprehensive treatment of nonparametric statistical inference, including the most basic materials from classical nonparametrics, robustness, nonparametric estimation of a distribution function from incomplete data, curve estimation, and theory of resampling methodology. Offered in alternate years.
(II-III.)

241. Asymptotic Theory of Statistics (4)

Lecture--3 hours; term paper. Prerequisite: course 231C; courses 235A-235B-235C desirable. Topics in asymptotic theory of statistics chosen from weak convergence, contiguity, empirical processes, Edgeworth expansion, and semiparametric inference. Offered in alternate years. (III.)

242. Introduction to Statistical Programming (4)

Lecture--3 hours; laboratory--1 hour. Prerequisite: courses 130A and 130B or equivalent. Essentials of statistical computing using a general-purpose statistical language. Topics include algorithms; design; debugging and efficiency; object-oriented concepts; model specification and fitting; statistical visualization; data and text processing; databases; computer systems and platforms; comparison of scientific programming languages. Offered in alternate years. –II

243. Computational Statistics (4)

Lecture--3 hours; laboratory--1 hour. Prerequisite: courses 130A and 130B or equivalent, and Mathematics 167 or Mathematics 67 or equivalent. Numerical analysis; random number generation; computer experiments and resampling techniques (bootstrap, cross validation); numerical optimization; matrix decompositions and linear algebra computations; algorithms (markov chain monte carlo, expectation-maximization); algorithm design and efficiency; parallel and distributed computing. Offered in alternate years. --II

250 Topics in Applied and Computational Statistics (4)

Lecture--3 hours; laboratory/discussion--1 hour. Prerequisite: course 131B; course 232A is recommended by not required. Topics my include resampling, nonparametric and semiparametric methods, incomplete data analysis, diagnostics, multivariate and time series analysis, applied Bayesian methods, sequential analysis and quality control, categorical data analysis, spatial and image analysis, computational biology, functional data analysis, models for correlated data, learning theory.

251. Topics in Statistical Methods and Models (4)

Lecture--3 hours; discussion--1 hour. Prerequisite: course 231B or the equivalent. Topics may include Bayesian analysis, nonparametric and semiparametric regression, sequential analysis, bootstrap, statistical methods in high dimensions, reliability, spatial processes, inference for stochastic process, stochastic methods in finance, empirical processes, change-point problems, asymptotics for parametric, nonparametric and semiparametric models, nonlinear time series, robustness. May be repeated for credit with consent of instructor. Not offered every year.--II. (II.)

252. Advanced Topics in Biostatistics (4)

Lecture--3 hours; discussion/laboratory--1 hour. Prerequisite: course 222, 223. Biostatistical methods and models selected from the following: genetics, bioinformatics and genomics; longitudinal or functional data; clinical trials and experimental design; analysis of environmental data; dose-response, nutrition and toxicology; survival analysis; observational studies and epidemiology; computer-intensive or Bayesian methods in biostatistics. May be repeated for credit with consent of adviser when topic differs. (Same course as Biostatistics 252.) Offered in alternate years.--III.

260. Statistical Practice and Data Analysis (3)

Principles and practice of interdisciplinary collaboration in statistics, statistical consulting, ethical aspects, and basics of data analysis and study design. Emphasis on practical consulting and collaboration of statisticians with clients and scientists under instructor supervision.

280. Orientation to Statistical Research (2)

Seminar--2 hours. Prerequisite: consent of instructor. Guided orientation to original statistical research papers, and oral presentations in class of such papers by students under the supervision of a faculty member. May be repeated once for credit. (S/U grading only.)--III. (III.)

290. Seminar in Statistics (1-6)

Prerequisite: consent of instructor. Seminar on advanced topics in probability and statistics. (S/U grading only.)--I, II, III. (I, II, III.)

292. Graduate Group in Statistics Seminar (1-2)

Seminar--1-2 hours. Prerequisite: graduate standing. Advanced study in various fields of statistics with emphasis in applied topics, presented by members of the Graduate Group in Statistics and other guest speakers. (S/U grading only.)--III. (III.)

298. Directed Group Study (1-5)

Prerequisite: graduate standing, consent of instructor.

299. Individual Study (1-12)

Prerequisite: consent of instructor. (S/U grading only.)

299D. Dissertation Research (1-12)

Prerequisite: advancement to candidacy for Ph.D., consent of instructor. (S/U grading only.)

390. Methods of Teaching Statistics (2)

Lecture/discussion--1 hour; laboratory--1 hour. Prerequisite: graduate standing. Practical experience in methods/problems of teaching statistics at university undergraduate level. Lecturing techniques, analysis of tests and supporting material, preparation and grading of examinations, and use of statistical software. Emphasis on practical training. May be repeated for credit. (S/U grading only.)--I. (I.)

396. Teaching Assistant Training Practicum (1-4)

Prerequisite: consent of instructor; graduate standing. (S/U grading only.)—I, II, III. (I, II, III.)

Professional Courses

401. Methods in Statistical Consulting (3)

Lecture--3 hours; discussion--1 hour. Introduction to consulting, in-class consulting as a group, statistical consulting with clients, and in-class discussion of consulting problems. Clients are drawn from a pool of University clients. Students must be enrolled in the graduate program in Statistics or Biostatistics. May be repeated for credit with consent of graduate advisor. Not offered every year. (S/U grading only.)--I, II, III. (I, II, III.)