General Information | The Program | Requirements | Courses | Lower Division 10. Statistical Thinking (4) Lecture—3 hours; discussion/laboratory—1 hour. Prerequisite: two years of high school algebra. Statistics and probability in daily life. Examines principles of collecting, presenting and interpreting data in order to critically assess results reported in the media; emphasis is on understanding polls, unemployment rates, health studies; understanding probability, risk and odds. GE credit: SciEng or SocSci, Wrt | QL, SE.—F. (F.) 12. Introduction to Discrete Probability (4) Lecture—3 hours; laboratory—1 hour. Prerequisite: two years of high school algebra. Random experiments; countable sample spaces; elementary probability axioms; counting formulas; conditional probability; independence; Bayes theorem; expectation; gambling problems; binomial, hypergeometric, Poisson, geometric, negative binomial and multinomial models; limiting distributions; Markov chains. Applications in the social, biological, and engineering sciences. Offered in alternate years. GE credit: SciEng | QL, SE. 13. Elementary Statistics (4) Lecture—3 hours; discussion—1 hour. Prerequisite: two years of high school algebra or Mathematics D. Descriptive statistics; basic probability concepts; binomial, normal, Student's t, and chi-square distributions. Hypothesis testing and confidence intervals for one and two means and proportions. Regression. Not open for credit to students who have completed course 13V or higher. GE credit: SciEng | QL, SE.—F, W, S, Su. (F, W, S, Su.) 13Y. Elementary Statistics (4) Lecture—1.5 hours; web virtual lecture—5 hours. Prerequisite: two years of high school algebra or Mathematics D. Descriptive statistics; basic probability concepts; binomial, normal, Student's t, and chi-square distributions. Hypothesis testing and confidence intervals for one and two means and proportions. Regression. Not open for credit for students who have completed course 13, or higher. GE credit: SciEng | QL, SE. 32. Introductory Statistical Analysis Through Computers (4) Lecture—3 hours; laboratory—1 hour. Prerequisite: Mathematics 16B or 17C or 21B; ability to program in a high-level programming language. Probability concepts: Events and sample spaces; random variables; mass, density and distribution functions; parametric families; parameter estimation and confidence intervals; hypothesis testing; Central Limit Theorem. Recommended as alternative to course 13 for students with a background in calculus and programming. Only two units of credit allowed to students who have taken course 13, or 102; not open for credit to students who have taken course 100. GE credit: SciEng | QL, SE.—W, S. (W, S.) 90X. Seminar (1-2) Seminar—1-2 hours. Prerequisite: high school algebra and consent of instructor. Examination of a special topic in a small group setting. 98. Directed Group Study (1-5) Prerequisite: consent of instructor. (P/NP grading only.) 99. Special Study for Undergraduates (1-5) Prerequisite: consent of instructor. (P/NP grading only.) Upper Division 100. Applied Statistics for Biological Sciences (4) Lecture—3 hours; laboratory—1 hour. Prerequisite: Mathematics 16B or 17C or 21B. Descriptive statistics, probability, sampling distributions, estimation, hypothesis testing, contingency tables, ANOVA, regression; implementation of statistical methods using computer package. Only two units credit allowed to students who have taken course courses 13, 32 or 103; not open for credit to students who have taken course 102. GE credit: SciEng | QL, SE.—F, W, S, Su. (F, W, S, Su.) 101. Advanced Applied Statistics for the Biological Sciences (4) Lecture—3 hours; laboratory—1 hour. Prerequisite: course 100. Basic experimental designs, two- factor ANOVA without interactions, repeated measures ANOVA, ANCOVA, random effects vs. fixed effects, multiple regression, basic model building, resampling methods, multiple comparisons, multivariate methods, generalized linear models, Monte Carlo simulations. GE credit: SciEng | SE, QL.—S. (S.) 103. Applied Statistics for Business and Economics (4) Lecture—3 hours; discussion—1 hour. Prerequisite: course 13, 32, or 100; and Mathematics 16B or 17C or 21B. Descriptive statistics; probability; random variables; expectation; binomial, normal, Poisson, other univariate distributions; joint distributions; sampling distributions, central limit theorem; properties of estimators; linear combinations of random variables; testing and estimation; Minitab computing package. Two units credit given to students who have completed course 100. GE credit: SciEng | QL, SE.—F, W, S, Su. (F, W, S, Su.) 104. Applied Statistical Methods: Nonparametric Statistics (4) Lecture—3 hours; laboratory—1 hour. Prerequisite: course 13, 32, or 100. Sign and Wilcoxon tests, Walsh averages. Two-sample procedures. Inferences concerning scale. Kruskal-Wallis test. Measures of association. Chi square and Kolmogorov-Smirnov tests. Offered in alternate years. GE credit: SciEng | QL, SE.—S. (S.) 106. Applied Statistical Methods: Analysis of Variance (4) Lecture—3 hours; discussion/laboratory—1 hour. Prerequisite: course 13 or 32 or 100. Basics of experimental design. One-way and two-way fixed effects analysis of variance models. Randomized complete and incomplete block design. Multiple comparisons procedures. One-way random effects model. GE credit: SciEng | SE.—F, W, S, Su. (F, W, S, Su.) 108. Applied Statistical Methods: Regression Analysis (4) Lecture—3 hours; discussion—1 hour. Prerequisite: course 13, 32, or 100. Simple linear regression, variable selection techniques, stepwise regression, analysis of covariance, influence measures, computing packages. GE credit: SciEng | QL, SE, SL.—F, W, S, Su. (F, W, S, Su.) 130A. Mathematical Statistics: Brief Course (4) Lecture—3 hours; discussion—1 hour. Prerequisite: Mathematics 16B or 17C or 21B. Basic probability, densities and distributions, mean, variance, covariance, Chebyshev's inequality, some special distributions, sampling distributions, central limit theorem and law of large numbers, point estimation, some methods of estimation, interval estimation, confidence intervals for certain quantities, computing sample sizes. Only 2 units of credit allowed to students who have taken course 131A. GE credit: SciEng | QL, SE.—F. (F.) 130B. Mathematical Statistics: Brief Course (4) Lecture—3 hours; discussion—1 hour. Prerequisite: course 130A or 131A or Mathematics 135A. Transformed random variables, large sample properties of estimates. Basic ideas of hypotheses testing, likelihood ratio tests, goodness-of-fit tests. General linear model, least squares estimates, Gauss-Markov theorem. Analysis of variance, F-test. Regression and correlation, multiple regression. Selected topics. GE credit: SciEng | QL, SE.—W. (W.) 131A. Introduction to Probability Theory (4) Lecture—3 hours; discussion—1 hour. Prerequisite: Mathematics 21B, 21C and 22A. 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. Not open for credit to students who have completed Mathematics 135A. GE credit: SciEng | QL, SE.— F, S. (F, S.) 131B. Introduction to Mathematical Statistics (4) Lecture—3 hours; discussion—1 hour. Prerequisite: course 131A or consent of the instructor. Sampling, methods of estimation, sampling distributions, confidence intervals, testing hypotheses, linear regression, analysis of variance, elements of large sample theory and nonparametric inference. GE credit: SciEng | QL, SE.—W. (W.) 131C. Introduction to Mathematical Statistics (4) Lecture—3 hours; discussion—1 hour. Prerequisite: course 131B or consent of the instructor. Sampling, methods of estimation, sampling distributions, confidence intervals, testing hypotheses, linear regression, analysis of variance, elements of large sample theory and nonparametric inference. GE credit: SciEng | SE, QL.—S. (S.) 135. Multivariate Data Analysis (4) Lecture—3 hours; discussion—1 hour. Prerequisite: course 130B or 131B; and Mathematics 22A or 67.
Multivariate normal distribution; Mahalanobis distance; sampling distributions of the mean vector and covariance matrix;
Hotelling's T 137. Applied Time Series Analysis (4) Lecture—3 hours; laboratory—1 hour. Prerequisite: course 108. Time series relationships; univariate time series models: trend, seasonality, correlated errors; regression with correlated errors; autoregressive models; autoregressive moving average models; spectral analysis: cyclical behavior and periodicity, measures of periodicity, periodogram; linear filtering; prediction of time series; transfer function models. GE credit: SciEng | QL, SE.—F, W. (F, W.) 138. Analysis of Categorical Data (4) Lecture—3 hours; discussion—1 hour. Prerequisite: course 130B or 131B, or courses 106 and 108. Varieties of categorical data, cross-classifications, contingency tables, tests for independence. Multidimensional tables and log-linear models, maximum likelihood estimation; tests of goodness-of-fit. Logit models, linear logistic models. Analysis of incomplete tables. Packaged computer programs, analysis of real data. GE credit: SciEng | QL, SE.—F, W. (F, W.) 141. Statistical Computing (4) Lecture—3 hours; laboratory—1 hour. Prerequisite: one introductory class in Statistics (such as 13, 32, 100, or 102), or the equivalent. Organization of computations to access, transform, explore, analyze data and produce results. Concepts and vocabulary of statistical/scientific computing. GE credit: SciEng | QL, SE.—F. (F.) 141A. Fundamentals of Statistical Data Science (4) Lecture—3 hours; discussion—1 hour. Prerequisite: course 10 or course 13 or course 32 or course 100. Introduction to computing for data analysis and visualization, and simulation, using a high-level language (e.g., R). Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. open for credit to students who have taken course 141 or course 242.—F. (F.) 141B. Data & Web Technologies for Data Analysis (4) Lecture—3 hours; discussion—1 hour. Prerequisite: course 141A or Engineering: Computer Science 145. Essentials of using relational databases and SQL. Processing data in blocks. Scraping Web pages and using Web services/APIs. Basics of text mining. Interactive data visualization with Web technologies. Computational data workflow and best practices. Statistical methods.—W. (W.) 141C. Big Data & High Performance Statistical Computing (4) Lecture—3 hours; discussion—1 hour. Prerequisite: course 141A or Engineering: Computer Science 40. High-performance computing in high-level data analysis languages; different computational approaches and paradigms for efficient analysis of big data; interfaces to compiled languages; R and Python programming languages; high-level parallel computing; MapReduce; parallel algorithms and reasoning.—S. (S.) 144. Sampling Theory of Surveys (4) Lecture—3 hours; discussion/laboratory—1 hour. Prerequisite: course 130B or 131B; or courses 106 and 108. Simple random, stratified random, cluster, and systematic sampling plans; mean, proportion, total, ratio, and regression estimators for these plans; sample survey design, absolute and relative error, sample size selection, strata construction; sampling and nonsampling sources of error. Offered in alternate years. GE credit: SciEng | QL, SE.—S. 145. Bayesian Statistical Inference (4) Lecture—3 hours; laboratory—1 hour. Prerequisite: courses 130B or 131B. Subjective probability, Bayes Theorem, conjugate priors, non-informative priors, estimation, testing, prediction, empirical Bayes methods, properties of Bayesian procedures, comparisons with classical procedures, approximation techniques, Gibbs sampling, hierarchical Bayesian analysis, applications, computer implemented data analysis. Offered in alternate years. GE credit: SciEng | QL, SE.—W. (W.) 160. Practice in Statistical Data Science (4) Lecture—3 hours; discussion/laboratory—1 hour. Prerequisite: course 106; course 108; course 130B or course 131B; course 141 or course 141A. Principles and practice of interdisciplinary, collaborative data analysis; complete case study review and team data analysis project. GE credit: SciEng | QL, SE.—S. (S.) 190X. Seminar (1-2) Seminar—1-2 hours. Prerequisite: course 13, 32, 100, or 103. In-depth examination of a special topic in a small group setting.—F, W, S. (F, W, S.) 192. Internship in Statistics (1-12) Internship—3-36 hours; term paper. Prerequisite: upper division standing and consent of instructor. Work experience in statistics. (P/NP grading only.) 194HA-194HB. Special Studies for Honors Students (4-4) Independent study—12 hours. Prerequisite: senior qualifying for honors. Directed reading, research and writing, culminating in the completion of a senior honors thesis or project under direction of a faculty adviser. (Deferred grading only, pending completion of sequence.) GE credit: SciEng | SE. 198. Directed Group Study (1-5) Prerequisite: consent of instructor. (P/NP grading only.) 199. Special Study for Advanced Undergraduates (1-5) Prerequisite: consent of instructor. (P/NP grading only.) Graduate 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 (3) Lecture—2 hours; discussion/laboratory—1 hour. Prerequisite: introductory, upper-division Statistics course; some knowledge of vectors and matrices; courses 106 or 108 or the equivalent suggested. 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.—S. (S.) 205. Statistical Methods for Research with SAS (4) Lecture—3 hours; laboratory—1 hour. Prerequisite: introductory upper division statistics course and some knowledge of vectors and matrices; courses course 100, or 102, or 103 suggested 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.—S. (S.) 206. Statistical Methods for Research—I (4) 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.—F. (F.) 207. Statistical Methods for Research—II (4) 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.—F. (F.) 208. Statistical Methods in Machine Learning (4) 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.—S. (S.) 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.)—F. (F.) 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.)—W. (W.) 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.)—S. (S.) 225. Clinical Trials (4) Lecture—3 hours; discussion/laboratory—1 hour. Prerequisite: course/Biostatistics 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.—S. 226. Statistical Methods for Bioinformatics (4) Lecture—3 hours; discussion/laboratory—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.—(W.) 231A. Mathematical Statistics I (4) Lecture—3 hours; discussion—1 hour. Prerequisite: courses 131A-C, Mathematics 25 and Mathematics 125A or 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.—F. (F.) 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.—W. (W.) 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.—S. (S.) 232A. Applied Statistics I (4) Lecture—3 hours; laboratory—1 hour. Prerequisite: courses 106, 108, 131A, 131B, 131C, and Mathematics 167. Estimation and testing for the general linear model, regression, analysis of designed experiments, and missing data techniques.—F. (F.) 232B. Applied Statistics II (4) Lecture—3 hours; laboratory—1 hour. Prerequisite: courses 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.—W. (W.) 232C. Applied Statistics III (4) Lecture—3 hours; laboratory—1 hour. Prerequisite: courses 106, 108, 131C, 232B and Mathematics 167. Multivariate analysis: multivariate distributions, multivariate linear models, data analytic methods including principal component, factor, discriminant, canonical correlation and cluster analysis.—W. (W.) 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.—(S.) 235A. Probability Theory (4) Lecture—3 hours; term paper or discussion—1 hour. Prerequisite: Mathematics 125B and 135A 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.) —F. (F.) 235B. Probability Theory (4) Lecture—3 hours; term paper or discussion—1 hour. Prerequisite: Mathematics 235A/course 235A 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 235B.)—W. (W.) 235C. Probability Theory (4) Lecture—3 hours; term paper or discussion—1 hour. Prerequisite: 235A—course/Mathematics 235B 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 235C.)—S. (S.) 237A. Time Series Analysis (4) Lecture—3 hours; term paper. Prerequisite: course 131B or the equivalent. 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.—(F.) 237B. Time Series Analysis (4) Lecture—3 hours; term paper. Prerequisite: course 131B or the equivalent. 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.—(W.) 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.—W. 240A. Nonparametric Inference (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.—(W.) 240B. Nonparametric Inference (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.—(S.) 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.—(S.) 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.—W. 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.—W. 250. Topics in Applied and Computational Statistics (4) Lecture—3 hours; lecture/discussion—1 hour. Prerequisite: course 131A; course 232A recommended, not required. 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. May be repeated for credit with consent of graduate adviser. Offered irregularly.—F, W, S. 251. Topics in Statistical Methods and Models (4) Lecture—3 hours; discussion—1 hour. Prerequisite: course 231B or 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 if topics differ; only with consent of the graduate adviser. Offered irregularly.—F, W, S. (F, W, S.) 252. Advanced Topics in Biostatistics (4) Lecture—3 hours; discussion/laboratory—1 hour. Prerequisite: course/Biostatistics 222 and course/Biostatistics 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.—S. 260. Statistical Practice and Data Analysis (3) Lecture/discussion—3 hours. Prerequisite: working knowledge of advanced statistical software and completion of at least one of course 207 or 232B or the equivalent. Open to students enrolled in the graduate program in Statistics or Biostatistics, as the class also serves to provide professional service to clients and collaborators who work with the students. 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. May be repeated one time for credit.—F, W, S. (F, W, S.) 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 one time for credit. (S/U grading only.)—S. (S.) 290. Seminar in Statistics (1-6) Prerequisite: consent of instructor. Seminar on advanced topics in probability and statistics. (S/U grading only.)—F, W, S. (F, W, S.) 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.)—S. (S.) 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.) Professional 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.)—F. (F.) 396. Teaching Assistant Training Practicum (1-4) Prerequisite: consent of instructor; graduate standing. (S/U grading only.)—F, W, S. (F, W, S.) Professional 401. Methods in Statistical Consulting (3) Lecture—3 hours; discussion—1 hour. Students must be enrolled in the graduate program in Statistics or Biostatistics. 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. May be repeated for credit with consent of graduate adviser. Offered irregularly. (S/U grading only.)—F, W, S. (F, W, S.) |

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