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STAT 100 Numbers and Reasons (5) NW,QSR
Introduction to the history of how numbers work as part of good reasoning in both science and society.
STAT 111 Lectures in Applied Statistics (1) NW
Weekly lectures illustrating the importance of statisticians in a variety of fields, including medicine and the biological, physical, and social sciences. Contact instructor for information on emphasized fields of applications. Credit/no credit only. Offered: jointly with BIOST 111; Sp.
STAT 220 Basic Statistics (5) NW, QSR
Objectives and pitfalls of statistical studies. Structure of data sets, histograms, means, and standard deviations. Correlation and regression. Probability, binomial and normal. Interpretation of estimates, confidence intervals, and significance tests. (Students may receive credit for only one of 220, 311, and ECON 311.) Offered: AWSpS.
Instructor Course Description:
David K Blough
Grace Chiu
Oliver A. Will
Tamas Rudas
STAT 221 Statistical Concepts and Methods for the Social Sciences (5) QSR Morita
Develops statistical literacy. Examines objectives and pitfalls of statistical studies; study designs, data analysis, inference; graphical and numerical summaries of numerical and categorical data; correlation and regression; and estimation, confidence intervals, and significance tests. Emphasizes social science examples and cases. (Students may receive credit for only one of STAT 220, STAT 311, STAT/CS&SS/SOC 221, and ECON 311.) Offered: jointly with CS&SS 221/SOC 221; AWSp.
STAT 302 Statistical Software and Its Applications (2) QSR Morita
Introduction to data structures and basics of implementing procedures in statistical computing packages, selected from but not limited to R, SAS, STATA, MATLAB, SPSS, and Minitab. Provides a foundation in computation components of data analysis. Prerequisite: either STAT 311/ECON 311 or STAT 390/MATH 390. Offered: ASp.
STAT 311 Elements of Statistical Methods (5) NW, QSR
Elementary concepts of probability and sampling; binomial and normal distributions. Basic concepts of hypothesis testing, estimation, and confidence intervals; t-tests and chi-square tests. Linear regression theory and the analysis of variance. (Students may receive credit for only one of 220, 311, and ECON 311.) Prerequisite: either MATH 111, MATH 120, MATH 124, MATH 127, or MATH 144. Offered: AWSpS.
Instructor Course Description:
Grace Chiu
STAT 316 Design of Experiments and Regression Analysis (4) NW
Introduction to the analysis of data from planned experiments. Analysis of variance for multiple factors and applications of orthogonal arrays and linear graphs for fractional factorial designs to product and process design optimization. Regression analysis with applications in engineering. Prerequisite: IND E 315. Offered: jointly with IND E 316.
STAT 320 Evaluating Social Science Evidence (5) I&S, QSR
A critical introduction to the methods used to collect data in social science: surveys, archival research, experiments, and participant observation. Evaluates "facts and findings" by understanding the strengths and weaknesses of the methods that produce them. Case based. Offered: jointly with CS&SS 320/SOC 320; A.
Instructor Course Description:
Wanda Martina Morris
STAT 321 Case-Based Social Statistics I (5) I&S, QSR
Introduction to statistical reasoning for social scientists. Built around cases representing in-depth investigations into the nature and content of statistical and social-science principles and practice. Hands-on approach: weekly data-analysis laboratory. Fundamental statistical topics: measurement, exploratory data analysis, probabilistic concepts, distributions, assessment of statistical evidence. Offered: jointly with CS&SS/SOC 321; W.
Instructor Course Description:
Mark S. Handcock
STAT 322 Case-Based Social Statistics II (5) I&S, QSR
Continuation of CS&SS/SOC/STAT 321. Progresses to questions of assessing the weight of evidence and more sophisticated models including regression-based methods. Built around cases investigating the nature and content of statistical principles and practice. Hands-on approach: weekly data analysis laboratory. Prerequisite: CS&SS/SOC/STAT 321, or permission of instructor. Offered: jointly with CS&SS/SOC 322; Sp.
STAT 340 Introduction to Probability and Mathematical Statistics I (4) QSR Morita
Covers the fundamentals of probability and mathematical statistics; axioms of probability, conditional and joint probability, random variables, univariate and multivariate distributions and densities, and moments; bionomial, negative binomial, geometric, Poisson, normal, exponential distributions, and central limit theorem; and basic estimation and hypothesis testing theory. Prerequisite: either STAT 311/ECON 311 or STAT 390/MATH 390; either MATH 126 or MATH 136, either of which may be taken concurrently. Offered: A.
STAT 341 Introduction to Probability and Statistical Inference I (4) NW
Brief review of: sample spaces, random variables, probability. Distribution: binomial, normal, Poisson, geometric. Followed by: expectation, variance, central limit theorem. Basic concepts of estimation, testing, and confidence intervals. Maximum likelihood estimators and likelihood ratio tests, efficiency. Introduction to regression. Prerequisite: STAT 340. Offered: W.
STAT 342 Introduction to Probability and Statistical Inference II (4) NW
Brief review of: sample spaces, random variables, probability. Distribution: binomial, normal, Poisson, geometric. Followed by: expectation, variance, central limit theorem. Basic concepts of estimation, testing, and confidence intervals. Maximum likelihood estimators and likelihood ratio tests, efficiency. Introduction to regression. Prerequisite: STAT 341. Offered: Sp.
STAT 390 Probability and Statistics in Engineering and Science (4) NW
Concepts of probability and statistics. Conditional probability, independence, random variables, distribution functions. Descriptive statistics, transformations, sampling errors, confidence intervals, least squares and maximum likelihood. Exploratory data analysis and interactive computing. Students may receive credit for only one of 390, STAT/ECON 481, and ECON 580. Prerequisite: either MATH 126 or MATH 136. Offered: jointly with MATH 390; AWSpS.
STAT 391 Probability and Statistics for Computer Science (4) NW
Fundamentals of probability and statistics from the perspective of the computer scientist. Random variables, distributions and densities, conditional probability, independence. Maximum likelihood, density estimation, Markov chains, classification. Applications in computer science. Prerequisite: 2.5 in MATH 126; 2.5 in MATH 308; either CSE 326, CSE 373, CSE 417, or CSE 421.
STAT 394 Probability I (3) NW
Sample spaces; basic axioms of probability; combinatorial probability; conditional probability and independence; binomial, Poisson and normal distributions, central limit theorem. Prerequisite: either 2.0 in MATH 126, or 2.0 in MATH 136; recommended: MATH 324 or MATH 327. Offered: jointly with MATH 394; AWS.
Instructor Course Description:
Federico Marchetti
Matias Courdurier
STAT 395 Probability II (3) NW
Random variables; expectation and variance; laws of large numbers; normal approximation and other limit theorems; multidimensional distributions and transformations. Prerequisite: 2.0 in STAT/MATH 394. Offered: jointly with MATH 395; WSpS.
STAT 396 Probability III (3) NW
Characteristic functions and generating functions; recurrent events and renewal theory; random walk. Prerequisite: either 2.0 in MATH 395 or 2.0 in STAT 395. Offered: jointly with MATH 396; Sp.
STAT 403 Introduction to Resampling Inference (4) NW
Introduction to computer-intensive data analysis for experimental and observational studies in empirical sciences. Students design, program, carry out, and report applications of bootstrap resampling, rerandomization, and subsampling of cases. Credit allowed for 403 or 503 but not both. Prerequisite: either STAT 220, STAT 301, STAT/ECON 311, STAT 341, STAT 361, STAT/MATH 390, or STAT/ECON 481. Offered: Sp.
STAT 421 Applied Statistics and Experimental Design (4) NW
Computer-aided data analyses using comparisons between batches, analysis of variance and regression. Evaluation of assumptions, data transformation, reliability of statistical measures (jackknife, bootstrap). Fisher-Gosset controversy. Prerequisite: either STAT 342, STAT/MATH 390, or STAT/ECON 481; recommended: MATH 308. Offered: A.
STAT 423 Applied Regression and Analysis of Variance (4) NW
Regression analysis. Problems in interpreting regression coefficients. Estimation, including two-stage least squares. Guided regression: building linear models, selecting carriers. Regression residuals. Analysis of variance. Nonparametric regression. Factorial designs, response surface methods. Prerequisite: either STAT 342, STAT/MATH 390, STAT 421, or STAT/ECON 481; recommended: MATH 308. Offered: W.
Instructor Course Description:
Matthew Stephens
STAT 425 Introduction to Nonparametric Statistics (3) NW
Overview of nonparametric methods, such as rank tests, goodness of fit tests, 2 x 2 tables, nonparametric estimation. Useful for students with only a statistical methods course background. Prerequisite: STAT/MATH 390. Offered: jointly with BIOST 425; when demand is sufficient.
STAT 427 Introduction to Analysis of Categorical Data (4) NW
Techniques for analysis of count data. Log-linear models, logistic regression, and analysis of ordered response categories. Illustrations from the behavioral and biological sciences. Computational procedures. Prerequisite: either STAT 342, STAT 362, or STAT 421.
STAT 428 Multivariate Analysis for the Social Sciences (4) NW
Multivariate techniques commonly used in the social and behavioral sciences. Linear models for dependence analysis (multivariate regression, MANOVA, and discriminant analysis) and for interdependence analysis (principal components and factor analysis). Techniques applied to social science data using computer statistical packages. Prerequisite: either STAT 342, STAT 362, or STAT 421.
STAT 480 Sampling Theory for Biologists (3) NW
Theory and applications of sampling finite populations including: simple random sampling, stratified random sampling, ratio estimates, regression estimates, systematic sampling, cluster sampling, sample size determinations, applications in fisheries and forestry. Other topics include sampling plant and animal populations, sampling distributions, estimation of parameters and statistical treatment of data. Prerequisite: Q SCI 482; recommended: Q SCI 483. Offered: jointly with Q SCI 480; even years.
STAT 481 Introduction to Mathematical Statistics (5) NW
Probability, generating functions; the d-method, Jacobians, Bayes theorem; maximum likelihoods, Neyman-Pearson, efficiency, decision theory, regression, correlation, bivariate normal. (Credit allowed for only one of 390, 481, and ECON 580.) Prerequisite: STAT/ECON 311; either MATH 136 or MATH 126 with either MATH 308 or MATH 309. Recommended: MATH 324. Offered: jointly with CS&SS/ECON 481; A.
STAT 486 Experimental Design (3) NW
Topics in analysis of variance and experimental designs: choice of designs, comparison of efficiency, power, sample size, pseudoreplication, factor structure. Prerequisite: Q SCI 482; recommended: Q SCI 483. Offered: jointly with Q SCI 486.
STAT 491 Introduction to Stochastic Processes (3) NW
Random walks, Markov chains, branching processes, Poisson process, point processes, birth and death processes, queuing theory, stationary processes. Prerequisite: either 2.0 in MATH 395 or STAT 395. Offered: jointly with MATH 491; A.
Instructor Course Description:
Soumik Pal
STAT 492 Stochastic Calculus for Option Pricing (3) NW
Introductory stochastic calculus mathematical foundation for pricing options and derivatives. Basic stochastic analysis tools, including stochastic integrals, stochastic differential equations, Ito's formula, theorems of Girsanov and Feynman-Kac, Black-Scholes option pricing, American and exotic options, bond options. Prerequisite: MATH STAT 394-5. Offered: jointly with MATH 492; W.
STAT 495 Service Learning: K-12 Tutoring Experience (1-5, max. 5) Morita
Tutoring mathematics in local K-12 schools. Credit/no credit only. Offered: AWSp.
STAT 498 Special Topics (1-5, max. 15) NW
Reading and lecture course intended for special needs of students.
STAT 499 Undergraduate Research (1-5, max. 15)
Offered: AWSpS.
STAT 502 Design and Analysis of Experiments (4)
Design of experiments covering concepts such as randomization, blocking, and confounding. Analysis of experiments using randomization tests, analysis of variance, and analysis of covariance. Prerequisite: either STAT 342, MATH/STAT 390, ECON/STAT 481, ECON 580 or equivalent; MATH 308 or equivalent. Offered: A.
STAT 504 Applied Regression (4)
Least squares estimation. Hypothesis testing. Interpretation of regression coefficients. Categorical independent variables. Interactions. Assumption violations: outliers, residuals, robust regression; nonlinearity, transformations, ACE, CART; nonconstant variance. Variable selection and model averaging. Prerequisite: either STAT 342, STAT/MATH 390, STAT 421, STAT/ECON 481, or SOC 425; recommended: MATH 308. Offered: jointly with CS&SS 504.
STAT 506 Applied Probability and Statistics (4)
Discreet and continuous random variables, independence and conditional probability, central limit theorem, elementary statistical estimation and inference, linear regression. Emphasis on physical applications. Prerequisite: some advanced calculus and linear algebra. Offered: jointly with AMATH 506.
STAT 512 Statistical Inference (4)
Review of random variables; transformations, conditional expectation, moment generating functions, convergence, limit theorems, estimation; Cramer-Rao lower bound, maximum likelihood estimation, sufficiency, ancillarity, completeness. Rao-Blackwell theorem. Hypothesis testing: Neyman-Pearson lemma, monotone likelihood ratio, likelihood-ratio tests, large-sample theory. Contingency tables, confidence intervals, invariance. Introduction to decision theory. Prerequisite: STAT 395 and STAT 421, STAT 423, STAT 504, or BIOST 512 (concurrent registration permitted for these three). Offered: A.
STAT 513 Statistical Inference (4)
Review of random variables; transformations, conditional expectation, moment generating functions, convergence, limit theorems, estimation; Cramer-Rao lower bound, maximum likelihood estimation, sufficiency, ancillarity, completeness. Rao-Blackwell theorem. Hypothesis testing: Neyman-Pearson lemma, monotone likelihood ratio, likelihood-ratio tests, large-sample theory. Contingency tables, confidence intervals, invariance. Introduction to decision theory. Prerequisite: STAT 512. Offered: W.
STAT 516 Stochastic Modeling of Scientific Data (4-)
Markovian and semi-Markovian models, point processes, cluster models, queuing models, likelihood methods, estimating equations. Prerequisite: STAT 511 or STAT 396. Offered: A.
Instructor Course Description:
Volodymyr Minin
STAT 517 Stochastic Modeling of Scientific Data (4)
Markovian and semi-Markovian models, point processes, cluster models, queuing models, likelihood methods, estimating equations. Prerequisite: STAT 516. Offered: W.
STAT 518 Stochastic Modeling Project (4)
Supervised, applied project based on stochastic modeling of scientific data. Prerequisite: STAT 517. Offered: Sp.
STAT 519 Time Series Analysis (3)
Descriptive techniques. Stationary and nonstationary processes, including ARIMA processes. Estimation of process mean and autocovariance function. Fitting ARIMA models to data. Statistical tests for white noise. Forecasting. State space models and the Kalman filter. Robust time series analysis. Regression analysis with correlated errors. Statistical properties of long memory processes. Prerequisite: STAT 513. Offered: A.
STAT 520 Spectral Analysis of Time Series (4)
Estimation of spectral densities for single and multiple time series. Nonparametric estimation of spectral density, cross-spectral density, and coherency for stationary time series, real and complex spectrum techniques. Bispectrum. Digital filtering techniques. Aliasing, prewhitening. Choice of lag windows and data windows. Use of the fast Fourier transform. The parametric autoregressive spectral density estimate for single and multiple stationary time series. Spectral analysis of nonstationary random processes and for randomly sampled processes. Techniques of robust spectral analysis. Prerequisite: one of STAT 342, STAT 390, STAT 481, or IND E 315. Offered: jointly with E E 520; W.
STAT 521 Advanced Probability (3)
Measure theory and integration, independence, laws of large numbers. Fourier analysis of distributions, central limit problem and infinitely divisible laws, conditional expectations, martingales. Prerequisite: either MATH 426 or MATH 576. Offered: jointly with MATH 521; A.
STAT 522 Advanced Probability (3)
Measure theory and integration, independence, laws of large numbers. Fourier analysis of distributions, central limit problem and infinitely divisible laws, conditional expectations, martingales. Prerequisite: either MATH 426 or MATH 576. Offered: jointly with MATH 522; W.
STAT 523 Advanced Probability (3)
Measure theory and integration, independence, laws of large numbers. Fourier analysis of distributions, central limit problem and infinitely divisible laws, conditional expectations, martingales. Prerequisite: either MATH 426 or MATH 576. Offered: jointly with MATH 523; Sp.
STAT 524 Design of Medical Studies (3)
Emphasis on randomized controlled clinical trials. Bias elimination, controls, treatment assignment and randomization, precision, replication, power and sample size calculations, stratification, and ethics. Suitable for students in biostatistics and other scientific fields. Prerequisite: BIOST 511 or equivalent, and one of STAT 421, STAT 423, BIOST 513, BIOST 518, or EPI 512; or permission of instructor. Offered: jointly with BIOST 524; even years.
STAT 527 Nonparametric Regression and Classification (3) Minim, Raftery, Richardson, Wakefield
Covers techniques for smoothing and classification including spline models, kernel methods, generalized additive models, and the averaging of multiple models. Describes measures of predictive performance, along with methods for balancing bias and variance. Prerequisite: either STAT 502 and STAT 504 or BIOST 514 and BIOST 515. Offered: jointly with BIOST 527; Sp.
STAT 529 Sample Survey Techniques (3)
Design and implementation of selection and estimation procedures. Emphasis on human populations. Simple, stratified, and cluster sampling; multistage and two-phase procedures; optimal allocation of resources; estimation theory; replicated designs; variance estimation; national samples and census materials. Prerequisite: either STAT 421, STAT 423, STAT 504, QMETH 500, BIOST 511, or BIOST 517, or equivalent; or permission of instructor. Offered: jointly with BIOST 529/CS&SS 529.
STAT 530 Wavelets: Data Analysis, Algorithms, and Theory (3)
Review of spectral analysis. Theory of continuous and discrete wavelets. Multiresolution analysis. Computation of discrete wavelet transform. Time-scale analysis. Wavelet packets. Statistical properties of wavelet signal extraction and smoothers. Estimation of wavelet variance. Prerequisite: some Fourier theory and linear algebra; Math or STAT 390, ECON or STAT 481, or STAT 513; or IND E 315. Offered: jointly with E E 530; Sp.
STAT 533 Theory of Linear Models (3)
Examines model structure; least squares estimation; Gauss-Markov theorem; central limit theorems for linear regression; weighted and generalized least squares; fixed and random effects; analysis of variance; blocking and stratification; and applications in experimental design. Prerequisite: STAT 421 or STAT 423; and STAT 513, BIOST 515, and a course in matrix algebra. Offered: jointly with BIOST 533; Sp.
STAT 534 Statistical Computing (3)
Introduction to scientific computing. Includes programming tools, modern programming methodologies, (modularization, object oriented design), design of data structures and algorithms, numerical computing and graphics. Uses C++ for several substantial scientific programming projects. Prerequisite: experience with programming in a high level language. Offered: jointly with BIOST 534; Sp.
STAT 535 Statistical Computing (3)
Introduction to scientific computing. Includes programming tools, modern programming methodologies, (modularization, object oriented design), design of data structures and algorithms, numerical computing and graphics. Uses C++ for several substantial scientific programming projects. Prerequisite: experience with programming in a high level language. Offered: jointly with BIOST 535; A.
STAT 536 Analysis of Categorical and Count Data (3)
Analysis of categorical data in the social sciences. Binary, ordered, and multinomial outcomes, event counts, and contingency tables. Focuses on maximum likelihood estimations and interpretations of results. Prerequisite: SOC 424, SOC 425, SOC 426, or equivalent; recommended: CS&SS 505 and CS&SS 506, or equivalent. Offered: jointly with SOC 536/CS&SS 536; annually.
Instructor Course Description:
Christopher A Adolph
STAT 538 Statistical Computing (3)
Introduction to scientific computing. Includes programming tools, modern programming methodologies, (modularization, object oriented design), design of data structures and algorithms, numerical computing and graphics. Uses C++ for several substantial scientific programming projects. Prerequisite: experience with programming in a high level language. Offered: jointly with BIOST 538; W.
Instructor Course Description:
Werner Stuetzle
STAT 542 Multivariate Analysis (3)
Multivariate normal distribution; partial and multiple correlation; Hotelling's T2; Bartlett's decomposition; various likelihood ratio tests; discriminant analysis; principal components; graphical Markov models. Prerequisite: STAT 582 or permission of instructor. Offered: alternate years.
STAT 544 Bayesian Statistical Methods (3)
Statistical methods based on the idea of a probability distribution over the parameter space. Coherence and utility. Subjective probability. Likelihood principle. Conjugate families. Structure of Bayesian inference. Limit theory for posterior distributions. Sequential experiments. Exchangeability. Bayesian nonparametrics. Empirical Bayes methods. Prerequisite: STAT 513 or permission of instructor. Offered: alternate years.
STAT 547 Derivatives: Theory, Statistics, and Computing (4)
Covers theory, computation, and statistics of options and derivatives pricing, including options on stocks, stock indices, futures, currencies, and interest rate derivatives. Prerequisite: STAT 506 or equivalent, or permission of instructor. Recommended: ECON 424.
STAT 549 Statistical Methods for Portfolios (4)
Covers the fundamentals of modern statistical portfolio construction and risk measurement, including theoretical foundations, statistical methodology, and computational methods using modern object-oriented software for data analysis, statistical modeling, and numerical portfolio optimization. Prerequisite: ECON 424 or equivalent, or permission of instructor.
STAT 550 Statistical Genetics I: Mendelian Traits (3)
Mendelian genetic traits. Population genetics; Hardy-Weinberg, allelic variation, subdivision. Likelihood inference, information and power; latent variables and EM algorithm. Pedigree relationships and gene identity. Meiosis and recombination. Linkage detection. Multipoint linkage analysis. Prerequisite: STAT 390 and STAT 394, or permission of instructor. Offered: jointly with BIOST 550; A.
STAT 551 Statistical Genetics II: Quantitative Traits (3)
Statistical basis for describing variation in quantitative traits. Decomposition of trait variation into components representing genes, environment and gene-environment interaction. Methods of mapping and characterizing quantitative trait loci. Prerequisite: STAT/BIOST 550; STAT 423 or BIOST 515; or permission of instructor. Offered: jointly with BIOST 551; W.
STAT 552 Statistical Genetics III: Design and Analysis (3)
Overview of probability models, inheritance models, penetrance. Association and linkage. The lod score method. Affected sib method. Fitting complex inheritance models. Design mapping studies; multipoint, disequilibrium, and fine-scale mapping. Ascertainment. Prerequisite: STAT/BIOST 551; GENET 371; or permission of instructor. Offered: jointly with BIOST 552; Sp.
STAT 560 Hierarchical Modeling for the Social Sciences (4)
Explores ways in which data are hierarchically organized, such as voters nested within electoral districts that are in turn nested within states. Provides a basic theoretical understanding and practical knowledge of models for clustered data and a set of tools to help make accurate inferences. Prerequisite: SOC 504-505-506 or equivalent; recommended: CS&SS 505-506 or equivalent. Offered: jointly with CS&SS 560/SOC 560.
Instructor Course Description:
Adrian Dobra
STAT 561 Special Topics in Applied Statistics (1-5, max. 15)
Data analysis, spectral analysis or robust estimation. Prerequisite: permission of instructor.
STAT 562 Special Topics in Applied Statistics (1-5, max. 15)
Data analysis, spectral analysis or robust estimation. Prerequisite: permission of instructor.
STAT 564 Bayesian Statistics for the Social Sciences (4)
Statistical methods based on the idea of probability as a measure of uncertainty. Topics covered include subjective notion of probability, Bayes' Theorem, prior and posterior distributions, and data analysis techniques for statistical models. SOC 504-505-506 or equivalent; recommended: CS&SS 505; CS&SS 506. Offered: jointly with CS&SS 564.
STAT 566 Causal Modeling (4)
Construction of causal hypotheses. Theories of causation, counterfactuals, intervention vs. passive observation. Contexts for causal inference: randomized experiments; sequential randomization; partial compliance; natural experiments, passive observation. Path diagrams, conditional independence and d-separation. Model equivalence and causal under-determination. Prerequisite: course in statistics, SOC 504-505-506 or equivalent; recommended: CS&SS 505-506 or equivalent. Offered: jointly with CS&SS 566.
STAT 567 Statistical Analysis of Social Networks (4)
Statistical and mathematical descriptions of social networks. Topics include graphical and matrix representations of social networks, sampling methods, statistical analysis of network data, and applications. Prerequisite: SOC 504-505-506 or equivalent; recommended: CS&SS 505; CS&SS 506. Offered: jointly with CS&SS 567.
Instructor Course Description:
Mark S. Handcock
STAT 570 Advanced Applied Statistics and Linear Models (3)
Generalized linear models, REML in mixed models for randomized blocks, split plots, longitudinal data. Generalized estimating equations, empirical model building, cross validation, recursive partitioning, generalized additive models, projection pursuit. Prerequisite: STAT 513; STAT 533 or STAT 421 and STAT 423, and a course in matrix algebra for STAT 570. Offered: jointly with BIOST 570; A.
STAT 571 Advanced Applied Statistics and Linear Models (3)
Generalized linear models, REML in mixed models for randomized blocks, split plots, longitudinal data. Generalized estimating equations, empirical model building, cross validation, recursive partitioning, generalized additive models, projection pursuit. Prerequisite: STAT 570. Offered: jointly with BIOST 571; W.
STAT 572 Advanced Applied Statistics and Linear Models (3)
Student presentations and discussion on selected methodological research articles focusing on regression modeling. Prerequisite: STAT 571. Offered: jointly with BIOST 572; Sp.
STAT 573 Statistical Methods for Categorical Data (3)
Advanced topics in generalized linear models and the analysis of categorical data: overdispersion, quasilikelihood, parameters in link and variance functions, exact conditional inference, random effects, saddlepoint approximations. Credit/no credit only. Prerequisite: STAT 571 and STAT 582. Offered: jointly with BIOST 573; alternate years.
STAT 574 Multivariate Statistical Methods (3)
Use of multivariate normal sampling theory, linear transformations of random variables, one- and two-sample tests, profile analysis, partial and multiple correlation, multivariate ANOVA and least squares, discriminant analysis, principal components, factor analysis, robustness, and some special topics. Some computer use included. Prerequisite: STAT 570 or permission of instructor. Offered: jointly with BIOST 574; alternate years.
STAT 576 Statistical Methods for Survival Data (3)
Statistical methods for censored survival data. Covers parametric and nonparametric methods, Kaplan-Meier survival curve estimator, comparison of survival curves, log-rank test, regression models including the Cox proportional hazards model, competing risks. Prerequisite: STAT 581 and either STAT 423, BIOST 515, or Q SCI 483, or equivalent. Offered: jointly with BIOST 576; alternate years.
STAT 577 Advanced Design and Analysis of Experiments (3)
Concepts important in experimental design and analyzing data from planned experiments. Multi-way layouts, randomized block designs, incomplete block designs, Lating and Graeco-Latin squares, factorial and fractional designs, split-plot designs, optimal design theory, response surface experiments. Prerequisite: either BIOST 515, BIOSTAT 533, STAT 502, STAT 504, STAT 533, or permission of instructor. Offered: jointly with BIOST 577.
STAT 578 Special Topics in Advanced Biostatistics (*, max. 30)
Advanced-level topics in biostatistics offered by regular and visiting faculty. Prerequisite: permission of instructor. Offered: jointly with BIOST 578; AWSpS.
Instructor Course Description:
Michal Kulich
Elizabeth A. Sheppard
STAT 579 Data Analysis and Reporting (2)
Analysis of real data to answer scientific questions. Common data-analytic problems. Sensible approaches to complex data. Graphical and tabular presentation of results. Writing reports for scientific journals, research collaborators, consulting clients. Graduate standing in statistics or biostatistics or permission of instructor. Offered: jointly with BIOST 579; AWSp.
STAT 581 Advanced Theory of Statistical Inference (3)
Limit theorems, asymptotic methods, asymptotic efficiency and efficiency bounds for estimation, maximum likelihood estimation, Bayes methods, asymptotics via derivatives of functionals, sample-based estimates of variability: (bootstrap and jackknife); robustness; estimation for dependent data, nonparametric estimation and testing. Prerequisite: STAT 513; either MATH 426 or MATH 576. Offered: A.
Instructor Course Description:
Jon A Wellner
STAT 582 Advanced Theory of Statistical Inference (3)
Limit theorems, asymptotic methods, asymptotic efficiency and efficiency bounds for estimation, maximum likelihood estimation, Bayes methods, asymptotics via derivatives of functionals, sample-based estimates of variability: (bootstrap and jackknife); robustness; estimation for dependent data, nonparametric estimation and testing. Prerequisite: STAT 581. Offered: W.
STAT 583 Advanced Theory of Statistical Inference (3)
Limit theorems, asymptotic methods, asymptotic efficiency and efficiency bounds for estimation, maximum likelihood estimation, Bayes methods, asymptotics via derivatives of functionals, sample-based estimates of variability: (bootstrap and jackknife); robustness; estimation for dependent data, nonparametric estimation and testing. Prerequisite: STAT 582. Offered: Sp.
STAT 586 Martingales: Survival Analysis (3)
Theory of counting processes and martingales to provide unified study of survival analysis methods. Focus on survival distribution estimators, censored data rank statistics, regression methods with censored survival data. Development of small samples moments, asymptotic distributions, and efficiencies. Prerequisite: STAT 521 or STAT 583 or permission of instructor; recommended: STAT 576. Offered: jointly with BIOST 586; W.
STAT 590 Statistics Seminar (*, max. 15)
Credit/no credit only. Prerequisite: permission of graduate program coordinator. Offered: AWSp.
STAT 591 Special Topics in Statistics (1-5, max. 15)
Distribution-free inference, game and decision theory, advanced theory of estimation (including sequential estimation), robustness, advanced probability theory, stochastic processes or empirical processes. Prerequisite: permission of instructor. Offered: A.
STAT 592 Special Topics in Statistics (1-5, max. 15)
Distribution-free inference, game and decision theory, advanced theory of estimation (including sequential estimation), robustness, advanced probability theory, stochastic processes or empirical processes. Prerequisite: permission of instructor. Offered: W.
STAT 593 Special Topics in Statistics (1-5, max. 15)
Distribution-free inference, game and decision theory, advanced theory of estimation (including sequential estimation), robustness, advanced probability theory, stochastic processes or empirical processes. Prerequisite: permission of instructor. Offered: Sp.
Instructor Course Description:
Finn Lindgren
Peter Guttorp
Marina Meila-Predoviciu
STAT 598 Techniques of Statistical Consulting (1)
Seminar series covering technical and non-technical aspects of statistical consulting, including skills for effective communication with clients, report writing, statistical tips and rules of thumb, issues in survey sampling, and issues in working as a statistical consultant in academic, industrial, and private-practice settings. Prerequisite: entry code. Offered: jointly with BIOST 598; ASp.
STAT 599 Statistical Consulting (*, max. 12)
Consulting experience in data analysis, applied statistics. Student required to provide consulting services to students and faculty three hours per week. Credit/no credit only. Prerequisite: permission of graduate program coordinator. Offered: AWSpS.
STAT 600 Independent Study or Research (*)
Prerequisite: permission of graduate program coordinator. Offered: AWSpS.
STAT 700 Master's Thesis (*)
Prerequisite: permission of graduate program coordinator. Offered: AWSpS.
STAT 800 Doctoral Dissertation (*)
Prerequisite: permission of graduate program coordinator. Offered: AWSpS.
