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Provides an overview of the field of statistics, including methods of summarizing and analyzing data, statistical reasoning for learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Emphasis is on presenting underlying concepts rather than covering a variety of different methodologies. No credit to those who have completed or are enrolled in Soc 210, Stat 265, 350, 400, 405, 412 , IOE 265, Econ 404 or 405 or Environ/NRE 438. Stat. 125 - Games, Gambling and Coincidences Emphasizes problem solving and modeling related to games, gambling and coincidences, touching on many fundamental ideas in discrete probability, finite Markov chains, dynamic programming and game theory. First Year Seminar. Stat. 265 - Probability and Statistics for Engineers (IOE 265) Graphical representation of data; axioms of probability; conditioning, Bayes Theorem; discrete distributions (geometric, binomial, Poisson); continuous distributions (Normal Exponential, Weibull), point and interval estimation, likelihood functions, test of hypotheses for means, variances, and proportions for one and two populations. No credit granted to those who have completed or are enrolled in Stat 350, 400, 405, 412 Econ 404, 405 or Environ/NRE 438. Stat. 350 - Introduction to Statistics & Data Analysis A one term course in applied statistical methodology from an analysis-of-data viewpoint. Frequency distributions; measures of location; mean, median, mode; measures of dispersion; variance; graphic presentation; elementary probability; populations and samples; sampling distributions; one sample univariate inference problems, and two sample problems; categorical data; regression and correlation; and analysis of variance. Use of computers in data analysis. No credit granted to those who have completed or are enrolled in Econ 404, 405, or IOE 265 or Stats 265, 400, 405, 412 or Environ/NRE 438. Stat. 400 - Applied Statistical Methods Statistics and the scientific method; observational study versus designed experiment; visualization; introduction to probability; statistical inference; confidence intervals; one-sample tests of hypothesis; two-sample problems; analysis of variance (ANOVA); blocked designs; tests for association and independence (chi-square tests); regression and correlation; and non-parametric tests. No credit granted to those who have completed or are enrolled in Econ 404, 405, or IOE 265 or Stats 265, 350, 405, 412 or Environ/NRE 438. Stat. 401 - Applied Statistical Methods II An intermediate course in applied statistics, covering a range of topics in modeling and analysis of data including: review of simple linear regression, two-sample problems, one-way analysis of variance; multiple linear regression, diagnostics and model selection; two-way analysis of variance, multiple comparisons, and other selected topics. Prereq: Math 115 and Stats 350, 400 or 405, or Econ 405, or Environ/NRE 438. No credit granted if completed or enrolled in Stat 413. Stat. 403 - Introduction to Quantitative Research Methods This course introduces methods for planning, executing, and evaluating research studies based on experiments, surveys, and observational datasets. In addition to learning a toolset of methods, students will read and report on recent research papers to learn how study design and data analysis are handled in different fields. Prereq: Math 115, one of Stat 265, 350, 400, 405 or 412. Prereq: Stat 470 or Stat 480 Stat. 405 - Introduction to Statistics (Econ 405) The purpose of this course is to provide students with an understanding of the principles of statistical inference. Topics include probability, experimental and theoretical derivation of sampling distributions hypothesis testing, estimation, and simple regression. (Students are advised to elect the sequel, Econ 406.) Prereq: Math 116 or 118. Juniors and seniors may elect this course concurrently with Econ 101 or 102. No credit granted to those who have completed or are enrolled in Stat 265, 400 or 412. Students with credit for Econ 404 can only elect Stat 405 for 2 credits and must have permission of instructor. Stat. 406 - Introduction to Statistical Computing Selected topics in statistical computing including basic numerical aspects, iterative statistical methods, principles of graphical analysis, simulation and Monte Carlo methods, generation of random variables, stochastic modeling, importance sampling, numerical and Monte Carlo integration. Prereq: One of Stat 401, 412 or 425. Stat. 408 - Statistical Principles for Problem Solving: A Systems Approach Our purpose is to help students use quantitative reasoning to facilitate learning. Specifically, we introduce statistical and mathematical principles, and then use these as analogues in a variety of real world situation. The notion of a system, a collection of components that come together repeatedly for a purpose, provides an excellent framework to describe many real world phenomena and provides a way to view the quality of an inferential process. No credit granted to those who have completed or are enrolled in Stat 170. Stat. 412 - Introduction to Probability & Statistics An introduction to probability theory; statistical models, especially sampling models; estimation and confidence intervals; testing statistical hypotheses; and important applications, including the analysis of variance and regression. Prereq: Prior or concurrent enrollment in Math 215 and CS 183. No credit granted to those who have completed or are enrolled in Econ 405 or Stat 265, 400 or 405. One credit granted to those who have completed Stat 350. Stat. 413 - The General Linear Model and Its Applications Introduces students to the general linear model and its assumptions, and covers such topics as the geometry of the model, projections, least squares estimation, residuals, normal distribution theory results, inference on parameters, diagnostic tools, and applications in analysis of variance, design, and time series. Prereq: Stat 350 and Math 217; prior or concurrent enrollment in Stat/Math 425. Non-Stat majors who have not taken Math 217 should elect Stat 401. Two credits granted to those who have completed Stat 401. Stat. 414 - Topics in Applied Statistics Topics in applied statistics, including random and mixed effects ANOVA models, analysis of covariance and repeated measures designs, ridge regression, splines, logit-probit analysis, log-linear models, topics in multivariate analysis (MANOVA), discriminate analysis, profile analysis, topics in time series analysis, and basics of survival analysis. Prereq: Stat 413 and permission of instructor. Stat. 415 - Data Mining and Statistical Learning This course covers the principles of data mining, exploratory analysis and visualization of complex data sets, and predictive modeling. The presentation balances statistical concepts (such as over-fitting data, and interpreting results) and computational issues. Students are exposed to algorithms, computations, and hands-on data analysis in the weekly discussion sessions. Advisory Pre-requisite: MATH 215 and 217, and one of STATS 401, 406, 412 or 426 Stat. 425 - Introduction to Probability (Math 425) Basic concepts of probability; expectation, variance, covariance; distribution functions; and bivariate, marginal, and conditional distributions. Prereq: Math 215, 255, or 285. Stat. 426 - Introduction to Theoretical Statistics An introduction to theoretical statistics for students with a background in probability. Probability models for experimental and observational data, normal sampling theory, likelihood-based and Bayesian approaches to point estimation, confidence intervals, tests of hypotheses, and an introduction to regression and the analysis of variance. Prereq: Stat 425. Stat. 430 - Applied Probability Review of probability theory; introduction to random walks; counting and Poisson processes; Markov chains in discrete and continuous time; equations for stationary distribution introduction to Brownian motion. Selected applications such as branching processes, financial modeling, genetic models, the inspection paradox, inventory and queuing problems, prediction, and/or risk analysis. Prereq: Stat 425. Stat. 449 - Topics in Biostatistics (Biostat 449) Introduction to biostatistical topics: clinical trials, cohort and case-control studies; experimental versus observational data; issues of causation, randomization, placebos; case control studies; survival analysis; diagnostic testing; image analysis of PET and MRI scans; statistical genetics; longitudinal studies; and missing data. Prereq: Stat 401 or permission of instructor Stat. 466 - Statistical Quality Control (IOE 466/MFG 466) Quality improvement philosophies; Modeling process quality, statistical process control, control charts for variables and attributes, CUSUM and EWMA, short production runs, multivariate quality control, auto correlation, engineering process control, economic design of charts, fill control, precontrol, adaptive schemes, process capability, specifications and tolerances, gage capability studies, acceptance sampling by attributes and variables, international quality standards. Prereq: Stat 265 and 401 or IOE 366. Stat. 470 - Introduction to the Design of Experiments Introduces students to basic concepts for planning experiments and to efficient methods of design and analysis. Topics covered include concepts such as randomization, replication and blocking; analysis of variance and covariance and the general linear model; fractional factorial designs, blocked designs, and split-plot designs. Prereq: One of Stats 401, 412, 425 or Math 425. Stat. 480 - Survey Sampling Techniques Introduces students to basic ideas in survey sampling, moving from motivating examples to abstraction to populations, variables, parameters, samples and sample design, statistics, sampling distributions, Horvitz-Thompson estimators, basic sample design (simple random, cluster, systematics, multiple stage), various errors and biases, special topics. Prereq: One of Stats 401, 412, 425 or Math 425. Stat. 489 - Independent Study in Statistics Individual study of advanced topics in statistics, reading and/or research in applied or theoretical statistics. Permission of instructor required. Stat. 499 - Honors Seminar Advanced topics, reading and/or research in applied or theoretical statistics. Permission of Departmental
Honors advisor required: Stat. 500 - Applied Statistics I Linear models; definitions, fitting, identifiability, collinearity, Gauss-Markov theorem, variable selection, transformation, diagnostics, outliers and influential observations. ANOVA and ANCOVA. Common designs. Applications and real data analysis are stressed, with students using the computer to perform statistical analyses. Prereq: Math 417 and Stat 350 or 426. Stat. 501 - Applied Statistics II Generalized linear models including logistics regression, Poisson regression, contingency tables. Random effects and repeated measures. Modern regression techniques. Regression and classification trees. Neural networks. Prereq: Stat 500. Stat. 503 - Applied Multivariate Analysis Topics in applied multivariate analysis including Hotelling's T-squared, multivariate ANOVA, discriminant functions, factor analysis, principal components, canonical correlations, and cluster analysis. Selected topics from: maximum likelihood and Bayesian methods, robust estimation and survey sampling. Applications and data analysis using the computer is stressed. Prereq: Stat 500 Stat. 504 - Statistical Consulting Introduces students to key aspects of statistical consulting and data analysis activities. Problem solving and real applications; data analysis, reports, presentations. Prereq: Stat 401 (403) or 500 Stat. 505 - Econometric Analysis I (Econ 671) Econ 671 and 672 form the basic required sequence in econometrics for all doctoral students. Their purpose is to provide Ph.D. students with the training needed to do the basic quantitative analysis generally understood to be part of the background of all modern economists. This includes: the theory and practice of testing hypotheses, statistical estimation theory, the basic statistical theory underlying the linear model, an introduction to econometric methods, and the nature of the difficulties which arise in applying statistical procedures to economic research problems. Permission of instructor required to register. Stat. 508 - Statistical Analysis of Financial Data This course will cover basic topics involved in modeling and analysis of financial data. These include linear and non-linear regression, nonparametric and semi-parametric regression, selected topics on the analysis of multivariate data and dimension-reduction, and time series analysis. Examples and data from financial applications will be used to motivate and illustrate the methods. Stat 520: Mathematical Methods in Statistics This course provides the mathematical background for theoretical Ph.D.-level courses in statistics and probability. The course reviews basic notions from matrix algebra and real analysis. It then introduces students to measure theory and integration. In particular, the content covers definition of measures and measurable functions, convergence theorems, Lebesgue integration, Lp spaces, signed measures, Radon-Nikodym theorem, integration on product spaces. Prereq: Math 451 or equivalent course in real analysis. Stat. 525 - Probability Theory (Math 525) Axiomatic probability; combinatorics; random variables and their distributions; special distributions; joint, marginal and conditional distributions; expectation; the mean, variance, and moment generating function; induced distributions; sums of independent random variables; the law of large numbers; the central limit theorem. Optional topics drawn from: random walks, Markov chains, and/or martingales. Advisory: Math 451 (strongly recommended) . Stats 425 would be helpful. Students with credit for Math 425/Stat 425 can elect Math 525/Stat 525 for only one credit. Stat. 526 - Discrete State Stochastic Processes (Math 526) Review of discrete distributions; generating functions; compound distributions, renewal theorem; modeling of systems as Markov chains; first properties; Chapman-Kolmogorov equations; return and first passage times; classification of states and periodicity; absorption probabilities and the forward equation; stationary distributions and the backward equation; ergodicity; limit properties; application to branching and queueing processes; examples from engineering, biological and social sciences; Markov chains in continuous time; embedded chains; the M/G/1 queue; Markovian decision processes; application to inventory problems; other topic at instructor's discretion. Prereq: Stat 525 or EECS 501 Stat. 531 - Analysis of Time Series (Econ 677) Decomposition of series; trends and regression as a special case of time series; cyclic components; smoothing techniques; the variate difference method; representations including spectrogram, periodogram, etc.; stochastic difference equations autoregressive schemes, moving averages; large sample inference and prediction; covariance structure and spectral densities; hypothesis testing and estimation and applications and other topics. Prereq: Stat 426 Stat. 535 - Reliability (IOE 562) This course covers the important reliability concepts and methodology that arise in modeling, assessing, and improving product reliability and in analyzing field and warranty data. Topics are selected from the following: Basic reliability concepts; common parametric models for component reliability; censoring schemes; analysis of time-to-failure data; accelerated testing for reliability assessment; modeling and analyzing repairable systems reliability; analysis of warranty and field-failure data; maintenance policies and availability; reliability improvement through experimentation. Prereq: Stat 425 and 426 (or IOE 316 and 366) Stat 545 - Data Analysis in Molecular Biology (Biostat 646, Bioinformatics 545) The course will cover statistical methods used to analyze data in experimental molecular bilogy, with an emphasis on gene and protein expression array data. Topics: data acquisition, databases, low level processing, normalization, quality control, statistical inference (group comparisons, cyclicity, survival), multiple comparisons, statistical learning algorithms, clustering, visualization, and case studies. Prereq: Graduate
standing and Stat 400 (or equivalent) or permission of instructor. Stat. 547 - Probabilistic Modeling in Bioinformatics (Math 547) Probabilistic models of proteins and nucleic acids. Analysis of DNA/RNA and protein sequence data. Algorithms for sequence alignment, statistical analysis of similarity scores, hidden Markov models, neural networks training, gene finding, protein family profiles, multiple sequence alignment, sequence comparison and structure prediction. Analysis of expression array data. Prereq: Stat 425 or Biol 427 or Biol Chem 415; basic programming skills desirable. Graduate standing and permission of instructor. Stat. 548 - Computations in Probabilistic Modeling in Bioinformatics (Math 548) This will be a computational laboratory course designed in parallel with Math/Stat 547. Weekly hands-on problems will be presented on the algorithms presented in the course, the use of public sequence databases, the design of hidden Markov models. Concrete examples of homology, gene finding, structure analysis. Prereq: Stat 425 or Biol 427 or Biol Chem 415; basic programming skills desirable. Graduate standing and permission of instructor. Stat. 550 - Bayesian Decision Analysis (IOE 560/SMS 576) Axiomatic foundations for personal probability and utility; interpretation and assessment of personal probability and utility; formulation of Bayesian decision problems; risk functions, admissibility; likelihood principle and properties of likelihood functions; natural conjugate prior distributions; improper and finitely additive prior distributions; examples of posterior distributions, including the general regression model and contingency tables; Bayesian credible intervals and hypothesis tests; applications to a variety of decision-making situations. Prereq: Stat 425 Stat. 553 - Conceptual Foundations of Statistical Inference (Philosophy 553) This course will focus on conceptual issues in the foundations of probability theory and statistics. It is intended for graduate students with modest prior background in statistics. Probability theory will be reviewed, and elementary statistical techniques will be discussed. Course will evaluate the main philosophical interpretations of the probability calculus and resulting paradigms of statistical inference. Prereq: A course in statistical theory (e.g. Stat 405, Psych 613, Econ 405) and graduate or advanced undergraduate standing, or permission of instructor. Stat. 560 - Introduction to Nonparametric Statistics (Bios 685) Confidence intervals and tests for quantiles, tolerance regions, and coverages; estimation by U statistics and linear combination or order statistics; large sample theory for U statistics and order statistics; the sample distribution and its uses including goodness-of-fit tests; rank and permutation tests for several hypotheses including a discussion of locally most powerful rank and permutation tests; and large sample and asymptotic efficiency for selected tests. Prereq: Stat 426 Stat. 570 - Design of Experiments (IOE 570) Basic topics and ideas in the design of experiments: randomization and randomization tests; the validity and analysis of randomized experiments; randomized blocks; Latin and Graeco-Latin squares; plot techniques; factorial experiments; the use of confounding and response surface methodology; weighing designs, lattice and incomplete block and partially balanced in complete block designs. Prereq: Stat 500 or background in regression. Graduate standing. Stat. 575 - Econometric Theory I (Econ 775) A course in econometric theory stressing the statistical foundations of the general linear model. The course involves a development of the required theory in mathematical statistics; and derivations and proofs of main results associated with statistical inference in the general linear model. Prereq: Math 417 and 425 or Econ 653, 654, 673 and 674. Stat. 576 - Econometric Theory II (Econ 776) Generalized least squares, multivariate multiple regression, simultaneous equation models (including problems of identification, estimation by equation and system methods, and forecasting), introduction to asymptotic theory, and estimation problems in time series models. Prereq: Stat 575 Stat. 580 - Methods and Theory of Sample Design (Soc 717/Bios 617) Theory underlying sample designs and estimation procedures commonly used in survey practice. Simple random sampling, stratification systematic sampling, cluster sampling, multistage sampling, sampling with probability proportional to size, replicated sampling, multiphase sampling. Post-stratification, ratio, regression and difference estimation. Variance estimation with complex sample designs: Taylor series method, repeated replications, jackknife repeated replications. Nonresponse weighting adjustments and imputation. Prereq: Three or more courses in Statistics and preferably a course in methods of survey sampling. Stat 600: Linear Models This is an advanced introduction to regression modeling and prediction, including traditional and modern computationally-intensive methods. The following topics will be covered: (1) Theory and practice of linear models, including the relevant distribution theory, estimation, confidence and prediction intervals, testing, model and variable selection, generalized least squares, robust fitting, and diagnostics; (2) Generalized linear models, including likelihood formulation, estimation and inference, diagnostics, and analysis of deviance; and (3) Large and small-sample inference as well as inference via the bootstrap, cross-validation, and permutation tests. Prereq: Knowledge of linear algebra; Knowledge of regression and analysis of variance at the level of Stat 500; Knowledge of probability and statistical theory at the level of Biostat 601/602. Stat 601: Analysis of Multivariate and Categorical Data This is an advanced introduction to the analysis of multivariate and categorical data. Topics include: (1) dimension reduction techniques, including principal component analysis, multidimensional scaling and extensions; (2) classification, starting with a conceptual framework developed from cost functions, Bayes classifiers, and issues of over-fitting and generalization, and continuing with a discussion of specific classification methods, including LDA, QDA, and KNN; (3) discrete data analysis, including estimation and testing for log-linear models and contingency tables; (4) large-scale multiple hypothesis testing, including Bonferroni, Westphal-Young and related approaches, and false discovery rates; (5) shrinkage and regularization, including ridge regression, principal component regression, partial least squares, and the lasso; (6) clustering methods, including hierarchical methods, partitioning methods, K-means, and model-based clustering. Prereq: Stat 600 Stat 605: Advanced Topics in Modeling and Data Analysis This course covers recent developments in statistical modeling and data analysis. Topics include: (1) classification and machine learning, including support vector machines, recursive partitioning, and ensemble methods; (2) methods for analyzing sets of curves, surfaces and images, including functional data analysis, wavelets, independent component analysis, and random field models; (3) modern regression, including splines and generalized additive models, (4) methods for analyzing structured dependent data, including mixed effects models, hierarchical models, graphical models, and Bayesian networks; and (5) clustering, detection, and dimension reduction methods, including manifold learning, spectral clustering, and bump hunting. Prereq: Stat 601 Stat. 606 - Statistical Computing This course is an advanced introduction to numerical techniques used in statistics, modeling, and data analysis. Course topics include: background in numerical analysis (concepts of convergence rate and computational complexity), classical numerical techniques (root finding, quadrature), combinatorial enumerations, numerical linear algebra (linear solvers, matrix factorizations, eigenvalue problems), optimization (Newton-Raphson, gradient optimization, EM algorithm, constrained optimization, stochastic optimization), and simulation (pseudo-random numbers, importance and rejection sampling, Markov chain methods). The course will cover both theory and implementation. Statistical applications will include frequentist and Bayesian model estimation and inference for complex models including hierarchical linear and nonlinear models, random effects and latent variable models, signal and image processing methods, clustering and classification methods, and visualization. Prereq: Some experience in computer programming is required. Knowledge of regression and analysis of variance at the level of Stat 500; Knowledge of probability and statistical theory at the level of Biostat 601/602. Stat. 610 - Statistical Inference This course introduces students to the theory of statistical inference. It starts with a review of topics in probability theory including densities, expectation, random vectors and covariance matrices, independence, and conditioning. It then introduces exponential families and sufficiency and develops the theory of point estimation including unbiased and Bayesian estimation, conditional distributions, variance bounds and information. The theory of hypothesis testing is also covered, including uniformly most powerful tests and the duality between testing and interval estimation. Additional topics that may be covered include curved exponential families, equivariant estimation, and empirical Bayes and shrinkage estimators. Prereq: Math 451, Stat 425, and Stat 426 or equivalent courses in probability, statistics and real analysis Stat. 611 - Large Sample Theory This course covers topics in large sample theory that are central for statistical inference, including: (1) modes of convergence, central limit theorems for averages and medians, and asymptotic relative efficiency; (2) estimating equations including the law of large numbers for random functions, consistency and asymptotic normality for maximum likelihood and M-estimators, the E-M algorithm, and asymptotic confidence intervals; (3) large sample theory for likelihood ratio tests. In addition, simultaneous inference and nonparametric regression are also covered. Other possible topics include theory for two-sided tests and tests in higher dimensions. Prereq: Stat 610 Stat. 612 - Advanced Topics in Theoretical Statistics This course deals with selected topics in theoretical statistics at an advanced level. Topics include stochastic convergence in metric spaces, empirical processes including the empirical distribution function, functional delta method and applications including large sample theory for nonparametric density estimation, and the large sample theory for bootstrap methods. Other possible topics include semi-parametric models and efficiency issues. Prereq: Stat 520 or equivalent course in measure theory and Stat 611Stat. 617 - Advanced Topic in Quantitative Methodology This course explores and critiques advanced methods for conducting quantitative research in the social sciences. A special topic is chosen for a particular semester, with relevant methods drawn from a wide variety of disciplines, including economics, education, epidemiology, psychology, sociology, and statistics. Particular attention is paid to quasi- experimental and observational research design. Prereq: Graduate level courses in Statistics at the level of 500 and 501 or permission of instructor. Stat. 620 - Applied Probability and Stochastic Modeling This course is an introduction to stochastic models that capture the evolution in time of various random phenomena and/or dynamical systems. Such phenomena/systems arise extensively in diverse areas of research, ranging from biology, to data networks and production planning. Emphasis is placed on modeling aspects as well as development of the underlying theory. Topics covered include: Markov chains in discrete and continuous time, Poisson and renewal processes, Brownian motion. Applications of these models in key scientific and engineering areas, such as genetics, epidemics, computational algorithms, computer and communications networks, inventory systems financial and risk management, are discussed. Prereq: Math 451 or equivalent knowledge of real analysis. Knowledge of probability at the level of Biostat 601 or Math 525.Stat. 621 - Theory of Probability II This course is an introduction to measure-theoretic probability theory, with emphasis on rigorous treatment of the various topics discussed in the course. Topics to be covered include: (i) constructions of probability spaces, Kolmogorov’s consistency theorem; independence of families of random variables, Borel-Cantelli lemmas and 0-1 laws; (ii) various modes of convergence (in probability, almost surely, in Lp, in distribution) and properties of weak convergence, (iii) laws of large numbers, (iv) central limit theorems for sequences and triangular arrays, (v) conditional expectations and distributions and (vi) discrete time martingale theory. In addition, Brownian motion, continuous time martingales and elements of ergodic theory may be covered. Prereq: Stat 520 or equivalent course in measure theory, Stat 620 Stat. 625 - Probability and Random Processes I (Math 625) Axiomatics; measures and integration in abstract spaces. Fourier analysis, characteristic functions. Conditional expectation, Kolmogoroff extension theorem. Stochastic processes; Wiener-Levy, infinitely divisible, stable. Limit theorems, law of the iterated logarithm. Prereq: Math 597. Graduate standing. Stat. 626 - Probability and Random Processes II (Math 626) Selected topics from among: diffusion theory and partial differential equations; spectral analysis; stationary processes, and ergodic theory; information theory; martingales and gambling systems; theory of partial sums. Prereq: Stat 625. Graduate standing. Stat. 630 - Topics in Applied Probability Advanced topics in applied probability, such as queueing theory, inventory problems, branching processes, stochastic difference and differential equations, etc. The course will study one or two advanced topics in detail. Prereq: Permission of instructor. Stat. 631 - Advanced Time Series Analysis Prereq: Stat 500, 610, 611. Graduate standing. Stat. 640 - Multivariate Statistical Models (Bios 890) Wishart distribution, multivariate linear models, multivariate regression, Hotelling's T-square and its applications, discriminant analysis, canonical correlations, principal components analysis, growth curves. Prereq: Math 417 and either Stat 611 or Biostat 602. Graduate standing and permission of instructor. Stat. 642 - Linear Statistical Models I (Bios 851) Gauss-Markov theorem; one-way, two-way analysis of variance, and complete higher-way layouts; regression; the general linear model and hypothesis; least squares theory; analysis of covariance; missing observations; multiple comparisons procedures; incomplete blocks, split plot designs, and Latin squares; variance component models, mixed models; treatment of residuals; robustness of the methods. Special topics in the second semester. Prereq: Math 417 and either Stat 611 or Biostat 602. Graduate standing. Stat. 645 - Topics in Reliability and Maintainability (IOE 645/MFG 645) Advanced topics in reliability and maintainability. Examples include models for component and system reliability, probabilistic design, physics of failure models, degradation modeling and analysis, models form maintainability and availability, and maintenance and monitoring policies. Prereq: IOE 515/Stat 526 and IOE 562/Stat 535. Stat. 670 - Advanced Design and Analysis of Experiments This is an advanced course on the design and analysis of experiments. It will cover topics from orthogonal arrays, optimal designs, minimum aberration designs, parameter design, response surface methodology, computer experiments, and experiments with split-plot structure. Emphasis will be placed on new concepts/tools and recent advances. Prereq: Stat 570 or permission of instructor. Stat. 680 - Theory of Sampling Recent developments in the foundations and methodology of sampling finite populations. Identifiability of units, likelihood of units, likelihood functions, admissibility of standard estimators, randomization, use of prior information in design and inference. Models for non-sampling errors including bias, response error and non-response. Other topics of current interest. Prereq: Stat 426 and 575. Graduate standing. Stat. 700 - Special Topics in Applied Statistics I Selected topics in applied statistics. Prereq: Stats 501 and graduate standing. Stat. 701 - Special Topics in Applied Statistics II Selected topics in applied statistics. Prereq: Stats 501 and graduate standing. Stat. 710 - Special Topics in Theoretical Statistics I Special Topics. Prereq: Graduate standing and permission of instructor. Stat. 711 - Special Topics in Theoretical Statistics II Special topics. Prereq: Graduate standing and permission of instructor. Stat. 725 - Topics in Advanced Probability I (Math 725) Prereq: Stat 626. Graduate standing Stat. 726 - Topics in Advanced Probability II (Math 726) Prereq: Stat 626, 725; Math 725. Graduate standing. Stat. 750 - Directed Reading Designed for individual students who have an interest in a specific topic (usually that has stemmed from a previous course). An individual instructor must agree to direct such a reading, and the requirements are specified when approval is granted. Prereq: Graduate standing (INDEPENDENT). Stat. 808 - Seminar in Applied Statistics I - Graduate standing. Stat. 809 - Seminar in Applied Statistics II - Graduate standing. Stat. 810 - Literature Proseminar I This course is designed to acquaint students with classical papers in mathematics and applied statistics and probability theory, to encourage them in critical independent reading and to permit them to gain pedagogical experience during the course of their graduate training. Prereq: Graduate standing and permission of instructor. Stat. 811 - Literature Proseminar II This course is designed to acquaint students with classical papers in mathematics and applied statistics and probability theory, to encourage them in critical independent reading and to permit them to gain pedagogical experience during the course of their graduate training. Prereq: Graduate standing and permission of instructor. Stat 816 - Interdisciplinary Seminar in the Physical Sciences The seminar will consider statistical questions that arise in the physical sciences. Topics will be drawn from current research projects, will vary each semester. Meetings will feature lectures by faculty from the University and selected visitors. Students will be expected to complete a course project and present to group. Prereq: Graduate standing and Statistics 426 (or equivalent) or permission of instructor. Stat. 817 - Interdisciplinary Seminar in Quantitative Social Science Methodology (Educ 817/Psych 817/Soc 810) This seminar will meet to consider methodological issues that arise in research in the social sciences. Themes for each meeting will arise from ongoing research projects at the University of Michigan. Visiting researchers will provide a brief account of their aims and data before defining the methodological challenge for which they desire discussion. Prereq: Graduate level courses in Statistics at the level of Stat 500 and 501 or permission of instructor. Stat. 818 - Seminar in Theoretical Statistics I - Graduate standing Stat 819 - Seminar in Theoretical Statistics II - Graduate standing
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