301 Introduction to Statistical Methods. 3 cr. Distributions, measures of central tendency, dispersion and shape, the normal distribution; experiments to compare means, standard errors, confidence intervals; effects of departure from assumption; method of least squares, regression, correlation, assumptions and limitations; basic ideas of experimental design. P: Open to Fr. Stdts may receive degree cr for no more than one of the following: Stat 201, 224, 301, 324, and 371.
309 Introduction to Mathematical Statistics. (Crosslisted with Math) 4 cr. Probability and combinatorial methods, discrete and continuous, univariate and multivariate distributions, expected values, moments, normal distribution and derived distributions, estimation. P: For majors in math and stats, Math 223 or 234.
310 Introduction to Mathematical Statistics. (Crosslisted with Math) 4 cr. Unbiased estimation, maximum likelihood estimation, confidence intervals, tests of hypotheses, Neyman-Pearson fundamental lemma, likelihood ratio test, applications to general linear model and analysis of variance, categorical data analysis, nonparametric methods. P: For majors in Math and Stat, Math 309 or Stat 309.
311 Introduction to Mathematical Statistics. 4 cr. Elements of probability, important discrete distributions, acceptance sampling by attributes, sample characteristics, probability distributions and population characteristics, the normal distribution, acceptance sampling plans based on sample means and variances, sampling from the normal, the central limit theorem, point and interval estimation. P: Math 223 or con reg.
312 Introduction to Mathematical Statistics. 4 cr. Tests of hypotheses, control charts, goodness of fit tests; order statistics and nonparametric tests; regression theory, analysis of variance. P: For majors in Engineering or Nat Sci, Stat 311.
324 Introductory Applied Statistics for Engineers. 3 cr. Descriptive statistics, probability concepts and distributions, random variables. Hypothesis tests and confidence intervals for one- and two-sample rpoblems. Linear regression, model checking, and inference. Analysis of variance and basic ideas in experimental design. Math 222. Stdts may receive degree cr for no more that one of the following: Stat 201, 224, 301 and 324. Open to Fr.
333 Applied Regression Analysis. 3 cr. An introduction to regression with emphasis on the practical rather than the theoretical aspects. Begins with fitting a straight line, converts this problem into matrix terms and then proceeds to fitting and evaluation of general linear models. P: Cons inst.
349 Introduction to Time Series. 3 cr. Autocorrelation, elements of spectral analysis; dynamic models; auto-regressive and moving average models; identification and fitting; forecasting; seasonal adjustment; applications in the social sciences and environmental studies. P: Stat 301 or equiv, or cons inst.
351 Introductory Nonparametric Statistics. 3 cr. Distribution free statistical procedures or methods valid under nonrestrictive assumptions: basic tools; counting methods; order statistics, ranks; distribution free tests and associated interval and point estimators; sign test; signed rank tests; rank tests; Mann Whitney Wilcoxon procedures; Kolmogorov Smirnov tests; permutation methods; methods for discrete data with zeros and ties; computer techniques and programs; discussion and comparison with parametric methods. P: Stat 201 or 301 or 224 or cons inst.
371 Introductory Applied Statistics for the Life Sciences. 3 cr. The course will provide students in the life sciences with an introduction to modern statistical practice. Topics include: exploratory data analysis, probability and random variables; one-sample testing and confidence intervals, role of assumptions, sample size determination, two-sample inference; basic ideas in experimental design, analysis of variance, linear regression, goodness-of fit; biological applications. P: Math 112 & 113 or Math 114. Open to Fr. Stdts may receive cr for no more than one of the following crses: Stat 201, 224, 301, 324, & 371.
411 An Introduction to Sample Survey Theory and Methods. 3 cr. An elementary development of the statistical theory (and methods) used to design and analyze the results from sample surveys. Topics: basic tools, simple random sampling, ratio and regression estimation, stratification, systematic sampling, cluster (area) sampling, unequal probability sampling, sampling on successive occasions, non-sampling errors, analytical sample surveys. For illustration and clarification, examples drawn from diverse areas of application. P: Stat 224, 201, 301 or an equiv intro statistics course.
421 Applied Categorical Data Analysis. 3 cr. Methods of analyzing multidimensional contingency tables, emphasis on practical applications. The use of computing packages for analysis of such data. Model selection, testing goodness of fit, estimation of parameters, measures of association and methods for detecting sources of significance. P: Stat 301 or cons inst.
424 Statistical Experimental Design for Engineers. (Crosslisted with ME) 3 cr. Concepts of randomization, blocking, confounding, transformations, replication; block designs, factorial and fractional methodology, evolutionary operation, and response-surface methodology. P: Stat 224.
426 Reliability. (Crosslisted with ME) 3 cr. Engineering reliability, analysis of failure data, estimates of hazard rates and failure distributions for the reliability of components and/or systems, acceptance sampling plans for quality control. P: Stat 224 or cons inst.
431 Introduction to the Theory of Probability. (Crosslisted with Math) 3 cr. Probability in discrete sample spaces; combinatorial analysis; conditional probabilities, stochastic independence, Laplace limit theorem, Poisson distribution, laws of large numbers, random variables, central limit theorem, applications. P: Math 223 or 234.
441 Introduction to Biostatistics for Pharmacy. 3 cr. Introduction to statistical methods used in pharmaceutical and related biomedical applications. Topics include exploratory data analysis of random samples, theory of probability and population reference distributions, statistical inference and hypothesis testing, regression methods, and survival analysis techniques. P: Admission to School of Pharmacy, Pharm.D. prgm.
456 Applied Multivariate Analysis. 3 cr. Theory and applications of multivariate statistical methods. Basic concepts and statistical reasoning which underlie the techniques of multivariate analysis. Ideas rather than derivations stressed although basic models discussed to give the student some feeling for their adequacy in particular situations. Current applications in the functional areas of accounting, finance, marketing and management. Acquaintance with and use of existing computer programs in the multivariate analysis area. P: Gen Bus 304, Stat 314 or equiv.
475 Introduction to Combinatorics. (Crosslisted with Math, Comp Sci) 3 cr. Problems of enumeration, distribution, and arrangement. Inclusion-exclusion principle. Generating functions and linear recurrence relations. Combinatorial identities. Graph coloring problems. Finite designs. Systems of distinct representatives and matching problems in graphs. Potential applications in the social, biological, and physical sciences. Puzzles. Problem solving. P: Math 320 or 340 or cons inst.
525 Linear Programming Methods. (Crosslisted with Comp Sci, I SY E, Math) 3 cr. Real linear algebra over polyhedral cones; theorems of the alternative for matrices. Formulation of linear programs. Duality theory and solvability. The simplex method and related methods for efficient computer solution. Perturbation and sensitivity analysis. Applications and extensions, such as game theory, linear economic models, and quadratic programming. P: Math 443 or 320 or 340 or cons inst.
541 Introduction to Biostatistics. (Crosslisted with B M I) 3 cr. Course designed for the biomedical researcher. Topics include: descriptive statistics, hypothesis testing, estimation, confidence intervals, t-tests, chi-squared tests, analysis of variance, linear regression, correlation, nonparametric tests, survival analysis and odds ratio. Biomedical applications used for each topic. P: Math 221 or equiv or cons inst.
542 Introduction to Clinical Trials I. (Crosslisted with B M I) 3 cr. Intended for biomedical researchers interested in the design and analysis of clinical trials. Topics include definition of hypotheses, measures of effectiveness, sample size, randomization, data collection and monitoring, and issues in statistical analysis. Statistics graduate students should take Stat 641. P: Stat 541 or equiv or cons inst.
546 Practicum in Clinical Trial Data Analysis and Interpretation. (Crosslisted with B M I) 3 cr. Provides practice in analysis and interpretation of existing datasets from national and international clinical trials in a variety of diseases. Students will develop a research question, review clinical protocols, and analyze available data to prepare a report. P: Stat 541 or 572 & Stat 542 or 641.
571 Statistical Methods for Bioscience I. (Crosslisted with Forest, Hort) 4 cr. Descriptive statistics, distributions, one- and two-sample normal inference, power, one-way Anova, simple linear regression, categorical data, non-parametric methods; underlying assumptions and diagnostic work. P: College algebra: Grad st or cons inst.
572 Statistical Methods for Bioscience II. (Crosslisted with Forest, Hort) 4 cr. Continuation of Forestry 571. Polynomial regression, multiple regression, two-way Anova with and without interaction, split-plot design, subsampling, analysis of covariance, elementary sampling, introduction to bioassay. P: Stats/Forestry/Hort 571.
575 Statistical Methods for Spatial Data. 3 cr. Detecting and quantifying spatial patterns and modeling in the presence of such patterns. Spatial Point Patterns: testing nonrandomness, simulating and characterizing patterns. Lattice Data: spatial autocorrelation and regression. Geostatistics: variograms, ordinary and universal kriging, inference, assessing assumptions, and extensions. P: Stat 333 & 424; or Stat/Forest/Hort 572; or cons inst.
609 Mathematical Statistics I. 3 cr. Review of probability, random variables and vectors and their distributions, moments and inequalities, generating functions, transformations of random variables, sampling and distribution theory, convergence concepts for sequences of random variables, laws of large numbers, central limit and other limit theorems. P: Stat 309 or 431, Math 340, Math 521, or equiv or cons inst.
610 Introduction to Statistical Inference. 4 cr. Conditioning, distribution theory, approximation to distributions, modes of convergence, limit theorems, statistical models, parameter estimation, comparision of estimators, confidence sets, theory of hypothesis tests, introduction to Bayesian inference and nonparametric estimation. P: Stat 309 or Stat 431, Math 521, Math 340 or equiv or cons inst.
632 Introduction to Stochastic Processes. (Crosslisted with Math, I SY E, OTM) 3 cr. Markov chains: classification, recurrence, transcience, limit theory. Renewal theory, Markov processes, birth-death processes. Applications to queueing, branching, and other models in science, engineering and business. Topics drawn from semi-Markov processes, martingales, Brownian motion. P: Math 431, or Stat 309 & 310, or Stat 311 & 312, or Stat 313 or 314.
641 Statistical Methods for Clinical Trials. 3 cr. Statistical issues in the design of clinical trials, basic survival analysis, data collection and sequential monitoring. Intended for statistics graduate students; those with medical backgrounds should take Stat 542. P: Math/Stat 310 or equiv or cons inst.
642 Statistical Methods for Epidemiology. 3 cr. Methods for analysis of case-control, cross sectional, and cohort studies. Covers epidemiologic study design, measures of association, rates, classical contingency table methods, and logistic and Poisson regression. P: Stat 310 or equiv or cons inst.
643 Practicum in Coordinating Center Methods. (Crosslisted with B M I) 3 cr. Practicum in the operation of a coordinating center in a clinical trial or epidemiologic study. Covers organization, randomization, forms design and collection, quality control and other operational responsibilities of coordinating centers. P: Stat 641 or 642 or cons inst.
692 Special Topics in Statistics. 1-3 cr. Content varies. Consult department or Timetable for information. P: Cons inst.
698 Directed Study. 1-6 cr. P: Graded on a Cr/N basis; requires cons inst.
699 Directed Study. 1-6 cr. P: Graded on a lettered basis; requires cons inst.
701 Applied Time Series Analysis, Forecasting and Control I. 3 cr. Theory and application of discrete time series models illustrated with forecasting problems. Principles of iterative model building. Representation of dynamic relations by difference equations. Autoregressive integrated Moving Average models. Identification, fitting, diagnostic checking of models. Seasonal model application to forecasting in business, economics, ecology, and engineering used at each stage, which the student analyzes using computer programs which have been specially written and extensively tested. P: Stat 310 or equiv.
702 Applied Time Series Analysis, Forecasting and Control II. 3 cr. Further theory and application of discrete time series models illustrated by transfer function estimation, intervention analysis, and forecasting and control problems for multiple time series. Illustrations at each stage with real examples from business, economics, ecology and engineering which the student analyzes using the computer. P: Stat 701.
709 Mathematical Statistics. (Crosslisted with Math) 4 cr. Introduction to measure theoretic probability; derivation and transformation of probability distributions; generating functions and characteristic functions; conditional expectation, sufficiency, and unbiased estimation; methods of large sample theory including laws of large numbers and central limit theorems; order statistics. P: Cons inst or one yr adv calculus and Math, Stat 431, Math, Stat 310.
710 Mathematical Statistics. (Crosslisted with Math) 4 cr. Estimation, efficiency, Neyman-Pearson theory of hypothesis testing, confidence regions, decision theory, analysis of variance, and distribution of quadratic forms. P: Stat, Math 709.
726 Nonlinear Optimization I. (Crosslisted with Comp Sci, I SY E, Math) 3 cr. Theory and algorithms for nonlinear optimization, focusing on unconstrained optimization. Line-search and trust-region methods; quasi-Newton methods; conjugate-gradient and limited-memory methods for large-scale problems; derivative-free optimization; algorithms for least-squares problems and nonlinear equations; gradient projection algorithms for bound-constrained problems; and simple penalty methods for nonlinearly constrained optimization. P: Familiarity with basic mathematical analysis and either Math 443 or 320; or cons inst.
731 Probability and Analysis. 3 cr. Abstract measure theory and theory of integration with probabilistic and statistical applications. Extension of measures, basic integration theorems, Radon-Nikodym Theorem, Fubini's Theorem. Modes of convergence of sequences of random variables and their relationships. Moment and probability inequalities. Scheff theorem for probability density functions. Slutsky theorems with some statistical applications. Helly-Bray type theorems. Conditional expectation and conditional probabilities. Convergence theorems, laws of large numbers, central limit theorems, characteristic functions, weak convergence topics. P: Cons inst.
732 Large Sample Theory of Statistical Inference. 3 cr. Stochastic modes of convergence. Asymptotic theory of normed sums of random variables with applications to asymptotic normality of estimators. Methods for deriving limit distributions of nonlinear statistics. Asymptotic relative efficiencies. Asymptotic confidence regions and tests of hypotheses. Models of non-identically distributed or dependent random variables. P: Either Stat 709, 731, or 831 or cons inst.
741 Survival Analysis Theory and Methods. 3 cr. Theory and practice of analytic methods for censored survival data, including nonparametric and parametric methods, the proportional hazards regression model, and a review of current topics in survival analysis. P: Stat 610 or 710 or equiv or cons inst.
749 Mathematical Models and Response Surface Methodology I. 3 cr. Two-level factorial and fractional factorial designs, applications, blocking, polynomial models, first-order designs, second-order designs, several responses, determination of optimum conditions, canonical reduction, design criteria involving variance and bias. P: Cons inst or Stat 310.
750 Theory of Linear Models. 3 cr. Theory of general linear, mixed-effects. and generalized linear models, regression and Anova models, least squares, distribution theory, F-tests from likelihood ratio, sums of squares distribution theory, generalized least squares, random and mixed models, restricted likelihood estimation, generalized linear models. P: Stat 609 or 849 or cons inst.
751 Sequential Analysis. 3 cr. Sequential tests of simple hypotheses and their optimal properties; composite hypotheses including derivation of the sequential t-test; sequential estimation; stochastic approximation, topics in sequential analysis. P: Stat 310 or equiv.
760 Multivariate Analysis I. 3 cr. Multivariate normal distribution, estimation of mean and covariance matrix; Wishart distribution; distribution of partial and multiple correlation coefficients; Hotelling's T¿2¿, principal components. P: Cons inst or Stat 710.
761 Decision Trees for Multivariate Analysis. 3 cr. Tree construction, including finding splits, tree-pruning and error estimation. Categorical predictor variables, missing or censored data, prior class-probabilities, and unequal misclassification costs. Selection bias. Comparison with other statistics and machine-leaarning methods. Extensions to piecewise linear and non-least squares regression models. P: Cons inst.
765 Stochastic Models I. 3 cr. Further applications of stochastic models; renewal theory; theory of regenerative events; theory of queues; Markovian and semi-Markovian processes; models of epidemics and of accident occurrence. P: Stat 710 or cons inst.
766 Stochastic Models II. 3 cr. Continuation of 765. P: Stat 765.
771 Statistical Computing. 3 cr. The design of statistical software including special techniques for probability distributions, methods of simulation of random processes, numerical methods for linear models and multivariate analysis, and methods for nonlinear models. P: Stat 333 or equiv or cons inst.
775 Introduction to Bayesian Decision and Control I. (Crosslisted with Econ, Gen Bus) 3 cr. Common sampling models in business and economic problems, information from data, likelihood function of parameters, choices of models, Bayes' Theorem, subjective basis for probability, sequential nature of Bayesian inference, prior and posterior distributions of parameters in binomial, poisson, exponential and normal populations, comparison of two normal distributions, predictive distributions, decision theory, utility, risk aversion, extensive form of analysis, two-action problems, point estimation, best population problems, economics of sampling. P: Stat 309, 313, or 311 or equiv.
776 Introduction to Bayesian Decision and Control II. (Crosslisted with Econ, Gen Bus) 3 cr. Dependence of observations in economic and business data, trend, moving averages, autoregressive series, non-stationary models and their applications, Bayesian estimation of parameters, adaptive forecasting, control theory, elements of difference calculus, dynamics, models for inventory control. P: Stat/Gen Bus/Econ 775 or cons inst.
803 Experimental Design I. (Crosslisted with Math) 3 cr. Summary of matrix algebra required, theory of estimable functions, incomplete blocks, balanced incomplete block designs, partially balanced incomplete block designs. P: Stats 310 or cons inst.
804 Experimental Design II. 3 cr. P: Stat 803.
809 Non Parametric Statistics. 3 cr. Statistical procedures valid under unrestrictive assumptions; sign test; confidence intervals; efficiency comparisons; signed rank procedures; Walsh sums; point estimators; two sample rank tests; zeros, ties, and other problems of discrete data; order statistics; Winsorized and truncated point estimators and connection with gross error models; permutation procedures; combinatorial problems, and computer applications. P: Stat 710 or cons inst.
810 Non-Parametric Statistics. 3 cr. P: Stat 809.
811 Sample Survey Theory and Method. 3 cr. Simple random sampling; systematic sampling; probability sampling; stratified sampling; subsampling with units of equal and unequal size; double sampling; multi-stage and multi-phase sampling; ratio and regression estimates; model-based and model-assisted approaches; variance estimation; non-response. P: Stats 610 or 710 or equiv.
824 Nonlinear Regression Analysis with Engineering Applications. 3 cr. Engineering application of statistical design techniques; sequential design strategies; nonlinear model building; model discrimination. P: Stat 333, 424 or 849; or cons inst.
826 Theory of Life Testing and Reliability. 3 cr. The statistical theory of reliability and life testing. Probabilistic failure models, complete and censored data, robustness considerations, nonparametric life test procedures, system reliability, redundancy optimization and related topics, application of stochastic processes in reliability, group testing. P: Stat 710 or cons inst.
829 Decision Theory. 3 cr. Statistical inference and decision theory, decision functions, game theory, normal forms, extensive forms, zero sum games, the minimax theorem, sequential games, axiomatic treatment of utility, complete classes of decision functions and strategies. P: Stats 710 or cons inst.
830 Decision Theory. 3 cr. Estimation theory, minimax and Bayes' estimation procedures, hypothesis testing, power and regret functions, invariant test and minimax tests, programming, dynamic programming from the decision point of view. P: Stat 829.
831 Theory of Probability. (Crosslisted with Math) 3 cr. An introduction to measure theoretic probability and stochastic processes.Topics include foundations, independence, zero-one laws, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, random walks, conditional expectations. P: Math 629, 721, or con reg in 721, or con inst.
832 Theory of Probability. (Crosslisted with Math) 3 cr. Continuation of 831. Possible topics include martingales, weak convergence of measures, introduction to Brownian motion. P: Cons inst.
833 Topics in the Theory of Probability. (Crosslisted with Math) 3 cr. Topics in probability and stochastic processes. P: Cons inst.
834 Empirical Processes and Semiparametric Inference. 1-3 cr. Empirical process methods in statistics; semiparametric models; stochastic convergence in metric spaces; Glivenko-Cantelli and Donsker theorems; entropy calculations; bootstrapped empirical processes; functional delta method; Z-estimators; M-estimators; rates of convergence; semiparametric efficiency; semiparametric estimating equations; nonparametric maximum likelihood. P: Math/Stat 710 or Math/Stat 832 or cons inst.
840 Statistical Model Building and Learning. 3 cr. Theory of reproducing kernel Hilbert spaces in statistical model building; bounded linear functionals and representer theory; smoothing splines; Anova spines; degees of freedom for signal and the bias-variance tradeoff; Bayesian confidence intervals; model selection. P: Stat 710 or cons inst.
841 Nonparametric Statistics and Machine Learning Methods. 3 cr. Statistical function estimation and classification; reproducing kernel machines, support vector machines; high dimensional model selection and estimation; Bayesian, empirical Bayesian interpretation of nonparametric learning methods; log density Anova and graphical models; tree ensemble methods including bagging, boosting, and random forest. P: Stat 840.
842 Hypothesis Testing. 3 cr. Measure theoretic background, exponential families; uniformly most powerful tests, least favorable priors; unbiased tests, invariant tests, and applications to exponential families and the general linear hypothesis. P: Stat 710 or cons inst.
849 Theory and Application of Regression and Analysis of Variance I. 3 cr. Theory and applications of the general linear model; graphical methods; simultaneous inference; regression diagnostics; analysis of variance of fixed, random and mixed effects models; Ancova: violations of assumptions. P: Stat 310, 312 or 314.
850 Theory and Application of Regression and Analysis of Variance II. 3 cr. Theory and applications of the general linear model; graphical methods; simultaneous inference; regression diagnostics; analysis of variance of fixed, random and mixed effects models; Ancova: violations of assumptions. P: Stat 849.
851 Generalized Linear Models. 3 cr. Methods for developing, fitting and checking models beyoud the classical linear model framework. Binary, ordinal and categorical models will be examined, as well as the non-Gaussian continuous case and more advanced topics. P: Stat 850 or con reg or cons inst.
853 Bayesian Inference. 3 cr. Sampling theory and its critique, subjective probability, likelihood principles, Bayes theorem, Bayesian analysis of Normal theory inference problems, the Behrens-Fisher problem, assessment of model assumptions, robustness of inference, analysis of variance, estimation of variance components, empirical Bayes, some aspects of multivariate problems. P: Stat 710.
860 Estimation of Functions from Data. 3 cr. Statistical and appoximation theoretic methods of estimating functions and values of functionals from experimental data; experimental design and data analysis problems that arise as problems in approximation theory; convergence theorems; ill-posed inverse problems; Banach and Hilbert space penalty functionals. P: Stat 710 or cons inst.
990 Research. 1-12 cr. Content varies.
992 Seminar. 1-3 cr. Content varies. P: Cons inst.
993 Information Theory and Statistics. 3 cr.
996 Colloquium. 1 cr.
997 Seminar on Statistical Methods in Business and Economics. 2 cr. P: Cons inst.
998 Statistical Consulting. 3 cr. Consulting apprenticeship. P: 9 cr in statistics and cons inst.