Graduate Statistics Courses
Stat 500. Applied Statistics. (3) Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and two-way ANOVA, chi-square tests, diagnostics. Prerequisite: one undergraduate course in statistics.
Stat 501. Regression Methods. (3) Analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; stepwise, piecewise, and logistic regression. Prerequisite: 6 credits of statistics or Stat 500; matrix algebra.
Stat 502. Analysis of Variance and Design of Experiments. (3) Analysis of variance and design concepts; factorial, nested, and unbalanced data; ANCOVA; blocked, Latin square, split-plot, repeated measures designs. Prerequisite: Stat 462 or Stat 501.
Stat 503. Design of Experiments. (3) Design principles; optimality; confounding in split-plot, repeated measures, fractional factorial, response surface, and balanced/partially balanced incomplete block designs. Prerequisites: Stat 502; Stat 462 or Stat 501.
Stat 504. Analysis of Discrete Data. (3) Models for frequency arrays; goodness-of-fit tests; two-, three-, and higher-way tables; latent and logistics models. Prerequisites: Stat 460, Stat 502 or Stat 512; matrix algebra.
Stat 505. Applied Multivariate Statistical Analysis. (3) Analysis of multivariate data; T-squared tests; partial correlation; discrimination; MANOVA; cluster analysis; regression; growth curves; factor analysis; principal components; canonical correlations. Prerequisites: Stat 501 and Stat 502; matrix algebra.
Stat 506. Sampling Theory and Methods. (3) Theory and application of sampling from finite populations. Prerequisites: calculus; Stat 500 or equivalent.
Stat 507. Epidemiologic Research Methods. (3) Research and quantitative methods for analysis of epidemiologic observational studies. Non-randomized, intervention studies for human health, and disease treatment. Prerequisites: Stat 250 or equivalent.
Stat 508. Applied Statistical Distribution Theory. (3) Analysis of data involving nonnormal families of distributions; model building and selection, parameterizations, inferential algorithms, transformations, simulations, displays, interpretations. Prerequisites: Stat 401.
Stat 509. Design and Analysis of Clinical Trials. (3) An introduction to the design and statistical analysis of randomized and observational studies in biomedical research. Prerequisite: Stat 500.
Stat 510. Applied Time Series Analysis. (3) Identification of models for empirical data collected over time. Use of models in forecasting. Prerequisite: Stat 462, 501, or 511.
Stat 511. Regression Analysis and Modeling. (3) Multiple regression methodology using matrix notation; linear, polynomial, and nonlinear models; indicator variables; AOV models; piece-wise regression, autocorrelation; residual analyses. Prerequisites: Stat 500 or equivalent; matrix algebra, calculus.
Stat 512. Design and Analysis of Experiments. (3) AOV, unbalanced, nested factors; CRD, RCBD, Latin squares, split-plot, and repeated measures; incomplete block, fractional factorial, response surface designs; confounding. Prerequisite: Stat 511.
Stat 513. Theory of Statistics I. (3) Probability models, random variables, expectation, generating functions, distribution theory, limit theorems, parametric families, exponential families, sampling distributions. Prerequisite: Math 230.
Stat 514. Theory of Statistics II. (3) Sufficiency, completeness, likelihood, estimation, testing, decision thoery, Bayesian inference, sequential procedures, multivariate distributions and inference, nonparametric inference. Prerequisite: Stat 513.
Stat 515. Stochastic Processes I. (3) Conditional probability and expectation, Markov chains, the exponential distribution and Poisson processes. Prerequisite: Stat (Math) 414 or Stat 513.
Stat (Math) 516. Stochastic Processes. (3) Markov chains; generating functions; limit theorems; continuous time and renewal processes; martingales, submartingales, and supermartingales; diffuse processes; applications. Prerequisite: Stat (Math) 416.
Stat (Math) 517. Probability Theory I (3) Measure theoretic foundation of probability, distribution functions and laws, types of convergence, central limit problem, conditional probability, special topics. Prerequisite: Math 403.
Stat (Math) 518. Probability Theory II (3) Measure theoretic foundation of probability, distribution functions and laws, types of convergence, central limit problem, conditional probability, special topics. Prerequisite: Stat (Math) 517.
Stat (Math) 519. Topics in Stochastic Processes. (3) Selected topics in stochastic processes, including Markov and Wiener processes; stochastic integrals, optimal filtering. Prerequisites: Stat (Math) 516, 517.
Stat 524. Ecometrics. (3) Stochastic models and statistical methods in ecological problems; population dynamics, spatial patterns in populations of one, two, or more species. Prerequisite: Stat (Math) 414 or Stat 418.
Stat 525. Survival Analysis I. (3) Location estimation, 2- and k- sample problems, matched pairs, tests for association and covariance analysis when the data are censored. Prerequisites: Stat 512, 514.
Stat 526. Survival Analysis II. (3) Asymptotic theory for Kaplan-Meier estimator, 2- and k- sample rank tests, rank regression, proportional hazards regression. Advanced special topics. Prerequisite: Stat 525.
Stat (Biol) 527. Quantitative Ecology. (3) Introduction to quantitative population and community ecology, with emphasis on problems, concepts, and methods using mathematical, statistical, and computational analysis. Prerequisites: Stat (Math) 409, Biol 210.
Stat (Biol) 528. Statistical Ecology Spectrum. (3) Overview of research and instruction of particular interest to quantitative ecology faculty in the Ecology program. Prerequisite: Stat (Biol) 527.
Stat 540. Statistical Computing. (3) Computational foundations of statistics; algorithms for linear and nonlinear models, discrete algorithms in statistics, graphics, missing data, Monte Carlo techniques. Prerequisites: Stat (Math) 415; Stat 501 or 511; matrix algebra.
Stat 544. Categorical Data Analysis I (3) Two-way tables; generalized linear models; logistic and conditional logistic models; loglinear models; fitting strategies; model selection; residual analysis. Prerequisites: Stat 512, 514.
Stat 545. Categorical Data Analysis II (3) Generalized logit modes; symmetry and agreement models; repeated measures; longitudinal data; delta method; asymptotic distributions; ML & WLS; advanced special topics. Prerequisite: Stat 544.
Stat 548. Statistical Distribution Theory. (3) Analytical study of nonnormal models and methods in reliability theory, survival analysis, records evaluation, scale/scale-free analysis, and directional statistics. Prerequisites: Stat (Math) 414, or 416.
Stat 551. Linear Models I. (3) A coordinate-free treatment of the theory of univariate linear models, including multiple regression and analysis of variance models. Prerequisites: Math (Stat) 415 or Stat 514; Stat 512; Math 436 or Math 441.
Stat 552. Linear Models II. (3) Treatment of other normal models, including generalized linear, repeated measures, random effects, mixed, correlation, and some multivariate models. Prerequisite: Stat 551.
Stat 553. Asymptotic Tools. (3) Study of large-sample theory without measure theory. Prerequisites: Stat 513-514.
Stat 561. Statistical Inference I. (3) Classical optimal hypothesis test and confidence regions, Bayesian inference, Bayesian computation, large sample relationship between Bayesian and classical procedures. Prerequiste: Stat 514.
Stat 562. Statistical Inference II (3) Basic limit theorems; asymptotically efficient estimators and tests; local asymptotic analysis; estimating equations and generalized linear models. Prerequisite: Stat 561.
Stat 564. Theory of Nonparametric Statistics. (3) Estimation and testing based on nonparametric procedures for location and regression models. Distribution theory and asymptotic efficiency. Prerequisites: Stat (Math) 415 or Stat 514.
Stat 565. Multivariate Analysis. (3) Theoretical treatment of methods for analyzing multivariate data, including Hotelling's T-squared, MANOVA, discrimination, principal components, and canonical analysis. Prerequisites: Stat 505, 551.
Stat 572. Statistical Decision Theory I. (3) Structure of statistical games, optimal strategies, fixed sample-size games. Prerequisite: Stat (Math) 415 or Stat 514.
Stat 580. Statistical Consulting Practicum I. (2) General principals of statistical consulting and statistical consulting experience. Preparation of reports, presentations, and communication aspects of consulting are discussed. Prerequisites: Stat 502; Stat 503, 504, or 506.
Stat 581. Statistical Consulting Practicum II. (1) Statistical consulting experience including client meetings, development of recommendation reports, and discussion of consulting solutions. Prerequisites: Stat 580.
Stat 590. Colloquium (1-3)
Stat 596. Individual Studies (1-9)
Stat 597. Special Topics (1-9)