For the undergraduate curriculum in liberal arts and sciences, major in statistics, leading to the degree bachelor of science, see Liberal Arts and Sciences, Curriculum.

The curriculum in liberal arts and sciences with a major in statistics is designed to prepare students for (1) entry level statistics positions requiring the B.S. degree in statistics in business, industry or commerce, nonprofit institutions, and in state or federal government; (2) graduate study in statistics. Entry-level positions include the following types of work: statistical design, analysis and interpretation of experiments and surveys; data processing and analysis using modern computation facilities and statistical computing systems; application of statistical principles and methods in commercial areas such as finance, insurance, industrial research, marketing, manufacturing, and quality control. Nonprofit organizations such as large health study institutions have entry-level positions for B.S. graduates in statistics. Also, there are opportunities for work in statistics that require a major in a subject-matter field and a minor in statistics.

Students completing the undergraduate degree in statistics should have a broad understanding of the discipline of statistics. They should have a clear comprehension of the theoretical basis of statistical reasoning and should be proficient in the use of modern statistical methods and computing. Such graduates should have an ability to apply and convey statistical concepts and knowledge in oral and written form. They should be aware of ethical issues associated with polling and surveys and in the summarization of the outcomes of statistical studies.

Undergraduate majors in this department usually include in their programs: (a) Statistics 101 or an alternative introductory course (104 or 226), (b) Mathematics 165, 166, 265 (or 165H, 166H, 265H), 307 (or 317) and Computer Science 207, and (c) Statistics 341, 342, 401, 402, 421, 479, 480.

These courses plus at least two additional courses in statistics at the 400 level or above constitute the major. With the permission of the department, I E/Stat 361 may be substituted for one of these 400 level courses. It is advisable to have a minor in a field of application.

The department offers a minor in statistics which may be earned by completing an introductory course in statistics plus additional courses from 341, 342, 361, and 400 level or above to yield a total of at least 15 credits in statistics courses.

English and Speech proficiency requirement: The department requires a grade of C or better in each of Engl 150 and 250 (or 250H), and completion of one of Engl 302 or 314 with a grade of C- or better. The department requires a passing grade in ComSt 102 or Sp Cm 212.

Students intending to do graduate work in statistics normally will take additional courses in mathematics.

The department offers work for the degrees master of science and doctor of philosophy with a major in statistics, and for a minor for students majoring in other departments. Within the statistics major the student choose to emphasize topics such as experimental design, probability, statistical methods, statistical theory, statistical computing, survey sampling, quality control, spatial statistics, time series, reliability, or applied statistics (e.g., bioinformatics, biometrics, econometrics, environmental statistics, psychometrics, sociometrics, etc.). A major in operations research leading to a master of science degree is offered in cooperation with the Department of Industrial and Manufacturing Systems Engineering. The doctor of philosophy degree is offered as a co-major with other graduate programs. Such programs have included graduate majors in Agronomy, Animal Ecology, Animal Science, Bioinformatics, Chemical and Biological Engineering, Computer Science, Electrical Engineering, Ecology, Evolution and Organismal Biology (EEOB), Economics, Educational Leadership and Policy Studies, Food Science and Human Nutrition, Genetics, Development and Cell Biology (GDCB), Industrial and Manufacturing Systems Engineering, Mathematics, Meteorology, Psychology and Sociology.

M.S. graduates have a basic understanding of statistical theory and methods. Elective courses in statistics provide the opportunity for the student to emphasize particular areas within the field of statistics, based on interest and future career goals. Communication skills are developed through course projects, assistantship duties and creative components. Ph.D. graduates study advanced theory and methods and are able to do independent research in statistics and collaborative research outside of statistics.

Prerequisite to major graduate work is the completion of an undergraduate curriculum essentially equivalent to the curriculum in liberal arts and sciences at this institution including at least a year of calculus.

The degree master of science may be earned on either a thesis or nonthesis basis. The nonthesis option requires the completion of at least 34 credits of acceptable graduate work, including the completion of a creative component and satisfactory performance on a written examination. The thesis option requires the completion of 30 credits of acceptable graduate work, including the completion of a thesis and satisfactory performance on a written examination. Ph.D. candidates must complete at least 72 semester credit hours (half or more from Iowa State) with a minimum 3.0 (B) average and submit an original thesis representing a substantial contribution to statistics as a science. The department encourages students to prepare themselves in foreign languages and in computer languages, but specific requirements for the degrees master of science and doctor of philosophy are at the discretion of the student's advisory committee.

The department participates in the interdepartmental programs in bioinformatics and computational biology, ecology and evolutionary biology, forensic research, genetics, human computer interaction, and nutrition.

Courses open for nonmajor graduate credit: 330, 361, 401, 402, 404, 406, 407, 415, 416, 421, 430, 432, 447, 451, 479, 480, 493, 495, 496, 528.

** Stat 401. Statistical Methods for Research Workers. ** (3-2) Cr. 4. F.S.SS. * Prereq: 101 or 104 or 105 or 226. * Graduate students without an equivalent course should contact the department. Methods of analyzing and interpreting experimental and survey data. Statistical concepts and models; estimation; hypothesis tests with continuous and discrete data; simple and multiple linear regression and correlation; introduction to analysis of variance and blocking. Nonmajor graduate credit.

** Stat 401I. Statistical Methods for Field Biologists. ** (Cross-listed with Ia LL). Cr. 4. Alt. SS., offered 2008. Introduction to the design and implementation of ecological and environmental field studies and statistical analyses, interpretation, and presentation of field data. Fundamentals of experimental design; hypotheses testing with continuous and discrete data; simple and multilinear regression and correlation; introduction of analysis of variance; and data presentation. Individual and/or group projects will be used to collect field data.

** Stat 402. Statistical Design and the Analysis of Experiments. ** (3-0) Cr. 3. F.S. * Prereq: 401. * The role of statistics in research and the principles of experimental design. Experimental units, randomization, replication, blocking, subdividing and repeatedly measuring experimental units; factorial treatment designs and confounding; extensions of the analysis of variance to cover general crossed and nested classifications and models that include both classificatory and continuous factors. Determining sample size. Nonmajor graduate credit.

** Stat 404. Regression for Social and Behavioral Research. ** (2-2) Cr. 3. F. * Prereq: 401. * Lorenz, Roberts. Applications of generalized linear regression models to social science data. Assumptions of regression; diagnostics and transformations; analysis of variance and covariance; path analysis. Nonmajor graduate credit.

** Stat 406. Statistical Methods for Spatial Data. ** (3-0) Cr. 3. Alt. S., offered 2008. * Prereq: Six hours of statistics at the 400-level. * The analysis of spatial data; geostatistical methods and spatial prediction; discrete index random fields and Markov random field models; models for spatial point processes. Emphasis on application and practical use of spatial statistical analysis. Nonmajor graduate credit.

** Stat 407. Methods of Multivariate Analysis. ** (2-2) Cr. 3. F. * Prereq: 401, knowledge of matrix algebra. * Carriquiry, Cook. Techniques for analyzing multivariate data including comparing group mean vectors using Hotelling's T2, multivariate analysis of variance, reducing variable dimension with principal components, grouping/classifying observations with cluster analysis and discriminant analysis. Imputation of missing multivariate observations. Nonmajor graduate credit.

** Stat 415. Advanced Statistical Methods for Research Workers. ** (2-2) Cr. 3. Alt. S., offered 2008. * Prereq: 401. * Advanced statistical methods using modern computer methods for modeling and analyzing data. Examples from a wide variety of scientific and engineering disciplines. Nonmajor graduate credit.

** Stat 416. Statistical Design and Analysis of Microarray Experiments. ** (3-0) Cr. 3. S. * Prereq: Stat 401. * Introduction to two-color microarray technology including cDNA and oligo microarrays; introduction to single-channel platforms (Affymetrix GeneChips); the role of blocking, randomization, and biological and technical replication in microarray experiments; design of single-channel and two-color microarray experiments with factorial treatment structure; normalization methods; methods for identifying differentially expressed genes including mixed linear model analyses, empirical Bayes analyses, and resampling based approaches; adjustments for multiple testing; clustering and classification problems for microarray data; emphasis on practical use of methods. Nonmajor graduate credit.

** Stat 421. Survey Sampling Techniques. ** (2-2) Cr. 3. S. * Prereq: 231 or 328 or 401. * Concepts of sample surveys and the survey process; methods of designing sample surveys, including: simple random, stratified, and multistage sampling designs; methods of analyzing sample surveys including ratio, regression, domain estimation and nonresponse. Nonmajor graduate credit.

** Stat 430. Empirical Methods for Computer Science. ** (3-0) Cr. 3. S. * Prereq: Stat 330 or an equivalent course. * Programs and systems as objects of empirical studies; exploratory data analysis; analysis of designed experiments - analysis of variance, hypothesis testing, interaction among variables; linear regression, logistic regression, Poisson regression; parameter estimation, prediction, confidence regions, dimension reduction techniques, model diagnostics and sensitivity analysis; simulation techniques and bootstrap methods; applications to performance assessment - comparison of multiple systems; communicating results of empirical studies. Nonmajor graduate credit.

** Stat 432. Applied Probability Models. ** (3-0) Cr. 3. F. * Prereq: 231 or 341 or 447. * Probabilistic models in biological, engineering and the physical sciences. Markov chains; Poisson, birth-and-death, renewal, branching and queing processes; applications to bioinformatics and other quantitative problems. Nonmajor graduate credit.

** Stat 447. Statistical Theory for Research Workers. ** (4-0) Cr. 4. F.S.SS. * Prereq: Math 151 and permission of instructor, or Math 265. * Primarily for graduate students not majoring in statistics. Emphasis on aspects of the theory underlying statistical methods. Probability, probability density and mass functions, distribution functions, moment generating functions, sampling distributions, point and interval estimation, maximum likelihood and likelihood ratio tests, introduction to posterior distributions and Bayesian analysis. Credit for both Stat 341 and 447 may not be applied toward graduation. Nonmajor graduate credit.

** Stat 451. Applied Time Series. ** (3-0) Cr. 3. S. * Prereq: 231 or 328 or 401. * Meeker. Methods for analyzing data collected over time; review of multiple regression analysis. Elementary forecasting methods: moving averages and exponential smoothing. Autoregressive-moving average (Box-Jenkins) models: identification, estimation, diagnostic checking, and forecasting. Transfer function models and intervention analysis. Nonmajor graduate credit.

** Stat 457. Applied Categorical Data Analysis. ** (3-0) Cr. 3. Alt. S., offered 2009. * Prereq: Stat 401 (or equivalent). * Statistical methods for the analysis of categorical data: estimation of proportions, chi-square tests, sample size determination, measures of association and relative risk, measures of agreement, logistic regression, Poisson regression and log-linear models, matched-pair and repeated measures designs, conditional inference. Applications to social, behavioral, and health sciences. Nonmajor graduate credit.

** Stat 479. Computer Processing of Statistical Data. ** (3-0) Cr. 3. F. * Prereq: 401. * Marasinghe. Structure, content and programming aspects of the Statistical Analysis System (SAS) software package. Advanced techniques in the use of SAS for data analysis including statistical graphics, regression diagnostics, and complex analysis of variance models. If time permits, the SAS macro programming language will be introduced. Nonmajor graduate credit.

** Stat 480. Statistical Computing Applications. ** (3-0) Cr. 3. S. * Prereq: 231 or 328 or 401. * Modern statistical computing. Data management; spread sheets, verifying data accuracy, transferring data between systems. Data and graphical analysis with microcomputer statistical software packages. Macro programming. Algorithmic programming concepts and applications. Simulation. Software reliability. Nonmajor graduate credit.

** Stat 490. Independent Study. ** Cr. arr. Repeatable. * Prereq: 10 credits in statistics. * No more than 9 credits in Stat 490 may be counted toward graduation.

H. Honors.

** Stat 493. Workshop in Statistics. ** (2-0) Cr. 2. Alt. S., offered 2009. * Prereq: 101 or 104 or 226. * Off-Campus only. Introduction to methods for analyzing data from surveys and experiments. Summarizing data, analysis of data from simple random samples and more complex survey designs, experimental design, estimation and hypothesis testing for data from simple experiments, good and bad graphical presentations of results. Designed for master of agriculture program only. Nonmajor graduate credit.

** Stat 495. Applied Statistics for Industry I. ** (3-0) Cr. 3. Alt. F., offered 2008. * Prereq: 101 or 104 or 105 or 226; Math 166 (or 166H). * Graduate students without an equivalent course should consult the department. Statistical thinking applied to industrial processes. Assessing, monitoring and improving processes using statistical methods. Analytic/enumerative studies; graphical displays of data; process monitoring; control charts; capability analysis. Nonmajor graduate credit.

** Stat 496. Applied Statistics for Industry II. ** (3-0) Cr. 3. Alt. S., offered 2009. * Prereq: 495. * Statistical design and analysis of industrial experiments. Concepts of control, randomization and replication. Simple and multiple regression; factorial and fractional factorial experiments; reliability; analysis of lifetime data. Nonmajor graduate credit.

**Courses primarily for graduate students, open to qualified undergraduate students**

** Stat 500. Statistical Methods. ** (3-2) Cr. 4. F. * Prereq: 101. * Introduction to methods for analyzing data from experiments and observational data. Design-based and model-based inference. Estimation, hypothesis testing, and model assessment for 2 group and k group studies. Experimental design and the use of pairing/blocking. Analysis of discrete data. Correlation and regression, prediction, model selection and diagnostics. Simple mixed models including nested random effects and split plot experimental designs. Use of the SAS statistical software.

** Stat 501. Multivariate Statistical Methods. ** (3-0) Cr. 3. S. * Prereq: 500 or 402; 447 or 542; knowledge of matrix algebra. * Statistical methods for analyzing and displaying multivariate data: simultaneous analysis of multiple responses, multivariate analysis of variance; summarizing high dimensional data with principal components, factor analysis, canonical correlations, multidimensional scaling; grouping similar items with cluster analysis; classification methods; dynamic graphics. Statistical software: SAS, S-Plus or R, and GGobi.

** Stat 503. Exploratory Methods and Data Mining. ** (2-2) Cr. 3. Alt. S., offered 2009. * Prereq: 401, 341 or 447. * Approaches to finding the unexpected in data; pattern recognition, classification, association rules, graphical methods, classical and computer-intensive statistical techniques, and problem solving. Emphasis is on data-centered, non-inferential statistics for large or high-dimensional data, topical problems, and building report writing skills.

** Stat 505. Environmental Statistics. ** (3-0) Cr. 3. Alt. S., offered 2008. * Prereq: 341 or 447; 401. * Statistical methods and theory for environmental data and questions. Topics include below detection limit data, trend analysis, assessment of toxicity, environmental risk assessment, safety determination, and setting environmental standards. Use of parametric and non-parametric methods.

** Stat 506. Statistical Methods for Spatial Data. ** (3-0) Cr. 3. Alt. S., offered 2009. * Prereq: 447 or 542. * The analysis of spatial data; geostatistical methods and spatial prediction; discrete index random fields and Markkov random field models; models for spatial point processes.

** Stat 511. Statistical Methods. ** (3-0) Cr. 3. S. * Prereq: 500 or 402 or 404; 447 or 542 and current enrollment in 543; knowledge of matrix algebra. * Introduction to the general theory of linear models, least squares and maximum likelihood estimation, hypothesis testing, interval estimation and prediction, analysis of unbalanced designs. Models with both fixed and random factors. Introduction to non-linear and generalized linear models, bootstrap estimation, local smoothing methods. Requires use of R statistical software.

** Stat 512. Design of Experiments. ** (3-0) Cr. 3. F. * Prereq: 511. * Basic ideas of experimental design and analysis; completely randomized, randomized complete block, and Latin Square designs; randomization analysis; factorial experiments, confounding, fractional replication; split-plot and incomplete block designs; crossover designs.

** Stat 513. Response Surface Methodology. ** (3-0) Cr. 3. Alt. S., offered 2009. * Prereq: 402 or 512, knowledge of elementary matrix theory and matrix formulation of regression. * Morris. Analysis techniques for locating optimum and near-optimum operating conditions: standard experimental designs for first- and second-order response surface models; design performance criteria; use of data transformations; mixture experiments; optimization for multiple-response problems. Requires use of statistical software with matrix functions.

** Stat 515. Theory and Applications of Nonlinear Models. ** (3-0) Cr. 3. Alt. F., offered 2008. * Prereq: 447 or 543, 511. * Construction of nonlinear statistical models; random and systematic model components, additive error nonlinear regression with constant and non-constant error variances, generalized linear models, transform both sides models. Iterative algorithms for estimation and asymptotic inference. Basic random parameter models, beta-binomial and gamma-Poisson mixtures. Requires use of instructor-supplied and student-written R functions.

** Stat 516. Statistical Design and Analysis of Microarray Experiments. ** (3-0) Cr. 3. S. * Prereq: Stat 500; 447 or 542. * Introduction to two-color microarray technology including cDNA and oligo microarrays; introduction to single-channel platforms (Affymetrix GeneChips); the role of blocking, randomization, and biological and technical replication in microarray experiments; design of single-channel and two-color microarray experiments with factorial treatment structure; normalization methods; methods for identifying differentially expressed genes including mixed linear model analyses, empirical Bayes analyses, and resampling based approaches; adjustments for multiple testing; clustering and classification problems for microarray data; emphasis on current research topics in microarray statistics. Nonmajor graduate credit.

** Stat 521. Theory and Applications of Sample Surveys. ** (3-0) Cr. 3. S. * Prereq: 401; 447 or 542. * Practical aspects and basic theory of design and estimation in sample surveys for finite populations. Simple random, systematic, stratified, cluster multistage and unequal-probability sampling. Horvitz-Thompson estimation of totals and functions of totals: means, proportions, regression coefficients. Linearization technique for variance estimation. Model-assisted ratio and regression estimation. Two-phase sampling and sampling on two occasions. Non-response effects. Imputation.

** Stat 522. Advanced Applied Survey Sampling. ** (3-0) Cr. 3. Alt. F., offered 2007. * Prereq: Stat 521 or both Stat 421 and Stat 477. * Advanced topics in survey sampling and methodology: clustering and stratification in practice, adjustments and imputation for missing data, variance estimation in complex surveys, methods of panel and/or longitudinal surveys, procedures to increase response rates, and computing. Examples are taken from large, well-known surveys in various subject areas. Prior exposure to mathematical statistics, probability, and at least one course in survey sampling theory is assumed.

** Stat 528. Applied Business Statistics. ** (2-2) Cr. 3. F.SS. * Prereq: 226 and enrollment in MBA, not for STAT majors. * Application of statistical methods to problems in business and economics; review of multiple regression; residual analysis; model building; analysis of variance; introduction to experimental design concepts; time series analysis and forecasting. Nonmajor graduate credit.

** Stat 531. Quality Control and Engineering Statistics. ** (Cross-listed with I E). (3-0) Cr. 3. Alt. S., offered 2009. * Prereq: Stat 401; 342 or 447. * Wu. Statistical methods and theory applicable to problems of industrial process monitoring and improvement. Statistical issues in industrial measurement; Shewhart, CUSUM, and other control charts; feedback control; process characterization studies; estimation of product and process characteristics; acceptance sampling, continuous sampling and sequential sampling; economic and decision theoretic arguments in industrial statistics.

** Stat 533. Reliability. ** (Cross-listed with I E). (3-0) Cr. 3. Alt. S., offered 2008. * Prereq: 342 or 432 or 447. * Meeker. Probabilistic modeling and inference in reliability; analysis of systems; Bayesian aspects; product limit estimator, probability plotting, maximum likelihood estimation for censored data, accelerated failure time and proportional hazards regression models with applications to accelerated life testing; repairable system data; planning studies to obtain reliability data.

** Stat 534. Ecological Statistics. ** (3-0) Cr. 3. Alt. F., offered 2007. * Prereq: 447 or 542. * Dixon. Statistical methods for non-standard problems, illustrated using questions and data from ecological field studies. Specific topics include: Estimation of abundance and survival from mark-recapture studies. Deterministic and stochastic matrix models of population trends. Estimation of species richness and diversity. Ordination and analysis of complex multivariate data. Statistical methods discussed will include randomization and permutation tests, spatial point processes, bootstrap estimation of standard error, partial likelihood and Empirical Bayes methods.

** Stat 536. Statistics for Population Genetics. ** (Cross-listed with GDCB). (3-0) Cr. 3. Alt. F., offered 2008. * Prereq: 401, 447; Gen 320 or Biol 313. * Statistical models for population genetics covering: selection, mutation, migration, population structure, and linkage disequilibrium. Applications to gene mapping (case-control, TDT), inference about population structure, DNA and protein sequence analysis, and forensic and paternity identification.

** Stat 537. Statistics for Molecular Genetics. ** (Cross-listed with GDCB). (3-0) Cr. 3. Alt. S., offered 2009. * Prereq: 401, 447; Gen 320 or Biol 313. * Statistical models, inference, and computational tools for linkage analysis, quantitative trait analysis, and molecular evolution. Topics include; quantitative trait models, variance component mapping, interval and composite-interval mapping, and phylogenetic tree reconstruction.

** Stat 542. Theory of Probability and Statistics I. ** (4-0) Cr. 4. F. * Prereq: 341; Math 414 or 465. * Sample spaces, probability, conditional probability; Random variables, univariate distributions, expectation, moment generating functions; Common theoretical distributions; Joint distributions, conditional distributions and independence, covariance; Probability laws and transformations; Introduction to the Multivariate Normal distribution; Sampling distributions, order statistics; Convergence concepts, the central limit theorem and delta method; Basics of stochastic simulation.

** Stat 543. Theory of Probability and Statistics II. ** (3-0) Cr. 3. S. * Prereq: 542. * Point estimation including method of moments, maximum likelihood estimation, exponential family, Bayes estimators, Loss function and Bayesian optimality, unbiasedness, sufficiency, completeness, Basu s theorem; Interval estimation including conficence intervals, prediction intervals, Bayesian interval estimation; Hypothesis testing including Neyman-Pearson Lemma, uniformly most powerful tests, likelihood ratio tests; Bayesian tests; Nonparametric methods, bootstrap.

** Stat 544. Bayesian Statistics. ** (3-0) Cr. 3. S. * Prereq: 543. * Specification of probability models; subjective, conjugate, and noninformative prior distributions; hierarchical models; analytical and computational techniques for obtaining posterior distributions; model checking, model selection, diagnostics; comparison of Bayesian and traditional methods.

** Stat 546. Nonparametric Methods in Statistics. ** (3-0) Cr. 3. Alt. S., offered 2008. * Prereq: 511, 542. * Chen, Opsomer. Overview of parametric versus nonparametric methods of inference; introduction to nonparametric smoothing methods for estimating density and regression functions; smoothing parameter selection; applications to semiparametric models and goodness-of-fit tests of a parametric model.

** Stat 551. Time Series Analysis. ** (3-0) Cr. 3. F. * Prereq: 447 or 542. * Concepts of trend and dependence in time series data; stationarity and basic model structures for dealing with temporal dependence; moving average and autoregressive error structures; analysis in the time domain and the frequency domain; parameter estimation, prediction and forecasting; identification of appropriate model structure for actual data and model assessment techniques. Possible extended topics include dynamic models and linear filters.

** Stat 554. Introduction to Stochastic Processes. ** (Cross-listed with MAth). Cr. 3. F. * Prereq: Stat 542. * Markov chains on discrete spaces in discrete and continuous time (random walks, Poisson processes, birth and death processes) and their long-term behavior. Optional topics may include branching processes, renewal theory, introduction to Brownian motion.

** Stat 557. Statistical Methods for Counts and Proportions. ** (3-0) Cr. 3. F. * Prereq: 500 or 401; 543 or 447. * Statistical methods for analyzing simple random samples when outcomes are counts or proportions; measures of association and relative risk, chi-squared tests, loglinear models, logistic regression and other generalized linear models, tree-based methods. Extensions to longitudinal studies and complex designs, models with fixed and random effects. Use of statistical software: SAS, S-Plus or R.

** Stat 565. Methods in Biostatistics. ** (Cross-listed with Tox). (3-0) Cr. 3. Alt. F., offered 2007. * Prereq: Stat 500 or 401; Stat 543 or 447. * Statistical methods useful for biostatistical problems. Topics include analysis of cohort studies, case-control studies and randomized clinical trials, techniques in the analysis of survival data and longitudinal studies, approaches to handling missing data, and meta-analysis. Examples will come from recent studies in cancer, AIDS, heart disease, psychiatry and other human and animal health studies. Use of statistical software: SAS, S-Plus or R.

** Stat 566. Survival Analysis for Biomedical Applications. ** (3-0) Cr. 3. Alt. F., offered 2008. * Prereq: Stat 543 and Stat 511. * Statistical methods for analyzing time to event and survival data. Estimation of survivor and hazard functions, proportional hazards models, diagnostic procedures, time dependent covariates. Extensions to cases with multiple or correlated end points. Applications to medical studies involving cancer treatments, liver and bladder diseases, auto-immune disorders, bone fractures, surgery mortality rates. Implementation of SAS, S Plus, and R. Nonmajor graduate credit.

** Stat 568. Bioinformatics II (Advanced Genome Informatics). ** (Cross-listed with BCB, GDCB, Com S). (3-0) Cr. 3. S. * Prereq: BCB 567, BBMB 301, Biol 315, Stat 401, Stat 432, credit or enrollment in Gen 411. * Advanced sequence models. Basic methods in molecular phylogeny. Hidden Markov models. Genome annotation. DNA and protein motifs. Introduction to gene expression analysis.

** Stat 570. Bioinformatics IV (Computational Functional Genomics and Systems Biology). ** (Cross-listed with BCB, GDCB, Com S, Cpr E). (3-0) Cr. 3. S. * Prereq: BCB 567, Biol 315, Com S 363, Gen 411, Stat 401, Stat 432. * Algorithmic and statistical approaches in computational functional genomics and systems biology. Biological Information Integration - knowledge (ontology) driven and statistical approaches. Qualitative, probabilistic, and dynamic network models. Modeling, analysis, simulation and inference of transcriptional regulatory modules and networks, protein-protein interaction networks. Metabolic networks; cells and systems.

** Stat 579. Introduction to Statistical Computing. ** (0-2) Cr. 1. F. * Prereq: Enrollment in 500. * An introduction to the logic of programming, numerical algorithms, and graphics. The R statistical programming environment will be used to demonstrate how data can be stored, manipulated, plotted, and analyzed using both built-in functions and user extensions. Concepts of modularization, looping, vectorization, conditional execution, and function construction will be emphasized.

** Stat 580. Statistical Computing. ** (3-0) Cr. 3. S. * Prereq: 579 and 447 or 542. * Introduction to scientific computing for statistics using tools and concepts in R: programming tools, modern programming methodologies, modularization, design of statistical algorithms. Introduction to C programming for efficiency; interfacing R with C. Building statistical libraries. Use of algorithms in modern subroutine packages, optimization and integration. Implementation of simulation methods; inversion of probability integral transform, rejection sampling, importance sampling. Monte Carlo integration.

** Stat 590. Special Topics. ** Cr. arr. Repeatable.

A. Theory

B. Methods

C. Design of Experiments

D. Sample Surveys

** Stat 598. Cooperative Education. ** Cr. R. F.S.SS. * Prereq: Permission of the department chair. * Off-campus work periods for graduate students in a field of statistics.

** Stat 599. Creative Component. ** Cr. arr.

**Courses for graduate students**

** Stat 601. Advanced Statistical Methods. ** (3-2) Cr. 4. F. * Prereq: 511, 543. * Emphasis on the approaches statisticians take toward the statistical formulation of scientific problems. Students should develop an understanding of the way that various concepts of probability are used in problem formulation, analysis, and inference, and the ability to develop one or more appropriate analyses for a variety of problems. Specific methodological topics include permutation procedures and design-based analysis; model building with single and multiple stochastic components; estimation based on least-squares, likelihood functions, modified likelihood functions, sample reuse, and Bayesian analysis; inference in the sample space, parameter space, and belief space. Development of various analyses for real problems, including statistical formulation and necessary computations.

** Stat 606. Advanced Spatial Statistics. ** (3-0) Cr. 3. Alt. S., offered 2009. * Prereq: 506, 642. * Consideration of advanced topics in spatial statistics, including areas of current research. Topics may include construction of nonstationary covariance structures including intrinsic random functions, examination of edge effects, general formulation of Markov random field models, spatial subsampling, use of pseudo-likelihood and empirical likelihood concepts in spatial analysis, the applicability of asymptotic frameworks for inference, and a discussion of appropriate measures for point processes.

** Stat 611. Theory and Applications of Linear Models. ** (3-0) Cr. 3. F. * Prereq: 500 or 402 or 404, 542 or 447, a course in matrix algebra. * Wu. Matrix preliminaries, estimability, theory of least squares and of best linear unbiased estimation, analysis of variance and covariance, distribution of quadratic forms, extension of theory to mixed and random models, inference for variance components.

** Stat 612. Advanced Design of Experiments. ** (3-0) Cr. 3. Alt. S., offered 2008. * Prereq: 512. * Design optimality criteria, approximate design and general equivalence theory, computational approaches to constructing optimal designs for linear models. Advanced topics of current interest in the design of experiments, including one or more of: distance based design criteria and construction of spatial process models, screening design strategies for high-dimensional problems, and design problems associated with computational experiments.

** Stat 615. Nonlinear Mixed Models: Theory, Methods and Applications. ** (3-0) Cr. 3. Alt. S., offered 2008. * Prereq: Stat 601 and Stat 611. * The linear mixed effects (LME) model, the generalized linear mixed effects model (GLMM), quasilikelihood estimation, generalized estimating equations, nonlinear mixed effects (NLME) model, application in longitudinal data analysis, growth curve analysis and small area estimation, method of model diagnostics and influential analysis. The knowledge of general statistical inference is assumed.

** Stat 621. Advanced Theory of Survey Statistics. ** (3-0) Cr. 3. Alt. F., offered 2008. * Prereq: 521. * Advanced topics of current interest in the design of surveys and analysis of survey data, including: asymptotic theory for design and model-based estimators, use of auxiliary information in estimation, variance estimation techniques, small area estimation, non-response modeling and imputation.

** Stat 642. Advanced Probability Theory. ** (Cross-listed with MAth). (4-0) Cr. 4. F. * Prereq: 542. * Measure spaces, extension theorem and construction of Lebesgue-Stieljes measures on Euclidean spaces, Lebesgue integration and the basic convergence theorems, Lp-spaces, absolute continuity of measures and the Radon Nikodym theorem, absolute continuity of functions on R and the fundamental theorem of Lebesgue integration, product spaces and Fubini-Tonelli Theorems, convolutions. Fourier series and transforms, probability spaces; Kolmogorov's existence theorem for stochastic processes; expectation; Jensen's inequality and applications, independence, Borel-Cantelli lemmas; weak and strong laws of large numbers and applications, renewal theory.

** Stat 643. Advanced Theory of Statistical Inference. ** (4-0) Cr. 4. S. * Prereq: 543, 642. * Weak convergence of probability distributions; characteristic functions; continuity theorem; Lindberg-Feller central limit theorem and its ramifications; conditional expectation and probability; sufficiency, completeness; Elements of decision theory; Neyman-Pearson theory of testing hypotheses. Uniformly most powerful tests, introduction to unbiased tests, likelihood ratio tests, Asymptotic theory of maximum likelihood estimation and likelihood ratio tests; Invariance.

** Stat 645. Advanced Stochastic Processes. ** (Cross-listed with MAth). (3-0) Cr. 3. S. * Prereq: Permission of instructor. * Weak convergence. Random walks and Brownian motion. Martingales. Stochastic integration and Ito's Formula. Stochastic differential equations and applications.

** Stat 647. Multivariate Analysis. ** (3-0) Cr. 3. Alt. F., offered 2008. * Prereq: 543, knowledge of matrix algebra. * Multivariate normal distribution, estimation of the mean vector and the covariance matrix, multiple and partial correlation, Hotelling's T2 statistic, Wishart distribution, multivariate regression, principle components, discriminant analysis, high dimensional data analysis, latent variables.

** Stat 648. Seminar on Theory of Statistics and Probability. ** Cr. arr. F. * Prereq: 643. *

** Stat 651. Time Series. ** (3-0) Cr. 3. Alt. S., offered 2008. * Prereq: 551, 642. * Stationary and nonstationary time series models, including ARMA, ARCH, and GARCH. Covariance and spectral representation of time series. Fourier and periodogram analyses. Predictions. CLT for mixing processes. Estimation and distribution theory. Long range dependence.

** Stat 680. Statistical Computing - II. ** (3-0) Cr. 3. F. * Prereq: 543 and 580. * Normal approximations to likelihoods. The delta-method and propagation of errors. Topics in the use of the E-M algorithm including; its use in the exponential family, computation of standard errors, acceleration. Resampling methods: brief theory and application of the jackknife and the bootstrap. Randomization tests. Stochastic simulation: Markov Chain, Monte Carlo, Gibbs' sampling, Hastings-Metropolis algorithms, critical slowing-down and remedies, auxiliary variables, simulated tempering, reversible-jump MCMC and multi-grid methods.

** Stat 690. Advanced Special Topics. ** Cr. arr. Repeatable. * Prereq: Permission of instructor. *

A. Theory

B. Methods

C. Design of Experiments

D. Sample Surveys

E. Statistical Computing

F. Graphics

** Stat 699. Research. ** Cr. arr. Repeatable.