Virginia Tech Statistics Department

Course Information

General Information

**For course enrollment problems please e-mail higgvt@vt.edu

Course Duplications | Computer Literacy | Course Projects

Courses Offered

Undergraduate Courses | Graduate Courses | Syllabi

Course Duplications:

No credit will be given for more than one course in each of the following groups (in parentheses) of partially duplicated courses: (3005, 3615, 4604). (3006, 3616, 4604, 4706). (4105, 4705, 4714, 4724). No credit will be given for: 2004 if taken with or after any other statistics course; 2404 if taken with or after any of 3104, 4105, 4705, 4714, 4724; 3604 if taken with or after any statistics course except 2004, 2404, 3104. MSci 2405 may not be used as a substitute for credit as a statistics course. Exceptions to this rule may be granted if the student was officially registered as a Business major at the time MSci 2405 was taken.

Computer Literacy:

Many statistics courses involve the use of statistical software; primarily MINITAB or SAS. Experience with the software is not expected, but students should have familiarity with either the Windows or MacIntosh operating system and access to a computer. These courses are specified by "WIN/MAC" under prerequisites.

Course Projects:

Many of the upper-division course descriptions below include the word "Project." Those courses will usually include a major term project, either individually or in small groups. These projects are designed to give students the kind of insight and experience in realistic statistical practice that cannot be obtained in classroom lectures or short-term homework assignments.

Undergraduate Statistics Courses (STAT)

Link to all undergraduate course syllabi

2004: INTRODUCTORY STATISTICS

Fundamental concepts and methods of statistics with emphasis on interpretation of statistical arguments. An introduction to design of experiments, data analysis, correlation and regression, concepts of probability theory, sampling errors, confidence intervals, and hypothesis tests. See also Course Duplications. Pre: MATH 1015. (4H,3C). I,II,III,IV.

2964: FIELD STUDY

Pass/fail only. Variable credit course.

3005-3006: STATISTICAL METHODS

3005:: Basic statistical methodology: exploratory data techniques, estimation, inference, comparative analysis by parametric, nonparametric, and robust procedures. Analysis of variance (one-way), multiple comparisons, and categorical data.

3006: Analysis of variance, simple and multiple, linear and nonlinear regression, analysis of covariance. Use of MINITAB.
Pre: MATH 1206 and WIN/MAC. (3H,3C). 3005: I,II,III; 3006: I,II,IV.

3094: INTRODUCTION TO PROGRAMMING IN SAS

Introduction to basic programming techniques: creating DATA and PROC statements, libraries, functions, programming syntax, and formats. Other topics include loops, SAS Macros, and PROC IML. Emphasis is placed on using these tools for statistical analyses. Pre: STAT 3005 or equivalent. (3H, 3C)

3104: PROBABILITY AND DISTRIBUTION

Probability theory, including set theoretic and combinatorial concepts; in-depth treatment of discrete random variables and distributions, with some introduction to continuous random variables; introduction to estimation and hypothesis testing.

3504: NONPARAMETRIC STATISTICS

Statistical methodology based on ranks, empirical distributions, and runs. One and two sample tests, ANOVA, correlation, goodness of fit, and rank regression, R-estimates and confidence intervals. Comparisons with classical parametric methods. Emphasis on assumptions and interpretation. Pre: WIN/MAC and one of 3006, 4106, 4604, 4706. (3H,3C). I. Even years.

3604: STATISTICS FOR THE SOCIAL SCIENCES

Statistical methods for nominal, ordinal, and interval levels of measurement. Topics include descriptive statistics, elements of probability, discrete and continuous distributions, one and two sample tests, measures of association. Emphasis on comparison of methods and interpretations at different measurement levels. See also Course Duplications. Pre: MATH 1015. (3H,3C). I,II,IV.

3615-3616: BIOLOGICAL STATISTICS

Descriptive and inferential statistics in a biological context.
3615: Fundamental principles, one- and two-sample parametric inference, simple linear regression, frequency data.
3616: One- and two-way ANOVA, multiple regression, correlation, nonparametrics, using the MINITAB computer package.
Pre: WIN/MAC for 3616. (3H,3C). 3615: I,II,III; 3616: II, IV.

3704: STATISTICS FOR ENGINEERING APPLICATIONS

Introduction to statistical methodology with emphasis on engineering experimentation: probability distributions, estimation, hypothesis testing, regression, and analysis of variance. Only one of the courses 3704, 4604, 4705, and 4714 may be taken for credit. Pre: MATH 2224. (2H,2C) I,II.

4004: METHODS OF STATISTICAL COMPUTING

The objective of this course is to develop a fundamental understanding on simulation-based statistical computing method and the necessary programming skills. The course will cover following major components: 1) The programming and statistical analyses using the R statistical programming language; 2) generation of random variable including inverse transformation method, acceptance-rejection method, and transformation method; 3) Monte Carlo method in statistics computing; 4) bootstrap method and permutation test. Pre: 4105, 4214. (4H,3C)

4024: COMMUNICATION IN STATISTICAL COLLABORATIONS

Theory and examples of effective communication in the context of statistical collaborations. Practice developing the communication skills necessary to be effective statisticians using peer feedback and self-reflection. Topics include helping scientists answer their research questions, writing about and presenting statistical concepts to a non-statistical audience, and managing an effective statistical collaboration meeting. Pre: Senior standing in the Department of Statistics, 4105, 4204; Co: 4214 (3H, 3C).

4105-4106: THEORETICAL STATISTICS

4105: Probability theory, counting techniques, conditional probability; random variables, moments; moment generating functions; multivariate distributions; transformations of random variables; order statistics.
4106: Convergence of sequences of random variables; central limit theorem; methods of estimation; hypothesis testing; linear models; analysis of variance.
Pre: MATH 2224. (3H,3C). 4105: I; 4106: II.

4204: EXPERIMENTAL DESIGNS

Fundamental principles of designing and analyzing experiments with application to problems in various subject matter areas. Discussion of completely randomized, randomized complete block, and latin square designs, analysis of covariance, split--plot designs, factorial and fractional designs, incomplete block designs. Project. Pre: WIN/MAC and one of 3006, 3616, 4106, 4706, 5605, 5615. (3H,3C). I.

4214: METHODS OF REGRESSION ANALYSIS

Multiple regression including variable selection procedures; detection and effects of multicollinearity; identification and effects of influential observations; residual analysis; use of transformations. Non-linear regression, the use of indicator variables, and logistic regression. Use of SAS. Project. Pre: WIN/MAC and one of 3006, 3616, 4106, 4706, 5606 or 5616. (3H,3C). I.

4444: APPLIED BAYESIAN STATISTICS

Introduction to Bayesian methodology with emphasis on applied statistical problems: data displaying, prior distribution elicitation, posterior analysis, models for proportions, means and regression. Pre: MATH 2224. (3H,3C)

4504: APPLIED MULTIVARIATE ANALYSIS

Non-mathematical study of multivariate analysis. Multivariate analogs of univariate test and estimation procedures. Simultaneous inference procedures. Multivariate analysis of variance, repeated measures, inference for dispersion and association parameters, principal components analysis, discriminant analysis, cluster analysis. Use of SAS. Project. Pre: WIN/MAC and one of 3006, 4706, 5606, 5616. (3H,3C). Even years.

4514: CONTINGENCY TABLE ANALYSIS

Statistical techniques for frequency data. Goodness-of-fit. Tests and measures of association for two-way tables. Log-linear models for multidimensional tables. Parameter estimation, model selection, incomplete tables, ordinal categories, logistic regression. Use of BMDP and SPSSx. Project. Pre: WIN/MAC and one of 3006, 3616, 4106, 4706, 5606, 5616. (3H,3C). II. Even years.

4524: SAMPLE SURVEY METHODS

Statistical methods for the design and analysis of survey sampling. Fundamental survey designs. Methods of randomization specific to various survey designs. Estimation of population means, proportions, totals, variances, and mean squared errors. Design of questionnaires and organization of a survey. Project. Pre: One of 3006, 3616, 4106, 4706, 5606, 5616. (3H,3C). I. Odd years.

4534: APPLIED STATISTICAL TIME SERIES ANALYSIS

An applied course in time series analysis. A uniform coverage of both time domain and frequency domain methods that are used in the physical, biological, and social sciences and by applied statisticians. Pre: One of 3006, 4106, 4706, 4714, 5606, 5616; WIN/MAC, Math 1206. (3H,3C). II. Odd years.

4604: STATISTICAL METHODS FOR ENGINEERS

Introduction to statistical methodology with emphasis on engineering applications: probability distributions, estimation, hypothesis testing, regression, analysis of variance, quality control. Only one of the courses 4604, 4705, and 4714 may be taken for credit. Pre: MATH 1206 and WIN/MAC. (3H,3C). I,II.

4705-4706: PROBABILITY AND STATISTICS FOR ENGINEERS

Basic concepts of probability and statistics with emphasis on engineering applications.
4705: Probability, random variables, distribution theory, sampling distributions, estimation, hypothesis testing.
4706: Hypothesis testing, simple and multiple regression, analysis of variance, factorial experiments. Only one of the courses 4604, 4705, and 4714 may be taken for credit.
Pre: MATH 2224 and WIN/MAC. (3H,3C). 4705: I,II,III; 4706: I,II.

4714: PROBABILITY AND STATISTICS FOR ELECTRICAL ENGINEERS

Introduction to the concepts of probability, random variables, estimation, hypothesis testing, regression, and analysis of variance with emphasis on application in electrical engineering. Only one of the courses 4604, 4705, and 4714 may be taken for credit. Pre: MATH 2224. (3H,3C). I,II,III.

4724: STATISTICAL THEORY FOR ECONOMISTS

Probability, random variables, marginal and conditional distributions, mathematical expectations, sampling distributions, properties of estimators, maximum likelihood and least squares estimation, confidence intervals, hypothesis tests, linear regression. Emphasis on preparation for graduate study in econometrics. Pre: 3006 and MATH 2016. (3H,3C). I.

4804 (AAEC 4804): ELEMENTARY ECONOMETRICS

Economic applications of mathematical and statistical techniques: regression, estimators, hypothesis testing, lagged variables, discrete variables, violations of assumptions, simultaneous equations. Pre: STAT 3005 or 3604 and AAEC 1006. (3H,3C). II.

4974: INDEPENDENT STUDY

Variable credit course.

4984: SPECIAL STUDY

Variable credit course.

4994: UNDERGRADUATE RESEARCH

Variable credit course.

Advanced Undergraduate Courses (Stat)

All 4000-level statistics courses may be taken for graduate credit by non-statistics majors. Statistics graduate students may not take 4000-level statistics courses for graduate credit.

4004: METHODS OF STATISTICAL COMPUTING
4105-4106: THEORETICAL STATISTICS
4204: EXPERIMENTAL DESIGNS
4214: METHODS OF REGRESSION ANALYSIS
4504: APPLIED MULTIVARIATE ANALYSIS
4514: CONTINGENCY TABLE ANALYSIS
4524: SAMPLE SURVEY METHODS
4604: STATISTICAL METHODS FOR ENGINEERS
4705-4706: PROBABILITY AND STATISTICS FOR ENGINEERS
4714: PROBABILITY AND STATISTICS FOR ELECTRICAL ENGINEERS
4724: STATISTICAL THEORY FOR ECONOMISTS
4804 (AGEC 4804): ELEMENTARY ECONOMETRICS

Link to all undergraduate course syllabi

Graduate Statistics Courses (STAT)

5014: Introduction to Statistical Program Packages
5024: Communication in Statistical Collaborations
5034: Inference Fundamentals with Applications to Categorical Data
5044: Regression and ANOVA
5104: Probability and Distribution Theory
5114: Statistical Inference
5124: Linear Models Theory
5204: Experimental Design and Analysis I
5204G: Advanced Experimental: Concepts and Applications
5214G: Advanced Regression Analysis
5304: Statistical Computing
5314: Statistical Simulation
5324: Statistical Methods for Analyzing Unbalanced Data
5334: Exploratory and Robust Data Analysis
5344: Linear and Nonlinear Programming
5354: Structured Process Improvement
5404: Nonparametric Statistics
5414: Time Series Analysis I
5424: Statistical Decision Theory
5434: Applied Stochastic Processes
5444: Bayesian Statistics
5454: Reliability Theory/Survival Analysis
5464 (ISE 5464): Queuing Theory
5474 (ISE 5474): Statistical Theory of Quality Control
5484: Sequential Analysis
5504: Multivariate Statistical Methods
5504G: Advanced Applied Multivariate Methods
5514: Topics in Regression
5524: Sample Survey Theory
5525 (CS 5525): Data Analytics I
5526 (CS 5526): Data Analytics II
5534: Analysis of Multivariate Categorical Data
5544: Spatial Statistics
5554: Variance Components
5564: Statistical Genetics
5574: Response Surface Design and Analysis I
5584 (AAEC 5584): Basic Topics in Econometrics
5594: Topics in Biostatistics
5605-5606: Biometry
5615-5616: Statistics in Research
5634: Hierarchical Modeling
5674: Methods in Biostatistics
5754: Internship in Statistics
5894: Final Examination
5904: Project and Report
5924: Graduate Seminar
5974: Independent Study
5984: Special Study
5994: Research and Thesis
6105: Measure and Probability
6114: Advanced Topics in Statistical Inference
6124: Stochastic Modeling and Inference
6404: Advanced Topics in Nonparametric Statistics
6414: Time Series Analysis II
6424: Multivariate Statistical Analysis
6434: Stationary and Related Processes
6464 (ISE 6464): Queuing Networks
6474: Advanced Topics in Bayesian Statistics
6484 (ISE 6484): Seminar in Applied Probability
6494: Advanced Topics in Mathematical Statistics
6504: Experimental Design and Analysis II
6514: Advanced Topics in Regression
6574: Response Surface Design and Analysis II
6584 (AAEC 6584): Advanced Topics in Econometrics
6634 (EDRE 6634): Advanced Statistics for Education
6644 (EDRE 6644): Advanced Research Design and Methodology
7994: Research and Dissertation

Link to all graduate course syllabi.

5014: INTRODUCTION TO STATISTICAL PROGRAM PACKAGES

Introduction to managing, plotting, and analyzing data in R and SAS. Pass/Fail only. Pre: Graduate standing in Department of Statistics. (1H,1C). I.

5024: COMMUNICATION IN STATISTICAL COLLABORATIONS

Developing the communication skills necessary to be effective interdisciplinary statistical collaborators. Topics include explaining and presenting statistical concepts to a non-statistical audience, learning how to help scientists answer their research questions, and managing an effective statistical collaboration meeting. Pre: Graduate standing in the Department of Statistics, 5014; Co: 5124, and 5204 (3H, 3C).

5034: INFERENCE FUNDAMENTALS WITH APPLICATIONS TO CATEGORICAL DATA

Fundamental ideas of statistical estimation and testing; principles and methods for standard one-sample and two-sample settings; applications to categorical data problems. Topics include point and interval estimation, including small and large sample procedures, the likelihood principle; hypothesis testing including exact and large-sample tests; nonparametric and resampling inference; one-way, two-way and multi-way analysis of variance. Pre: Graduate standing, Department of Statistics, MATH/STAT 4584. Co: 5014. (3H, 3C).

5044: REGRESSION AND ANALYSIS OF VARIANCE

Principles and methods of data analysis employing linear models for both continuous response variables and categorical variables. Topics include both classical descriptive measures and modern computer-based techniques for data visualization; simple, polynomial, multiple and weighted regression; Box-Cox transformation; model diagnosis; variable selection; categorical data analysis; the generalized linear model. Pre: Graduate standing, Department of Statistics, 5034, MATH/STAT 4584. Co: 5014. (3H,3C).

5104: PROBABILITY AND DISTRIBUTION THEORY

Fundamental concepts of probability, random variables and their distributions, functions of random variables, mathematical expectations, and stochastic convergence. Pre: MATH 4526; (3H,3C). I.

5114: STATISTICAL INFERENCE

Decision theoretic formulation of statistical inference, concept and methods of point and confidence set estimation, notion and theory of hypothesis testing, relation between confidence set estimation and hypothesis testing. Co: 5104. (3H,3C). II.

5124: LINEAR MODELS THEORY

A study of the theory underlying the general linear model and general linear hypothesis. Applications in linear regression (full rank) and analysis of variance. Pre: 5114 and MATH 5524; (3H,3C). II.

5204: EXPERIMENTAL DESIGN AND ANALYSIS I

Principles and concepts of experimental design; systematic overview and discussion of basic designs from the point of view of blocking, error reduction, and treatment structure; and development of analysis based on linear models. Includes designs with one or more blocking factors, split-plot designs, repeated measures, fractional factorials, orthogonal arrays. Pre: 5044, 5104; (3H,3C). II.

5204G: EXPERIMENTAL DESIGN: CONCEPTS AND APPLICATIONS

Fundamental principles of designing and analyzing experiments with application to problems in various subject matter areas. Discussion of completely randomized, randomized complete block, and Latin square designs, analysis of covariance, split--plot designs, factorial and fractional designs, incomplete block designs. Project. Pre: one of 3006, 3616, 4106, 4706, 5605, 5615. (3H,3C). I.

5214G: ADVANCED REGRESSION ANALYSIS

5304: STATISTICAL COMPUTING

A comprehensive course in simulation based sampling methodology designed to develop an understanding of computational methods for statistics. Topics covered include Monte Carlo integration, importance sampling, Markov chain Monte Carlo, particle methods, Kalman filtering. Both theoretical and applied aspects will be emphasized. Pre: 5034, 5044, (3H,3C).I. Alternate years.

5314: STATISTICAL SIMULATION

Special computer techniques used in statistical simulation. Pseudo-random number generators, stochastic simulation, variance reduction techniques, and Monte Carlo applications. Pre: 5114 and Knowledge of R; (3H,3C). II. Odd years.

5324: STATISTICAL METHODS FOR ANALYZING UNBALANCED DATA

Discussion of statistical and computational aspects of methods for analyzing nonorthogonal data: estimable functions, estimation and testing of effects, and variance components for fixed, mixed, and random effects linear models; interpretation of output from existing computer packages. Pre: 5124, 5204 (3H,3C). I. Alternate years.

5334: EXPLORATORY AND ROBUST DATA ANALYSIS

Analysis of data by graphical and numerical techniques, statistical analysis of non-Gaussian data, topics in robust estimation for location, regression and anova models via parametric and nonparametric methods, and Monte Carlo and bootstrap techniques. Pre: 5114, (3H,3C). I. Alternate years.

5344: LINEAR AND NONLINEAR PROGRAMMING

Mathematical formulation and solution of linear and nonlinear programming problems; simplex algorithms (Kuhn-Tucker conditions, duality theory); and discussion of various applications in statistics. Pre:5124; (3H,3C). I. Even years.

5354: STRUCTURED PROCESS IMPROVEMENT

An introduction to the selection, management, leadership and execution of structured process improvement projects. Topics include effective roadmaps for process improvement, team facilitation and leadership, project selection and management, sampling, process capability analysis, data transformations, variance component analysis, response surface methodology (including full and fractional factorial designs, Plackett-Burman designs, central composite designs, Box-Behnken designs, analysis of variance, regression, and multi-response optimization), and statistical process control. Pre: 5034,5044. (3H, 3C) I.

5404: NONPARAMETRIC STATISTICS

Introduction to theory and methods of nonparametric statistical inference. General linear rank statistics, tests and estimation of location, dispersion, regression, and association. Selected topics. Pre: 5034, 5044, 5114; (3H,3C). I. Odd years.

5414: TIME SERIES ANALYSIS I

Analysis of serially dependent data -, including stationary and nonstationary time series, Box-Jenkins modeling, trend elimination, prediction, unit root testing, intervention analysis, transfer function models, and applications in economics and engineering. Pre: 5114; (3H,3C). I. Alternate years.

5424: STATISTICAL DECISION THEORY

Decision theoretic approach to statistics including admissibility, minimax, and Bayes decisions. Theory and applications of Empirical Bayes. Pre: 5114; (3H,3C). II. Odd years.

5434:APPLIED STOCHASTIC PROCESSES

Stochastic processes in statistical applications including Markov chains, Poisson processes, renewal processes, branching processes, random walks, martingales, Brownian motion and related stationary Gaussian processes. Pre: 5104. (3H, 3C). II

5444: BAYESIAN STATISTICS

Introductory course of Bayesian statistics on basic concepts of probability, Bayesian inference of Normal, Binomial, Poisson, Uniform and other common distributions, selections of prior information, Bayesian decision theory, Bayesian analysis of regression and analysis of variance and Bayesian foundation. Pre: 5114; (3H,3C). II. Even years.

5454: RELIABILITY THEORY/SURVIVAL ANALYSIS

Reliability Theory: Basic concepts of lifetime distributions, types of censoring, inference procedures for exponential, Weibull and extreme value distributions, nonparametric estimation of survival function, kernel density estimation, accelerated life testing, and goodness of fit tests. Pre: 5044, 5104, 5114; (3H,3C). II. Alternate years.

Survival Analysis: An applied statistics course for learning models and methods for time-to-event data with focus on biological and biomedical applications. Topics includes types of censoring and truncation; likelihood construction; survival function estimation; nonparametric two or more samples tests; Cox semiparametric regression, time-dependent covariates; regression diagnostics; competing risks; frailty model. Pre: STAT 5044, 5104, 5114 (or equivalent courses); 3H, 3C. I.

5464 (ISE 5464): QUEUING THEORY

Classic models of queues including M/M/1, M/GI/1, and GI/M/s. Topics in queue length processes, waiting time processes, busy period processes, and traffic processes. Pre: STAT 5434 or ISE 5414; (3H,3C). I. Even years.

5474 (ISE 5474): STATISTICAL THEORY OF QUALITY CONTROL

Development of statistical concepts and theory underlying procedures used in quality control applications. Sampling inspection procedures, the sequential probability ratio test, continuous sampling procedures, process control procedures, and experimental design. Pre: STAT 5104, 5114; (3H,3C). I. Alternate years.

5484: SEQUENTIAL ANALYSIS

Introduction to sequential tests, sequential probability ratio and other tests, approximation to OC and ASN function, tests for continuous parameter processes, sequential tests between three hypotheses, invariant tests, and sequential estimation. Pre: 5114; (3H,3C). I. Alternate years.

5504: MULTIVARIATE STATISTICAL METHODS

Methods useful for description and inference for multivariate data. Multivariate distributions, location and dispersion problems for one and two samples, multivariate analysis of variance, linear models, repeated measurements, principal components, factor analysis, biplots, discriminant and canonical variate analysis, cluster analysis, multidimensional scaling and correspondence analysis. Uses SAS or R. Pre: STAT 5124 (3H,3C). I. Even years.

5504G: ADVANCED APPLIED MULTUVARIATE METHODS

Non-mathematical study of multivariate analysis. Multivariate analogs of uinivariate test and estimation procedures. Simultaneous inference procedures. Multivariate analysis of variance, repeated measures, inference for dispersion and association parameters, principle components analysis, discriminant analysis, cluste analysis. Pre: STAT 5606 or STAT 5616. Graduate Standing required.

5514: TOPICS IN REGRESSION

Classical and modern techniques in regression analysis are discussed for linear, nonlinear and generalized linear models (including logistic and Poisson regression). Use of modern regression techniques to diagnose model performance. Diagnostics to detect unusual observations and collinearity. The study of biased estimation methods (including ridge and principal component regression) and numerical methods used in regression. Pre: 5114, 5124; (3H,3C). I.

5524: SAMPLE SURVEY THEORY

Theory of sample surveys including major sampling designs, sample size determination, estimation and interval estimation, and questionnaire design. Pre: 5034,5044; (3H,3C). I. Alternate years.

5525 (CS 5525): DATA ANALYTICS I

Basic principles of data mining, including data analysis under uncertainly, modeling of data mining problems, data mining algorithms, scalability, and data integration and management, Applications of data mining in areas such as bioinformatics, electronic commerce, and environmetrics. Cross listed with CS 5624. Pre: 5034, 5044 or graduate standing in CS; (3H, 3C). I.

5526 (CS 5526): DATA ANALYTICS II

Advanced techniques in supervised, unsupervised, and visualized learning in high dimensional spaces. Methodology will be focused on theoretical, probabilistic, as well as applied aspects of data analytics. Methods will include generalized linear models in high dimensional spaces, regularization, lasso and related methods, principal component regression (pca), tree methods, and random forests. Clustering methods including K means, hierarchical clustering, biclustering, and model-based clustering will be examined. Distance-based learning methods such as multi dimensional scaling, the self organizing map, graphical/network models, and isomap will be demonstrated in conjunction with probabilistically based alternative methods. Supervised learning will consist of discriminant analyses, supervised pca, support vector machines, and kernel methods. Pre: 5444, 5505. (3H, 3C), II.

5534: ANALYSIS OF MULTIVARIATE CATEGORICAL DATA

Log-linear models for unconstrained and ordinal multidimensional contingency tables; testing and estimation; random and structural zeros; model building; logit models and logistic regression; and use of major statistical packages. Pre: 5124; (3H,3C). I. Alternate years.

5544: SPATIAL STATISTICS

Spatial data structures: geostatistical data, lattices, and point patterns. Stationary and isotropic random fields. Autocorrelated data structures. Semivariogram estimation and spatial prediction for geostatistical data. Mapped and sampled point patterns. Regular, completely random, and clustered point processes. Spatial regression and neighborhood analyses for data on lattices. Pre: 5124 (3H,3C). III. Even years.

5554: VARIANCE COMPONENTS

Theoretical treatment of the general problem of estimating and testing hypotheses about variance components within the framework of random effects and mixed linear models; derivation of different estimation procedures and their statistical proper- ties; and discussion of balanced and unbalanced data and of designs for estimating variance components. Pre: 5124; (3H,3C). I. Alternate years.

5564: STATISTICAL GENETICS

Probabilistic approach to behavior of random mating populations, effects of inbreeding and elementary selection, population fitness, and natural selection. Statistical concepts in quantitative inheritance for random and non-random mating populations, correlation between relatives, and artificial selection. Pre: 5034,5044 and BIOL 3004; (3H,3C). II. Alternate years.

5574: RESPONSE SURFACE DESIGN AND ANALYSIS I

Use of response surface analysis to design and analyze industrial experiments. First and second order models. First and second order experimental designs. Use of model diagnostics for finding optimum operating conditions. Pre: 5204; (3H,3C). I. Odd years.

5584 (AAEC 5584): BASIC TOPICS IN ECONOMETRICS

Introduction to the concepts and methods in application of econometric analysis to problems of economic research. Pre: 4724; (3H,3C). II.

5594: TOPICS IN BIOSTATISTICS

Course with variable content; specialized application of statistical theory and methodology to biological and medical sciences; topics include bioassay, epidemiology, survival analysis, and statistical ecology. Pre: 5114; (3H,3C). III. Odd years.

5605-5606: BIOMETRY

5605: Descriptive statistics, the normal distribution, estimation, hypothesis testing, simple linear regression, and one-way analysis of variance and the use of JMP® software (a product of SAS) with applications to the biological sciences.

5606: Experimental design, nested and factorial analysis of variance, linear regression and correlation, multiple regression, and the use of JMP® software (a product of SAS), with applications to the biological sciences. 5606: Pre: 5605 or 5615. (3H,3C). 5605: I; 5606: II.

5615-5616: STATISTICS IN RESEARCH

5615: Concepts in statistical inference, including basic probability, estimation, and test of hypothesis, point and interval estimation and inferences; simple linear regression; one-way analysis of variance and categorical data analysis.

5616: Experimental designs: basic concepts; completely randomized designs; randomized complete block designs; balanced incomplete block designs; Latin square designs; factorial treatment designs; mixed effects designs; split-plot designs. Multiple linear regression: general formulation, estimation and inference, variable selection, and model diagnostics. Pre: 5615: 1 year calculus; 5616: 5615(3H,3C). 5615: I 5616: II.

5634: HIERARCHICAL MODELING

An applied statistics course for learning hierarchical modeling techniques to assess data with atypical features, such as non-normal responses (e.g., binary, discrete survival, continuous mixtures), censored/missing observations, multivariate responses, repeated measures, and nested structures. Classical and Bayesian techniques will be used to assess the models developed in class. Stat 5444 is highly recommended. Programming experience required. Pre: 5044, 5104 (or equivalent courses); 3H, 3C. II.

5674: METHODS IN BIOSTATISTICS

Understanding the basis of descriptive and inferential methods applied in the biological and medical sciences. Topics include graphical and numerical exploratory data analysis, basic probability concepts and important probability distribution, sampling distribution, basic statistical inference methods including estimation, hypothesis testing, linear regression and analysis of variance. Additional topics include Chi-Square test, relative risk and odds ratio, and some basic nonparametric statistics. Student will learn to use JMP statistical software to to calculate and graph descriptive statistics, and to perform the calculations of statistical inference.

5754: INTERNSHIP IN STATISITCS

A variable credit (from 1 to 3 hours) course, to be taken by statistics students who intern at an appropriate company or government agency performing statistical analysis under supervision of a corporate, or government, affiliate faculty member. Pre: Graduate student stand in the Department of Statistics. Variable credit course. I, II, III, IV, V.

5894: FINAL EXAMINATION

Pass/fail only. (3H,3C).

5904: PROJECT AND REPORT

Internship in Statistics. Variable credit course. I,II,III,IV,V.

5924: GRADUATE SEMINAR

Special topics in statistical theory and applications. May be taken for credit two times (max. 2C). Pass/Fail only. Pre: Graduate standing; (1H,1C). I,II.

5974: INDEPENDENT STUDY

Pass/fail only. Variable credit course.

5984: SPECIAL STUDY

Special topics in statistics. Topics vary by semester. Including topics in environmetrics, ethics and the law, highly computational methods, Markov Chain Monte Carlo, mixed linear and nonlinear models. Variable credit course.

5994: RESEARCH AND THESIS

Variable credit course.

6105: MEASURE AND PROBABILITY

Sigma algebra; construction of probability measure and space; independent events; general measure and measurability; integral and Lp spaces; convergence of random variables. Pre: stat 5104 (3H, 3C), I.

6114: ADVANCED TOPICS IN STATISTICAL INFERENCE

Advanced course in the theory of inference for graduate students in statistics and other qualified graduate students. Develops foundations, sufficiency, information, estimation, hypothesis testing, asymptotics, and unbiasedness. Pre: 5114; (3H,3C). II.

6124: Stochastic Modeling and Inference

Data analyses and inferential techniques applied to data described by stochastic processes. Emphasize inferential techniques for Markov models, Poisson processes, point processes, birth/death processes, and cluster processes. Techniques for inference will apply to both stationary and nonstationary processes. Relationships between deterministic partial differential equation models and stochastic models will be examined. Modeling applications in time series, spatial analyses, genetics, epidemiology, text mining, and other application areas will be discussed. Pre: 5434 (3H, 3C). II.

6404: ADVANCED TOPICS IN NONPARAMETRIC STATISTICS

Topics of current interest in research for nonparametric theory and methods, using recent advanced texts and journal articles. Pre: 5404, 6114; (3H,3C). II. Odd years. Alternate years.

6414: TIME SERIES ANALYSIS II

Weakly and strictly stationary stochastic processes; ergodic and ensemble theory; time and frequency domain; spectral decomposition theory; Hilbert space geometry; and multivariate spectra. Pre: 5414; (3H,3C). II. Alternate years.

6424: MULTIVARIATE STATISTICAL ANALYSIS

Foundations of multivariate analysis. Distribution theory of vectors and matrices, inequalities, limit theory, the structure of some multivariate location-scale parameter families, derived distributions, invariant distributions, the principle of invariance in estimation and testing for multivariate location and scale parameters, and robust aspects of normal-theory multivariate procedures. Pre: 5504; (3H,3C). II. Odd years.

6434: STATIONARY AND RELATED PROCESSES

Stationary processes, harmonic analysis, prediction, ARMA and moving average processes, martingales, and elementary stochastic integrals and differential equations. Pre: 5104; (3H,3C). I. Alternate years.

6464 (ISE 6464): QUEUING NETWORKS

Applications of queuing theory results to queuing networks. Topics include reversibility, insensitivity, product forms for queue length processes, and traffic processes including traffic flow within the network. Pre: ISE 5644, 6504; (3H,3C).

6474: ADVANCED TOPICS IN BAYESIAN STATISTICS

This course introduces advanced Bayesian computing and methods and demonstrates their usefulness in challenging applied settings. Topics include Bayesian computing; model selection and criterion; nonparametric priors; semiparametric/nonparametric Bayesian approaches for random effects and survival analysis. Pre: stat 5114; 5124; 5514; 5444.

6484 (ISE 6484): SEMINAR IN APPLIED PROBABILITY

Working seminar open to anyone doing research in applied probability. The purpose is to review student research progress through a series of seminars offered by them and to present new research results offered by faculty attending. May be taken more than once. Pre: Enrollment in Ph.D. program; (1H,1C). I,II.

6494: ADVANCED TOPICS IN MATHEMATICAL STATISTICS

Advanced treatment beyond standard course offerings in topics such as theory of inference, nonparametrics, sequential analysis, and limit theory. May be repeated for credit with different topics. Pre: 5114 and consent; (3H,3C). II.

6504: EXPERIMENTAL DESIGN AND ANALYSIS II

Theoretical treatment of optimality and construction of various types of designs, selected from incomplete block designs, fractional factorials, split-plot designs, regression designs, computer experiment designs, and others according to class interest. Pre: 5124, 5204; (3H,3C). I. Alternate years.

6514: ADVANCED TOPICS IN REGRESSION

Advanced subjects in modern regression techniques and diagnostics. Advanced study of nonlinear models, generalized linear models, generalized estimating equations, nonparametric regression, mixed linear and nonlinear models. Pre: 5124 and 5514; (3H,3C). II. Even years.

6574: RESPONSE SURFACE DESIGN AND ANALYSIS II

Advanced techniques and theory in response surface analysis and design. Robustness of designs. Thorough study of the notion of rotatability. Optimal design criteria and designs for estimating slopes of response surfaces. Mixture designs. Study of model misspecification. Pre: 5574; (3H,3C). II. Odd years.

Link to all graduate course syllabi.

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