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Department of Statistics

www.stat.ncsu.edu

Statistics is the body of scientific methodology that deals with the logic of experiment and survey design, the efficient collection and presentation of quantitative information, and the formulation of valid and reliable inferences from sample data. The Department of Statistics provides instruction, consultation, and computational services on research projects for other departments of all colleges at North Carolina State University including the Agricultural Research Service. Department staff are engaged in research in statistical theory and methodology. This range of activities furnishes a professional environment for training students in the use of statistical procedures in the physical, biological and social sciences and in industrial research and development.

Opportunities

The importance of sound statistical thinking in the design and analysis of quantitative studies is reflected in the abundance of job opportunities for statisticians. Industry relies on statistical methods to control the quality of goods in the process of manufacturing and to determine the acceptability of goods produced. Statistical procedures based on scientific sampling have become basic tools in such diverse fields as weather forecasting, environmental monitoring, opinion polling, crop and livestock estimation, market research, and business trends prediction. The development and testing of new drugs and therapies requires statistical expertise, and advances in genomic science provide tremendous opportunities for statistical work. Because one can improve the efficiency and use of increasingly complex and expensive experiment and survey data, the statistician is in demand wherever quantitative studies are conducted.

Scholarships and Awards

The Department of Statistics recognizes the importance of superior academic performance through the awarding of scholarships and certificates of merit. Scholarships are available for the freshman year for the purpose of attracting academically superior students. There are four named departmental scholarships: John L. Wasik Freshman Scholarship, Francis E. McVay Scholarships, Dr. Jackson A. and Viola H. Rigney Scholarship and SAS Institute Scholarships. The North Carolina Sate University chapter of Mu Sigma Rho, the national statistics honorary fraternity, accepts as members students who have had superior performance in statistics courses. Each year the department recognizes exceptional seniors with awards in the areas of community engagement, academic achievement, and research.

Honors Program

The Department of Statistics allows exceptional undergraduate students to design a program of study that typically includes advanced courses not ordinarily taken by statistics majors and one or two semesters of independent study or research. Students in the program complete a minimum of 9 credit hours in courses drawn from at least two of the following three categories: MA 426, or other courses designated as appropriate by the honors adviser, 500-level courses in statistics or mathematics, and 400- or 500-level courses in independent study. Interested students should contact the Honors Adviser in the statistics department for additional information.

Curricula

The undergraduate curriculum provides basic training for a career in statistics or for graduate study and leads to the Bachelor of Science in Statistics. In addition to statistics, the curriculum includes study in mathematics, computer science, and the biological/physical sciences. While fulfilling their major elective requirements, students can either elect a minor or distribute their study across disciplines exploring the application of statistics in other fields such as agriculture and life sciences, computer science, economics and business, industrial engineering, and the social sciences. A cooperative work-study option is also available.

Specific curriculum requirements are available on the Registration and Records website.

Minor in Statistics

The Department of Statistics offers a minor in statistics to majors in any field except statistics. The importance of statistical reasoning to solve real-world problems has been recognized by the business, government, and scientific communities. This minor program will provide students with an opportunity to become competent in the use of statistical methods to summarize information and/or provide answers to policy/research questions. Students completing this program of study will also be provided with experience in statistical computing. Please see the Director of Undergraduate Programs.

Please refer to https://oucc.dasa.ncsu.edu/statistics-17stm/ for information about a minor in statistics.

Head

L. Stefanski


Associate Head

D. Boos


Assistant Head

A. Motsinger-Reif


Director of Biomathematics Graduate Program

A. Lloyd


Director of the Center for Quantitative Sciences in Biomedicine

M. Davidian


Director of Bioinformatics Research Center

S. Muse


Director of Undergraduate Programs in Statistics

S. Muse


William Neal Reynolds Professor

M. Davidian

D. Dickey

K. Pollock

Z. Zeng


Drexel Professors of Statistics

L.A. Stefanski


Cox Distinguished Professor of Statistics

A.A. Tasiatis


Alumni Distinguished Graduate Professor

M. Davidian


Alumni Distinguished Undergraduate Professors

J.M. Hughes-Oliver

T. Reiland


Alumni Distinguished Research Professor

A.A. Tsiatis


Professors

P. Bloomfield

H.D. Bondell

D.D. Boos

M. Davidian

D.A. Dickey

S. Ghosal

S.K. Ghosh

K. Gross

M. Gumpertz

J. Hughes-Oliver

S. Lahiri

W. Lu

S. Muse

D.L. Solomon

L.A. Stefanski

J. Thorne

A.A. Tsiatis

J. Tzeng

A. Wilson

F. Wright

Z. Zeng

D. Zhang


Associate Professors

E.Laber

A. Maity

D. Martin

R.Martin

A. Motsinger-Reif

J.A. Osborne

B. Reich

T.W. Reiland

C.E. Smith

R. Song

A. Staicu

Y. Wu


Research Associate Professor

C. Arellano

N. Sedransk


Assistant Professors

E. Chi

J. Guinness

J. Jeng

J. Stallings


Teaching Professor

R. Woodard


Teaching Assistant Professor

B.Alhanti

J.Duggins

J. Post


Research Assistant Professor

E. Griffith


Professor Emeriti

T. Gerig

J. Monahan

W. Swallow


Associate Faculty

K. Pollock

D. Reif

ST - Statistics Courses

ST 101 Statistics by Example 3.

Sampling, experimental design, tables and graphs, relationships among variables, probability, estimation, hypothesis testing. Real life examples from the social, physical and life sciences, the humanities and sports. Credit not allowed if student has prior credit for another ST course.

ST 114 Statistical Programming 3.
Restriction: Statistics majors only.

This is an introductory course in computer programming for statisticians using Python. Emphasis is on designing algorithms, problem solving, and forming good coding practices: methodical development of programs from specifications; documentation and style; appropriate use of control structures such as loops, of data types such as arrays; modular program organization; version control. Students will become acquainted with core statistical computational problems through examples and coding assignments, including computation of histograms, boxplots, quantiles, and least squares regression.

ST 305 Statistical Methods 4.
Prerequisite: MA 141; Corequisite: ST 307.

Basic concepts of data collection, sampling, and experimental design. Descriptive analysis and graphical displays of data. Probability concepts, and expectations. Normal and binomial distributions. Sampling distributions and the Central Limit Theorem. Confidence intervals and hypothesis testing. Tests for means/proportions of two independent groups. One factor analysis of variance. Understanding relationships among variables; correlation and simple linear regression. Computer use is emphasized.

ST 307 Introduction to Statistical Programming- SAS 1.
Corequisite: ST 305 or ST 312 or ST 372 or Prerequisite: ST 350 or BUS 350.

An introduction to using the SAS statistical programming environment. The course will combine lecture and a virtual computing laboratory to teach students how to use the SAS sytem for: basic data input and manipulation; graphical displays of univariate and bivariate data; one- and two-sample analyses of means; simple linear regression; one-way ANOVA. Documentation of code and writing of statistical reports will be included.

ST 308 Introduction to Statistical Programming - R 1.

Introduction to the statistical programming language R. The course will cover: reading and manipulating data; use of common data structures (vectors, matrices, arrays, lists); basic graphical representations.

ST 311 Introduction to Statistics 3.

Examining relationships between two variables using graphical techniques, simple linear regression and correlation methods. Producing data using experiment design and sampling. Elementary probability and the basic notions of statistical inference including confidence interval estimation and tests of hypothesis. One and two sample t-tests, one-way analysis of variance, inference for count data and regression. Credit not allowed if student has prior credit for another ST course or BUS 350.

ST 312 Introduction to Statistics II 3.
Prerequisite: ST 311.

A further examination of statistics and data analysis. Inference for comparing multiple samples, experimental design, analysis of variance and post-hoc tests. Inference for correlation, simple regression, multiple regression, and curvilinear regression. Analysis of contingency tables and categorical data. No credit for students who have credit for ST 305.

ST 350 Economics and Business Statistics 3.
Prerequisite: MA 114.

Introduction to statistics applied to management, accounting, and economic problems. Emphasis on statistical estimation, inference, simple and multiple regression, and analysis of variance. Use of computers to apply statistical methods to problems encountered in management and economics.

ST 361 Introduction to Statistics for Engineers 3.
Prerequisite: College algebra.

Statistical techniques useful to engineers and physical scientists. Includes elementary probability, frequency distributions, sampling variation, estimation of means and standard deviations, basic design of experiments, confidence intervals, significance tests, elementary least squares curve fitting. Credit not allowed for both ST 361 and ST 370 or ST 380.

ST 370 Probability and Statistics for Engineers 3.
Prerequisite: MA 241.

Calculus-based introduction to probability and statistics with emphasis on Monte Carlo simulation and graphical display of data on computer workstations. Statistical methods include point and interval estimation of population parameters and curve and surface fitting (regression analysis). The principles of experimental design and statistical process control introduced. Credit not allowed for both ST 370 and ST 361 or ST 380.

ST 371 Introduction to Probability and Distribution Theory 3.
Prerequisite: MA 241, Corequisite: MA 242.

Basic concepts of probability and distribution theory for students in the physical sciences, computer science and engineering. Provides the background necessary to begin study of statistical estimation, inference, regression analysis, and analysis of variance.

ST 372 Introduction to Statistical Inference and Regression 3.
Prerequisite: ST 371.

Statistical inference and regression analysis including theory and applications. Point and interval estimation of population parameters. Hypothesis testing including use of t, chi-square and F. Simple linear regression and correlation. Introduction to multiple regression and one-way analysis of variance.

ST 380 Probability and Statistics for the Physical Sciences 3.
Prerequisite: MA 241.

Introduction to probability models and statistics with emphasis on Monte Carlo simulation and graphical display of data on computer laboratory workstations. Statistical methods include point and interval estimation of population parameters and curveand surface fitting (regression analysis). Credit not allowed for both ST 380 and ST 361 or ST 370.

ST 401 Experiences in Data Analysis 4.
Prerequisite: Permission of Instructor and either ST 311 or ST 305.

This course will allow students to see many practical aspects of data analysis. Each section of this course will expose students to the process of data analysis in a themed area such as biostatistics or environmental statistics. Students will see problems of data collection and analysis through a combination of classroom demonstrations, hands on computer activities and visits to local industries.

ST 405 Applied Nonparametric Statistics 3.
Prerequisite: (ST 422 and ST 430) or ST 511 or equivalent.

Statistical methods requiring relatively mild assumptions about the form of the population distribution. Classical nonparametric hypothesis testing methods, Spearman and Kendall correlation coefficients, permutation tests, bootstrap methods, and nonparametric regressions will be covered.

ST 412 Long-Term Actuarial Models 3.
Prerequisite: MA 241 or MA 231, Corequisite: MA 421, BUS(ST) 350, ST 301, ST 305, ST 311, ST 361, ST 370, ST 371, ST 380 or equivalent.

Long-term probability models for risk management systems. Theory and applications of compound interest, probability distributions of failure time random variables, present value models of future contingent cash flows, applications to insurance, health care, credit risk, environmental risk, consumer behavior and warranties.

ST 413 Short-Term Actuarial Models 3.
Prerequisite: MA 241 or MA 231, and one of MA 421, ST 301, ST 305, ST 370, ST 371, ST 380, ST 421..

Short-term probability models for risk management systems. Frequency distributions, loss distributions, the individual risk model, the collective risk model, stochastic process models of solvency requirements, applications to insurance and businessdecisions.

ST 421 Introduction to Mathematical Statistics I 3.
Prerequisite: MA 242.

First of a two-semester sequence of mathematical statistics, primarily for undergraduate majors in Statistics. Introduction to probability, univariate and multivariate probability distributions and their properties, distributions of functions of random variables, random samples and sampling distributions. Credit is not allowed for both ST 421 and MA 421.

ST 422 Introduction to Mathematical Statistics II 3.
Prerequisite: ST 421 or MA 421.

Second of a two-semester sequence of mathematical statistics, primarily for undergraduate majors in Statistics. Random samples, point and interval estimators and their properties, methods of moments, maximum likelihood, tests of hypotheses, elements of nonparametric statistics and elements of general linear model theory.

ST 430 Introduction to Regression Analysis 3.
Prerequisites: (ST 305 or ST 312 or ST 372) and ST 307 and (MA 305 or MA 405).

Regression analysis as a flexible statistical problem solving methodology. Matrix review; variable selection; prediction; multicolinearity; model diagnostics; dummy variables; logistic and non-linear regression. Emphasizes use of computer.

ST 431 Introduction to Experimental Design 3.
Prerequisite: (ST 305 or ST 312 or ST 372) and ST 307.

Experimental design as a method for organizing analysis procedures. Completely randomized, randomized block, factorial, nested, latin squares, split-plot and incomplete block designs. Response surface and covariance adjustment procedures. Stresses use of computer.

ST 432 Introduction to Survey Sampling 3.
Prerequisite: (ST 305 or ST 312 or ST 372) and ST 307.

Design principles pertaining to planning and execution of a sample survey. Simple random, stratified random, systematic and one- and two-stage cluster sampling designs. Emphasis on statistical considerations in analysis of sample survey data. Class project on design and execution of an actual sample survey.

ST 433 Applied Spatial Statistics 3.
P: ST 422 and ST 430.

Introduction to statistical models and methods for analyzing various types of spatially referenced data. The focus is on applications with real data and their analysis with statistical programs such as R and SAS. Students are required to write, modify, and run computer code in order to complete homework assignments and final projects.

ST 434 Applied Time Series 3.
P: ST 422 and ST 430.

Statistical models and methods for the analysis of time series data using both time domain and frequency domain approaches. A brief review of necessary statistical concepts and R will be given at the beginning. Analyses of real data sets using the statistical software packages will be emphasized.

ST 435 Statistical Methods for Quality and Productivity Improvement 3.
Prerequisite: (ST 305 or ST 312 or ST 372) and ST 307.

Use of statistics for quality control and productivity improvement. Control chart calculations and graphing, process control and specification; sampling plans; and reliability. Computer use will be stressed for performing calculations and graphing.

ST 437 Applied Multivariate and Longitudinal Data Analysis 3.
P: ST 422 and ST 430.

An introduction to use of statistical methods for analyzing multivariate and longitudinal data collected in experiments and surveys. Topics covered include multivariate analysis of variance, discriminant analysis, principal components analysis, factor analysis, covariance modeling, and mixed effects models such as growth curves and random coefficient models. Emphasis is on use of a computer to perform statistical analysis of multivariate and longitudinal data.

ST 440 Applied Bayesian Analysis 3.
P: ST 422 and ST 430.

Introduction to Bayesian concepts of statistical inference; Bayesian learning; Markov chain Monte Carlo methods using existing software (SAS and OpenBUGS); linear and hierarchical models; model selection and diagnostics.

ST 442 Introduction to Data Science 3.
P: (MA 305 or MA 405) and (ST 305 or ST 312 or ST 370 or ST 372) and (CSC 111 or CSC 112 or CSC 113 or CSC 116 or ST 114 or ST 445).

Overview of data structures, data lifecycle, statistical inference. Data management, queries, data cleaning, data wrangling. Classification and prediction methods to include linear regression, logistic regression, k-nearest neighbors, classification and regression trees. Association analysis. Clustering methods. Emphasis on analyzing data, use and development of software tools, and comparing methods.

ST 445 Introduction to Statistical Computing and Data Management 3.
Prerequisite: (ST 305 or ST 312 or ST 372) and ST 307.

Detailed discussion of the program data vector and data handling techniques that are required to apply statistical methods. Topics are based on the current content of the Base SAS Certification Exam and typically include: importing, validating, and exporting of data files; manipulating, subsetting, and grouping data; merging and appending data sets; basic detail and summary reporting; and code debugging. Additional topics with practical applications, such as graphics and advanced reporting, may also be introduced. Statistical methods for analyzing data are not covered in this course. Regular access to a computer for homework and class exercises is required. Previous exposure to SAS is expected.

ST 491 Statistics in Practice 3.
P: ST 430.

Mentored experience in applied statistical analysis. Students will work in small groups in collaboration with local scientists to answer real questions about real data. The experience involves mentoring by both the project scientist and the instructor.

ST 495 Special Topics in Statistics 1-6.

Offered as needed to present material not normally available in regular departmental course offerings, or for offering new courses on a trial basis.

ST 498 Independent Study In Statistics 1-6.
Prerequisite: Six hours of ST.

Detailed investigation of topics of particular interest to advanced undergraduates under faculty direction. Individualized/Independent Study and Research courses require a "Course Agreement for Students Enrolled in Non-Standard Courses" be completed by the student and faculty member prior to registration by the department.

ST 501 Fundamentals of Statistical Inference I 3.
Prerequisite: MA 242 or equivalent.

First of a two-semester sequence in probability and statistics taught at a calculus-based level. Probability: discrete and continuous distributions, expected values, transformations of random variables, sampling distributions. Credit not given for both ST 521 and ST 501.

ST 502 Fundamentals of Statistical Inference II 3.
Prerequisite: ST 501.

Second of a two-semester sequence in probability and statistics taught at a calculus-based level. Statistical inference: methods of construction and evaluation of estimators, hypothesis tests, and interval estimators, including maximum likelihood. Credit not given for both ST 522 and ST 502.

ST 503 Fundamentals of Linear Models and Regression 3.
P: ST 501; C: ST 502.

Estimation and testing in full and non-full rank linear models. Normal theory distributional properties. Least squares principle and the Gauss-Markov theorem. Estimability, analysis of variance and co variance in a unified manner. Practical model-building in linear regression including residual analysis, regression diagnostics, and variable selection. Emphasis on use of the computer to apply methods with data sets. Credit not given for both ST 552 and ST 503.

ST 505 Applied Nonparametric Statistics 3.
Prerequisite: (ST 422 and ST 430) or ST 511 or equivalent.

Statistical methods requiring relatively mild assumptions about the form of the population distribution. Classical nonparametric hypothesis testing methods, Spearman and Kendall correlation coefficients, permutation tests, bootstrap methods, and nonparametric regressions will be covered.

ST 506 Sampling Animal Populations 3.
Prerequisite: ST 512.

Statistical methods applicable to sampling of wildlife populations, including capture-recapture, removal, change in ratio, quadrant and line transect sampling. Emphasis on model assumptions and study design.

ST 507 Statistics For the Behavioral Sciences I 3.

A general introduction to the use of descriptive and inferential statistics in behavioral science research. Methods for describing and summarizing data presented, followed by procedures for estimating population parameters and testing hypotheses concerning summarized data.

ST 508 Statistics For the Behavioral Sciences II 3.
Prerequisite: ST 507.

Introduction to use of statistical design principles in behavioral science research. Presentation of use of a statistical model to represent structure of data collected from a designed experiment or survey study. Opportunities provided for use of a computer to perform analyses of data, to evaluate proposed statistical model and to assist in post-hoc analysis procedures. Least squares principles used to integrate topics of multiple linear regression analysis, the analysis of variance and analysis of covariance.

ST 511 Experimental Statistics For Biological Sciences I 3.
Prerequisite: ST 311 or Graduate standing.

Basic concepts of statistical models and use of samples; variation, statistical measures, distributions, tests of significance, analysis of variance and elementary experimental design, regression and correlation, chi-square.

ST 512 Experimental Statistics For Biological Sciences II 3.
Prerequisite: ST 511.

Covariance, multiple regression, curvilinear regression, concepts of experimental design, factorial experiments, confounded factorials, individual degrees of freedom and split-plot experiments. Computing laboratory addressing computational issues and use of statistical software.

ST 513 Statistics for Management I 3.
Prerequisite: Graduate standing.

Analysis of data to represent facts, guide decisions and test opinions in managing systems and processes. Graphical and numerical data analysis for descriptive and predictive decisions. Scatter plot smoothing and regression analysis. Basic statistical inference. Integrated use of computer.

ST 514 Statistics For Management and Social Sciences II 3.
Prerequisite: ST 507.

Linear regression, multiple regression and concepts of designed experiments in an integrated approach, principles of the design and analysis of sample surveys, use of computer for analysis of data.

ST 515 Experimental Statistics for Engineers I 3.
Prerequisite: ST 361 or Graduate standing.

General statistical concepts and techniques useful to research workers in engineering, textiles, wood technology, etc. Probability distributions, measurement of precision, simple and multiple regression, tests of significance, analysis of variance,enumeration data and experimental design.

ST 516 Experimental Statistics For Engineers II 3.
Prerequisite: ST 515.

General statistical concepts and techniques useful to research workers in engineering, textiles, wood technology, etc. Probability distributions, measurement of precision, simple and multiple regression, tests of significance, analysis of variance, enumeration data and experimental designs.

ST 519 Teaching and Learning of Statistical Thinking 3.
Prerequisite: ST 507 or ST 511.

This course is designed to bridge theory and practice on how students develop understandings of key concepts in data analysis, statistics, and probability. Discussion of students' understandings, teaching strategies and the use of manipulatives and technology tools. Topics include distribution, measures of center and spread, sampling, sampling distribution, randomness, and law of large numbers. Must complete a first level graduate statistics course ( ST 507, ST 511, or equivalent) before enrolling.

ST 520 Statistical Principles of Clinical Trials 3.
Corequisite: ST 501 or ST 521 or ST 701.

Statistical methods for design and analysis of clinical trials and epidemiological studies. Phase I, II, and III clinical trials. Principle of Intention-to-Treat, effects of non-compliance, drop-outs. Interim monitoring of clinical trials and data safety monitoring boards. Introduction to meta-analysis. There is also discussion of Epidemiological methods time permitting.

ST 524 Statistics In Plant Science 3.
Prerequisite: ST 512.

Principles and techniques of planning, establishing and executing field and greenhouse experiments. Size, shape and orientation of plots; border effects; estimation of size of experiments for specified accuracy; subsampling plots and yields for laboratory analysis; combining data from a series of years and/or locations; rotation experiments; repeated measures data; multiple comparisons in variety trial results; selection of predictors in multiple regression; introduction to interspecies and intraspecies plant competition experiments and models.

ST 533 Applied Spatial Statistics 3.
P: ST 512 or ST 514 or ST 515 or ST 517.

Introduction to statistical models and methods for analyzing various types of spatially referenced data. The focus is on applications with real data and their analysis with statistical programs such as R and SAS. Students are required to write, modify, and run computer code in order to complete homework assignments and final projects.

ST 534 Applied Time Series 3.
P: ST 512 or ST 514 or ST 515 or ST 517.

Statistical models and methods for the analysis of time series data using both time domain and frequency domain approaches. A brief review of necessary statistical concepts and R will be given at the beginning. Analyses of real data sets using the statistical software packages will be emphasized.

ST 535 Statistical Methods for Quality and Productivity Improvement 3.
Prerequisite: (ST 305 or ST 312 or ST 372) and ST 307.

Use of statistics for quality control and productivity improvement. Control chart calculations and graphing, process control and specification; sampling plans; and reliability. Computer use will be stressed for performing calculations and graphing.

ST 537 Applied Multivariate and Longitudinal Data Analysis 3.
P: ST 512 or ST 514 or ST 515 or ST 517.

An introduction to use of statistical methods for analyzing multivariate and longitudinal data collected in experiments and surveys. Topics covered include multivariate analysis of variance, discriminant analysis, principal components analysis, factor analysis, covariance modeling, and mixed effects models such as growth curves and random coefficient models. Emphasis is on use of a computer to perform statistical analysis of multivariate and longitudinal data.

ST 540 Applied Bayesian Analysis 3.
P: ST 512 or ST 514 or ST 515 or ST 517.

Introduction to Bayesian concepts of statistical inference; Bayesian learning; Markov chain Monte Carlo methods using existing software (SAS and OpenBUGS); linear and hierarchical models; model selection and diagnostics.

ST 542 Statistical Practice 3.
Prerequisites: (ST 512 or ST 514) and (ST 502 or ST 522).

This course will provide a discussion-based introduction to statistical practice geared towards students in the final semester of their Master of Statistics degree.

ST 546 Probability and Stochastic Processes I 3.
Prerequisite: MA 421 and MA 425 or MA 511.

Modern introduction to Probability Theory and Stochastic Processes. The choice of material is motivated by applications to problems such as queueing networks, filtering and financial mathematics. Topics include: review of discrete probability and continuous random variables, random walks, markov chains, martingales, stopping times, erodicity, conditional expectations, continuous-time Markov chains, laws of large numbers, central limit theorem and large deviations.

ST 555 Statistical Programming I 3.

An introduction to the data-handling techniques that are required to apply statistical methods including the importing, validating, and exporting of data files; manipulating, subsetting, and grouping data; merging and appending data sets; and basic reports including tables and graphics. Students learn SAS, the industry standard for statistical practice, and the R language commonly used in upper level statistics courses. Regular access to computer for homework and class exercises is required. Credit for both ST 445 and ST 555 is not allowed.

ST 556 Statistical Programming II 3.
P: ST 555 or Base SAS Certification.

Statistical procedures for importing/managing complex data structures using SQL, automated analysis using macro programming, basic simulation methods and text parsing/analysis procedures. Students learn SAS, the industry standard for statistical practice. Regular access to a computer for homework and class exercises is required.

ST 557 Using Technology to Teach Statistics 3.
P: ST 508 or ST 512.

This course will provide statistics educators with an in-depth introduction to applying technology for teaching college statistics. In this course, students will explore a variety of available statistical packages, demonstration applets, and other technologies for teaching statistics. Students will learn pedagogy t help them structure learning activities around these technologies. Students will also learn to identify key elements in technologies that support pedagogical goals.

ST 561 Intermediate Econometrics 3.
Prerequisite: ECG 700 and ST 514.

Formalization of economic hypotheses into testable relationships and application of appropriate statistical techniques. Major attention to procedures applicable for single equation stochastic models expressing microeconomic and macroeconomic relation-ships. Statistical considerations relevant in working with time series and cross sectional data in economic investigations. Survey of simultaneous equation models and the available estimation techniques.

ST 562 Data Mining with SAS Enterprise Miner 3.
Prerequisite: ST 512 or ST 514 or ST 515 or ST 517.

This is a hands-on course using modeling techniques designed mostly for large observational studies. Estimation topics include recursive splitting, ordinary and logistic regression, neural networks, and discriminant analysis. Clustering and association analysis are covered under the topic "unsupervised learning," and the use of training and validation data sets is emphasized. Model evaluation alternatives to statistical significance include lift charts and receiver operating characteristic curves. SAS Enterprise Miner is used in the demonstrations, and some knowledge of basic SAS programming is helpful.

ST 563 Introduction to Statistical Learning 3.
Prerequisite: ST 512 or ST 514 or ST 515 or ST 517.

This course will introduce common statistical learning methods for supervised and unsupervised predictive learning in both the regression and classification settings. Topics covered will include linear and polynomial regression, logistic regression and discriminant analysis, cross-validation and the bootstrap, model selection and regularization methods, splines and generalized additive models, principal components, hierarchical clustering, nearest neighbor, kernel, and tree-based methods, ensemble methods, boosting, and support-vector machines.

ST 590 Special Topics 1-6.

ST 601 Seminar 1.

ST 610 Topics in Stat 1-99.

Special topics in Statistics.

ST 630 Independent Study 1-3.

ST 635 Readings 1-3.

ST 641 Statistical Consulting 1.
Prerequisite: ST 512 and ST 522.

Participation in regularly scheduled supervised statistical consulting sessions with faculty member and client. Consultant's report written for each session. Regularly scheduled meetings with course instructor and other student consultants to present and discuss consulting experiences.

ST 685 Master's Supervised Teaching 1-3.
Prerequisite: Master's student.

Teaching experience under the mentorship of faculty who assist the student in planning for the teaching assignment, observe and provide feedback to the student during the teaching assignment, and evaluate the student upon completion of the assignment.

ST 690 Master's Examination 1-9.
Prerequisite: Master's student.

For students in non thesis master's programs who have completed all other requirements of the degree except preparing for and taking the final master's exam.

ST 693 Master's Supervised Research 1-9.
Prerequisite: Master's student.

Instruction in research and research under the mentorship of a member of the Graduate Faculty.

ST 695 Master's Thesis Research 1-9.
Prerequisite: Master's student.

Thesis Research.

ST 696 Summer Thesis Research 1.
Prerequisite: Master's student.

For graduate students whose programs of work specify no formal course work during a summer session and who will be devoting full time to thesis research.

ST 699 Master's Thesis Preparation 1-9.
Prerequisite: Master's student.

For students who have completed all credit hour requirements and full-time enrollment for the master's degree and are writing and defending their thesis. Credits Arranged.

ST 701 Statistical Theory I 3.

Probability tools for statistics: description of discrete and absolutely continuous distributions, expected values, moments, moment generating functions, transformation of random variables, marginal and conditional distributions, independence, orderstatistics, multivariate distributions, concept of random sample, derivation of many sampling distributions.

ST 702 Statistical Theory II 3.
Prerequisite: ST 701.

General framework for statistical inference. Point estimators: biased and unbiased, minimum variance unbiased, least mean square error, maximum likelihood and least squares, asymptotic properties. Interval estimators and tests of hypotheses: confidence intervals, power functions, Neyman-Pearson lemma, likelihood ratio tests, unbiasedness, efficiency and sufficiency.

ST 705 Linear Models and Variance Components 3.
Corequisite: ST 702.

Theory of estimation and testing in full and non-full rank linear models. Normal theory distributional properties. Least squares principle and the Gauss-Markoff theorem. Estimability and properties of best linear unbiased estimators. General linear hypothesis. Application of dummy variable methods to elementary classification models for balanced and unbalanced data. Analysis of covariance. Variance components estimation for balanced data.

ST 706 Nonlinear Programming 3.
Prerequisite: OR(IE,MA) 505 and MA 425.

An advanced mathematical treatment of analytical and algorithmic aspects of finite dimensional nonlinear programming. Including an examination of structure and effectiveness of computational methods for unconstrained and constrained minimization. Special attention directed toward current research and recent developments in the field.

ST 708 Applied Least Squares 3.
Prerequisite: ST 512.

Least squares estimation and hypothesis testing procedures for linear models. Consideration of regression, analysis of variance and covariance in a unified manner. Emphasis on use of the computer to apply these techniques to experimental (including unequal cell sizes) and survey situations.

ST 711 Design Of Experiments 3.
Prerequisite: ST 512.

Review of completely randomized, randomized complete block and Latin square designs and basic concepts in the techniques of experimental design. Designs and analysis methods in factorial experiments, confounded factorials, response surface methodology, change-over design, split-plot experiments and incomplete block designs. Examples used to illustrate application and analysis of these designs.

ST 715 Theory Of Sampling Applied To Survey Design 3.
Prerequisite: ST 422, ST 512.

Principles for interpretation and design of sample surveys. Estimator biases, variances and comparative costs. Simple random sample, cluster sample, ratio estimation, stratification, varying probabilities of selection. Multi-stage, systematic and double sampling. Response errors.

ST 721 Genetic Data Analysis 3.
Prerequisite: ST 430 and GN 411.

Analysis of discrete data, illustrated with genetic data on morphological characters allozymes, restriction fragment length polymorphisms and DNA sequences. Maximum likelihood estimation, including iterative procedures. Numerical resampling. Development of statistical techniques for characterizing genetic disequilibrium and diversity. Measures of population structure and genetic distance. Construction of phylogenetic trees. Finding alignments and similarities between DNA sequences. Locating genes with markers.

ST 730 Applied Time Series Analysis 3.
Prerequisite: ST 512.

An introduction to use of statistical methods for analyzing and forecasting data observed over time. Trigonometric regression, periodogram/spectral analysis. Smoothing. Autoregressive moving average models. Regression with autocorrelated errors. Linear filters and bivariate spectral analysis. Stress on methods and applications; software implementations described and used in assignments.

ST 731 Applied Multivariate Statistical Analysis 3.
Prerequisite: ST 512.

An introduction to use of multivariate statistical methods in analysis of data collected in experiments and surveys. Topics covered including multivariate analysis of variance, discriminant analysis, canonical correlation analysis and principal components analysis. Emphasis upon use of a computer to perform multivariate statistical analysis calculations.

ST 732 Applied Longitudianal Data Analysis 3.
Prerequisite: ST 512.

Statistics methods for analysis of multivariate data, focusing on data collected in form of repeated measurements. Multivariate normal distribution, Hotelling's T2, multivariate analysis of variance, repeated measures analysis of variance, growth curve models, mixed effects models. Methods for analyzing multivariate data in form of counts, categorical data and binary data, emphasizing recent approaches in statistical literature.

ST 733 Spatial Statistics 3.
Prerequisite: ST 552.

Introduction to the theory and methods of spatial data analysis including: visualization; Gaussian processes; spectral representation; variograms; kriging; computationally-efficient methods; nonstationary processes; spatiotemporal and multivariate models.

ST 740 Bayesian Inference and Analysis 3.
Prerequisite: ST 522.

Introduction to Bayesian inference; specifying prior distributions; conjugate priors, summarizing posterior information, predictive distributions, hierachical models, asymptotic consistency and asymptotic normality. Markov Chain Monte Carlo (MCMC) methods and the use of exising software(e.g., WinBUGS).

ST 744 Categorical Data Analysis 3.
Prerequisite: ST 512 and ST 522.

Statistical models and methods for categorical responses including the analysis of contingency tables, logistic and Poisson regression, and generalized linear models. Survey of asymptotic and exact methods and their implementation using standard statistical software.

ST 745 Analysis of Survival Data 3.
Prerequisite: ST 522.

Statistical methods for analysis of time-to-event data, with application to situations with data subject to right-censoring and staggered entry, including clinical trials. Survival distribution and hazard rate; Kaplan-Meier estimator for survival distribution and Greenwood's formula; log-rank and weighted long-rank tests; design issues in clinical trials. Regression models, including accelerated failure time and proportional hazards; partial likelihood; diagnostics.

ST 746 Introduction To Stochastic Processes 3.
Prerequisite: MA 405 and MA(ST) 546 or ST 521.

Markov chains and Markov processes, Poisson process, birth and death processes, queuing theory, renewal theory, stationary processes, Brownian motion.

ST 747 Probability and Stochastic Processes II 3.
Prerequisite: MA(ST) 546.

Fundamental mathematical results of probabilistic measure theory needed for advanced applications in stochastic processes. Probability measures, sigma-algebras, random variables, Lebesgue integration, expectation and conditional expectations w.r.t.sigma algebras, characteristic functions, notions of convergence of sequences of random variables, weak convergence of measures, Gaussian systems, Poisson processes, mixing properties, discrete-time martingales, continuous-time markov chains.

ST 748 Stochastic Differential Equations 3.
Prerequisite: MA(ST) 747.

Theory of stochastic differential equations driven by Brownian motions. Current techniques in filtering and financial mathematics. Construction and properties of Brownian motion, wiener measure, Ito's integrals, martingale representation theorem, stochastic differential equations and diffusion processes, Girsanov's theorem, relation to partial differential equations, the Feynman-Kac formula.

ST 750 Introduction to Econometric Methods 3.
Prerequisite: ST 421; Corequisite: ST 422.

Introduction to principles of estimation of linear regression models, such as ordinary least squares and generalized least squares. Extensions to time series and panel data. Consideration of endogeneity and instrumental variables estimation. Limited dependent variable and sample selection models. Attention to implementation of econometric methods using a statistical package and microeconomic and macroeconomic data sets.

ST 751 Econometric Methods 3.
Prerequisite: ST 421, ST 422.

Introduction to important econometric methods of estimation such as Least Squares, instrumentatl Variables, Maximum Likelihood, and Generalized Method of Moments and their application to the estimation of linear models for cross-sectional ecomomic data. Discussion of important concepts in the asymptotic statistical analysis of vector process with application to the inference procedures based on the aforementioned estimation methods.

ST 752 Time Series Econometrics 3.
Prerequisite: ECG(ST) 751.

The characteristics of macroeconomic and financial time series data. Discussion of stationarity and non-stationarity as they relate to economic time series. Linear models for stationary economic time series: autoregressive moving average (ARMA) models; vector autoregressive (VAR) models. Linear models for nonstationary data: deterministic and stochastic trends; cointegration. Methods for capturing volatility of financial time series such as autoregressive conditional heteroscedasticity (ARCH) models. Generalized Method of Moments estimation of nonlinear dynamic models.

ST 753 Microeconometrics 3.
Prerequisite: ECG 751.

The characteristics of microeconomic data. Limited dependent variable models for cross-sectional microeconomic data: logit/probit models; tobit models; methods for accounting for sample selection; count data models; duration analysis; non-parametricmethods. Panel data models: balanced and unbalanced panels; fixed and random effects; dynamic panel data models; limited dependent variables and panel data analysis.

ST 755 Advanced Analysis Of Variance and Variance Components 3.
Prerequisite: ST 512, ST 552.

Expected mean squares, exact and approximate tests of hypotheses for balanced and unbalanced data sets. Fixed, mixed and random models. Randomization theory. Estimation of variance components using regression, MINQUE and general quadratic unbiased estimation theory.

ST 756 Computational Molecular Evolution 3.
Prerequisite: GN 411 and ST 511.

Phylogenetic analyses of nucleotide and protein sequence data. Sequence alignment, phylogeny reconstruction and relevant computer software. Prediction of protein secondary structure, database searching, bioinformatics and related topics. Project required.

ST 757 Statistics for Molecular Quantitative Genetics 3.
Prerequisite: ST 512 and GN 703 or ST 721.

Genetic mapping data. Linkage map reconstruction, quantitative genetical models. Statistical methods and computer programs for mapping quantitative trait loci and estimating genetic architecture of quantitative traits.

ST 758 Computation for Statistical Research 3.
Prerequisite: ST 522 and ST 552.

Computational tools for research in statistics, including applications of numerical linear algebra, optimization and random number generation, using the statistical language R. A project encompassing a simulation experiment will be required.

ST 762 Nonlinear Statistical Models for Univariate and Multivariate Response 3.
Prerequisite: ST 512, ST 552.

Inference for general nonlinear parametric statistical models for univariate and multivariate continuous and discrete response, including generalized linear models, nonlinear models with nonconstant variance, and generalized estimating equation procedures for multivariate response, including repeated measurement data. Linear and quadratic estimating equations, models for covariance structure, effects of model misspecification and robustness. Survey of major theoretical results and implementation using standard statistical software.

ST 771 Biomathematics I 3.
Prerequisite: Advanced calculus, reasonable background in biology.

Role of theory construction and model building in development of experimental science. Historical development of mathematical theories and models for growth of one-species populations (logistic and off-shoots), including considerations of age distributions (matrix models, Leslie and Lopez; continuous theory, renewal equation). Some of the more elementary theories on the growth of organisms (von Bertalanffy and others; allometric theories; cultures grown in a chemostat). Mathematical theories oftwo and more species systems (predator-prey, competition, symbosis; leading up to present-day research) and discussion of some similar models for chemical kinetics. Much emphasis on scrutiny of biological concepts as well as of mathematical structureof models in order to uncover both weak and strong points of models discussed. Mathematical treatment of differential equations in models stressing qualitative and graphical aspects, as well as certain aspects of discretization. Difference equation models.

ST 772 Biomathematics II 3.
Prerequisite: BMA 771, elementary probability theory.

Continuation of topics of BMA 771. Some more advanced mathematical techniques concerning nonlinear differential equations of types encountered in BMA 771: several concepts of stability, asymptotic directions, Liapunov functions; different time-scales. Comparison of deterministic and stochastic models for several biological problems including birth and death processes. Discussion of various other applications of mathematics to biology, some recent research.

ST 773 Stochastic Modeling 3.
Prerequisite: BMA 772 or ST (MA) 746.

Survey of modeling approaches and analysis methods for data from continuous state random processes. Emphasis on differential and difference equations with noisy input. Doob-Meyer decomposition of process into its signal and noise components. Examples from biological and physical sciences, and engineering. Student project.

ST 779 Advanced Probability 3.
Prerequisite: MA 425 and ST 521..

Sets and classes, sigma-fields and related structures, probability measures and extensions, random variables, expectation and integration, uniform integrability, inequalities, L_p-spaces, product spaces, independence, zero-one laws, convergence notions, characteristic functions, simplest limit theorems, absolute continuity, conditional expectation and conditional probabilities, martingales.

ST 782 Time Series Analysis: Time Domain 3.
Prerequisite: ST 512 and ST 522.

Estimation inference for coefficients in autoregressive, moving average and mixed models and large sample. Distribution theory for autocovariances and their use in identification of time series models. Stationarity and seasonality. Extensions of theory and methods to multiple series including vector autoregressions, transfer function models, regression with time series errors, state space modelin.

ST 783 Time Series Analysis: Frequency Domain 3.
Prerequisite: ST 512 and ST 522.

Theory and methods of time series analysis from frequency point of view. Harmonic analysis, complex demodulation and spectrum estimation. Frequency domain structure of stationary time series and space-time processes. Sampling distributions of commonly used statistics.

ST 784 Multivariate Analysis 3.
Prerequisite: ST 522.

Survey of multivariate statistical theory. Multivariate distributions including the multinormal, Wishart, Hotelling's T, Fisher-Roy-Hsu, Wilks' and multivariate Beta distributions. Applications of maximum likelihood estimation, likelihood ratio testing and the union-intersection principle. Development of the theory of Hotelling's T tests and confidence sets, discriminant analysis, canonical correlation, multivariate analysis of variance and principal components.

ST 790 Advanced Special Topics 1-6.

ST 793 Advanced Statistical Inference 3.
Prerequisite: ST 522.

Statistical inference with emphasis on the use of statistical models, construction and use of likelihoods, general estimating equations, and large sample methods. Includes introduction to Bayesian statistics and the jackknife and bootstrap.

ST 801 Seminar 1.

ST 810 Advanced Topics in Statistics 1-3.

ST 830 Independent Study 1-3.

ST 835 Readings 1-3.

ST 841 Statistical Consulting 1.
Prerequisite: ST 512 and ST 522.

Participation in regularly scheduled supervised statistical consulting sessions with faculty member and client. Consultant's report written for each session. Regularly scheduled meetings with course instructor and other student consultants to present and discuss consulting experiences.

ST 885 Doctoral Supervised Teaching 1-3.
Prerequisite: Doctoral student.

Teaching experience under the mentorship of faculty who assist the student in planing for the teaching assignment, observe and provide feedback to the student during the teaching assignment, and evaluate the student upon completion of the assignment.

ST 890 Doctoral Preliminary Examination 1-9.
Prerequisite: Doctoral student.

For students who are preparing for and taking written and/or oral preliminary exams.

ST 893 Doctoral Supervised Research 1-9.
Prerequisite: Doctoral student.

Instruction in research and research under the mentorship of a member of the Graduate Faculty.

ST 895 Doctoral Dissertation Research 1-9.
Prerequisite: Doctoral student.

Dissertation Research.

ST 896 Summer Dissertation Research 1.
Prerequisite: Doctoral student.

For graduate students whose programs of work specify no formal course work during a summer session and who will be devoting full time to thesis research.

ST 899 Doctoral Dissertation Preparation 1-9.
Prerequisite: Doctoral student.

For students who have completed all credit hour requirements, full-time enrollment, preliminary examination, and residency requirements for the doctoral degree, and are writing and defending their dissertations.