THE PROGRAM OF STUDIES

Statistics is a scientific discipline by which statisticians assist other scientists and researchers in making informed decisions in the face of uncertainty. Statisticians use skills—not only in statistics, but in other disciplines such as mathematics, computer science, business, management, and engineering—to solve problems. The application of statistics is the embodiment of the scientific method.

The graduate curriculum is designed to equip students with decision- making skills necessary for successful careers as professional statisticians. Although a firm foundation in theoretical statistics is provided, most of the courses are applied in nature, offering approaches to the solution of important real-world problems.

One degree is offered through the Department of Statistics: Statistics—MS. A statistics minor is also offered.

About twenty to twenty-five students are currently enrolled in the master's program in statistics. Students with an undergraduate degree in statistics, or with a very strong mathematics background, can generally complete the master's program in a little over one year. Other students generally take two years to complete the program.

Statistics—MS

This program is designed to prepare students for work in industry and government or PhD work in statistics.

Admission and Entry.

• Semesters of entry and application deadlines: fall, spring, summer, February 1 (U.S. and international). Students with a BS degree in statistics enter fall semester; others enter  spring or summer. Contact the department for further information.
• Entrance examination: GRE general test; minimum 3.3 overall undergraduate GPA required. Every international applicant whose native language is not English is required to submit TOEFL scores (minimum 580).
• Prerequisite: Stat 311, 322, 341, 342; 337 or 501; Math 343, 344; CS 130; or equivalents to these. Students whose native language is not English may be required to take one or more ESL classes, depending on the outcome of an interview with the department.

Requirements for Degree.

• Credit hours (31): minimum 25 course work hours plus 6 thesis hours (Stat 699R).
• Required courses: Stat 520, 521, 522, 531, 536, 591R, 592, 611, 636; one course from Stat 534, 537, 541, 545, 563, 621, 631, 662, 690R, Math 315 or other approved course.
• Minor (optional): any approved minor.
• Thesis.
• Examinations: (A) methods examination (Stat 222, 322, 336, 337; or 322, 501, 502); (B) theory examination (Stat 322, 341, 342, 520); (C) oral examination on course work; (D) oral defense of thesis.

Statistics—Minor

The statistics minor is offered to strengthen the data analysis skills of graduate students in the various experimental areas where statistical methodologies are frequently applied.

Master's Level.

• 9 hours in statistics courses numbered 311 or above except 552 and 554 (maximum 3 hours of 300-400 level may apply toward a graduate minor).
• Methods examination (Stat 222, 322, 336, 337; or 322, 501, 502) or the theory examination (Stat 322, 341, 342, 520).

PhD Level.

• Stat 520, 521.
• 9 additional hours from statistics courses 500 and above except Stat 501, 552, 554.
• Methods examination (Stat 222, 322, 336, 337; or 322, 501, 502) and a theory examination (Stat 322, 341, 342, 520).

FINANCIAL ASSISTANCE

The department has limited funds to supplement students' financial needs, and such funds are only available within departmental and university guidelines. Assistance is available in the following forms: tuition awards, internships, research assistantships, and tuition scholarships. For those interested in pursuing research assistantships, a booklet describing current research proposals is available in the department library.

RESOURCES AND OPPORTUNITIES

Center for Statistical Research. The center operates with full access to all departmental resources to provide statistical expertise to faculty, graduate students, and off-campus researchers in other disciplines. Areas of particular strength are designing experiments and sample surveys and analyzing the resulting data. Problems are solved by application and adaptation of state-of-the-art methodology and development of new methodology as required.

Quality Science Laboratory. The role of the Quality Science Laboratory is to facilitate the study and development of tools and techniques for improving the quality of products and services in the industrial, service, and government sectors. The Department of Statistics has administrative responsibility for the laboratory, but it is used by students from various parts of campus for study in quality technologies as well as to further research in the technology of quality control and improvement. Through the support of various industries, the laboratory is furnished with the latest computer equipment and automated measurement equipment for the collection and evaluation of quality-related data.

Computing Facilities. The Department of Statistics provides several excellent general computer laboratories furnished with modern computing equipment and software suitable for word processing, statistical graphics, data analysis, and statistical computing. These laboratories are reserved for the use of students in the department.

The current research plan for the Department of Statistics as a whole includes the development of multi-source data methods and the development of statistical tools for total quality management. In addition to these two general interests, specific research interest for individual faculty are listed in the faculty section immediately following the course descriptions.

For a more detailed description of the graduate program requirements, send for a copy of the department's bulletin.

COURSE DESCRIPTIONS

Class Schedule

501. Statistics for Research Workers 1. (5)

Prerequisite: Math 110 or equivalent. Recommended: concurrent registration in Stat 211, 322.

Probability, estimation, tests of hypotheses, regression, analysis of variance, and nonparametric methods. For natural or social science students.

502. Statistics for Research Workers 2. (5)

Prerequisite: Stat 501 or equivalent.

Analysis of covariance, multiple regression, linear models, design of experiments, and sampling. For natural or social science students.

520. Statistical Theory 1. (3)

Prerequisite: Math 344, Stat 341, 342, and instructor's consent.

Axiomatic probability theory for discrete and continuous random variables; moment generating functions; conditional probability; stochastic independence; transformations; limiting distributions; stochastic convergence, central limit theorem.

521. Statistical Theory 2. (3)

Prerequisite: Stat 520.

Sufficiency and completeness; point and interval estimation; hypothesis testing; Cramer-Rao inequality; some asymptotic results; introduction to Bayesian methods.

522. Theory of Linear Models. (3)

Prerequisite: Stat 322, 520, Math 343.

Linear hypotheses, with application to regression and design.

531. Experimental Design. (3)

Prerequisite: Stat 337.

Power for basic designs, hierarchical designs, change-over designs, confounding in symmetric and asymmetric designs, incomplete block designs, bioassay and response surface designs.

532. Quality Improvement for Engineering. (3)

Prerequisite: Stat 361, Math 113.

Selected topics in statistical theory, analysis of variance, simple and multiple regression, response surface design and analysis, multilevel experimental designs, blocking designs, confounding.

534. Sampling. (3)

Prerequisite: Stat 334; Stat 341 or instructor's consent.

Estimation in systematic, simple random, stratified, cluster, and PPS sampling and mixtures of these; ratio estimation; sample size determination and principles of sample allocation.

536. Regression Analysis. (3)

Prerequisite: Stat 322; 336 or 501.

Multiple regression, introduction to model building and nonlinear estimation, examination of residuals, stepwise regression, subset selection procedures, biased estimation, and model validation.

537. Categorical Data Analysis. (3)

Prerequisite: Stat 337 or 502; 536.

Analysis of multiway contingency tables with linear and log-linear models using maximum likelihood and minimum modified chi-square estimates as appropriate.

541. Advanced Probability. (3)

Prerequisite: Stat 520 or instructor's consent.

Stochastic processes, Markov chains, generating functions, birth-death processes, random walks, the gambler's ruin problem, advanced combinatorial methods.

545. Stochastic Processes. (3)

Prerequisite: Stat 421 or 520.

Review of elementary probability: expectation, characteristic functions, limit theorems. Introductory random processes: definitions and properties, covariance and spectral density, time average, stationarity, ergodicity, linear system relations, mean square estimation, Markov processes.

552. Statistical Methods in Education 1. (3)

Prerequisite: Math 100 or equivalent.

Measures of central tendency, variability; correlation; simple linear regression; introduction to hypothesis testing and estimation. Computer applications. For graduate majors in education and related fields.

554. Statistical Methods in Education 2. (3)

Prerequisite: Stat 552.

Applications of analysis of variance and covariance, multiple regression, correlation, and nonparametric methods. Introduction to experimental design. For graduate majors in education and related fields.

563. Advanced Operations Research. (3)

Prerequisite: Stat 463, 520.

Stochastic simulations; integer, nonlinear, and stochastic programming; developments in inventory theory; Markovian decision processes; insurance risks.

591R. Graduate Seminar in Statistics. (0)

592. Statistical Consulting. (1)

599R. Cooperative Education: Statistics. (1-9)

Prerequisite: departmental consent.

On-the-job experience. Report required.

611. Multivariate Statistical Methods. (3)

Prerequisite: Stat 322; 337 or 502.

Inference about mean vectors and covariance matrices; multivariate analysis of variance and regression; canonical correlation; discriminant analysis; principal component analysis; factor analysis.

621. Advanced Theory of Statistics. (3)

Prerequisite: Math 344, Stat 521.

Theory of estimation, testing hypotheses, multiple regression, and multivariate analysis.

631. Advanced Experimental Design. (3)

Prerequisite: Stat 342, 531.

Response surface methods, optimal designs, mixture designs, designs for nonlinear models, multi-response experiments, robust designs.

636. Advanced Statistical Methods. (3)

Prerequisite: Stat 342, 322; 502 or 531; 536.

Analysis of variance with unequal subclass frequencies, including missing cells; analysis of covariance; orthogonal polynomials; multiple comparisons and related topics.

662. Advanced Industrial Statistics and Reliability. (3)

Prerequisite: Stat 342, 462; Math 344.

Sequential sampling, tolerance limits, life testing, and reliability.

690R. Advanced Special Topics. (3)

Prerequisite: instructor's consent.

695R. Readings in Statistics. (1-3)

Prerequisite: departmental consent.

699R. Master's Thesis. (1-6)

Prerequisite: departmental consent.

FACULTY 

BEUS, GARY B., Associate Professor. PhD, Virginia Polytechnic Institute, 1968. Statistical Education; Quality Control.

BRYCE, GALE REX, Professor. PhD, University of Kentucky, 1974. Industrial Quality Improvement.

CHRISTENSEN, HOWARD B., Professor. PhD, North Carolina State University, 1975. Nonparametrics; Sample Design.

COLLINGS, BRUCE J., Professor. PhD, University of North Carolina, 1981. Actuarial Science; Biostatistics; Combinatorics.

FELLINGHAM, GILBERT W., Associate Professor. PhD, University of Washington, 1990. Biostatistics; Combining Data; Missing and Marginal Data.

GRIMSHAW, SCOTT D., Assistant Professor. PhD, Texas A&M University, 1989. Statistical Computing; Industrial Quality Improvement; Modern Regression Methods.

HENDRIX, LELAND J., Professor. PhD, Brigham Young University, 1967. Experimental Design; Computer Applications.

HILTON, STERLING C., Assistant Professor. PhD, Johns Hopkins University, 1996. Longitudinal Data Analysis.

LAWSON, JOHN S., Associate Professor. PhD, Polytechnic Institute of New York, 1984. Industrial Statistics; Experimental Design.

MADRIGAL, J. L., Associate Professor. DPhil, Oxford, England, 1985. Strategic Decision Making; Industrial and Business Statistics; Biostatistics.

RENCHER, ALVIN C., Professor. PhD, Virginia Polytechnic Institute, 1968. Multivariate Analysis; Linear Models.

SCHAALJE, G. BRUCE, Assistant Professor. PhD, North Carolina State University, 1988. Design and Analysis of Experiments; Population Modeling; Application of Statistics in Biology and Agriculture.

SCOTT, DEL T., Professor. PhD, Pennsylvania State University, 1977. Statistical Computing; Categorical Data Analysis; Linear Models.

TOLLEY, H. DENNIS, Professor. PhD, University of North Carolina, 1974. Health and Actuarial Statistics.

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