updated 5/11/2009
Statistics
Donald Edwards, Chair
Professors
Mark P. Becker, Ph.D., Pennsylvania State University, 1985, Executive Vice President and Provost
Ian L. Dryden, Ph.D., University of Leeds, 1989
Donald G. Edwards, Ph.D., Ohio State University, 1981
James D. Lynch, Ph.D., Florida State University, 1974, Director of Center for Reliability and Quality Sciences
Edsel A. Peña, Ph.D., Florida State University, 1986
Associate Professors
John M. Grego, Ph.D., Pennsylvania State University, 1989, Director of Statistical Laboratory
Brian T. Habing, Ph.D., University of Illinois, UrbanaChampaign, 1998
Joshua M. Tebbs, Ph.D., North Carolina State University, 2000, Undergraduate Director
Assistant Professors
David B. Hitchcock, Ph.D., University of Florida, 2004
Xianzheng Huang, Ph.D., North Carolina State University, 2006
Lianming Wang, Ph.D., University of Missouri, 2006
Instructors
Georgiana R. Baker, M.S., University of South Carolina, 1996, Graduate Coordinator and Assistant to the Chair
Elizabeth D. Johnson, M.S., University of South Carolina, 2002
Maureen O. Petkewich, M.S., University of South Carolina, 2003
Adjunct Professors
J. Wanzer Drane, Ph.D., Emory University, 1967
James W. Hardin, Ph.D., Texas A&M, 1992
Huynh Huynh, Ph.D., University of Iowa, 1969
Andrew B. Lawson, Ph.D., University of St. Andrews (UK), 1991
Elizabeth H. Slate, Ph.D., Carnegie Mellon University, 1991
R. Cary Tuckfield, Ph.D., Indiana University, 1985
Machelle Wilson, Ph.D., University of California, Davis, 2002
Distinguished Professors Emeriti
Stephen D. Durham, Ph.D., University of California, Davis, 1969
William J. Padgett, Ph.D., Virginia Polytechnic Institute and State University, 1971
John D. Spurrier, Ph.D., University of Missouri, 1974
Overview
The department offers the Bachelor of Science degree with a major in statistics. The program provides a strong basis in both applied and theoretical statistics and prepares a student for the pursuit of graduate study in statistics or for employment by industry or government. In addition, the department serves many of the disciplines within the University through course offerings which provide basic statistical skills necessary to the pursuit of studies in these disciplines.
General Statistics Courses
STAT 110 is a first course in statistics at the freshman level which is devoted to the basic concepts of statistical thinking, including techniques of collecting, organizing, and presenting statistical data.
STAT 201 emphasizes the concepts and methods of statistical inference. Any of STAT 110, MATH 111, or MATH 115 provide good preparation for STAT 201.
STAT 509 or STAT 515 provide thorough introductions to statistical methods, followed by advanced methods in STAT 506, 518, 519, 520, 525, 530. STAT 511, 512, 513 provide an introduction to the theory of probability and mathematical statistics.
Degree Requirements
(128 hours)
The Bachelor of Science in Statistics degree program is designed to give the student a balance of skills in statistical theory and applied statistical data analysis. The program is broad, yet rigorous enough to prepare a student to work in business, industry, or government as a statistician, or to pursue graduate work in statistics.
1. General Education Requirements (4150 hours)
The following courses may fulfill some of the general education or cognate requirements and must be passed with a C or higher (in at most two attempts) for a B.S. degree in statistics: MATH 141, 142, 241, 526 (or 544); CSCE 145 or 206; ENGL 462 or 463. For an outline of other general education requirements, see "College of Arts and Sciences."
2. Major Requirements
General Major (27 hours)
Theory and Models: STAT 511, 512, and 513
Methods and Computation: One of STAT 509* or 515* and both of 516 and 517
Advanced Applications: Three STAT electives numbered 500* or above
Major with Emphasis in Actuarial Mathematics and Statistics (5758 hours)
Theory and Models: STAT 511, 512, and 513
Methods and Computation: One of STAT 509* or 515* and both of 516 and 517
Advanced Applications: STAT 510, 520, and one STAT elective numbered 500* or above
Cognate in Mathematics: MATH 241, 526 or 544, plus 6 credit hours chosen from MATH 550, 554, and 570
Minor in Risk Management and Insurance: ACCT 222, ECON 224, FINA 363 {=ECON 363}, 3 credit hours chosen from FINA 341 or FINA 444, separate from 3 credit hours chosen from FINA 442, 443, 444, or 445, plus 3 additional credit hours chosen from FINA 346, 442, 443, 444, 445, MGSC 392, 393, 594, ECON 420, 594, or BADM 499
Intensive Major (36 hours)
Same as the general major plus MATH 550, 554, and one additional elective selected from STAT courses numbered 500* and above, MATH 527, 555, 570, or MATH 574
*Major credit will be given for only one of STAT 509 or 515.
3. Cognate or Minor (1218 hours), see "College of Arts and Sciences"
4. Electives (2448 hours), see "College of Arts and Sciences"
Retention: To be retained in the program, a student must obtain a grade of C or higher in at most two attempts in all mathematics, computer science, and statistics courses required for graduation.
Sample Program (Minor in Mathematics)
Freshman Fall, Spring
MATH 141, 142 (4) (4) hours
CSCE 145, STAT 201 (4) (3) hours
ENGL 101, 102 (Grp. I) (3) (3) hours
Foreign Language 121, 122 (Grp. I) (4) (3) hours
ARTH 105 (Grp. III) () (3) hours
Total (15) (16) hours
Sophomore
MATH 241, 526 (3) (4) hours
STAT 515, 516 (3) (3) hours
MUSC 110 (Grp. III), GEOG 105 (Grp. IV) (3) (3) hours
Laboratory Science (Grp. V) (4) (4) hours
HIST 101, 102 (Grp. I) (3) (3) hours
Total (16) (17) hours
Junior
STAT 511, 512 (3) (3) hours
STAT 518 (3) () hours
MATH 550, 520 (3) (3) hours
PSYC 101 (Grp. IV), ENGL 462 (3) (3) hours
Electives (5) (6) hours
Total (17) (15) hours
Senior
STAT 517, 519 (3) (3) hours
MATH 554, 570 (3) (3) hours
STAT 513, 520 (3) (3) hours
Electives (7) (7) hours
Total (16) (16) hours
Integrated B.S./M.S. in Statistics
The integrated B.S./M.S. education plan is designed to permit outstanding statistics students to pursue both a B.S. degree and an M.S. degree in statistics in five years. During or after the semester in which the student completes 90 credit hours toward the B.S. degree in statistics, the student may apply to the department to participate in the integrated plan. The application will require the approval of the undergraduate advisor, the graduate director in statistics, and the dean of the College of Arts and Sciences. Provisional admission to and continuation in the integrated plan will require at least a 3.40 overall GPA and at least a 3.40 GPA in both undergraduate statistics and mathematics courses. Students opting for the plan should complete their B.S. requirements by the end of their eighth semester of study. Students admitted provisionally in the plan must take the general Graduate Record Examination before the end of their seventh semester. Continuation in the integrated plan after the eighth semester requires that the student be admitted into the graduate program in statistics through The Graduate School. Upon admission to the graduate program, the student may be eligible for financial assistance from the department. In the fifth year during the first or second summer of graduate study, the student may be eligible for a graduate teaching or research assistantship.
Cognate or Minor for Nonmajors
Students with majors in other departments may supplement their major program of study by selecting a cognate or minor in the statistical sciences.
Cognate in Statistics. All courses in statistics numbered 500 and above may be used for cognate credit.
Minor in Statistics. The minor consists of 18 hours of 500level statistics courses. Minor credit will be given for at most one of 509, 515.
Minor in Actuarial Mathematics and Statistics. The minor consists of the prerequisite courses MATH 141, 142, and 241, plus 18 hours of mathematics and statistics courses chosen as follows: MATH 511 {=STAT 511}, STAT 512, 513; and one course from three of the following four categories: 1) MATH 514 {=STAT 522}; 2) STAT 510, 520; 3) MATH 526, 544; 4) MATH 570, 574.
Course Descriptions (STAT)
 110  Introduction to Descriptive Statistics. (3) Computational and graphical techniques for organizing and presenting statistical data. Sample mean and sample variance, cross tabulation of categorical data, correlation and simple linear regression, quality control charts, statistical software.
 201  Elementary Statistics. (3) (Prereq: MATH 111 or 115 or STAT 110, or consent of department) An introductory course in the fundamentals of modern statistical methods. Topics include descriptive statistics, probability, random sampling, tests of hypothesis, estimation, simple linear regression, and correlation.
 205  Elementary Statistics for the Biological and Life Sciences. (3) (Prereq: MATH 111 or higher or consent of department) An introduction to fundamental statistical methods with applications in the biological and life sciences. Topics include descriptive statistics, probability, inference, and an overview of contingency tables, linear regression, and ANOVA.
 399  Independent Study. (36) Contract approved by instructor, advisor, and department chair is required for undergraduate students.
 506  Introduction to Experimental Design. (3) (Prereq: MATH 122 or MATH 142 or STAT 201) Techniques of experimentation based on statistical principles with application to quality improvement and other fields. Full and fractional factorial designs for factors at two levels; dispersion effects; related topics.
 509  Statistics for Engineers. (3) (Prereq: MATH 142 or equivalent) Basic probability and statistics with applications and examples in engineering. Elementary probability, random variables and their distribution, random processes, statistical inference, curve fitting, prediction, correlation and application to quality assurance, reliability, and life testing.
 510  Introduction to Applied Probability. (3) (Prereq: MATH 142 with a grade of C or higher) Probability spaces and Markov chains, random variables and expectations, tree measures and transition diagrams, balance equations and limiting distributions, queueing models and Little's Formula, simulation.
 511  Probability. {=MATH 511} (3) (Prereq: grade of C or higher in MATH 241) Probability and independence; discrete and continuous random variables; joint, marginal, and conditional densities; moment generating functions; laws of large numbers; binomial, Poisson, gamma, univariate and bivariate normal distributions.
 512  Mathematical Statistics. (3) (Prereq: STAT 511 or MATH 511 with a grade of C or higher) Sampling theory, discrete and continuous transformations, t and F distributions, independence of sample mean and S^{2}; limiting distributions, central limit theorem; quality of estimators, testing statistical hypotheses, confidence intervals, Bayesian estimates.
 513  Theory of Statistical Inference. (3) (Prereq: STAT 512 with a grade of C or higher) Hypothesis testing, NeymanPearson Theorem, best tests, likelihood ratio tests; sufficient statistics, RaoBlackwell theorem, completeness; efficiency, sequential probability ratio test, multiple comparisons.
 515  Statistical Methods I. (3) (Prereq: a grade of C or higher in MATH 111 or equivalent) Applications and principles of descriptive statistics, elementary probability, sampling distributions, estimation, and hypothesis testing. Inference for means, variances, proportions, simple linear regression, and contingency tables. Statistical packages such as SAS.
 516  Statistical Methods II. (3) (Prereq: a grade of C or higher in STAT 515 or STAT 509 or equivalent) Applications and principles of linear models. Simple and multiple linear regression, analysis of variance for basic designs, multiple comparisons, random effects, and analysis of covariance. Statistical packages such as SAS.
 517  Computing in Statistics. (3) (Prereq: STAT 509 or STAT 515 with a grade of C or higher) An introduction to statistical packages such as R and SAS with special focus on data management and computing procedures such as Monte Carlo simulation.
 518  Nonparametric Statistical Methods. (3) (Prereq: A grade of C or higher in STAT 515 or equivalent) Application of nonparametric statistical methods rather than mathematical development. Levels of measurement, comparisons of two independent populations, comparisons of two dependent populations, test of fit, nonparametric analysis of variance, and correlation.
 519  Sampling. (3) (Prereq: STAT 515 or equivalent) Techniques of statistical sampling in finite populations with applications in the analysis of sample survey data. Topics include simple random sampling for means and proportions, stratified sampling, cluster sampling, ratio estimates, and twostage sampling.
 520  Forecasting and Time Series. {=MGSC 520} (3) (Prereq: STAT 516 or MGSC 391) Time series analysis and forecasting using the multiple regression and BoxJenkins approaches.
 522  Financial Mathematics I. {=MATH 514} (Prereq: a grade of C or better in MATH 241) Probability spaces. Random variables. Mean and variance. Geometric Brownian Motion and stock price dynamics. Interest rates and present value analysis. Pricing via arbitrage arguments. Options pricing and the BlackScholes formula.
 523  Financial Mathematics II. {=MATH 515} (3) (Prereq: MATH 514 or STAT 522 with a grade of C or better) Convex sets. Separating Hyperplane Theorem. Fundamental Theorem of Asset Pricing. Risk and expected return. Minimum variance portfolios. Capital Asset Pricing Model. Martingales and options pricing. Optimization models and dynamic programming.
 525  Statistical Quality Control. {=MGSC 525} (3) (Prereq: STAT 509 or STAT 515 or MGSC 391) Statistical procedures for process control including CUSUM and Shewhart Control Charts, and lotacceptance sampling.
 528  Environmental Statistics. (3) (Prereq: STAT 516) Statistical analysis of environmental data. Review of multiple regression and ANOVA, nonlinear regression models and generalized linear models, analyses for temporally and spatially correlated data, and methods of environmental sampling.
 530  Applied Multivariate Statistics. (3) (Prereq: STAT 515 or PSYC 228 or MGSC 391 or equivalent) Introduction to fundamental ideas in multivariate statistics using case studies. Descriptive, exploratory, and graphical techniques; introduction to cluster analysis, principal components, factor analysis, discriminant analysis. Hotelling's T^{2} and other methods.
 582  Bayesian Networks and Decision Graphs. {=CSCE 582} (3) (Prereq: CSCE 350 and STAT 509) Normative approaches to uncertainty in artificial intelligence. Probabilistic and causal modeling with Bayesian networks and influence diagrams. Applications in decision analysis and support. Algorithms for probability update in graphical models.
 591  Data Analysis for Teachers. {=SMED 591} (3) Introduction to statistics for elementary, middle, and high school teachers. The fundamentals of data collection, descriptive statistics, probability, and inference with special focus on methods of teaching statistical reasoning. For I.M.A./M.A.T. (excluding mathematics)/M.Ed./M.T. and nondegree credit only.
 599  Topics in Statistics. (13) Course content varies and will be announced in the schedule of courses by suffix and title.
