DEPARTMENT OF STATISTICS
346 Computer Science;
(949) 824-5392: Fax: (949) 824-9863
E-mail: stat@uci.edu; World Wide Web: http:www.stat.uci.edu/
Hal
S. Stern, Department Chair
Faculty
Daniel L. Gillen: Biostatistics, survival analysis and longitudinal methods, group sequential methods, design and analysis of clinical trials, applications to biological and clinical studies
Wesley O. Johnson: Bayesian semi-parametric inference, survival analysis, prediction, specification of priors, applications in epidemiology, diagnostic testing, longitudinal and mixed modeling, asymptotics
Gang Liang: Statistical inference, graphical models, and machine learning
Hal S. Stern: Bayesian methodology, model diagnostics, applications to biological and social sciences, sports and statistics
David van Dyk: Statistical computation, Bayesian methodology, hierarchical modeling, causal inference, and application in astronomy and the physical and social sciences
Yaming Yu: Statistical computation, Bayesian methodology, and missing data problems
Statistics is the science concerned with developing and studying methods for collecting, analyzing, interpreting, and presenting empirical data. Statistical principles and methods are important for addressing questions in public policy, medicine, industry, and virtually every branch of science. Interest in statistical methods has increased dramatically with the abundance of large databases in fields like computer science (Internet and Web traffic), business and marketing (transaction records), and biology (the human genome and related data). It is the substantive questions in such various areas of application that drive the development of new statistical methods and motivate the mathematical study of the properties of these methods.
LOWER-DIVISION
7 Basic Statistics (4). Lecture, three hours; discussion, one to two hours. Introduces basic inferential statistics including confidence intervals and hypothesis testing on means and proportions, t-distribution, Chi Square, regression and correlation. F-distribution and nonparametric statistics included if time permits. Same as Mathematics 7. Statistics 7/Mathematics 7 and Biological Sciences 7 may not both be taken for credit. No credit for Statistics 7/Mathematics 7 if taken after Mathematics 67. (V) F, W offered for seniors only.
67 Introduction to Probability and Statistics for Computer Science (4). Lecture, three hours; discussion, two hours. Introduction to the basic concepts of probability and statistics with discussion of applications to computer science. Prerequisites: Mathematics 2B, 6A, and 6C or 3A. No credit for Statistics 7/Mathematics 7 or Biological Sciences 7 if taken after Statistics 67/Mathematics 67. Same as Mathematics 67.
UPPER-DIVISION
100A-B-C Foundations of Applied Statistics I, II, III (4-4-4). Lecture, four hours; laboratory, three hours. 100A-B: Descriptive statistical concepts and techniques most widely used in social science research. Weekly laboratories employ computer graphics to investigate concepts. 100A: Pass/Not Pass only. 100C: Classical statistical inference, limited to simple random sampling or simple randomization designs. Characteristics of sampling distributions; bias, standard error, mathematical models, estimation, hypothesis testing. Same as Social Sciences 100A-B-C and Social Ecology 166A-B-C. (V)
101 Introduction to Statistical Computing with SAS (4). Lecture, two hours; laboratory, two hours. Data definition, data acquisition, and data management using SAS procedures and commands. Statistical procedures available from the SAS Statistical Software Package. SAS/GRAPH procedures for producing statistical graphics. Prerequisites: completion of one year of statistics, or concurrent enrollment in Statistics 100C, or consent of instructor. Pass/Not Pass only. Same as Social Ecology 166E and Social Science 101E.
120A-B-C Introduction to Probability and Statistics (4-4-4). Lecture, three hours; discussion, one to two hours. Introductory course covering basic principles of probability and statistical inference. 120A: Axiomatic definition of probability, random variables, probability distributions, expectation. 120B: Point estimation, interval estimating, and testing hypotheses, Bayesian approaches to inference. 120C: Linear regression, analysis of variance, model checking. Prerequisites: for 120A-B: Mathematics 2A-B; 2D-2J or 4; for 120C: Statistics 120A-B; Mathematics 3A or 6C. Same as Mathematics 131A-B-C. Only one course from Statistics 120A, Mathematics 130A, and Mathematics 132A may be taken for credit.
199 Individual Study (2 to 5). Individual research or investigations under the direction of an individual faculty member. Prerequisite: consent of instructor.
