USC Interdisciplinary Statistics Group

The USC Interdisciplinary Statistics Group is coordinated by CER Faculty Fellow Rand Wilcox.

Overview

The USC interdisciplinary statistics group (ISG) consists of a wide range of quantitative experts located within various departments at USC. One goal of this group is to foster lines of communication among ISG members; another is to serve as a statistics resource for all faculty and students at USC.

Members of this group include experts on computational biology, probability, econometrics, psychometrics, and biostatistics.

Statistics, as a discipline, has grown and expanded tremendously in recent years making it possible to get a deeper, broader and more accurate understanding of what data are trying to tell us. ISG exists in part to help faculty and students take advantage of what contemporary tools and techniques of statistical analysis have to offer.

Upcoming Events

ISG Workshop: Jan. 21, 2008 (time TBA). Office of Research, Conference Room 329, USC Credit Union Building, 3720 S. Flower Street (parking available).

Susan Groshen, Dept. of Preventive Medicine: “Phase II Trials in Cancer Drug Development”
Delores Conway, Marshall School of Business: TBA

ISG Faculty

The Interdisciplinary Statistics Group is comprised of faculty from numerous departments and schools across USC:

• Business: Delores Conway, Mendel Fygenson, Gareth James, Jinchi Lu, John Rolph, Bert Steece
• Computational Biology: Laing Chen, Fengzhu Sun, Simon Tavare, Michael Waterman
• Economics: John Ham, Cheng Hsiao, Roger Moon, Hashem Pesaran, John Strauss
• Gerontology: Eileen Crimmins, Merril Silverstein
• Industrial/Systems Engineering: Geza Bottlik, James Moore, Kurt Palmer, Sheldon Ross, Detlof von Winterfeld
• Mathematics: Ken Alexander, Richard Arratia, Jay Bartroff, Peter Baxendale, Jason Fulman, Larry Goldstein, Cymra Haskell, Lei Li, Alan Schumitzky, Alexander Tartakovsky
• Policy, Planning, and Development: Yongheng Deng, Elizabeth Graddy, Christopher Weare
• Preventive Medicine: Stanley Azen, Kiros Berhane, Chih-Ping Chou, David Conti, James Gauderman , Susan Groshen, Bryan Langholz, Wendy Mack, Paul Marjoram, Kimberly Siegmund, Dan Stram, Duncan Thomas, Anny Xiang, Richard Watanabe
• Psychology: Laura Baker, Richard John, Jack McArdle, Rand Wilcox
• Sociology: Timothy Biblarz

Statistics Courses

Listed below is a partial list of graduate-level statistics course offered at various USC schools and divisions:

Preventive Medicine and Biostatistics

• PM 510L. Principles of Biostatistics
• PM 511a. Data Analysis. (Linear regression using SAS)
• PM 511b: Data Analysis. (Applied logistic regression and survival analysis).
• PM 513. Experimental Designs.
• PM 516a. Statistical Problem Solving
• PM 516b. Statistical Problem Solving.
• PM 517ab Research Methods in Epidemiology.
• PM 518ab Statistical Methods for Epidemiological Studies.
• PM 520 — Advanced Statistical Computing.
• PM 522a. Introduction to the Theory of Biostatistics.
• PM 533 Genetic and Molecular Epidemiology.
• PM 534 Statistical Genetics.
• PM 537 Chronic Disease Epidemiology.
• PM 538 Introduction to Biomedical Informatics.
• PM 544L. Multivariate Analysis.
• PM 552 Statistical Methods in Clinical Trials.
• PM 570 Statistical Methods in Human Genetics.
• PM 571. Applied Logistic Regression.
• PM 599 Programming in R.
• PM 599 Programming in SAS.
• PM 599. Multivariate Statistics in Behavioral Sciences in Preventive Medicine.
• PM603. Structural Equation Modeling.
• PM 610 Seminar in Biostatistics and Epidemiology.

Mathematics

• Math 541a Introduction to Mathematical Statistics.
• Math 541b Introduction to Mathematical Statistics.
• Math 542L Analysis of Variance and Regression.
• Math 578b Computational Molecular Biology.

Information and Operations Management.

• IOM 530. Applied Modern Statistical Learning Methods.
• GSBA (Graduate School of Business Administration)
• GSBA 603 Foundations of Statistical Inference.
• GSBA 625 Designing and Running Experiments.

Psychology

• Psych 501. Statistics in Psychological Research. (Introduces both classic and modern tools for analyzing data.)
• Psych 502. Analysis of Variance and Experimental Design.
• Psych 503. Regression and the General Linear Model.
• Psych 504. Research Design.
• Psych 520. Test Analysis.
• Psych 575. Multivariate Analysis of Behavioral Data.
• Psych 577. Analysis of Covariance Structures.
• Psych 578. Workshop in Quantitative Methods.

School of Education.

• EDPT 540. Introduction to Educational Measurement and Evaluation.
• EDPT 550. Statistical Inference.

School of Policy, Planning and Development.

• PPD 557 Quantitative Analysis I. (Applied Operations Research/Management).
• PPD 558 Quantitative Analysis II. (Applied multivariate statistics/econometrics).