Course Guide for Fall 2004

Graduate level courses in statistics and quantitative methods are listed by department (alphabetically). Some advanced undergraduate level courses are also listed. Links are provided to course descriptions. Please report problems or suggestions concerning this web page to sguo@email.unc.edu

Course Descriptions

Biostatistics

BIOS 160: Probability and Statistical Inference I

Prerequisite, Mathematics 33 or equivalent. Introduction to probability; discrete and continuous random variables; expectation theory; bivariate and multivariate distribution theory; regression and correlation; linear functions of random variables; theory of sampling; introduction to estimation and hypothesis testing.

BIOS 162: Intermediate Statistical Methods

Corequisites, Biostatistics 111, 150, or equivalents. Principles of study design, descriptive statistics, and sampling from finite and infinite populations, with particular attention to inferences about location and scale for one, two, or k sample situations. Both distribution-free and parametric approaches are considered. Gaussian, binomial, and Poisson models, one-way and two-way contingency tables, as well as related measures of association, are treated.

BIOS 165: Analysis of Categorical Data

Prerequisites, Biostatistics 145, 150, and 162, or permission of the instructor. Introduction to the analysis of categorized data: rates, ratios, and proportions; relative risk and odds ratio; Cochran-Mantel-Haenszel procedure; survivorship and life table methods; linear models for categorical data. Applications in demography, epidemiology, and medicine.

BIOS 170: Demographic Techniques I

Prerequisite, Biostatistics 101 or equivalent. Source and interpretation of demographic data; rates and ratios, standardization, complete and abridged life tables; estimation and projection of fertility, mortality, migration, and population composition.

BIOS 231: Bayesian Statistics

Prerequisite, Biostatistics 161 or equivalent. Bayesian perspective on statistical theory (foundations and inference); decision theory; applications of prior to posterior analysis, predictive inference; estimation of distributions via numerical and stochastic simulation techniques; empirical Bayes methodology.

BIOS 235: Statistical Computing

Prerequisites, Biostatistics 161; familiarity with at least one computer system and with either a computer language (C, FORTRAN, etc.) or a computer package (SAS, SPSS, etc.). Basic theory and application of computing as a tool in statistical research and practice. Topics include: algorithms and data structures, linear and nonlinear systems, function approximation, numerical integration, the EM algorithm, simulation, and document preparation.

BIOS 260: Advanced Probability and Statistical Inference I

Prerequisite, Biostatistics 161 or permission of the instructor. Generating and characteristic functions; discrete and continuous distributions; basic estimation theory; basic hypothesis testing theory; sampling distributions; probability inequalities; laws of large numbers; martingales and reversed martingales; stochastic convergence; central limit theorems; large sample theory for least squares estimation and maximum likelihood estimation; large sample theory for likelihood ratio tests; and introduction to methods for categorical data and generalized linear models

BIOS 262: Advanced Linear Models I

Prerequisites, Biostatistics 161 and 163, Mathematics 147, Mathematics 116 or 137. Theory and methods for continuous responses. Topics include matrix theory, the multivariate normal distribution, multivariate quadratic forms, estimability, reparameterization, linear restrictions and splines, estimation theory, weighted least squares, multivariate tests of linear hypotheses, multiple comparisons, confidence regions, prediction intervals, statistical power, mixed models, transformations and diagnostics, growth curve models, dose-response models, missing data.

Economics

ECON 210: Basic Quantitative Techniques

Topics from linear algebra, calculus, linear and nonlinear programming, and the theory of difference and differential equations with applications to economics.

ECON 221: Game Theory I

Prerequisite, Economics 200, 201, or permission of the instructor. Non-cooperative games in strategic and extensive form with perfect and imperfection information. Other topics from: information economics, mechanism design, auctions, repeated games, bargaining, bounded rationality, learning, evolutionary games, cooperative games.

ECON 271: Introduction to Econometric Theory

Probability theory, expectation, conditional expectation, modes of convergence, limit and interchange theorems, and the asymptotics of maximum likelihood, generalized method of
moments, and efficient method of moments.

ECON 273: Advanced Econometrics

Prerequisites, Economics 271, Economics 272, and Mathematics 147. Economics 273 constitutes a one-semester treatment of the fundamental theory of econometrics. Topics covered include asymptotic distribution theory, linear and nonlinear models, specification testing techniques, and simultaneous equations models.

Education

EDUC 184: Statistical Analysis of Educational Data I

Descriptive and inferential statistics for educational research, including an introduction to fundamentals of research design and computer data analysis.

EDUC 384: Statistical Analysis of Educational Data III

An extension of the general linear model to analysis of educational data with multiple dependent variables, with computer applications.

Epidemiology

EPID 269: Advanced Methods for Epidemiologic Data Analysis

Prerequisite: EPID 268. Permission of instructor required for non-majors. Concepts and applications, including historic overview, logistic regression, survival analysis, model building strategy, additive and multiplicative interaction, meta-analysis, and graphical exploration. Includes computer-based experience with real data. Two lecture and four lab hours per week.

Prerequisites, EPID 268 and 269, and BIOS 145. A seminar for advanced students exploring methodological issues in epidemiology, including measurement error, missing data, intermediate variables, complex study designs, meta-analysis, splines, and other topics.

Political Science

Psychology

Sociology

Statistics