2005-06 - Office of the Registrar - Duke University

decision functions and their properties. Minimax analysis and improper priors. Decision
theoretic Bayesian experimental design. Combining evidence and group decisions.
Prerequisite: Statistics 215 or consent of instructor. Instructor: Staff. 3 units.
231. Behavioral Decision Theory. 3 units. C-L: see Business Administration 525; also C-
L: Psychology 316
232. Statistical Analysis of Ecological Data. 3 units. C-L: Biology 266, Environment 241
234. Choice Theory. 3 units. C-L: see Business Administration 513
240. Applied Data Analysis for Environmental Sciences. 3 units. C-L: see Environment
210
242. Applied Regression Analysis. 3 units. C-L: see Environment 255
244. Linear Models. Multiple linear regression and model building. Exploratory data
analysis techniques, variable transformations and selection, parameter estimation and
interpretation, prediction, Bayesian hierarchical models, Bayes factors and intrinsic Bayes
factors for linear models, and Bayesian model averaging. The concepts of linear models
from Bayesian and classical viewpoints. Topics in Markov chain Monte Carlo simulation
introduced as required. Prerequisite: Statistics 213 and 290 or equivalent. Instructor: Staff.
3 units. C-L: Mathematics 217
250. Numerical Analysis. 3 units. C-L: see Computer Science 250; also C-L: Mathematics
221
253. Applied Stochastic Processes. 3 units. C-L: see Mathematics 216
270. Statistical Methods for Computational Biology. 3 units. C-L: see Bioinformatics &
Genome Technology 200
271. Statistical Genetics. 3 units. C-L: see Bioinformatics & Genome Technology 201
273. Genome Informatics and Sequence Analysis. 3 units. C-L: see Bioinformatics &
Genome Technology 203
277. Computational Methods for Macromolecular Structure. 3 units. C-L: see
Bioinformatics & Genome Technology 207
278. Gene Expression Analysis. 3 units. C-L: see Bioinformatics & Genome Technology
208
290. Statistical Laboratory. Introduction to statistical thinking, data management and
collection, sampling and design, exploratory data analysis, graphical and tabular displays,
summarizing data. Introduction to applied work. Computer orientation, statistical packages
and operating systems, especially unix on high-speed workstations, and the statistical
package S-Plus. Graphics and numerical computing. Examples from various disciplines.
Instructor: Staff. 3 units.
291. Independent Study. Directed reading and research. Consent of instructor and director
of graduate studies required. Instructor: Staff. Variable credit.
292. Independent Study. Directed reading and research. Consent of instructor and director
of graduate studies required. Instructor: Staff. Variable credit.
293. Special Topics in Statistics. Prerequisite: Statistics 213 or consent of instructor.
Credit/Non-Credit grading only. Instructor: Staff. 3 units.
294. Special Topics in Statistics. Prerequisite: Statistics 213 or consent of instructor.
Credit/Non-Credit grading only. Instructor: Staff. 3 units.
294A. Special Topics. 1.5 units.
295. First-Year Seminar. Weekly seminar covering a variety of statistical subjects.
Coregistration in Statistics 213 and Statistics 244 or consent of instructor. Instructor: Staff.
Variable credit.
356. Time Series and Forecasting. Time series data and models: trend, seasonality, and
regressions. Traditional models: EWMA, EWR, ARMA. Dynamic linear models (DLMs).
Institute of Statistics and Decision Sciences (STA) 273