ST 501 RESEARCH (1-16)
This course is repeatable for a maximum of 16 credits.
PREREQS:
Departmental approval required.
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ST 503 THESIS (1-16)
This course is repeatable for a maximum of 16 credits.
PREREQS:
Departmental approval required.
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ST 505 READING AND CONFERENCE (1-16)
This course is repeatable for a maximum of 16 credits.
PREREQS:
Departmental approval required.
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ST 506 PROJECTS (1-16)
Section 1: Projects. Section 2: Teaching Experience. Section 3: Directed Work.
This course is repeatable for a maximum of 16 credits.
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ST 507 SEMINAR (1)
Section 1: Attendance at consulting practicum, 1 credit. Section 3: Research Seminar, 1 credit. Section 4: Computing Facilities, 1 credit. All sections graded P/N.
This course is repeatable for a maximum of 99 credits.
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ST 509 CONSULTING PRACTICUM (2)
The student provides statistical advice, under faculty guidance, on university-related research projects.
This course is repeatable for a maximum of 99 credits.
PREREQS:
ST 507, section 1 and ST 553, or instructor approval required.
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ST 510 INTERNSHIP (1-16)
Graded P/N.
This course is repeatable for a maximum of 16 credits.
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ST 511 METHODS OF DATA ANALYSIS (4)
Graphical, parametric and nonparametric methods for comparing two samples; one-way and two-way analysis of variance; simple linear regression. Lec/lab.
PREREQS:
ST 209 or ST 351 or the equivalent. ST 511, ST 512, and ST 513 must be taken in order.
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ST 512 METHODS OF DATA ANALYSIS (4)
Multiple linear regression, including model checking, dummy variables, using regression to fit analysis of variance models, analysis of covariance, variable selection methods. Lec/lab.
PREREQS:
ST 511
and
ST 209 or ST 351 or the equivalent.
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ST 513 METHODS OF DATA ANALYSIS (4)
Principles of experimental design; randomized block and factorial designs; repeated measures; categorical data analysis, including comparison of proportions, tests of homogeneity and independence in cross-classified frequency tables, Mantel-Haenszel test, logistic regression, log-linear regression. Introduction to multivariate statistics. Lec/lab.
PREREQS:
ST 512
and
ST 209 or ST 351 or the equivalent.
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ST 515 DESIGN AND ANALYSIS OF PLANNED EXPERIMENTS (3)
Principles of experimental design; uses, construction and analysis of completely randomized, randomized block and Latin square designs; covariates; factorial treatments, split plotting; random effects and variance components.
PREREQS:
ST 352 or (ST 411 or ST 511)
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ST 521 INTRODUCTION TO MATHEMATICAL STATISTICS (4)
Probability, random variables, expectation, discrete and continuous distributions, multivariate distributions.
PREREQS:
MTH 253. ST 521 and ST 522 must be taken in order.
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ST 522 INTRODUCTION TO MATHEMATICAL STATISTICS (4)
Sampling distributions, Central Limit Theorem, estimation, confidence intervals, properties of estimators, and hypothesis testing.
PREREQS:
ST 521
and
MTH 253
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ST 531 SAMPLING METHODS (3)
Estimation of means, totals and proportions; sampling designs including simple random, stratified, cluster, systematic, multistage and double sampling; ratio and regression estimators; sources of errors in surveys; capture-recapture methods.
PREREQS:
ST 411 or ST 511
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ST 535 QUANTITATIVE ECOLOGY (3)
Overview of statistical methods that are useful for analyzing ecological data, including spatial pattern analysis, multivariate techniques, logistic regression, Bayesian statistics and computer-intensive methods. Consideration of special topics such as population dynamics, food webs and ecological indicators. Not offered every year.
PREREQS:
ST 412 or ST 512
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ST 539 SURVEY METHODS (3)
Survey design, sampling, data collection and analysis, general methodology. Not offered every year.
PREREQS:
ST 201 or ST 351
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ST 541 PROBABILITY, COMPUTING, AND SIMULATION IN STATISTICS (4)
Review of probability, including univariate distributions and limit theorems. Random-number generation and simulation of statistical distributions. Bootstrap estimates of standard error. Variance reduction techniques. Emphasis on the use of computation in statistics using the S-Plus or MATLAB programming language. Lec/lab.
PREREQS:
ST 422 or ST 522
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ST 543 APPLIED STOCHASTIC MODELS (3)
Development of stochastic models commonly arising in statistics and operations research, such as Poisson processes, birth-and-death processes, discrete-time and continuous-time Markov chains, renewal and Markov renewal processes. Analysis of stochastic models by simulation and other computational techniques.
PREREQS:
(ST 421 or ST 521) and experience with a high-level programming language or mathematical computation package.
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ST 551 STATISTICAL METHODS (4)
Properties of t, chi-square and F tests; randomized experiments; sampling distributions and standard errors of estimators, delta method, comparison of several groups of measurements; two-way tables of measurements.
PREREQS:
ST 422 or ST 522. Should concurrently enroll in MTH 341. ST 551, ST 552 and ST 553 must be taken in order.
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ST 552 STATISTICAL METHODS (4)
Simple and multiple linear regression including polynomial regression, indicator variables, weighted regression, and influence statistics, nonlineral regression and linear models for binary data.
PREREQS:
ST 551
and
ST 422 or ST 522. Concurrent enrollment in MTH 341.
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ST 553 STATISTICAL METHODS (4)
Principles and analysis of designed experiments, including factorial experiments, analysis of covariance, random and mixed effect models. Lec/lab.
PREREQS:
ST 552
and
concurrent enrollment in MTH 341.
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ST 555 ADVANCED EXPERIMENTAL DESIGN (3)
Designs leading to mixed models including split plots, repeated measures, crossovers and incomplete blocks. Introduction to experimental design in industry including confounding, fractional factorials and response surface methodology. Analysis of unbalanced data.
PREREQS:
ST 553
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ST 557 APPLIED MULTIVARIATE ANALYSIS (3)
Multivariate data structures, linear combinations; principal components, factor and latent structure analysis, canonical correlations, discriminant analysis; cluster analysis, multidimensional scaling. Not offered every year.
PREREQS:
(ST 412 or ST 512) and (MTH 252 or MTH 245)
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ST 559 BAYESIAN STATISTICS (3)
Bayesian statistics for data analysis. Characterizations of probability; comparative (Bayesian versus frequentist) inference; prior, posterior and predictive distributions; hierarchical modeling. Computational methods include Markov Chain Monte Carlo for posterior simulation.
PREREQS:
ST 562
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ST 561 THEORY OF STATISTICS (3)
Distributions of functions of random variables, joint and conditional distributions, sampling distributions, convergence concepts, order statistics.
PREREQS:
(ST 422 or ST 522). ST 561, ST 562, and ST 563 must be taken in order.
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ST 562 THEORY OF STATISTICS (3)
Sufficiency, exponential families, location and scale families; point estimation: maximum likelihood, Bayes, and unbiased estimators; asymptotic distributions of maximum likelihood estimators; Taylor series approximations.
PREREQS:
ST 561
and
ST 422 or ST 522
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ST 563 THEORY OF STATISTICS (3)
Hypothesis testing: likelihood ratio, Bayesian, and uniformly most powerful tests; similar tests in exponential families; asymptotic distributions of likelihood ratio test statistics; confidence intervals.
PREREQS:
ST 562
and
ST 422 or ST 522
|
ST 565 TIME SERIES AND SPATIAL STATISTICS (3)
Analysis of serially correlated data in both time and frequency domains. Autocorrelation and partial autocorrelation functions, autoregressive integrated moving average models, model building, forecasting; filtering, smoothing, spectral analysis, frequency response studies, spatial statistics, kriging. Offered alternate years.
PREREQS:
(ST 412 or ST 512) and (ST 422 or ST 522)
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ST 571 ENVIRONMENTAL SAMPLING (3)
Evaluation and design of environmental surveys with special reference to the statistical aspects of indicator development, cost effective response designs, and spatially distributed sampling. Involves group project work. Not offered every year.
PREREQS:
ST 422 or ST 522 or instructor approval required
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ST 573 ECOLOGICAL SAMPLING (3)
Sampling of animal populations, frameless sampling, detectability, line transects, circular plots, mark-recapture, line intercept sampling; spatial sampling, quadrats, kriging; adaptive sampling designs. Not offered every year.
PREREQS:
(ST 412 or ST 512) and (ST 421 or ST 521)
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ST 581 LINEAR PROGRAMMING (3)
Formulation and solution of linear programming models; development of the simplex method and related pivot algorithms; duality, postoptimality analysis, extensions and applications of linear programming; special classes of linear programming.
PREREQS:
MTH 341 or ST 448
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ST 583 NONLINEAR OPTIMIZATION (3)
Convex sets and convex functions; gradients, Hessians; necessary and sufficient conditions for optimality; nonlinear duality; algorithms for unconstrained and constrained optimization.
PREREQS:
MTH 254 and MTH 341
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ST 585 TOPICS IN OPERATIONS RESEARCH (1-3)
A two-part course consisting of a reading component and a research component. In the reading component, students select and work on topics from a designated list. The research component provides an opportunity for further exploration in a topic of their choosing.
This course is repeatable for a maximum of 6 credits.
PREREQS:
ST 543 or ST 581
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ST 599 SPECIAL TOPICS (1-4)
May be repeated for credit when topics vary.
This course is repeatable for a maximum of 16 credits.
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ST 601 RESEARCH (1-16)
This course is repeatable for a maximum of 16 credits.
PREREQS:
Instructor approval required.
|
ST 603 THESIS (1-16)
This course is repeatable for a maximum of 16 credits.
PREREQS:
Instructor approval required.
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ST 606 PROJECTS (1-16)
This course is repeatable for a maximum of 16 credits.
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ST 623 GENERALIZED REGRESSION MODELS I (3)
Maximum likelihood analysis for frequency data; regression-type models for binomial and Poisson data; iterative weighted least squares and maximum likelihood; analysis of deviance and residuals; overdispersion and quasi-likelihood models; log-linear models for multidimensional contingency tables.
PREREQS:
ST 553 and ST 563
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ST 625 GENERALIZED REGRESSION MODELS II (3)
Parametric methods for the analysis of censored survival data, based mostly on large-sample likelihood theory. Specific topics include the Kaplan-Meier estimator, the log-rank test, partial likelihood, and regression models, including the Cox proportional-hazards model and its generalizations.
PREREQS:
ST 553 or ST 563
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ST 651 LINEAR MODEL THEORY (3)
Least squares estimation, best linear unbiased estimation, parameterizations, multivariate normal distributions, distributions of quadratic forms, testing linear hypotheses, simultaneous confidence intervals. Offered alternate years.
PREREQS:
ST 553 and ST 563. ST 651 and ST 652 must be taken in order.
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ST 652 LINEAR MODEL THEORY (3)
Advanced topics including classification models, mixed-effects models and multivariate models. Offered alternate years.
PREREQS:
ST 651
and
ST 553 and ST 563
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ST 661 ADVANCED THEORY OF STATISTICS (3)
Exponential families, sufficient statistics; unbiased, equivariant, Bayes, and admissible estimation. Offered alternate years.
PREREQS:
ST 563 and MTH 511. ST 661, ST 662, and ST 663 must be taken in order.
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ST 662 ADVANCED THEORY OF STATISTICS (3)
Uniformly most powerful, unbiased, similar, and invariant tests. Offered alternate years.
PREREQS:
ST 661
and
ST 563 and MTH 511
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ST 663 ADVANCED THEORY OF STATISTICS (3)
First-order and higher-order asymptotics; likelihood ratio, score, and Wald tests; Edgeworth and saddlepoint approximations. Offered alternate years.
PREREQS:
ST 662
and
ST 563 and MTH 511
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2004 Oregon State University
