ST 201 PRINCIPLES OF STATISTICS (3)
Design of experiments, descriptive statistics, the normal curve, probability, chance variability, sampling, confidence intervals for averages and percentages.
PREREQS:
ST 201 and ST 202 must be taken in order.
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ST 202 PRINCIPLES OF STATISTICS (3)
Tests of significance for averages and percentages for one and two samples, students t curve, chi-square tests, nonparametric tests, correlation and regression. ST 202 and ST 209 cannot both be taken for credit.
PREREQS:
ST 201
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ST 209 PRINCIPLES OF HYPOTHESIS TESTING (1)
Tests of significance for averages and percentages for one and two samples, Student's t curve, limitations of significance testing. Self-paced. May be taken concurrently with a self-paced section of ST 201. ST 209 and ST 202 cannot both be taken for credit.
PREREQS:
ST 201*
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ST 211 INTRODUCTION TO HYPOTHESIS TESTING (1)
Hypothesis testing for means and proportions using one and two samples. ST 211 serves as a transition between ST 201 and ST 352. Self-paced. May be taken concurrently with a self-paced section of ST 201. ST 211 and ST 351 cannot both be taken for credit.
PREREQS:
ST 201*
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ST 314 INTRODUCTION TO STATISTICS FOR ENGINEERS (3)
Probability, common probability distributions, sampling distributions, estimation, hypothesis testing, control charts, regression analysis, experimental design.
PREREQS:
MTH 252
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ST 351 INTRODUCTION TO STATISTICAL METHODS (4)
Descriptive statistics, random variables, normal distribution, sampling distributions, confidence intervals and hypothesis tests for means using one and two samples. Lec/lab. ST 211 and ST 351 cannot both be taken for credit.
PREREQS:
ST 351 and ST 352 must be taken in order.
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ST 351H INTRODUCTION TO STATISTICAL METHODS (4)
Descriptive statistics, random variables, normal distribution, sampling distributions, confidence intervals and hypothesis tests for means using one and two samples. Lec/lab.
PREREQS:
Honors College approval required.
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ST 352 INTRODUCTION TO STATISTICAL METHODS (4)
Simple and multiple linear regression, correlation, analysis of categorical data. Lec/lab.
PREREQS:
ST 211 or (ST 351 or ST 351H)
and
ST 351 and ST 352 must be taken in order.
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ST 406 PROJECTS (1-16)
Section 1: Projects, graded P/N. Section 2: Teaching Experience, graded P/N. Section 3: Directed Work, graded P/N.
This course is repeatable for a maximum of 16 credits.
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ST 407 SEMINAR (1)
Attendance at consulting practicum. Graded P/N.
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ST 410 INTERNSHIP (1-16)
Graded P/N.
This course is repeatable for a maximum of 16 credits.
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ST 411 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 411, ST 412 and ST 413 must be taken in order.
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ST 412 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 411
and
ST 209 or ST 351 or the equivalent.
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ST 413 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 412
and
ST 209 or ST 351 or the equivalent.
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ST 415 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 421 INTRODUCTION TO MATHEMATICAL STATISTICS (4)
Probability, random variables, expectation, discrete and continuous distributions, multivariate distributions.
PREREQS:
MTH 253. ST 421 and ST 422 must be taken in order.
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ST 422 INTRODUCTION TO MATHEMATICAL STATISTICS (4)
Sampling distributions, Central Limit Theorem, estimation, confidence intervals, properties of estimators, and hypothesis testing.
PREREQS:
ST 421
and
MTH 253
|
ST 431 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 435 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 439 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 441 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 MATLAB programming language. Lec/lab.
PREREQS:
ST 422 or ST 522
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ST 443 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 473 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 481 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
and
ST 448
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ST 483 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 or MTH 254H) and MTH 341
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ST 499 SPECIAL TOPICS (1-4)
This course is repeatable for a maximum of 8 credits.
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2004 Oregon State University
