Statistics
The Department of Statistics offers undergraduate service courses and an undergraduate minor, as well as graduate courses and programs leading to the MS and PhD degrees or to a minor for an advanced degree in other fields. Students planning to major in statistics at the graduate level should have a minimum of mathematics through multivariable calculus, linear algebra, and an upper-division sequence in mathematical statistics.
Survey Research Center
Established in 1973, the Oregon State University Survey Research Center (OSU-SRC) provides comprehensive survey services including proposal development, questionnaire design and layout, survey administration and data collection, survey analysis and professional report writing. Our staff offers customized options, working with our clients to determine the best approach to collect survey data based on the study objectives, population of interest, and budgetary concerns. Our past and current clients include federal, state, and local agencies, national non-profit organizations, and OSU-affiliated entities. The OSU-SRC maintains several contracts with clients to provide our services on a recurrent basis, from monthly, annually, to ever few years.
Operating as a center for research in survey methodology, the OSU-SRC routinely conducts experiments using self-administered surveys with an aim to contribute to survey methodology research. The OSU-SRC subsequently publishes related material in scientific journals and presents experimental findings at professional meetings. The OSU-SRC provides expertise using survey best practices to maximize response rates and reduce non-response bias. Various sampling plans are examined for each survey to minimize total survey error. The OSU-SRC also offers consulting for OSU community members on research-based survey design and analysis.
Lan Xue, Interim Department Head
239 Weniger Hall
Oregon State University
Corvallis, OR 97331-8574
Phone: 541-737-6577
Email: statistics.office@oregonstate.edu
Website: https://stat.oregonstate.edu/
Faculty & Staff
ST 199, SPECIAL TOPICS, 3 Credits
This course can only be taken once unless instructor permission is provided.
ST 201, PRINCIPLES OF STATISTICS, 4 Credits
Study design, descriptive statistics, the use of probability in statistical arguments, sampling, hypothesis tests and confidence intervals for means and proportions. NO LONGER TAUGHT. REPLACED WITH COMMON COURSE NUMBER ST 243Z.
Equivalent to: ST 201H, ST 243Z
Recommended: High school algebra
Available via Ecampus
ST 202, PRINCIPLES OF STATISTICS, 4 Credits
Comparisons of means and proportions between two populations (t-tests, chi-square tests, nonparametric tests), simple linear regression, correlation.
Prerequisite: ST 201 with D- or better or ST 243Z with D- or better
Available via Ecampus
ST 243Z, ELEMENTARY STATISTICS I, 4 Credits
A first course in statistics focusing on the interpretation and communication of statistical concepts. Introduces exploratory data analysis, descriptive statistics, sampling methods and distributions, point and interval estimates, hypothesis tests for means and proportions, and elements of probability and correlation. Technology will be used when appropriate.
Equivalent to: ST 201
Recommended: High school algebra
Available via Ecampus
ST 314, INTRODUCTION TO STATISTICS FOR ENGINEERS, 3 Credits
Probability, common probability distributions, sampling distributions, estimation, hypothesis testing, control charts, regression analysis, experimental design.
Prerequisite: MTH 252 with D- or better or MTH 252H with D- or better
Equivalent to: ST 314H
Available via Ecampus
ST 351, INTRODUCTION TO STATISTICAL METHODS, 4 Credits
Study designs, descriptive statistics, collecting and recording data, probability distributions, sampling distributions for means and proportions, hypothesis testing and confidence intervals for means and proportions in one- and two-sample inference, and chi-square tests. Lec/lab.
Equivalent to: ST 351H
Recommended: High school algebra with statistics
Available via Ecampus
ST 351H, INTRODUCTION TO STATISTICAL METHODS, 4 Credits
Study designs, descriptive statistics, collecting and recording data, probability distributions, sampling distributions for means and proportions, hypothesis testing and confidence intervals for means and proportions in one- and two-sample inference, and chi-square tests. Lec/lab.
Attributes: HNRS – Honors Course Designator
Equivalent to: ST 351
Recommended: High school algebra with statistics
ST 352, INTRODUCTION TO STATISTICAL METHODS, 4 Credits
Randomization tests and other nonparametric tests for one- and two-sample inference, simple and multiple linear regression, correlation, one- and two-way analysis of variance, logistic regression. Lec/lab.
Prerequisite: ST 351 with D- or better or ST 351H with D- or better
Available via Ecampus
ST 405, READING AND CONFERENCE, 1-16 Credits
This course is repeatable for 16 credits.
ST 406, PROJECTS, 1-16 Credits
Section 1: Projects. Section 2: Teaching Experience. Section 3: Directed Work.
This course is repeatable for 16 credits.
ST 407, SEMINAR, 1 Credit
Attendance at consulting practicum.
ST 410, INTERNSHIP, 1-16 Credits
This course is repeatable for 16 credits.
ST 411, METHODS OF DATA ANALYSIS, 4 Credits
Graphical, parametric and nonparametric methods for comparing two samples; one-way and two-way analysis of variance; simple linear regression.
Recommended: ST 351
ST 412, METHODS OF DATA ANALYSIS, 4 Credits
Multiple linear regression, including model checking, dummy variables, using regression to fit analysis of variance models, analysis of covariance, variable selection methods.
Prerequisite: ST 411 with D- or better
Recommended: ST 351
ST 413, METHODS OF DATA ANALYSIS, 4 Credits
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.
Prerequisite: ST 412 with D- or better
Recommended: ST 351
ST 415, DESIGN AND ANALYSIS OF PLANNED EXPERIMENTS, 3 Credits
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.
Prerequisite: ST 352 with D- or better or ST 411 with D- or better
ST 421, INTRODUCTION TO MATHEMATICAL STATISTICS, 4 Credits
Probability, random variables, expectation, discrete and continuous distributions, multivariate distributions.
Recommended: MTH 253
ST 422, INTRODUCTION TO MATHEMATICAL STATISTICS, 4 Credits
Sampling distributions, Central Limit Theorem, estimation, confidence intervals, properties of estimators, and hypothesis testing.
Prerequisite: ST 421 with D- or better
Recommended: MTH 253
ST 431, SAMPLING METHODS, 3 Credits
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.
Available via Ecampus
ST 436, R PROGRAMMING FOR DATA, 3 Credits
Focus on R programming from a data science perspective. Combine tools from the tidyverse set of packages to import, clean, prepare, and visualize data. Master basic data types, writing functions, automating repetitive tasks, and good practices for producing readable, reusable, and efficient R code.
Recommended: ST 201 or ST 351, experience working with data in spreadsheets or a point and click interface, or familiarity with calculating summary statistics (e.g., mean, median, standard deviation) and reading basic statistical charts (e.g., barcharts, histograms, scatterplots); critical thinking, problem solving, and reasoning skills
Available via Ecampus
ST 439, SURVEY METHODS, 3 Credits
Discusses survey design, survey administration, and modes of survey data collection. Emphasizes development of the questionnaire including survey question structures, questionnaire flow, and question formulation. Designs survey using Qualtrics given a specific population. Discusses recommended timings of survey delivery and accompanying letters to recruit participants.
Prerequisite: ST 201 with D- or better or ST 243Z with D- or better or ST 351 with D- or better or ST 351H with D- or better
Available via Ecampus
ST 441, PROBABILITY, COMPUTING, AND SIMULATION IN STATISTICS, 4 Credits
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.
Prerequisite: ST 422 with D- or better
ST 443, APPLIED STOCHASTIC MODELS, 3 Credits
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.
Prerequisite: ST 421 with D- or better
Recommended: Experience with a high-level programming language or mathematical computation package
ST 499, SPECIAL TOPICS, 1-4 Credits
This course is repeatable for 8 credits.
ST 501, RESEARCH, 1-16 Credits
This course is repeatable for 16 credits.
ST 503, THESIS, 1-16 Credits
This course is repeatable for 999 credits.
ST 505, READING AND CONFERENCE, 1-16 Credits
This course is repeatable for 16 credits.
Available via Ecampus
ST 506, PROJECTS, 1-16 Credits
Section 1: Projects. Section 2: Teaching Experience. Section 3: Directed Work.
This course is repeatable for 16 credits.
Available via Ecampus
ST 507, SEMINAR, 1 Credit
This course is repeatable for 99 credits.
ST 509, CONSULTING PRACTICUM, 2 Credits
The student provides statistical advice, under faculty guidance, on university-related research projects.
This course is repeatable for 99 credits.
ST 510, INTERNSHIP, 1-16 Credits
This course is repeatable for 16 credits.
ST 511, METHODS OF DATA ANALYSIS, 4 Credits
Graphical, parametric and nonparametric methods for comparing two samples; one-way and two-way analysis of variance; simple linear regression.
Recommended: ST 351
ST 512, METHODS OF DATA ANALYSIS, 4 Credits
Multiple linear regression, including model checking, dummy variables, using regression to fit analysis of variance models, analysis of covariance, variable selection methods.
Prerequisite: ST 511 with C or better
Recommended: ST 351
ST 513, METHODS OF DATA ANALYSIS, 4 Credits
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.
Prerequisite: ST 512 with C or better
Recommended: ST 351
ST 515, DESIGN AND ANALYSIS OF PLANNED EXPERIMENTS, 3 Credits
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.
Recommended: ST 352 or ST 411 or ST 511
Available via Ecampus
ST 516, FOUNDATIONS OF DATA ANALYTICS, 4 Credits
Foundations of estimation and hypothesis testing; desirable properties of estimators; maximum likelihood; one- and two-sample problems; theoretical results are explored through simulations and analysis using R. Offered via Ecampus only.
Recommended: ST 351
Available via Ecampus
ST 517, DATA ANALYTICS I, 4 Credits
Methods for modeling quantitative data and statistical learning--simple and multiple linear regression; linear mixed effects models; data imputation; prediction and cross-validation; scaling up to large datasets. Simulations and data analysis using R. Offered via Ecampus only.
Prerequisite: ST 516 with C+ or better
Available via Ecampus
ST 518, DATA ANALYTICS II, 4 Credits
Statistical methods and data analysis techniques for count data. Topics include tests for tables of counts, logistic regression, log-linear regression, generalized linear mixed models, and issues for large datasets. Data analysis in R.
Prerequisite: ST 517 with C+ or better
Available via Ecampus
ST 521, INTRODUCTION TO MATHEMATICAL STATISTICS, 4 Credits
Probability, random variables, expectation, discrete and continuous distributions, multivariate distributions.
Recommended: MTH 253
ST 522, INTRODUCTION TO MATHEMATICAL STATISTICS, 4 Credits
Sampling distributions, Central Limit Theorem, estimation, confidence intervals, properties of estimators, and hypothesis testing.
Prerequisite: ST 521 with C or better
Recommended: MTH 253
ST 525, APPLIED SURVIVAL ANALYSIS, 3 Credits
Statistical methods for analyzing survival data or time-to-event data, which may be censored and/or truncated. Specific topics can vary term to term, and could include Kaplan-Meier estimator; K-sample hypothesis tests for survival data; Accelerated failure time model; Cox proportional hazard regression model.
Prerequisite: ST 516 with C or better and ST 517 [C] and ST 518 [C]
Available via Ecampus
ST 531, SAMPLING METHODS, 3 Credits
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.
Available via Ecampus
ST 536, R PROGRAMMING FOR DATA, 3 Credits
Focus on R programming from a data science perspective. Combine tools from the tidyverse set of packages to import, clean, prepare, and visualize data. Master basic data types, writing functions, automating repetitive tasks, and good practices for producing readable, reusable, and efficient R code.
Available via Ecampus
ST 537, DATA VISUALIZATION, 3 Credits
Perceptual principles for displaying data; critique and improvement of data visualizations; use of color in visualization; principles of tidy data; strategies for data exploration; select special topics.
Prerequisite: ST 512 with C or better or ST 517 with C or better or ST 552 with C or better
Recommended: Familiarity with linear regression and using R
Available via Ecampus
ST 538, MODERN STATISTICAL METHODS FOR LARGE AND COMPLEX DATA SETS, 3 Credits
Provides students with the tools and experience to analyze big and messy data and work effectively in a data science team. Covers the tools to handle big data and answer statistical questions based on the data. Includes three big data analysis projects that students work on in groups. Focuses on proper use of modern data analysis techniques related to regression, classification and clustering for data coming from a variety of application fields. R will be the lingua franca.
Prerequisite: ST 512 with C or better or ST 517 with C or better or ST 552 with C or better or ST 412 with C or better
Available via Ecampus
ST 539, SURVEY METHODS, 3 Credits
Discusses survey design, survey administration, and modes of survey data collection. Emphasizes development of the questionnaire including survey question structures, questionnaire flow, and question formulation. Designs survey using Qualtrics given a specific population. Discusses recommended timings of survey delivery and accompanying letters to recruit participants.
Recommended: ST 243Z or ST 351
Available via Ecampus
ST 541, PROBABILITY, COMPUTING, AND SIMULATION IN STATISTICS, 4 Credits
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.
ST 543, APPLIED STOCHASTIC MODELS, 3 Credits
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.
Recommended: (ST 421 or ST 521) and experience with a high-level programming language or mathematical computation package.
ST 551, STATISTICAL METHODS, 4 Credits
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.
Recommended: Concurrent enrollment in MTH 341 and (ST 422 or ST 522)
ST 552, STATISTICAL METHODS, 4 Credits
Simple and multiple linear regression including polynomial regression, indicator variables, weighted regression, and influence statistics, nonlinear regression and linear models for binary data.
Prerequisite: ST 551 with C or better
ST 553, STATISTICAL METHODS, 4 Credits
Principles and analysis of designed experiments, including factorial experiments, analysis of covariance, random and mixed effect models. Lec/lab.
Prerequisite: ST 552 with C or better
ST 555, ADVANCED EXPERIMENTAL DESIGN, 3 Credits
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.
Prerequisite: ST 553 with C or better
ST 557, APPLIED MULTIVARIATE ANALYSIS, 3 Credits
Multivariate data structures, linear combinations; principal components, factor and latent structure analysis, canonical correlations, discriminant analysis; cluster analysis, multidimensional scaling. Not offered every year.
ST 558, MULTIVARIATE ANALYTICS, 3 Credits
Basics of matrix algebra, principal components analysis, cluster analysis, factor analysis, multidimensional scaling.
Prerequisite: ST 518 with C- or better
Available via Ecampus
ST 559, BAYESIAN STATISTICS, 3 Credits
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.
Recommended: ST 562
ST 561, THEORY OF STATISTICS, 4 Credits
Distributions of functions of random variables, joint and conditional distributions, sampling distributions, convergence concepts, order statistics.
ST 562, THEORY OF STATISTICS, 4 Credits
Sufficiency, exponential families, location and scale families; point estimation: maximum likelihood, Bayes, and unbiased estimators; asymptotic distributions of maximum likelihood estimators; Taylor series approximations.
Prerequisite: ST 561 with C or better
ST 563, THEORY OF STATISTICS, 4 Credits
Hypothesis testing: likelihood ratio, Bayesian, and uniformly most powerful tests; similar tests in exponential families; asymptotic distributions of likelihood ratio test statistics; confidence intervals.
Prerequisite: ST 562 with C or better
ST 565, TIME SERIES, 3 Credits
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, Offered winter term in even years.
ST 566, TIME SERIES ANALYTICS, 3 Credits
Focuses on statistical and analytical tools for analyzing data that are observed sequentially over time. Specific topics can vary term to term, and could include methods for exploratory time series analysis, linear time series models (ARMA, ARIMA), forecasting, spectral analysis and state-space models. The focus will be on applied problems, though some mathematical statistics is necessary for a solid understanding of the statistical issues. This course is designed for students in Data Analytics MS and Certificate programs.
Prerequisite: ST 516 with C or better and ST 517 [C] and ST 518 [C]
Available via Ecampus
ST 567, SPATIAL STATISTICS, 3 Credits
The analysis of spatial data. Graphical tools for exploring spatial data, geostatistics, variogram estimation, kriging, areal models, hierarchical spatial models, and spatio-temporal modelling. Offered winter term in odd years.
ST 591, INTRODUCTION TO QUANTITATIVE GENOMICS, 3 Credits
Provides an overview of how genomic data is generated and analyzed. It focuses on the underlying biological motivation, theoretical concepts, and analytical challenges associated with genomic research, especially the generation of statistics that summarize genomic data. The class is organized as a combination of lectures and group literature review discussions. Students are expected to actively participate in the class. Students from diverse backgrounds, including quantitative, biological, and computations sciences, are encouraged to enroll.
Available via Ecampus
ST 592, STATISTICAL METHODS FOR GENOMICS RESEARCH, 3 Credits
Lectures include an overview of statistical methods commonly applied in genomics research. Specific methods can vary term to term, and could include cluster analysis, decision trees, dimension reduction tools, regression models, multiple testing adjustment, variable selection methods, etc. Journal clubs include team-based review and presentations of landmark papers in both statistical methodology and genomics research. Research experience includes whole-term collaboration between students from statistics and other disciplines on real projects.
Recommended: ST 411 or ST 511 or a higher level course such as ST 551
Available via Ecampus
ST 595, CAPSTONE PROJECT, 3 Credits
Integrates and applies the analytics skills learned in the MS in Data Analytics program to solve real-world problems and interpret and communicate results. Engages student teams in the entire process of solving data science projects in realistic settings, from placing the problem into appropriate statistical framework to applying suitable analytic methods to the problem. Emphasizes problem solving, written and oral communication skills.
Prerequisite: ST 516 with C or better and ST 517 [C] and ST 518 [C] and ST 558 [C]
This course is repeatable for 6 credits.
Available via Ecampus
ST 599, SPECIAL TOPICS, 1-4 Credits
This course is repeatable for 16 credits.
ST 601, RESEARCH, 1-16 Credits
This course is repeatable for 16 credits.
ST 603, THESIS, 1-16 Credits
This course is repeatable for 999 credits.
ST 606, PROJECTS, 1-16 Credits
Section 1: Projects; Section 2: Teaching Experience; Section 3: Directed Work.
This course is repeatable for 16 credits.
ST 623, GENERALIZED REGRESSION MODELS, 3 Credits
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; over-dispersion and quasilikelihood models; log-linear models for multidimensional contingency tables.
ST 625, SURVIVAL ANALYSIS, 3 Credits
Prepares students to understand and analyze survival data. Concepts to be discussed include: hazard function (failure rate function); nonparametric likelihood; empirical processes; empirical distribution function; censoring (mostly right independent censoring); Kaplan-Meier estimator; Bias of the KM estimator; Cox proportional hazards model; Accelerated Failure Time Model; Partial Likelihood; log-rank test.
Prerequisite: ST 553 with C or better or ST 563 with C or better
ST 651, LINEAR MODEL THEORY, 3 Credits
Least squares estimation, best linear unbiased estimation, parameterizations, multivariate normal distributions, distributions of quadratic forms, testing linear hypotheses, simultaneous confidence intervals. Offered alternate years.
ST 652, LINEAR MODEL THEORY, 3 Credits
Explores advanced topics in linear and generalized linear mixed models: estimation, tests, confidence intervals, prediction, model diagnostics, model selection.
Prerequisite: ST 651 with C or better
ST 661, ADVANCED THEORY OF STATISTICS, 3 Credits
Exponential families, sufficient statistics; unbiased, equivariant, Bayes, and admissible estimation. Offered alternate years.
ST 662, ADVANCED THEORY OF STATISTICS, 3 Credits
Uniformly most powerful, unbiased, similar, and invariant tests. Offered alternate years.
Prerequisite: ST 661 with C or better