Course Descriptions
For your first course in statistics, please see the Placement Information
| Course Number | Course Name and Description |
|---|---|
| STA 30 (10) |
Basic Statistics and Quantitative Literacy
Statistical concepts involved in making inferences, decisions, and predictions from data. Emphasis on applications, not formal technique. Instructor: Staff Prerequisites: Must have taken placement test and placed accordingly Audience: Introductory, Undergraduate Typically offered: Fall, Spring and Summer |
| STA 89S (49S) |
First Year Seminar
Topics vary each semester offered. Instructor: Staff Prerequisites: First year and Transfer students only Audience: Introductory Typically offered: Spring Only |
| STA 101 (101) |
Data Analysis and Statistical Inference
Introduction to statistics as a science of understanding and analyzing data. Major themes include data collection, exploratory analysis, inference, and modeling. Focus on principles underlying quantitative research in social sciences, humanities... Prerequisites: Placement exam Audience: Introductory, Undergraduate, Minor Typically offered: Fall, Spring and Summer |
| STA 102 (102) |
Intro Biostatistics
Reading and interpretation of statistical analyses from life science and medical literature. Topics include: basic concepts and tools of probability and conditional probability, independence, two-by-two tables, Simpson's paradox, medical... Prerequisites: Must have STA 30 (10) or sufficient score on placement exam Audience: Introductory, Undergraduate Typically offered: Fall and/or Spring |
| STA 110FS (80FCS) |
Focus Program - Introductory Special Topics in Statistics
This is a seminar course for focus students. Topics vary every semester. Prerequisites: Math 31 (21) is required. Audience: Introductory Typically offered: Occasionally |
| STA 111 (103) |
Probability/Stat Inference
Basic laws of probability, random events, independence and dependence, expectations, Bayes theorem. Discrete and continuous random variables, density, and distribution functions. Binomial and normal models for observational data. Introduction to... Prerequisites: MATH 21 (31) Audience: Introductory, Undergraduate Typically offered: Fall and/or Spring |
| STA 130 (113) |
Probability/Statistics In Engineering
Introduction to probability, independence, conditional independence, and Bayes' theorem. Discrete and continuous, univariate and multivariate distributions. Linear and nonlinear transformations of random variables. Classical and Bayesian... Prerequisites: MATH 212 (103) or equivalent Audience: Introductory, Undergraduate Typically offered: Fall and/or Spring |
| STA 210 (121) |
Regression Analysis
Extensive study of regression modeling. Multiple regression, weighted least squares, logistic regression, log-linear models, analysis of variance, model diagnostics and selection. Emphasis on applications. Examples drawn from a variety of fields... Prerequisites: 100-level STA course. Audience: Undergraduate, Major, Minor Typically offered: Fall and/or Spring |
| STA 230 (104) |
Probability
Probability models, random variables with discrete and continuous distributions. Independence, joint distributions, conditional distributions. Expectations, functions of random variables, central limit theorem. Instructor: Staff Prerequisites: MATH 202 (102) MATH 212 (103) or MATH 222 (105) Audience: Introductory, Undergraduate, Major Typically offered: Fall and/or Spring |
| STA 250 (114) |
Statistics
An introduction to the concepts, theory, and application of statistical inference, including the structure of statistical problems, probability modeling, data analysis and statistical computing, and linear regression. Inference from the... Prerequisites: MATH 221 (104) and STA 230 (104)/MATH 230 (135) Audience: Introductory, Undergraduate, Major, Minor Typically offered: Fall and/or Spring |
| STA 320 (130) |
Design and Analysis of Causal Studies
Design of randomized experiments and observational studies. Role of randomization, block designs, factorial designs, fractional factorial designs, matching. Analysis of variance, contrasts, propensity score matching, instrumental variables. ... Prerequisites: STA 210 (121) Audience: Undergraduate, Electives Typically offered: Occasionally |
| STA 321 (135) |
Statistics Of Surveys
Design and analysis of surveys, including random sampling, stratification, clustering, and multi-stage sampling. Design-based and model-based inference. Methods for handling missing data. Instructor: Reiter Prerequisites: STA 210 (121) Audience: Undergraduate, Electives Typically offered: Occasionally |
| STA 340 (140) |
Introduction to Statistical Decision Analysis
Quantitative methods for decision making under uncertainty. Probability theory, personal probabilities and utilities, decision trees, ROC curves, sensitivity analysis, dominant strategies, Bayesian networks and influence diagrams, Markov models... Prerequisites: STA 230 (104) Audience: Undergraduate, Electives Typically offered: Occasionally |
| STA 350S (180S) |
Statistical Methods in Bioinformatics
Statistical and analytical tools for bioinformatics and genomics. Methods for comparison, database search, and functional inference for DNA, RNA, and protein sequences; analysis of families of molecular sequences and structures; inference in... Prerequisites: Statistical Science 230 (104) required. Statistical Science 250 (114) Computer programming and molecular biology required. Audience: Undergraduate, Electives Typically offered: Occasionally |
| STA 360 (122) |
Bayesian And Modern Statistics
Principles of data analysis and advanced statistical modeling. Bayesian inference, prior and posterior distributions, multi-level models, model checking and selection, stochastic simulation by Markov Chain Monte Carlo. Instructor: Clyde, Reiter... Prerequisites: STA 230 (104) , STA 250 (114), and STA (210) 121 (or equivalent) Audience: Undergraduate, Major, Electives Typically offered: Fall and/or Spring |
| STA 393 (191) |
Research Independent Study
Individual research in a field of special interest, under the supervision of a faculty member, resulting in a substantive paper or written report containing significant analysis and interpretation of a previously approved topic. Consent of... Prerequisites: None Audience: Undergraduate Typically offered: Fall and/or Spring |
| STA 470S (145S) |
Introduction to Statistical Consulting
Participation by students in data analysis projects from the DSS Statistical Consulting Center. Projects led and directed by DSS faculty. Instructor: Lucas Prerequisites: STA 360 (122) Audience: Undergraduate, Electives Typically offered: Fall and/or Spring |
| STA 471S (175S) |
Computational Data Analysis
Data analysis, exploration, and representation. Scientific modeling and computation. Data mining for large datasets, algebraic decomposition methods, stochastic simulation for temporal models of dynamic processes, graphical and network data,... Prerequisites: STA 360 (122) Audience: Undergraduate, Electives Typically offered: Spring Only |
| STA 490S (393) |
Special Topics in Statistics
Special topics not covered in core courses and more advanced topics related to current research directions in statistics. Consent of instructor required. Prerequisites: None Audience: Undergraduate, Electives Typically offered: Occasionally |
| STA 497S (190AS) |
Research Seminar in Statistical Science I
Statistical and mathematical underpinnings of methodological research in statistical science. Student presentations of their statistical research in collaboration with, and under the supervision of, an DSS faculty mentor. Prerequisites: None Audience: Undergraduate, Major Typically offered: Fall Only |
| STA 498S (190) |
Research Seminar in Statistical Science II
Continuation of Statistics 497S (190AS). Statistical and mathematical underpinnings of methodological research in statistical science. Student presentations of their statistical research in collaboration with, and under the supervision of, a DSS... Prerequisites: STA 497S (STA 190AS) Audience: Undergraduate, Major Typically offered: Spring Only |
| STA 503 (234) |
Choice Theory
This seminar deals with the foundations and applications of the theory of rational choice, including Bayesian decision theory (subjective expected utility) as well as nonexpected utility theory, noncooperative game theory, and arbitrage theory... Prerequisites: None Audience: Graduate Typically offered: Occasionally |
| STA 601 (290) |
Bayesian and Modern Statistical Data Analysis
Principles of data analysis and modern statistical modeling. Exploratory data analysis. Introduction to Bayesian inference, prior and posterior distributions, predictive distributions, hierarchical models, model checking and selection, missing... Prerequisites: None Audience: Graduate, PhD, Master's, First Year Typically offered: Fall and/or Spring |
| STA 611 (213) |
Introduction to Mathematical Statistics
Formal introduction to basic theory and methods of probability and statistics: probability and sample spaces, independence, conditional probability and Bayes' theorem; random variables, distributions, moments and... Prerequisites: Multivariable calculus Audience: Graduate, Master's Typically offered: Fall Only |
| STA 613 (270) |
Statistical Methods/Computational Biology
Methods of statistical inference and stochastic modeling with application to functional genomics and computational molecular biology. Prerequisites: STA 611 (213). linear algebra, and multivariate calculus Audience: Introductory, Graduate, Master's Typically offered: Spring Only |
| STA 690 (293) |
Special Topics in Statistics
Current topics vary by semester; please see Current Course Schedule for latest offering. Instructor: Staff Prerequisites: None Audience: Graduate, PhD Typically offered: Fall and/or Spring |
| STA 701S (395) |
Readings in Statistical Science
Advanced seminar on topics at research frontiers in statistical sciences. Consent of instructor required. Instructor: Staff Prerequisites: None Audience: Graduate, PhD, Second Year, First Year Typically offered: Fall and/or Spring |
| STA 711 (205) |
Probability and Measure Theory
Introduction to probability spaces, the theory of measure and integration, random variables, and limit theorems. Distribution functions, densities, and characteristic functions; convergence of random variables and of their distributions; uniform... Prerequisites: Real Analysis Audience: Graduate, PhD, Master's, First Year Typically offered: Fall Only |
| STA 721 (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... Prerequisites: None Audience: Graduate, PhD, Master's, First Year Typically offered: Fall Only |
| STA 723 (245) |
Statistics Case Studies
Advanced Bayesian statistical modelling from an applied perspective; problems and data from a range of application areas; focus on statistical thought and practice with in-depth examination of applications; statistical topics drawn from... Prerequisites: STA 721 (244), suggested co-requisite STA 831 (214) Audience: Graduate, PhD, First Year Typically offered: Spring Only |
| STA 732 (215) |
Statistical Inference
Classical, likelihood, and Bayesian approaches to statistical inference. Foundations of point and interval estimation, and properties of estimators (bias, consistency, efficiency, sufficiency, robustness). Testing: Type I and II errors, power,... Prerequisites: STA 611 (213) and STA 721 (244) or consent of instructor Audience: Graduate, PhD, Master's, First Year Typically offered: Spring Only |
| STA 790 (294) |
Special Topics in Statistics
Current topics vary by semester; please see Current Course Schedule for latest offering. Instructor: Staff Prerequisites: STA 611 (213) or consent of instructor. Pass/Fail grading only. Audience: Graduate, PhD Typically offered: Fall and/or Spring |
| STA 811 (207) |
Probability
Theoretic probability. Triangular arrays, weak laws of large numbers, variants of the central limit theorem, rates of convergence of limit theorems, local limit theorems, stable laws, infinitely divisible distributions, general state space... Prerequisites: STA 711 (205) Audience: Graduate, PhD Typically offered: Fall and/or Spring |
| STA 831 (214) |
Probability/Statistical Models
Theory, modeling, and computational topics in probability and statistics: distribution theory and modeling, simulation and applied probability models in statistics, generation of random variables. Monte Carlo method and integration; Markov Chain... Prerequisites: STA 601 (290), STA 721 (244) and STA 732 (215) Audience: Graduate, PhD, Master's, First Year Typically offered: Spring Only |
| STA 832 (345) |
Multivariate Statistical Analysis
Review of matrix algebra, transformations, and Jacobians. The multivariate normal, Wishart, multivariate t, and related distributions are given special emphasis. Topics such as principal components, factor analysis, discrimination and... Prerequisites: STA 732 (244) and STA 841 (216) Audience: Graduate, PhD, Master's Typically offered: Occasionally |
| STA 841 (216) |
Generalized Linear Models
Likelihood-based and Bayesian inference of binomial, ordinal, and Poisson regression models, and the relation of these models to item response theory and other psychometric models. Prerequisites: STA 721 (244) Linear Models or consent of instructor Audience: Graduate, PhD, Master's, Second Year Typically offered: Fall Only |
| STA 851 (390) |
Statistical Consulting Workshop
Under faculty supervision, students address and solve consulting problems submitted to the Department of Statistical Science's campus-wide consulting program, and present their solutions to the class. May be taken more than once. Consent of... Prerequisites: None Audience: Graduate, PhD Typically offered: Fall and/or Spring |
| STA 863 (376) |
Advanced Modeling and Scientific Computing
An introduction to advanced statistical modeling and modern numerical methods useful in implementing statistical procedures for data analysis, model exploration, inference, and prediction. Topics include simulation techniques for maximization... Prerequisites: None Audience: Graduate, PhD, Master's, Second Year Typically offered: Spring Only |
| STA 941 (281) |
Modern Nonparametric Theory and Methods
Modern nonparametric approaches for exploring and drawing inferences from data. Topics may include: resampling methods, nonparametric density estimation, nonparametric regression and classification, bootstrapping, kernel methods, splines, local... Prerequisites: None Audience: PhD Typically offered: Occasionally |
| STA 942 (356) |
Time Series and Forecasting
Time series data and models: trend, seasonality, and regressions. Traditional models: EWMA, EWR, ARMA. Dynamic linear models (DLMs). Bayesian learning, forecasting, and smoothing. Mathematical structure of DLMs and related models. Intervention,... Prerequisites: Prerequisite: STA 732 (244) or equivalent. Audience: Graduate, PhD, Master's Typically offered: Occasionally |
| STA 944 (280) |
Spatial Statistics
Modeling data with spatial structure;point-referenced (geo-statistical)data, areal (lattice) data, and point process data; stationarity, valid covariance functions; Gaussian processes and generalizations; kriging; Markov random fields (CAR and... Prerequisites: None Audience: Graduate, PhD, Master's Typically offered: Occasionally |
| STA 961 (357) |
Stochastic Processes
Conditional probabilities and Radon-Nikodym derivatives of measures; tightness and weak convergence of probability measures, measurability and observability. Markov chains, Brownian motion, Poisson processes. Gaussian processes, birth-and-death... Prerequisites: STA 711 (205) Audience: Graduate, PhD, Master's Typically offered: Occasionally |
| STA 993 (291) |
Independent Study
Directed reading and research. Consent of instructor and director of graduate studies required. Prerequisites: None Audience: Graduate, PhD Typically offered: Fall and/or Spring |
| STA 994 (293) |
Independent Study
Directed reading and research. Consent of instructor and director of graduate studies required. Prerequisites: None Audience: Graduate, PhD Typically offered: Fall and/or Spring |
