Sampling and statistical studies; basic probability; random variables and their distributions; exploring data using graphical techniques and numerical summaries; exploring relationships between two variables: chi-sq. test of independence; correlation, linear regression; confidence intervals and…

Stochastic processes including discrete, continuous and conditional probability concepts. Definitions and properties of stochastic processes. Markov processes and chains, basic properties, transition matrices and steady state properties. Reliability renewal and queueing processes, expected…

First course on statistics emphasizing applications in social, behavioral sciences. Covers elementary topics, one and two sample inference, simple linear regression, some categorical data analysis. Uses point-and-click statistical software. Provides preparation for Introduction to Statistical…

Applied methods in regression analysis with implementation in R. Topics include linear regression with mathematical examination of model assumptions and inferential procedures; multiple regression and model building, including collinearity, variable selection and inferential procedures; ANOVA as…

Design of finite population sample surveys. Stratified, systematic, and multistage cluster sampling designs. Sampling with probability proportional to size. Auxiliary variables, ratio and regression estimators, non-response bias.

The methodology of multivariate statistics and machine learning for students specializing in statistics. Topics include inference on multivariate means, multivariate analysis of variance, principal component analysis, linear discriminant analysis, factor analysis, linear discrimination,…

Basic graphical techniques and control charts. Experimentation in quality assurance. Sampling issues. Other topics include process capability studies, error analysis, SPRT, estimation and reliability.

Offered spring semester every even-numbered year.

Network structures are increasingly common across the sciences, such as brain connectivity, gene-gene interaction, protein-protein interaction, the spread of diseases, social networks, etc. This course will introduce state-of-the-art concepts and algorithms concerning networks in statistics and…

Autoregressive, moving average, autoregressive-moving average, and integrated autoregressive-moving average processes, seasonal models, autocorrelation function, estimation, model checking, forecasting, spectrum, spectral estimators.

Techniques and applications of nonparametric statistical methods, estimates, confidence intervals, one sample tests, two sample tests, several sample tests, tests of fit, nonparametric analysis of variance, correlation tests, chi-square test of independence and homogeneity, sample size…

Basic statistical methods through one- and two-sample inference, regression, correlation, one-way analysis of variance, analysis of covariance, and simple methods of categorical data analysis. Course emphasizes implementation and interpretation of statistical methods. Statistical software (SAS)…

Introduction to theory and methods of the Bayesian approach to statistical inference and data analysis. Covers components of Bayesian analysis (prior, likelihood, posterior), computational algorithms, and philosophical differences among various schools of statistical thought.

Offered…

A second course in statistical computing, using the SAS programming language to read data, create and manipulate SAS data sets, writing and using SAS MACROS, and SAS programming efficiency. SAS-based implementation of Structured Query Language (SQL). Additional topics may include Hadoop and…

Programming techniques in modern statistical software, including SAS and R for students with some experience with computer programming. Topics include data input/output; data formats and types; data management; flow control, conditional execution, and program design; statistical graphics and…

Programming techniques in modern statistical software, including SAS and R for students with some experience with computer programming. Topics include data input/output; data formats and types; data management; flow control, conditional execution, and program design; statistical graphics and…

Statistical analysis and data manipulation in R and Python. Implementation of SQL. Topics include data input/output; data formats and types; data management; functions for statistical modeling; introduction to algorithms; flow control and program design; and programs for complex data…

Methods for comparing time-to-event data, including univariate parametric and nonparametric procedures, regression models, diagnostics, group comparisons, and use of relevant statistical computing packages.

Introduction to data analysis via linear models and logistic regression. Linear regression topics include estimation, inference, variable selection, diagnostics, and remediation. Basic design of experiments, analysis of variance, and logistic regression will also be covered, including an…

Theory and methods for constructing and analyzing designed experiments are considered. Basic concepts in design of experiments, analysis of covariance, completely randomized designs, randomized complete and incomplete block designs, row-column designs, repeated measures designs, factorial…

Concepts and basic properties of some special probability distributions, independence, moment generating functions, sampling distributions of statistics, limiting distributions.

Introduction to the fundamentals of statistical inference. Point estimation, including the properties of estimators and ways of evaluating or comparing them, confidence intervals, and hypothesis testing. Statistical inference in linear models, including regression and analysis of variance, is…

The methodology of categorical data analysis and its applications. The course covers descriptive and inferential methods for contingency tables, an introduction to generalized linear models, logistic regression, multinomial response models, regression for counts, and methods for categorical data…

Probability axioms, combinatorial analysis, random variables, univariate and multivariate distributions, expectations, conditional distributions, independence, and laws of large numbers.

Not offered on a regular basis.

Central limit theorems, random walks, Markov chains and processes, Brownian motion, branching and renewal processes, diffusion processes and queueing processes and applications.

Not offered on a regular basis.

Provides preparation for graduate study in statistics by surveying topics in linear algebra and other areas chosen to strengthen students' analytical and mathematical skills.

Builds the foundation in probability distribution theory that is necessary to learn statistical inference. Emphasizes mathematical rigor and includes topics such as probability laws; random variables and probability distributions; joint, marginal and conditional distributions; expectation and…

Supplemental study on probability distributions and their role in mathematical statistics. This course is an adjunct to Probability Distributions and provides opportunity for additional study of the material in that course. Problems, background material, and details of derivations and proofs…

The principles and theory behind statistical inference. It provides justification for many statistical procedures routinely used in practice and discusses principles and theory that can be used to develop reasonable solutions to new statistical problems.

Supplemental study on the methods, principles, and theory of statistical inference. This course is an adjunct to Statistical Inference and provides opportunity for additional study of the material in that course. Problems, background material, and details of derivations and proofs related to…

Research while enrolled for a master's degree under the direction of faculty members.

Thesis writing under the direction of the major professor.

Provides graduate teaching assistants with knowledge of pedagogical approaches and available support systems for teaching statistics courses. Special sections are reserved for international students, with focus on use of language, pedagogy, and cultural aspects of teaching in this country.

Teaches students the communication skills necessary to successfully collaborate with non-statisticians in an interdisciplinary setting. Students will learn methods for conducting successful interactions with non-statisticians and will have opportunities to practice written and oral communication…

Students will be matched with an active UGA researcher and be responsible for all aspects of a collaborative project with this researcher. In-class instruction will be provided to students on project management, presentation, and writing. Students will regularly present their progress, and the…

Methods for sampling the environment and analysis of environmental data are considered. Techniques are presented for estimation, hypothesis testing, and regression when data are non-normal and/or dependent. Statistical methods based on generalized linear models, linear mixed models, time series…

Calculus-based introduction to probability and mathematical statistics for students in Biostatistics, Epidemiology and other fields requiring a technical understanding of statistical inference. Random variables, expectation, laws of large numbers and central limit theorem. Point estimation,…

Calculus-based introduction to probability and mathematical statistics for students in Biostatistics, Epidemiology and other fields requiring a technical understanding of statistical inference. Random variables, expectation, laws of large numbers and central limit theorem. Point estimation,…

Tools and methods of statistical computing beginning with mathematical and computational underpinnings of statistical computation and progressing through Monte Carlo simulation, numerical linear algebra, optimization, numerical differentiation and integration, and simulation-based statistical…

Continuation of Statistical Computing I. Advanced statistical computing techniques will be covered. Topics may include advanced MCMC methods, Expectation-Maximization methods, machine-learning algorithms, constrained optimization, density estimation, nonparametric regression perfect sampling,…

Methods for analysis of genetic data, with an emphasis on gene mapping. Topics include quantitative genetics, covariance between relatives, estimation of genetic parameters, detection of genetic linkage in crosses and natural populations, association mapping, and QTL mapping. Emphasis on fitting…

Multilevel and hierarchical models for social and biological sciences. Empirical Bayes, James-Stein, maximum likelihood, and Bayesian estimation of model parameters. Interpreting and diagnosing multilevel models, model building, and uncertainty assessment.

Measurable spaces and measures, Lebesgue-Stieljes measure, independence, almost sure and in probability convergence, integration in probability spaces, product measures, absolute continuity of measures, weak law of large numbers, strong law of large numbers, weak convergence.

Methods for constructing and analyzing designed experiments are considered. Concepts of experimental unit, randomization, blocking, replication, and orthogonal contrasts are introduced. Designs include completely randomized design, randomized complete block design, Latin squares design, split-…

An introduction to the theory and methodology of multivariate statistics for students with training in linear models and mathematical statistics. Topics include the multivariate normal distribution, one and two population inference on population mean vectors, MANOVA, principal component analysis…

Statistical modeling using nonlinear regression is considered. Topics include fixed-effects nonlinear regression models, nonlinear least squares, computational methods and practical matters, growth models, and compartmental models. Nonlinear mixed-effects models are discussed, including model…

Recent development in model-based estimation in survey sampling. A super population approach to inference on finite population quantities will be taken. Both Bayesian and classical approaches to sampling and related applications including small area estimation will be emphasized.

Not…

Recent development in model-based estimation in survey sampling. A super population approach to inference on finite population quantities will be taken. Both Bayesian and classical approaches to sampling and related applications including small area estimation will be emphasized.

Not…

An introduction to the methodology of multivariate statistics for quantitatively-oriented students from various disciplines who have training in regression and analysis of variance. Topics include the multivariate normal distribution, one and two population inference on population mean vectors,…

Theory of the linear model is studied. Topics include a review of linear algebra; distribution theory; full and non-full rank linear models; ordinary and generalized least squares; maximum and restricted maximum likelihood estimation; prediction, inference, estimability, analysis of variance,…

Supplemental study on the theory of linear models and its relevance to applications. This course is an adjunct to Theory of Linear Models and provides opportunity for additional study of the material in that course. Problems, background material, and details of derivations and proofs related to…

Models and theories in spatial data, including geostatistics, lattice data, spatial point patterns, and space-time data. The course will focus on random field theory, various spatial regression models, model fitting, inferences and spatial prediction, with applications to agriculture,…

Advanced topics in time series analysis and forecasting. Linear and nonlinear time series will be discussed. Topics include stationary processes, autocorrelation functions, various univariate time series models, forecasting, and multivariate time series. The focus is mostly on theoretical topics…

Covers state-of-the-art knowledge on selected topics such as factorial experiments, fractional factorials, incomplete block designs, orthogonal arrays, crossover designs, response surface methodology, mixture experiments, optimal design theory for linear and nonlinear models, and design…

Selected topics in the theory of multivariate analysis at an advanced level.

Not offered on a regular basis.

Advanced programming and implementation of modern statistical techniques using statistical software such as R. Topics include Monte Carlo simulations, resampling techniques, penalized regression, generalized linear models, robust methods, nonlinear regression, multiple testing adjustment, and…

The theory and methodology of Bayesian statistical inference. Training in statistical modeling and data analysis under the Bayesian paradigm.

Concepts of statistical inference for students in the life sciences, including maximum likelihood, Bayesian inference, and stochastic modelling. The course focuses on Hidden Markov models, continuous time Markov chain (Poisson process, birth and death process, coalescent process), and their…

Development of computational methods to infer biological information from data, including DNA sequences, gene expression levels, epigenome, and microbiome data. Students will read research articles ranging from statistics to biology and conduct extensive data analysis. Focus on raw data…

The theory of statistical inference is presented at an advanced level, including both frequentist and Bayesian perspectives. This course provides justification of many statistical procedures routinely used in good practice of statistics and discusses principles and theory that can be used to…

This is a course on large sample statistical methods. In many situations exact properties of statistical procedures used are not known and hence large sample approximations have to be made. This course is concerned with asymptotic methods which can be used to study tests and estimators based on…

Nonparametric estimation and hypothesis testing, relative efficiency, exchangeable random variables, ranking and distribution free statistics, generalized U-Statistics, generalized linear rank statistics, limiting distributions of certain nonparametric statistics, density estimation and related…

Categorical data analysis and generalized linear models beginning with contingency tables and their analysis. Theory of generalized linear models will then be presented, followed by more detailed and application-oriented discussions of special cases, including logistic, log-linear models, and…

Extensions of classical and generalized linear models with emphasis on longitudinal data analysis. Course will focus on linear mixed models, and marginal and mixed-effect versions of generalized linear models for longitudinal discrete data. Emphasis will be placed on the application of these…

Discrete time Markov chains, continuous time Markov chains, queueing processes, renewal processes, Markov random fields, point processes, Brownian motion and diffusion.

Offered fall semester every odd-numbered year.

Selected topics concerning recent developments in statistics.

Students will learn about current research topics in statistics. Students will attend departmental colloquia and other presentations concerning research. In addition, students will meet with individual faculty members to discuss their current research activities and open problems in statistics…

Provides training in some of the skills, tools, and resources essential for conducting statistical research and for professional practice. Students will learn materials and methods for conducting statistical research as well as written and oral skills for communicating research. The course will…

Research while enrolled for a doctoral degree under the direction of faculty members.

Dissertation writing under the direction of the major professor.