STAT801A
Statistical Methods in Research: Non Calculus

Prerequisites: Introductory course in statistics.

This is an introductory, non-calculus based course for students who will not take statistics courses beyond STAT 802, 803 or 804. Students interested in taking more advanced statistics courses should register for STAT 801B.

Description: Statistical concepts and statistical methodology useful in descriptive, experimental, and analytical study of biological and other natural phenomena. Practical application of statistics rather than on statistical theory.

Course details
Credit Hours:4
Max credits per semester:4
Max credits per degree:4
Grading Option:Grade Pass/No Pass Option

Credit Hours:4

ACE:

STAT801B
Statistical Methods in Research: Calculus

Prerequisites: Introductory statistics course; at least one semester of calculus.

This course (not STAT 801A) is a pre-requisite for Stat 870, 873, 875 and 876. Can also be used as a pre-requisite for Stat 802 and 803.

Description: Statistical concepts and methodology useful for description, analysis and interpretation of experimental and observational studies. Practical application of statistics and essential background for subsequent courses in statistics.

This course is a prerequisite for: STAT 873; STAT 886

Course details
Credit Hours:4
Max credits per semester:4
Max credits per degree:4
Grading Option:Grade Pass/No Pass Option

Credit Hours:4

ACE:

STAT802
Design and Analysis of Research Studies

Prerequisites: STAT 318 or STAT 801A.

Description: Essential statistical characteristics of a research study intended to assess the impact of treatment, environmental or population conditions on response. Focus is on both designed experiments and on studies for which controlled experiments are not feasible but characteristics of controlled experiment must be mimicked to the extent possible. Methods to assess power and compare efficiency of alternative designs are considered. Course covers major design structures, including blocking, nesting, multilevel models, split-plot and repeated measures, and statistical analysis associated with these structures.

This course is a prerequisite for: AGRO 816E; ASCI 944, STAT 844; STAT 831; STAT 870; STAT 877; STAT 885

Course details
Credit Hours:4
Max credits per semester:4
Max credits per degree:4
Grading Option:Grade Pass/No Pass Option

Credit Hours:4

ACE:

STAT803
Ecological StatisticsCrosslisted with NRES 803

Prerequisites: STAT 801 or equivalent; prior experience with "R" software

Available online.

Description: Model-based inference for ecological data, generalized linear and additive models, mixed models, survival analysis, multi-model inference and information theoretic model selection, and study design.

Course details
Credit Hours:4
Max credits per semester:4
Max credits per degree:4
Grading Option:Grade Pass/No Pass Option

Credit Hours:4

ACE:

STAT804
Survey Sampling

Prerequisites: STAT 880 or IMSE 321

Description: Sampling techniques: simple random sampling, sampling proportions, estimation of sample size, stratified random sampling, ratio and regression estimates.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT810
Alpha Seminar

Prerequisites: Statistics graduate student

Description: Program requirements, resources available, tips for academic success, professional statistical organizations, career paths, history of statistics, ethics, statistical conferences, statistical blogs and online forums, frequentist and Bayesian paradigms, current research in department.

Course details
Credit Hours:1
Max credits per semester:1
Max credits per degree:1
Grading Option:Pass No-Pass

Credit Hours:1

ACE:

STAT811T
Statistics for Middle-Level Teachers

Prerequisites: A valid teaching certificate or permission. An undergraduate coruse in introductory statistics is desirable, but not essential.

Not open to MA or MS students in mathematics or statistics.

Description: Designed primarily to develop and equip middle-level teachers with the statistical knowledge they need for teaching. The course follows an inquiry/discovery design, dedicating much of class time to activities, discussion and group work. The course emphasizes topics in statistics that are part of the middle-school mathematics curriculum, as well as their application in other disciplines. The course also includes statistics that are used in education and school-based research.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT812T
Statistics for High School Teachers

Prerequisites: A valid secondary mathematics teaching certificate.

Not open to MA or MS students in mathematics or statistics.

Description: The statistical concepts typically taught in a high school statistics class, including linear regression, two-way tables, sampling distributions, statistical inference for means and proportions, chi-square tests, and inference for regression. Some experience with basic statistical concepts (mean, standard deviation, elementary probability) is necessary. The course is inquiry-based, and will emphasize applications and statistical thinking.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT821
Statistical Methods I

Prerequisites: Matrix Algebra; concurrently taking STAT 882, or passed STAT 882 with grade of B or higher, or passed STAT 880 with grade of B or higher.

Designed for Statistics MS majors and minors.

Description: Introduction to essential statistical methods and supporting design and modeling theory for professional statistical practice. First in a three semester sequence. Focus of this course on methods for single response variable and non-hierarchical study design.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Graded
Offered:FALL/SPR

Credit Hours:3

ACE:

STAT822
Statistical Methods II

Prerequisites: STAT 821; concurrently taking STAT 883, or passed STAT 883 with grade of B or higher, or passed STAT 880 with grade of B or higher.

Course is designed for Statistics MS majors and minors.

Description: A continuation of Statistical Methods I. Second in a three semester sequence on essential statistical methods and supporting design and modeling theory for professional statistical practice. Focus in this course of methods for single response variable and multiple sources of random variation.

This course is a prerequisite for: STAT 823; STAT 825; STAT 885; STAT 931

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Graded

Credit Hours:3

ACE:

STAT823
Statistical Methods III

Prerequisites: STAT 822; STAT 883 with grade of B or higher, or STAT 880 with grade of B or higher.

This course is designed for Statistics MS Majors.

Description: Introduction to essential statistical methods and supporting design and modeling theory for professional statistical practice. Third in a three semester sequence. Focus of this course on methods for situations that extend beyond the single-response-variable, designed study cases featured in Statistical Methods I and II. These include multivariate statistics, non-linear models, non- and semi-parametric statistics, observational studies, and other theory and methods deemed appropriate as statistical science continues to evolve.

This course is a prerequisite for: STAT 950; STAT 971; STAT 983

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Graded

Credit Hours:3

ACE:

STAT825
Principles of Statistical Consulting and Interdisciplinary Collaboration

Prerequisites: STAT 822 or instructor permission.

Description: Introduction to the role and purpose of statistical consulting and interdisciplinary collaboration. Topics include: asking good questions, dealing with difficult clients, communicating statistics to non-statisticians, determining solutions, and collaborating.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT830
Sensory EvaluationCrosslisted with FDST 430, FDST 830, STAT 430

Prerequisites: Introductory course in statistics.

Description: Food evaluation using sensory techniques and statistical analysis.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT831
Spatial Statistics

Prerequisites: STAT 802 and knowledge of matrix algebra or Stat 821

Offered odd-numbered calendar years.

Description: Statistical methods for modeling and analyzing correlated data, with emphasis on spatial correlation. Descriptive statistics, time series, correlograms, semivariograms, kriging and designing experiments in the presence of spatial correlation.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT832
Statistics in Sports

Description: Statistical methods useful for analyzing sports-related data. Descriptive statistics, graphical representations, experimental design, discriminant analysis and optimization.

Course details
Credit Hours:2
Max credits per semester:2
Max credits per degree:2
Grading Option:Grade Pass/No Pass Option

Credit Hours:2

ACE:

STAT841
Statistical Methods for High Throughput Biological Data

Prerequisites: STAT 801A or equivalent.

Description: Basic biological concepts. Multiple testing and false discovery rate. Second generation sequencing and statistical issues. ChIP-seq. RNA-seq. Empirical Bayes methods and software. Normalization, experimental design and commonly used models for microarray data. Metabolomics.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT842
Computational BiologyCrosslisted with BIOC 842, STAT 442, BIOC 442

Prerequisites: Any introductory course in biology, or genetics, or statistics.

Description: Databases, high-throughput biology, literature mining, gene expression, next-generation sequencing, proteomics, metabolomics, system biology and biological networks.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT843
Next-Generation Sequencing and Systems Biology

Prerequisites: Any introductory course in biology, statistics, computer science or mathematics

Description: Next-generation RNA and genome sequencing, systems biology. Regulatory networks of transcription, protein-protein interaction networks, theory and practice. Databases, data integration and visualization. Students present computational biology publications and projects.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Graded

Credit Hours:3

ACE:

STAT844
Quantitative Methods for Genomics of Complex TraitsCrosslisted with ASCI 944

Prerequisites: ASCI 861U or AGRO/ASCI/HORT 931 or BIOS 818 or equivalent; STAT 802 or 821 or equivalent.

Description: Quantitative genetic analysis of complex traits. Quantitative methodologies for connecting phenotypes with high-dimensional genomic information to understand polygenic traits from both prediction and inference perspectives.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Graded
Offered:SPRING

Credit Hours:3

ACE:

STAT847
Biometrical Genetics and Plant BreedingCrosslisted with AGRO 932

Prerequisites: AGRO 931

STAT 802 recommended. Offered odd-numbered calendar years.

Description: Theoretical concepts involved in planning breeding programs for the improvement of measurable morphological, physiological, and biochemical traits that are under polygenic control in crop plants of various types.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT850
Computing Tools for Statisticians

Prerequisites: STAT 801A or equivalent; STAT 462, 880, or equivalent is recommended.

Description: Introductions to statistical computing packages and document preparation software. Topics include: graphical techniques, data management, Monte Carlo simulation, dynamic document preparation, presentation software.

Course details
Credit Hours:2
Max credits per semester:2
Max credits per degree:2
Grading Option:Grade Pass/No Pass Option

Credit Hours:2

ACE:

STAT862W
Applied Variance Component Estimation in Livestock GeneticsCrosslisted with ASCI 862W

Prerequisites: ASCI 862V

This is a 5-week course taught by Speidel and Enns (Colorado State University). Permission required before registering. Contact the Animal Science Department at 402-472-6440.

Description: Principles in the estimation of (co)variance components and genetic parameters required to solve mixed models typical in livestock genetics. Focus on applied knowledge of approaches used to estimate the G and R sub-matrices of the mixed model equations. Demonstrate models commonly used in parameter estimation. Introduce scientific literature concerning implementation, and attributes of the solutions, of variance component estimation strategies.

Course details
Credit Hours:1
Max credits per semester:1
Max credits per degree:1
Grading Option:Graded

Credit Hours:1

ACE:

STAT868
An Introduction to R ProgrammingCrosslisted with ASCI 868

Prerequisites: Graduate Standing

This is a 5-week course taught by Maltecca (North Carolina State University).

Description: Introduction to the R environment for statistical computing, including use of R as a high-level programming language and as a gateway for more formal low-level languages. Material includes language structure, basic and advanced data manipulation, statistical analysis with R, and using R as a programming language.

This course is a prerequisite for: ASCI 869, STAT 869

Course details
Credit Hours:1
Max credits per semester:1
Max credits per degree:1
Grading Option:Graded

Credit Hours:1

ACE:

STAT869
MCMC Methods in Animal Breeding: A PrimerCrosslisted with ASCI 869

Prerequisites: ASCI 868.

This is a 5-week course taught by Maltecca (North Carolina State University).

Description: Principles of Markov Chain Monte Carlo (MCMC) methods in animal breeding. Materials include random variable generation, Monte Carlo integration, stochastic search, Expectation-maximization (EM) algorithm and Monte Carlo EM, Markov Chain principles, Metropolis-Hastings algorithm, Gibbs sample, and MCMC for genomic data. Illustrations developed using R software.

Course details
Credit Hours:1
Max credits per semester:1
Max credits per degree:1
Grading Option:Graded

Credit Hours:1

ACE:

STAT870
Multiple Regression Analysis

Prerequisites: STAT 801A, STAT 802 or STAT 821 concurrently

Description: Linear regression and related analysis of variance and covariance methods for models with two or more independent variables. Techniques for selecting and fitting models, interpreting parameter estimates, and checking for consistency with underlying assumptions. Partial and multiple correlation, dummy variables, covariance models, stepwise procedures, response surfaces estimation, and evaluation of residuals.

This course is a prerequisite for: STAT 874; STAT 875; STAT 878; STAT 974

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT871
Generalized, linear, and mixed models

Prerequisites: STAT 880 or concurrent STAT 883; MATH 314

For non-majors only.

Description: Methods and underlying theory for analyzing data based on generalized, linear, and mixed models.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT873
Applied Multivariate Statistical Analysis

Prerequisites: STAT 801A or STAT 801B

STAT 870 recommended

Description: Multivariate techniques used in research. Reduction of dimensionality and multivariate dependencies, principal components, factor analysis, canonical correlation, discriminant analysis, cluster analysis, multivariate extensions to the analysis of variance, and the general linear model.

This course is a prerequisite for: STAT 973

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT874
Nonparametric Statistics

Prerequisites: STAT 821 or STAT 870

Description: Introduction to nonparametric statistics - methodology and supporting theory. Focus of this course is broadly divided into three components: traditional (e.g. distribution-free hypothesis testing), function estimation (e.g. alternatives to parametric linear and nonlinear models) and modern methods that emphasize prediction (e.g., density estimation, robustness, computational methods, reproducing kernel Hilbert space methods).

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Graded
Offered:SPRING

Credit Hours:3

ACE:

STAT875
Categorical Data Analysis

Prerequisites: STAT 801A and STAT 870, or STAT 821

Description: Analysis of contingency tables. Regression models for binary, multi-category, and count responses. Tools for model building. Exact inference methods.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT876
Introduction to Survival Analysis

Prerequisites: STAT 801A.

Offered every other odd-numbered calendar year. Knowledge of at least one statistical package (SAS, R, Splus, SPS) is required.

Description: Application, theory and computational aspects of survival analysis. Survival and hazard functions; parametric models for survival data; censoring and truncation mechanisms; nonparametric estimation (confidence bands for the survival function, interval estimation of the mean and median survival time); univariate estimation of the hazard function; hypothesis testing; regression models (with fixed covariates, with time dependent covariates); and model diagnostics.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT877
Introduction to Mixed Model Analysis

Prerequisites: STAT 802

Description: Practical application of mixed models for data analysis, estimation, prediction, and testing. This course covers linear mixed models (LMM) for normally distributed data and generalized linear mixed models (GLMMs) for non-normally distributed data.

This course is a prerequisite for: STAT 902

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option
Offered:SPRING

Credit Hours:3

ACE:

STAT878
Time Series Analysis

Prerequisites: STAT 870 or STAT 821 and either STAT 880 or concurrent STAT 883

Description: Introduction to models for data observed over time. Both theoretical and practical aspects of time series models will be presented. Main topics include the Box-Jenkins model class, spectral analysis, and GARCH models. Forecasting will be emphasized throughout. The main statistical software package used will be R. Other statistical software packages and programming languages will be introduced as needed.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Graded

Credit Hours:3

ACE:

STAT880
Introduction to Mathematical Statistics

Prerequisites: MATH 208 or 107H; STAT 218 or equivalent

STAT 880 is not open to students earning a MA or MS degree in mathematics or statistics. This course requires command of material covered in MATH 107 or 107H, and STAT 218. It is also recommended to have command of materials covered in MATH 208 or 208H.

Description: Introductory mathematical statistics. Probability calculus; random variables, their probability distributions and expected values; sampling distributions; point estimation, confidence intervals and hypothesis testing theory and applications.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT882
Mathematical Statistics I-Distribution Theory

Prerequisites: MATH 208 or MATH 107H.

Description: Sample space, random variable, expectation, conditional probability and independence, moment generating functions, special distributions, sampling distributions, order statistics, limiting distributions and central limit theorem.

This course is a prerequisite for: ECON 917; STAT 821; STAT 883; STAT 973

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT883
Mathematical Statistics II-Statistical Inference

Prerequisites: STAT 882

Description: Interval estimation; point estimation, sufficiency and completeness; Bayesian procedures; uniformly most powerful tests, likelihood ratio test, goodness of fit tests.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT884
Applied Stochastic Models

Prerequisites: STAT 880 or IMSE 321 or equivalent

Description: Introduction to stochastic modeling in operations research. Includes the exponential distribution and the Poisson process, discrete-time and continuous-time Markov chains, renewal processes, queueing models, stochastic inventory models, stochastic models in reliability theory.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT885
Introduction to Data Mining and Machine Learning

Prerequisites: STAT 880 or STAT 883 and STAT 802 or STAT 822

Suggested co-requisite STAT 823; Some working knowledge of SQL would be very useful.

Description: The key topics represent the main areas of data mining and machine learning aimed at achieving predictive accuracy more than physical modeling. These topics are standard classification methods, regularization methods, visualization and geometry of data, leading to kernel methods. Finally, the course introduces trees, neural nets, and model averaging.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT886
Applied Bayesian Analysis

Prerequisites: STAT 801A or STAT 801B and either STAT 880 or concurrent STAT 883.

Description: Bayesian data analysis with emphasis on application and computation using R or similar software. Topics include: probability models, prior distributions, Bayes theorem, single parameter models, posterior predictive distribution, Gibbs sampling, MCMC simulations, regression models, generalized linear models, hierarchical models, model checking and diagnostics.

This course is a prerequisite for: STAT 986

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Graded

Credit Hours:3

ACE:

STAT889
Statistics Seminar

Prerequisites: Permission

Course details
Credit Hours:1
Max credits per semester:1
Max credits per degree:1
Grading Option:Grade Pass/No Pass Option

Credit Hours:1

ACE:

STAT892
Topics in Statistics and Probability

Prerequisites: Permission

Description: Special topics in either statistics or the theory of probability.

Course details
Credit Hours:1-5
Max credits per semester:5
Max credits per degree:24
Grading Option:Grade Pass/No Pass Option

Credit Hours:1-5

ACE:

STAT898
Statistics Project

Prerequisites: Permission

Course details
Credit Hours:1-5
Max credits per semester:5
Max credits per degree:5
Grading Option:Grade Pass/No Pass Option

Credit Hours:1-5

ACE:

STAT899
Masters Thesis

Prerequisites: Admission to the Masters Degree Program and permission of major adviser

Course details
Credit Hours:1-6
Max credits per semester:6
Max credits per degree:99
Grading Option:Grade Pass/No Pass Option

Credit Hours:1-6

ACE:

STAT902
Advanced Experimental Design

Prerequisites: STAT 821 or STAT 877.

Description: Advanced design concepts, theory and methods used in: construction, analysis and interpretation of incomplete block designs, split-plots, confounded and fractional factorials, screening designs, response surface methods, and other topics.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT904
Theory of Experimental Design

Prerequisites: Permission

Description: Theory of underlying construction and analysis of designed experiments. Multifactor designs, fractional factorials, incomplete block designs, row and column designs, orthogonal arrays, and response to surface designs. Optimality criteria. Mathematical and computer-aided design theory.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT930
Advanced Statistical Consulting and Interdisciplinary Collaboration

Prerequisites: Permission

For advanced Masters degree students or PhD students in Statistics.

Description: Exposure to more complex statistical consulting problems and how to resolve them. Topics include: major areas of consulting, interdisciplinary collaboration, and effective communication.

This course is a prerequisite for: STAT 997

Course details
Credit Hours:2
Max credits per semester:2
Max credits per degree:2
Grading Option:Grade Pass/No Pass Option

Credit Hours:2

ACE:

STAT931
Advanced Spatial and Spatio-temporal Statistics

Prerequisites: STAT 822 and STAT 883

Suggested prerequisite: STAT 831

Description: This course provides a development of theory and methods for spatial and spatio-temporal statistics. It provides the mathematical foundations and methodological development for topics such as MLE and in-fill asymptotics, non-Gaussian/non-stationary spatial processes, spatio-temporal models, and Bayesian methodology. R will be the main programing language.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT950
Computational Statistics I

Prerequisites: STAT 883; STAT 823 or concurrent enrollment.

Prior experience with "R" software is required.

Description: Statistical computing needed for research and advanced statistical analyses. Topics include: bootstrap, high performance computing, jackknife, Linux, Markov chain Monte Carlo, Monte Carlo simulation, numerical differentiation and integration, optimization, parallel processing, permutation tests.

This course is a prerequisite for: STAT 951

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT951
Computational Statistics II

Prerequisites: STAT 950

Knowledge of a high-level programming language is recommended

Description: A continuation of Computational Statistics I. Topics will be chosen from big data management and data analysis, data generation, high performance and throughput computing, importance sampling, machine learning, optimization, programming languages, web scraping, working with databases.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT970
Linear Models

Prerequisites: MATH 314/814.

Description: Methods and underlying theory for analyzing data based on linear statistical models. General linear model with specific models as special cases: including linear models applications.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT971
Advanced Statistical Modeling

Prerequisites: STAT 883 and STAT 823

Description: Development of theory for linear models, GLM, GLMMs, nonlinear models, and high dimensional (generalized) linear models. Mathematical foundations and methodological development for topics such as consistency and asymptotic distributions of estimators in linear model, GLM or GLMMs, theory and methods on EM algorithms for GLM and GLMM, multivariate GLMM models, theory of high dimensional linear models and generalized linear models.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT973
Theory of Multivariate Analysis

Prerequisites: STAT 873, STAT 882 and STAT 821 or equivalent

Description: Statistical inference concerning parameters of multivariate normal distributions with applications to multivariate datasets.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT974
Nonlinear Regression Analysis

Prerequisites: STAT 870 and introductory calculus.

Description: Basic concepts of nonlinear models and their associated applications. Estimating the parameters of these models under the classical assumptions as well as under relaxed assumptions. Major theoretical results and implementation using standard statistical software.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT980
Advanced Probability Theory I

Prerequisites: STAT 883

This course requires command of material covered in MATH 325 or equivalent.

Description: Construction of probability spaces, random variables and expectations, monotone and dominated convergence theorems, Fatou's lemma, modes of convergence, Kolmogorov law of large numbers, central limit theory, conditional probability given a sigma field.

This course is a prerequisite for: STAT 981; STAT 984; STAT 986

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT981
Advanced Probability Theory II

Prerequisites: STAT 980

Description: A continuation of STAT 980 providing depth in probability theory and stochastic processes. Topics include convergence properties of random variables and treatment of several important stochastic processes.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT982
Advanced Inference

Prerequisites: STAT 883

Description: Uniformly minimum variance unbiased estimators, decision-theoretic Bayes estimation, frequentist testing (likelihood ratio tests, Neyman-Pearson lemma, uniformly most powerful tests), Bayes testing and Bayes factors, nonparametric tests, multiple comparisons procedures.

This course is a prerequisite for: STAT 986

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT983
Statistical Learning

Prerequisites: STAT 823, STAT 883

Description: Model selection including sparsity methods and their oracle properties, information methods, cross-validation and stochastic search. Basic theory of kernel methods for regression. Classification: linear and quadratic discriminants, Bayes classifier, nearest neighbor methods, kernel methods for classification. Introduction to neural networks and recursive partitioning. Model averaging methods and measures of complexity. Cluster analysis.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT984
Asymptotics and Applications

Prerequisites: STAT 980

Description: A continuation of STAT 980 providing breadth in commonly occurring major subfields of statistics that rely heavily on probability theory. Large sample theory estimation, testing, expansion, and convergence in a variety of settings.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Grade Pass/No Pass Option

Credit Hours:3

ACE:

STAT986
Theoretical Foundations of Bayesian Analysis

Prerequisites: STAT 886; and at least one of STAT 980 or STAT 982.

Description: The foundational ideas and structure of Bayesian theory from its axiomatic and fundamental assumptions, including Savages axioms, complete class theorems, sequential properties, prior selection, model selection, Bayesian nonparametrics, and asymptotics for both the parametric and nonparametric cases.

Course details
Credit Hours:3
Max credits per semester:3
Max credits per degree:3
Grading Option:Graded

Credit Hours:3

ACE:

STAT992
Advanced Topics in Probability and Statistics

Prerequisites: Permission

Description: Special topics in either statistics or probability.

Course details
Credit Hours:1-5
Max credits per semester:5
Max credits per degree:24
Grading Option:Grade Pass/No Pass Option

Credit Hours:1-5

ACE:

STAT997
Practicum in Statistical Consulting

Prerequisites: STAT 930

Description: Participation in statistical consulting activities of the Statistics Department under faculty supervision. Prepare written reports to clients summarizing consultation results and to statistics supervisor summarizing statistical issues and findings.

Course details
Credit Hours:4
Max credits per semester:4
Max credits per degree:4
Grading Option:Grade Pass/No Pass Option

Credit Hours:4

ACE:

STAT999
Doctoral Dissertation

Prerequisites: Admission to Doctoral Degree Program and permission of supervisory committee

Course details
Credit Hours:1-24
Max credits per semester:24
Max credits per degree:99
Grading Option:Grade Pass/No Pass Option

Credit Hours:1-24

ACE: