Statistics Courses
Courses of Instruction (STAT)
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STAT 218

Prereqs:Removal of all entrance deficiencies in mathematics.The practical application of statistical thinking to contemporary issues; collection and organization of data; probability distributions; statistical inference; estimation; and hypothesis testing.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom ACE Outcomes: 3 
STAT 318

Prereqs:STAT 218 or equivalent.Tests for means/proportions of two independent groups, analysis of variance for completely randomized design, contingency table analysis, correlation, single and multiple linear regression, nonparametric procedures, design of experiments.This course is a prerequisite for: STAT 802
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
STAT 380Statistics and Applications Crosslisted as MATH 380

Probability calculus; random variables, their probability distributions and expected values; t, F and chisquare sampling distributions; estimation; testing of hypothesis; and regression analysis with applications.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom ACE Outcomes: 3 
STAT 412

Prereqs:Survey of elementary experimental designs and their analyses completely randomized, randomized block, factorial, and splitplot designs.
Credit Hours: 3 Course Delivery: Classroom 
STAT 414

FDST 430/830Sensory Evaluation Crosslisted as STAT 430/830

Prereqs:Introductory course in statistics.Offered fall semester of oddnumbered calendar years.Food evaluation using sensory techniques and statistical analysis.
Credit Hours: 3 Course Format: Lab 3, Lecture 2 Course Delivery: Classroom 
STAT 432

Prereqs:STAT 218 or equivalent.Spatial point patterns, test of randomness, Morans I statistic and similar measures, checking assumptions for independence of observations, variography, estimation (point and global), Kriging, nearest neighbor techniques, cokriging, mixed models and their role in designed spatial experiments.
Credit Hours: 3 Course Format: Lab, Lecture Course Delivery: Classroom 
STAT 442/842Computational Biology Crosslisted as BIOC 442/842

Prereqs:Any introductory course in biology, or genetics, or statistics.Databases, highthroughput biology, literature mining, gene expression, nextgeneration sequencing, proteomics, metabolomics, systems biology, and biological networks.
Credit Hours: 3 Course Format: Lab 2, Lecture 1 Course Delivery: Classroom 
STAT 450

STAT 380 or equivalent is strongly recommended.Sample space, random variable, expectation, conditional probability and independence, moment generating function, special distributions, sampling distributions, order statistics, limiting distributions, and central limit theorem.
Credit Hours: 4 Course Format: Lecture 3, Recitation 1 Course Delivery: Classroom 
Prereqs:Interval estimation; point estimation, sufficiency, and completeness; Bayesian procedures; uniformly most powerful tests, sequential probability ratio test, likelihood ratio test, goodness of fit tests; elements of analysis of variance and nonparametric tests.
Credit Hours: 4 Course Format: Lecture 3, Recitation 1 Course Delivery: Classroom 
STAT 494

Prereqs:Permission.Special topics in either statistics or the theory of probability.
Credit Hours: 15 Max credits per degree: 24 Course Delivery: Classroom 
STAT 496

Prereqs:Prior arrangement with a faculty member and submission of proposed study plan to department office.This course has no description.
Credit Hours: 15 Max credits per degree: 5 Course Delivery: Classroom 
STAT 801

Prereqs:Introductory course in statistics.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.
Credit Hours: 4 Course Format: Lab 2, Lecture 3 Course Delivery: Classroom 
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, splitplot and repeated measures, and statistical analysis associated with these structures.
Credit Hours: 4 Course Format: Lab 2, Lecture 3 Course Delivery: Classroom 
NRES 803Ecological Statistics Crosslisted as STAT 803

Prereqs:STAT *801 or equivalent.Available online.Modelbased inference for ecological data, generalized linear and additive models, mixed models, survival analysis, multimodel inference and information theoretic model selection, and study design.
Credit Hours: 4 Course Format: Lab 1, Lecture 3 Course Delivery: Classroom, Web 
STAT 804

STAT 810

Prereqs:Statistics graduate student.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.
Credit Hours: 1 Course Format: Lecture 1 Course Delivery: Classroom 
STAT 811T

Prereqs: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.Designed primarily to develop and equip middlelevel 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 middleschool mathematics curriculum, as well as their application in other disciplines. The course also includes statistics that are used in education and schoolbased research.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
STAT 812T

Prereqs:A valid secondary mathematics teaching certificate or permission.Not open to MA or MS students in mathematics or statistics.The statistical concepts typically taught in a high school statistics class, including linear regression, twoway tables, sampling distributions, statistical inference for means and proportions, chisquare tests, and inference for regression. Some experience with basic statistical concepts (mean, standard deviation, elementary probability) is necessary. The course is inquirybased, and will emphasize applications and statistical thinking.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
STAT 821

Prereqs:Matrix Algebra; concurrently taking STAT 882, or passed 882 or equivalent with grade of B or higher.Designed for Statistics MS majors and minorsIntroduction 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 nonhierarchical study design.
Credit Hours: 4 Course Format: Lab 2, Lecture 3 Course Delivery: Classroom 
STAT 822

Prereqs:Course is designed for Statistics MS majors and minors.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
Credit Hours: 4 Course Format: Lab 2, Lecture 3 Course Delivery: Classroom 
STAT 823

This course is designed for Statistics MS Majors.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 singleresponsevariable, designed study cases featured in Statistical Methods I and II. These include multivariate statistics, nonlinear models, non and semiparametric statistics, observational studies, and other theory and methods deemed appropriate as statistical science continues to evolve.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
Introduction to the role and purpose of statistical consulting and interdisciplinary collaboration. Topics include: asking good questions, dealing with difficult clients, communicating statistics to nonstatisticians, determining solutions, and collaborating.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
STAT 831

Prereqs:STAT *802Offered oddnumbered calendar years.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.
Credit Hours: 3 Course Delivery: Classroom 
STAT 832

Offered evennumbered calendar years.Statistical methods useful for analyzing sportsrelated data. Descriptive statistics, graphical representations, experimental design, discriminant analysis and optimization.
Credit Hours: 2 Course Format: Lecture 2 Course Delivery: Classroom 
Prereqs:STAT *801 or equivalent.Basic biological concepts. Multiple testing and false discovery rate. Second generation sequencing and statistical issues. ChIPseq. RNAseq. Empirical Bayes methods and software. Normalization, experimental design and commonly used models for microarray data. Metabolomics.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
Prereqs:Any introductory course in biology, statistics, computer science or mathematics.Nextgeneration RNA and genome sequencing, systems biology. Regulatory networks of transcription, proteinprotein interaction networks, theory and practice. Databases, data integration and visualization. Students present computational biology publications and projects.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
STAT 850

Introductions to statistical computing packages and document preparation software. Topics include: graphical techniques, data management, Monte Carlo simulation, dynamic document preparation, presentation software.
Credit Hours: 2 Course Format: Lecture 2 Course Delivery: Classroom 
ASCI 862WApplied Variance Component Estimation in Livestock Genetics Crosslisted as STAT 862W

Prereqs:This is a 5week course taught by Speidel and Enns (Colorado State University). Permission required before registering. Contact the Animal Science Department at 4024726440.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 submatrices 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.
Credit Hours: 1 Course Format: Lecture 3 Course Delivery: Web 
ASCI 868An Introduction to R Programming Crosslisted as STAT 868

Prereqs:Graduate Standing.This is a 5week course taught by Maltecca (North Carolina State University).Introduction to the R environment for statistical computing, including use of R as a highlevel programming language and as a gateway for more formal lowlevel 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
Credit Hours: 1 Course Format: Lecture 3 Course Delivery: Classroom, Web 
ASCI 869MCMC Methods in Animal Breeding: A Primer Crosslisted as STAT 869

Prereqs:This is a 5week course taught by Maltecca (North Carolina State University).Principles of Markov Chain Monte Carlo (MCMC) methods in animal breeding. Materials include random variable generation, Monte Carlo integration, stochastic search, Expectationmaximization (EM) algorithm and Monte Carlo EM, Markov Chain principles, MetropolisHastings algorithm, Gibbs sample, and MCMC for genomic data. Illustrations developed using R software.
Credit Hours: 1 Course Format: Lecture 3 Course Delivery: Classroom, Web 
STAT 870

Prereqs:STAT *801, *802Linear 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 974
Credit Hours: 3 Course Delivery: Classroom 
Prereqs:STAT *801Multivariate techniques used in research. Reduction of dimensionality and multivariate dependencies, principle components, factor analysis, canonical correlation, classification procedures, discriminant analysis, cluster analysis, multidimensional scaling, multivariate extensions to the analysis of variance, and the general linear model.This course is a prerequisite for: STAT 973
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
STAT 874

Prereqs:STAT *801 or 880Statistical methods useful when data does not adhere to classical distributional assumptions. Analysis of interval/ordinal/categorical data for one, two and k sample problems, correlation and regression, goodnessoffit methods and related topics.
Credit Hours: 3 Course Delivery: Classroom 
STAT 875

Prereqs:STAT *801, *802 or *870 recommendedMeasures of associating contingency tables analysis, chisquared tests, loglinear and logistic models, generalized estimating equations, planning studies involving categorical data.
Credit Hours: 3 Course Delivery: Classroom 
STAT 876

Prereqs:STAT 876 is offered every other oddnumbered calendar year. Knowledge of at least one statistical package (SAS, R, Splus, SPS) is required.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.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
STAT 880 is not open to students earning a MA or MS degree in mathematics or statistics.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.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
Sample space, random variable, expectation, conditional probability and independence, moment generating functions, special distributions, sampling distributions, order statistics, limiting distributions and central limit theorem.
Credit Hours: 3 Course Delivery: Classroom 
Prereqs:

STAT 884

Introduction to stochastic modeling in operations research. Includes the exponential distribution and the Poisson process, discretetime and continuoustime Markov chains, renewal processes, queueing models, stochastic inventory models, stochastic models in reliability theory.
Credit Hours: 3 Course Delivery: Classroom 
STAT 889

Prereqs:PermissionThis course has no description.
Credit Hours: 1 Course Delivery: Classroom 
STAT 892

Prereqs:PermissionSpecial topics in either statistics or the theory of probability.
Credit Hours: 15 Max credits per degree: 24 Course Delivery: Classroom 
STAT 898

Prereqs:PermissionThis course has no description.
Credit Hours: 15 Max credits per degree: 5 Course Delivery: Classroom 
STAT 899

Prereqs:Admission to the Masters Degree Program and permission of major adviserThis course has no description.
Credit Hours: 16 Course Delivery: Classroom 
STAT 902

Prereqs:STAT *802.Advanced design concepts and methods used in research: construction, analysis and interpretation of incomplete block designs, splitplots, confounded and fractional factorials, response surface methods, and other topics.
Credit Hours: 3 Course Format: Lecture 2 Course Delivery: Classroom 
STAT 904

Prereqs:PermissionTheory 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 computeraided design theory.
Credit Hours: 3 Course Delivery: Classroom 
Prereqs:PermissionSTAT 930 is for advanced Masters degree students or PhD students in Statistics.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
Credit Hours: 2 Course Format: Lab 4, Lecture 1 Course Delivery: Classroom 
AGRO 932Biometrical Genetics and Plant Breeding Crosslisted as STAT 932

Prereqs:STAT *802 recommended. Offered oddnumbered calendar years.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.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
STAT 950

Prereqs:STAT *883; STAT 971 or concurrent enrollment; prior experience with “R” software.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
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
STAT 951

Prereqs:STAT 950; knowledge of a highlevel programming language is recommended.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.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
Prereqs:STAT *801 and *802Concepts and matrix operations useful to expanding determinants, computing matrix inverses, determining ranks and linear (in)dependence, and finding latent roots and latent vectors. Introduction to matrix algebra applications in regression analyses and linear models.
Credit Hours: 2 Course Format: Lecture 2 Course Delivery: Classroom 
STAT 970

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.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
STAT 971

Prereqs:Statistical modeling beyond the “general linear model” normallydistributed data, fixedeffectsonly case. Focus on, but not limited to, the theory and practice of generalized and mixed linear models. Issues include translation of study design to plausible models, inference space, data and model scale, conditional vs. marginal models, correlated data, zeroinflated data, likelihoodbased estimation and inference.
Credit Hours: 3 Course Delivery: Classroom 
STAT 972

Prereqs:Offered oddnumbered calendar years.Design and analysis of random effects and mixed models Basic theoretical background for models with fixed effects, distribution of quadratic forms, quadratic estimators including ANOVA methods, likelihood estimators including ML and REML, computing strategies, and optimal design for nested and cross classifications.
Credit Hours: 3 Course Delivery: Classroom 
STAT 973

STAT 974

Prereqs:STAT 870 and introductory calculus.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.
Credit Hours: 3 Course Delivery: Classroom 
STAT 980

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.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
STAT 981

Prereqs:This course is intended primarily for Statistics PhD students who theory oriented. PhD students interested in applications and computing may also take this course for depth in probability theory.A primarily theoretical continuation of STAT 980. Depth in probability theory and stochastic processes. Mathematical foundations undergirding the use of probabilistic reasoning in statistics. Topics include searching examinations of the proofs of major theorems in statistics and a sophisticated treatment of several important stochastic processes.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
STAT 982

Prereqs:STAT *883 and STAT 980Uniformly minimum variance unbiased estimators, consistency and asymptotic normality of the maximum likelihood estimator, decisiontheoretic Bayes estimation, frequentist testing (likelihood ratio tests, NeymanPearson lemma, uniformly most powerful tests), Bayes testing and Bayes factors, nonparametric tests, multiple comparisons procedures.This course is a prerequisite for: STAT 983
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
STAT 983

Prereqs:Model selection including sparsity methods and their oracle properties, information methods, crossvalidtion 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.
Credit Hours: 3 Course Delivery: Classroom 
STAT 984

Prereqs:This course is intended primarily for Statistics PhD students who are applications and computing oriented. PhD students who are theory oriented may also take this course for breadth.A continuation of STAT 980 aiming at breadth of familiarity with commonly occuring major subfields of statistics that rely heavily on probability theory.
Credit Hours: 3 Course Format: Lecture 3 Course Delivery: Classroom 
Prereqs:PermissionSpecial topics in either statistics or probability.
Credit Hours: 15 Max credits per degree: 24 Course Delivery: Classroom 
STAT 997

Prereqs: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.
Credit Hours: 4 Course Format: Field Course Delivery: Classroom 
STAT 999

Prereqs:Admission to Doctoral Degree Program and permission of supervisory committeeThis course has no description.
Credit Hours: 124 Course Delivery: Classroom