# STAT Courses

STAT
218

Prereqs: Removal of all entrance deficiencies in mathematics.

Credit toward the degree may be earned in only one of: CRIM 300 or ECON 215 or EDPS 459 or SOCI 206. Credit toward the degree cannot be earned in STAT 218 if taken after or taken in parallel with STAT/MATH 380.

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
380

Statistics and Applications
Crosslisted as MATH 380

Credit toward the degree can not be earned in STAT 218 if taken after or taken in parallel with STAT/MATH 380.

Probability calculus; random variables, their probability distributions and expected values; t, F and chi-square 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: STAT 380.

Survey of elementary experimental designs and their analyses completely randomized, randomized block, factorial, and split-plot designs.

Credit Hours: | 3 |

Course Delivery: | Classroom |

STAT
414

STAT
430/830

Sensory Evaluation
Crosslisted as FDST 430/830

Prereqs: Introductory course in statistics.

Offered fall semester of odd-numbered 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
442/842

Computational Biology
Crosslisted as BIOC 442/842

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

Databases, high-throughput biology, literature mining, gene expression, next-generation 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: STAT 462.

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: | 1-5 |

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: | 1-5 |

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, split-plot and repeated measures, and statistical analysis associated with these structures.

Credit Hours: | 4 |

Course Format: | Lab 2, Lecture 3 |

Course Delivery: | Classroom |

STAT
803

Ecological Statistics
Crosslisted as NRES 803

Prereqs: STAT *801 or equivalent; prior experience with "R" software.

Available online.

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.

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 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.

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, 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.

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 minors.

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.

Credit Hours: | 3 |

Course Format: | Lecture |

Course Delivery: | Classroom |

STAT
822

Prereqs: STAT 821; STAT 882; STAT 883: must be concurrently enrolled in 883 or passed STAT 883 or equivalent with a grade of B or higher.

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: | 3 |

Course Format: | 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 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.

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 non-statisticians, determining solutions, and collaborating.

Credit Hours: | 3 |

Course Format: | Lecture 3 |

Course Delivery: | Classroom |

STAT
831

Prereqs: STAT *802

Offered odd-numbered 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 even-numbered calendar years.

Statistical methods useful for analyzing sports-related 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. ChIP-seq. RNA-seq. 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.

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.

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 |

STAT
862W

Applied Variance Component Estimation in Livestock Genetics
Crosslisted as ASCI 862W

Prereqs: 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.

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.

Credit Hours: | 1 |

Course Format: | Lecture 3 |

Course Delivery: | Web |

STAT
868

An Introduction to R Programming
Crosslisted as ASCI 868

Prereqs: Graduate Standing.

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

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.

Credit Hours: | 1 |

Course Format: | Lecture 3 |

Course Delivery: | Classroom, Web |

STAT
869

MCMC Methods in Animal Breeding: A Primer
Crosslisted as ASCI 869

Prereqs: ASCI 868.

This is a 5-week 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, 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.

Credit Hours: | 1 |

Course Format: | Lecture 3 |

Course Delivery: | Classroom, Web |

STAT
870

Prereqs: STAT *801, *802

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 974

Credit Hours: | 3 |

Course Delivery: | Classroom |

Prereqs: STAT *801

Multivariate 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 880

Statistical 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, goodness-of-fit methods and related topics.

Credit Hours: | 3 |

Course Delivery: | Classroom |

STAT
875

Prereqs: STAT *801, *802 or *870 recommended

Measures of associating contingency tables analysis, chi-squared tests, log-linear and logistic models, generalized estimating equations, planning studies involving categorical data.

Credit Hours: | 3 |

Course Delivery: | Classroom |

STAT
876

Prereqs: STAT 801.

STAT 876 is offered every other odd-numbered 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 882.

STAT
884

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.

Credit Hours: | 3 |

Course Delivery: | Classroom |

STAT
889

Prereqs: Permission

This course has no description.

Credit Hours: | 1 |

Course Delivery: | Classroom |

STAT
892

Prereqs: Permission

Special topics in either statistics or the theory of probability.

Credit Hours: | 1-5 |

Max credits per degree: | 24 |

Course Delivery: | Classroom |

STAT
898

Prereqs: Permission

This course has no description.

Credit Hours: | 1-5 |

Max credits per degree: | 5 |

Course Delivery: | Classroom |

STAT
899

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

This course has no description.

Credit Hours: | 1-6 |

Course Delivery: | Classroom |

STAT
902

Prereqs: STAT *802.

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

Credit Hours: | 3 |

Course Format: | Lecture 2 |

Course Delivery: | Classroom |

STAT
904

Prereqs: Permission

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.

Credit Hours: | 3 |

Course Delivery: | Classroom |

Prereqs: Permission

STAT 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 |

STAT
932

Biometrical Genetics and Plant Breeding
Crosslisted as AGRO 932

Prereqs: AGRO 931

STAT *802 recommended. Offered odd-numbered 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 high-level 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 *802

Concepts 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: STAT 802, STAT 882, STAT 883, STAT 970 and take Statistics MS Comprehensive Exam prior to start of STAT 971.

Statistical modeling beyond the “general linear model” normally-distributed data, fixed-effects-only 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, zero-inflated data, likelihood-based estimation and inference.

Credit Hours: | 3 |

Course Delivery: | Classroom |

STAT
972

Prereqs: STAT 970

Offered odd-numbered 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: STAT 980.

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 980

Uniformly minimum variance unbiased estimators, consistency and asymptotic normality of the maximum likelihood estimator, 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 983

Credit Hours: | 3 |

Course Format: | Lecture 3 |

Course Delivery: | Classroom |

STAT
983

Prereqs: STAT 982.

Model selection including sparsity methods and their oracle properties, information methods, cross-validtion 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: STAT 980.

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: Permission

Special topics in either statistics or probability.

Credit Hours: | 1-5 |

Max credits per degree: | 24 |

Course Delivery: | Classroom |

STAT
997

Prereqs: STAT 930

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 committee

This course has no description.

Credit Hours: | 1-24 |

Course Delivery: | Classroom |