Statistics
Courses for STAT (STAT)
AGRO
932
Biometrical Genetics and Plant Breeding LINKCrosslisted as STAT 932| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
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.
FDST
430/830
Sensory Evaluation LINKCrosslisted as STAT 430/830| Credit Hours: | 3 |
| Course Format: | Lecture 2, Lab 3 |
| Course Delivery: | Classroom |
Prereqs:
Introductory course in statistics.
Offered fall semester of odd-numbered calendar years.
Food evaluation using sensory techniques and statistical analysis.
NRES
803
Ecological Statistics LINKCrosslisted as STAT 803| Credit Hours: | 4 |
| Course Format: | Lab 1, Lecture 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
STAT *801 or equivalent.
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.
STAT
442/842
Computational Biology LINKCrosslisted as BIOC 442/842| Credit Hours: | 3 |
| Course Format: | Lab 2, Lecture 1 |
| Course Delivery: | Classroom |
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.
STAT
801
Statistical Methods in Research LINK| Credit Hours: | 4 |
| Course Format: | Lab 2, Lecture 3 |
| Campus: | |
| Course Delivery: | Classroom |
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.
STAT
802
Experimental Design LINK| Credit Hours: | 4 |
| Course Format: | Lab 2, Lecture 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
STAT *801.
Suitability and efficiency of various designs in conducting experimental investigations in related areas and the statistical analysis of the data.
STAT
804
Survey Sampling LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
Sampling techniques: simple random sampling, sampling proportions, estimation of sample size, stratified random sampling, ratio and regression estimates.
STAT
831
Spatial Statistics LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
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.
STAT
832
Statistics in Sports LINK| Credit Hours: | 2 |
| Course Format: | Lecture 2 |
| Campus: | |
| Course Delivery: | Classroom |
Offered even-numbered calendar years.
Statistical methods useful for analyzing sports-related data. Descriptive statistics, graphical representations, experimental design, discriminant analysis and optimization.
STAT
841
Statistical methods for Micro-array and Related Technologies LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
STAT *801 or equivalent.
Basic biological concepts. Image analysis for two-color and oligonucleotide micro-arrays. Normalization, experimental design and mixed linear models for micro-array data. Empirical Bayes methods and false discovery rate. Clustering and gene category based methods. Tiling micro-arrays, massively parallel signature sequencing, and other related technologies.
STAT
843
Next-Generation Sequencing and Systems Biology LINK| 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.
STAT
870
Multiple Regression Analysis LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
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.
STAT
873
Applied Multivariate Statistical Analysis LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Campus: | |
| 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.
STAT
874
Nonparametric Statistics LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
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.
STAT
875
Categorical Data Analysis LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
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.
STAT
880
Introduction to Mathematical Statistics LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Campus: | |
| 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.
STAT
882
Mathematical Statistics I-Distribution Theory LINK| Credit Hours: | 3 |
| Campus: | |
| 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.
STAT
883
Mathematical Statistics II-Statistical Inference LINK| Credit Hours: | 3 |
| Campus: | |
| 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.
STAT
884
Applied Stochastic Models LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
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.
STAT
885
Applied Statistics I LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
General linear models for estimation and testing problems analysis and interpretation for various experimental designs.
STAT
889
Statistics Seminar LINK| Credit Hours: | 1 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Permission
STAT
892
Topics in Statistics and Probability LINK| Credit Hours: | 1-5 |
| Max credits per degree: | 24 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Permission
Special topics in either statistics or the theory of probability.
STAT
898
Statistics Project LINK| Credit Hours: | 1-5 |
| Max credits per degree: | 5 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Permission
STAT
899
Masters Thesis LINK| Credit Hours: | 1-6 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Admission to the Masters Degree Program and permission of major adviser
STAT
902
Advanced Experimental Design LINK| Credit Hours: | 3 |
| Course Format: | Lecture 2 |
| Campus: | |
| Course Delivery: | Classroom |
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.
STAT
904
Theory of Experimental Design LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
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.
STAT
930
Principles of Statistical Consulting LINK| Credit Hours: | 2 |
| Course Format: | Lecture 2 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Permission
STAT 930 is primarily for graduate students in statistics.
Role and purpose of consulting. Statistical issues: understanding the client’s problem and choosing an appropriate procedure. Interpersonal issues: client expectations, difficult clients, working effectively with people and teamwork.
STAT
950
Bootstrap Methods and Their Application LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
STAT *883; STAT *870 or 970; prior experience with “R” software
Application, theory, and computational aspects of the bootstrap. Parametric, nonparametric, and jackknife re-sampling; influence function and nonparametric delta method; bootstrap confidence intervals and hypothesis tests; permutation tests; applications to regression; implementation using statistical software.
STAT
960
Matrix Algebra Applications in Statistics LINK| Credit Hours: | 2 |
| Course Format: | Lecture 2 |
| Campus: | |
| 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.
STAT
970
Linear Models LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Campus: | |
| Course Delivery: | Classroom |
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.
STAT
971
Advanced Statistical Modelling LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Advanced theory and methods for statistical analysis. Systematic development of the needs and requirement of statistical modelling in research. Distribution and estimation theory for analysis of categorical data, survival data, data with correlated errors. Theory and practice of generalized linear models, mixed linear models. Introduction to non-linear models.
STAT
972
Variance Component Estimation LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
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.
STAT
973
Theory of Multivariate Analysis LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
STAT 873 or equivalent
Statistical inference concerning parameters of multivariate normal distributions with applications to multiple decision problems.
STAT
974
Nonlinear Regression Analysis LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
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.
STAT
980
Advanced Probability Theory LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
MATH *825
Probability spaces and random variables, expectations and fundamental inequalities, characteristic functions, four types of convergence, central limit theorem, introduction to stochastic processes.
STAT
982
Statistics Theory I LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
MATH *825 and STAT *883
General decision problems, admissibility, mini-max and Bayes rules, invariance and unbiasedness, families of distributions problems in estimation theory.
STAT
983
Statistics Theory II LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
UMP tests, likelihood ratio tests, confidence ellipsoid multiple decision and multiple comparisons, sequential decision problems.
STAT
992
Advanced Topics in Probability and Statistics LINK| Credit Hours: | 1-5 |
| Max credits per degree: | 24 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Permission
Special topics in either statistics or probability.
STAT
997
Practicum in Statistical Consulting LINK| Credit Hours: | 4 |
| Course Format: | Field |
| Campus: | |
| Course Delivery: | Classroom |
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.
STAT
999
Doctoral Dissertation LINK| Credit Hours: | 1-24 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Admission to Doctoral Degree Program and permission of supervisory committee
Description
For a brief description of the program, application requirements and contact information, view the graduate program summary.
Interim Department Chair: Stephen Kachman, Ph.D.
Graduate Committee: Professors Eskridge (chair), Bilder, Parkhurst, Zhang
Graduate programs are offered leading to a master of science and a doctor of philosophy in statistics.
Master of Science Degree
The program of study for the masters degree may be under Options I, II, or III, with Option III the most common and Option I, the thesis option, rare. The primary aim of the statistics masters program is to provide students with an education that equips them to be competent practitioners of applied statistics. Programs can be tailored to emphasize applications in the biological sciences, environmental sciences, economics, engineering, agriculture, survey statistics or other areas of interest. Competence includes mastery of statistical theory and methods, significant exposure to disciplines with which statisticians interact, facility with statistical consulting tools, and training and experience with statistical consulting. Programs can also be tailored to prepare students who plan to go on for doctoral study.
Requirements are designed to allow flexibility in designing programs around individual student needs. Students are expected to take a common core consisting of two semesters of mathematical statistics, (STAT 882 and 883), two semesters of statistical modeling (STAT 970 and 971), one semester of design and analysis of experiments (STAT 802), and one semester of multivariate methods (STAT 873). In addition, students must attain proficiency in a statistical computing language, gain statistical consulting experience and become familiar with at least one discipline to which statistics is applied. Students are required to pass a comprehensive examination based on ability to integrate material from the core curriculum. Students who choose a non-thesis option are required to complete a project. All students must present a seminar as part of their masters program.
Doctor of Philosophy Degree
The goal of the statistics PhD program is to train students to conduct original methodological and/or theoretical research in statistics and to apply advanced statistical methods to scientific problems. Students are expected to take advanced graduate classes in the theory and applications of statistics and other relevant classes. The PhD program requires a qualifying exam, a PhD comprehensive exam and a final oral exam. The Statistics PhD Qualifying Examination is intended to verify mastery of tasks that require integration among fundamental statistics courses, (STAT 802, 882, 883, and 970). Each PhD student in statistics must complete courses in advanced statistical modeling (STAT 971), advanced probability (STAT 980) and the two-semester advanced statistical inference sequence (STAT 982 and 983). In addition, students must complete twelve hours of 900-level classes excluding STAT 970, 997 and 999. The PhD requires 90 hours of graduate credit, including a dissertation. At least 45 hours must be completed at UNL after the filing of the program of studies which must be approved by the student’s PhD graduate committee. The PhD program typically includes 20 to 25 hours of dissertation research. In addition there is a research tool requirement.