Graduate Degree Program Summary
Graduate programs offered
Earn a Graduate Degree
- MS in Statistics
- PhD in Statistics
Areas of Study
These informal areas of focus may help to shape your course of study but they will not appear on transcripts.
- Environmental and Agricultural Statistics
- Mathematical Statistics
- Statistics in Sports
- Survey and Behavioral Statistics
Online and Distance OpportunitiesSome online coursework may be available for your program; contact dept. for details.
Application checklist and deadlines
1. Required by Graduate Studies
Submit these items as part of the standard steps to admission.
2. Required by Statistics
After you apply, allow one business day for us to set up your access so you can complete these requirements via MyRED.
- Entrance exam(s): GRE
- Minimum English proficiency:
- Financial Consideration: Paper TOEFL 600, Internet TOEFL 100, IELTS 6.5
- Otherwise: Paper TOEFL 550, Internet TOEFL 79, IELTS 6.5
- Three recommendation letters
- Statement of purpose
When sending GRE or TOEFL scores, UNL's institution code is 6877 and a department code is not needed.
Application Deadlines for Statistics
- For Financial Consideration: January 15 for Fall. October 15 for Spring.
- Otherwise: April 1 for Fall. October 31 for Spring.
Application/admission is for entry in a specific term and year. UNL's academic year is divided into 3 terms: Fall (August-December), Spring (January-May), and Summer (multiple sessions May-August). Some UNL programs accept new students only in certain terms and/or years; if your desired entry term isn't mentioned here, you may want to consult the department for clarification.
Statistics as a discipline develops methods to collect and interpret data. Modern statistical methods are used in many areas, such as medicine, the biological and social sciences, economics, finance, manufacturing, marketing research, management, government, and research institutes.
Students who have majored in mathematics or statistics and have an interest in applications of these fields or students who have majored in other areas and have a good mathematics background should seriously consider a career in statistics. It is quite common for students to enter a statistics graduate program with only a few statistics courses since statistics is primarily a graduate discipline.
The Department of Statistics is involved in research, teaching, and statistical consulting for the entire university. Some of our faculty are involved in projects funded by federal agencies such as the National Science Foundation, Environmental Protection Agency, and United States Department of Agriculture. Because of its activities, the collaborative work with other disciplines gives graduate students a wide range of opportunities to work with individuals in these disciplines and to learn practical applications of statistical principles from direct experience. In addition, Ph.D. students have the opportunity to double major in statistics and economics.
Cost of attendanceCost differs from one student to another. For details see Tuition, Fees, and Funding or try our Cost Estimator.
Faculty and research
Where available, faculty names link to bios or homepages and conversation icons () link to directory listings with address, phone, and email.
Categorical Data Analysis; Statistical Computing; Group Testing; Statistics in Sports
Environmental Statistics; Nonlinear Models
Statistical Methodology with an Emphasis on High Dimensional and Predictive Methods; Statistical Computation; Bioinformatics/Computational Biology; Multi-Type Data Analysis; Bacterial Genomics/Metagenomics
Decision Analysis; Design of Experiments; Biological Modeling
Mixed Linear Models; Breeding and Genetics
Mixed Linear Models; Plant and Animal Breeding and Genetics; Statistical Computing; Bioinformatics
Computational Biology (Bioinformatics); Biostatistics; Epigenetics; Optimization; Machine Learning
Spatial Variability; Design of Experiments; Linear Models; Statistics in Sports
Generalized Linear Models; Design of Experiments; Statistics in Developing Countries
Non-Parametric and Semi-Parametric Statistical Methods; Functional Data Analysis; Survival Analysis; Measurement Error; Time Series Analysis, and their Applications in Public Health, Biology, Agriculture, Finance, Etc.
Theory, Methodology and Practice in Analyzing High Dimensional Data with Complex Structure
Bayes and Empirical Bayes Analysis; Estimating Animal Abundance
Multivariate Spatial and Spatial-Temporal Models, Extreme Theory and Fixed Domain Asymptotics of Multivariate Gaussian Random Fields