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Statistics

Graduate Program Summary

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Graduate Degrees Offered

M.S.; Ph.D.
Areas of Study
  • Bioinformatics
  • Biometry
  • Environmental and Agricultural Statistics
  • Mathematical Statistics
  • Statistics in Sports
  • Survey and Behavioral Statistics



  

Statistics

Application Checklist and Deadlines

Required by the Office of Graduate Studies


  

Required by Statistics


Application Deadline

For Financial Consideration
   Fall: March 1   Spring: October 15
Otherwise
   Fall: June 15   Spring: October 31

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Description of Program

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.



 

Graduate Bulletin

The Graduate Bulletin provides course descriptions, program requirements, and more:

Faculty and Research

Chris Bilder Categorical Data Analysis; Statistical Computing; Group Testing; Statistics in Sports
Erin Blankenship Environmental Statistics; Nonlinear Models
Kent Eskridge Decision Analysis; Design of Experiments; Biological Modeling
Kathy Hanford Mixed Linear Models; Breeding and Genetics
Stephen Kachman Mixed Linear Models; Plant and Animal Breeding and Genetics; Statistical Computing; Bioinformatics
David Marx Spatial Variability; Design of Experiments; Linear Models; Statistics in Sports
Allan McCutcheon Categorical Data Analysis; Survey Research Methods and Design; Cross-National and Comparative Survey Research
Anne Parkhurst Chaos and Nonlinear Modeling; Multivariate Analysis; Time Series
Ananya Roy Bayesian Methodology; Generalized Linear Models; Small Area Estimation; Robust Statistics; Biostatistics
Julia Soulakova Dose-Finding Strategies; Analysis of Single or Combination Drug Studies; Statistics in Behavioral Medicine
Walter Stroup Generalized Linear Models; Design of Experiments; Statistics in Developing Countries
Dong Wang Bioinformatics; Statistical Genetics; Non-Parametric Methods
Shunpu Zhang Bayes and Empirical Bayes Analysis; Estimating Animal Abundance
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