Department of Mathematical Sciences

Statistics and Probability

Graduate study in statistics and probability has taken on a new look and increased importance in the last two decades due to dramatically increased computational power and the aggressive and highly successful application of statistical methods by our competitors in the world market-place. In particular, the Japanese have extensively employed design of experiments, data analysis, and statistical process control to improve the quality of their processes and the quality of their manufactured products. Recently a number of major U.S. corporations began emulating the Japanese approach by getting management to support the introduction of "statistical thinking" throughout the company, and requiring that the people running their processes have sufficient formal training in statistics to properly implement and monitor statistical process control programs.


    • A. Brown : Bayesian analysis, neuroimaging data analysis, large-scale inference
    • C. Gallagher : limit theorems, time series, modeling heavy-tailed data
    • Y. Li : Bayesian statistics, model selection, Bayesian nonparametrics, high dimensional problems
    • R. Lund : time series, applied probability, statistics in climatology
    • C. McMahan : Categorical data analysis, group testing, survival analysis, Bayesian estimation, statistical computing
  • C. Park : statistical computing, simulation, robust inference
  • X. Sun :  statistical decision theory, Bayesian Statistics, multivariate analysis, and bioinformatics
  • R.L. Taylor : laws of large numbers, density estimation, bootstrap estimation, statistical education
  • C. L. Williams : biostatistics, computational statistics, categorical data


Whether one is interested in applying statistical methods to problems in government or industry, or would like to engage in teaching and research at a university, a program can be tailored to meet these objectives within the constructs of the graduate program at Clemson. In addition to comprehensive training in statistical theory and methodology, students are exposed to areas such as combinatorics, mathematical programming, and scientific computing. While these areas are not part of a traditional statistics program, knowledge of them is becoming essential to the application and development of statistical methods. Thus, the Mathematical Sciences Department at Clemson is an ideal place to pursue the study of statistics. Students who choose to pursue the PhD degree may do research within the Department of Mathematical Sciences or they may enroll in the Management Science PhD program which is jointly administered by Mathematical Sciences and the Department of Management. That program stresses the use of analytic models and quantitative methods for decision making.

Courses (Course Descriptions)

  • Linear Models I (8010)
  • Linear Models II (8020)
  • Statistical Inference (8040)
  • Data Analysis (8050)
  • Nonparametric Statistics (8060)
  • Applied Multivariate Analysis (8070)
  • Reliability Theory and Life Testing (8080)
  • Time Series Analysis (8090)
  • Mathematical Statistics (8810)
  • Statistics for Experimenters (8840)
  • Advanced Data Analysis (8850)
  • Probability Theory I (9010)
  • Probability Theory II (9020)

Course Substitution Policy  

Sample Curricula

  • Sample M.S. Program for Well-Prepared Students
    • Fall:  8000, 8040, 8530
    • Spring:  8050, 8600, 8810
    • Summer:  8210
    • Fall:  8010, 8070/8090, 8100
    • Spring:  8020, 8030, 8060/8080/9810, 8920
  • Sample M.S. Program for Students Lacking Advanced Calculus*
    • Fall:  8000, 8040, 6530
    • Spring:  8530, 8050, 8810
    • Summer:  8600
    • Fall:  8010, 8070/8090, 8100
    • Spring:  8020, 8060/8080/9810, 8210, 8920

*If lacking any other prerequisite course, substitute this for 6530 in the Fall.

Additional Statistics Links