### Overview

The School of Math together with the School of Industrial and Systems Engineering offer graduate work leading to the Master of Science in Statistics. The emphasis in this joint program is on statistics as a science applicable in a technological environment. Although this program can lead to further work toward a Doctoral degree in Applied Statistics, Mathematics, or Bioinformatics, it is designed Primarily to provide the background for a successful professional career in statistics.

Career fields for graduates of this program may be found in all areas of research, industry, and government. The program, which can be completed in twelve months, is designed to provide the graduates with competence in the collection, analysis, and interpretation of data and a sound understanding of statistical principles. Students work with faculty actively engaged in research and prepared to teach the latest developments in statistics.

The program is quite flexible with regard to students' background. In particular, anyone interested in statistics who holds or anticipates an undergraduate degree in engineering, mathematics, or science is encouraged to apply. It is important, however, that all prospective students' previous coursework include (1) a multivariate calculus course, (2) a calculus based probability course, (3) a linear algebra course, and (4) familiarity with some computer programming language.

### Requirements

The core courses for the MS in Statistics degree are taken in Math and in ISyE. Choices of the remaining courses in the program are quite flexible; students can concentrate their studies on a specific area of application such as Operations Research, Psychology, Mechanical Engineering, etc., or, in preparation for the PhD, can take more mathematical courses. The MS degree in Statistics is awarded upon successful completion of the courses in the program as described below according to the stipulations of the Institute catalog. Electives are to be chosen in consultation with a faculty member.

Core: | 12 hrs |

Statistics Electives: | 15 hrs |

Free Electives: | 3 hrs |

Total: | 30 hrs |

**Core Courses**

Math 4261 Mathematical Statistics I

Math 4262 Mathematical Statistics II

ISyE 6413 Design and Analysis of Experiments

ISyE 6414 Statistical Modeling and Regression Analysis

**Statistics Electives**

Math 4317 Real Analysis

Math 6262 Statistical Estimation

Math 6263 Testing Statistical Hypotheses

Math 6266 Linear Statistical Models

Math 6267 Multivariate Statistical Analysis

ISyE 6402 Time-Series Analysis

ISyE 6404 Nonparametric Data Analysis

ISyE 6405 Statistical Methods for Manufacturing Design and Improvement

ISyE 6412 Theoretical Statistics

ISyE 6416 Computational Statistics

ISyE 6420 Bayesian Statistics

BME/ISyE 6421 Biostatistics

Math/ISyE 6781 Reliability Theory

Math/ISyE 6783 Financial Data Analysis

ISyE 6810 System Monitoring and Prognostics

ISyE 7400 Advanced Design of Experiments

ISyE 7401 Advanced Statistical Modeling

ISyE 7405 Multivariate Data Analysis

ISyE 7406 Data Mining

ISyE 7441 Theory of Linear Models

### Affiliated Faculty

- Dave Goldsman - Comparisons via stochastic simulation; Statistical ranking and selection (Professor, Ph. D., Cornell University)
- Christian HoudrÃ© - Nonparametric statistics; Statistical methods in finance and bioinformatics (Professor, Ph.D., McGill University)
- Xiaoming Huo - Multiscale statistical methods, Data mining(Assistant Professor, Ph.D., Stanford University)
- Vladimir Koltchinskii - Probability theory; mathematical statistics (Professor, Ph.D., Kiev University)
- Karim Lounici - High-dimensional statistical learning problems; Sup-norm rates of convergence (Assistant Professor, Ph.D., University Paris 7)
- J. C. Lu - Statistics for manufacturing; Reliability; Degradation modeling (Professor, Ph.D., University of Wisconsin)
- Brani Vidakovic - Multiscale methods; Statistical methods in geophysics; Turbulence; Bayesian decision theory (Professor, Ph.D. Purdue University)
- Jeff Wu - Design and analysis of experiments; Quality engineering; Product/process improvement; Bioinformatics (Ph.D. University of California - Berkeley)

### Contact Information

Specific questions about the program may be directed to Professor Vladimir Koltchinskii. General questions about admission into the School of Math should be sent to the School's Director of Graduate Studies. Questions about admission into the School of Industrial and Systems Engineering should be directed to the ISyE's Associate Chair for Graduate Studies.