Statistical analysis is one of the primary tools of modern scientific reasoning. Since the need to draw correct and defensible conclusions from data collected in the presence of uncertainly arises so widely, statistical techniques are useful in almost all disciplines. There is a consequent demand for well-educated statisticians, and the demand is growing.
The Committee on Statistics offers a program leading to the Master of Science in Statistics. The program is interdisciplinary in the sense that it draws upon resources from several academic units. The program prepares students for careers in business, education, industry, and government. Graduates understand the theory that is fundamental to proper use of statistics, are knowledgeable about the tools of applied statistics, and are familiar with computer software packages available for doing statistical computations. In addition, students interested in research in statistics can obtain the background needed to begin a doctoral program. (There are Ph.D. programs offered in the Department of Industrial Engineering and the School of Mathematical and Statistical Sciences with specialization in Statistics.)
One of the factors behind the demand for statisticians is the dual nature of statistics: it is both theoretical and applied. Applied statisticians work closely with scientists and managers in the interpretation and control of stochastic phenomena. Theorists study new procedures often suggested by such applications, using advanced mathematical and computational methods. The interaction between theory and application is so important that many statisticians do both theoretical and applied work.
The requirements for the Master of Science in Statistics reflect this dual interactive nature. The Committee on Statistics is composed of faculty from the Departments of Economics, Health Management and Policy, Information Systems, and Supply Chain Management in the W. P. Carey College of Business, the Department of Industrial Engineering in the Ira A. Fulton School of Engineering, and the School of Mathematical and Statistical Science in the College of Liberal Arts and Sciences. The program relies primarily on courses offered by these departments. Additional members of the Committee on Statistics are from the Division of Mathematical and Natural Sciences in the New College of Interdisciplinary Arts and Sciences on the West campus.
Completion of the degree requires 30 semester hours of graduate credit (ten three-hour courses). The degree can be earned under an applied project option or under a thesis option.
| Project |
Thesis |
|
| Theory Courses |
9 hours |
9 hours |
| Elective Cources |
18 hours |
15 hours |
| Special Courses |
3 hours |
6 hours |
Both programs include three required theory courses: theory of probability, mathematical statistics, and theory of statistical linear models. These courses are fundamental to the education of any statistician and are necessary for more advanced graduate study.
The elective courses, chosen by the student with the approval of supervising faculty, allow the student to emphasize an area of special interest. Possibilities include, but are not limited to, the following: applied data analysis, Bayesian analysis, biostatistics, categorical data analysis, data mining, design of experiments, industrial statistics and six sigma methodology, linear models, multivariate analysis, sampling and survey research, smoothing methods, statistical computing, statistical process control, and time series analysis.
The remaining hours culminate in an applied project report or a master's thesis on a topic in the student's specialty area. The student must defend the report or thesis in an oral examination. No foreign language or written comprehensive examinations are required.
The Graduate College requires a grade point average of 3.0 or better (4.0 =A) in the last two years of work leading to the bachelor's degree. All applicants whose native language is not English must submit a TOEFL score. Other requirements are specified in the Graduate Catalog.
Application must be made via the internet. A completed application, official transcripts of all postsecondary academic work, and the application fee must be received by the Graduate College. Applicants to this program must, in addition, submit a brief statement of goals and arrange to have three letters of academic recommendation submitted to the Director of the Committee on Statistics.
Most applicants have earned the bachelor's degree in a quantitative area such as statistics, business analysis, mathematics, engineering, or computer science, but this is not required for admission to the program. However, applicants should have completed an introductory calculus sequence and one course each in advanced calculus, linear algebra, computer programming, and introductory applied statistics. Applicants who lack any of these prerequisite courses should expect to complete the prerequisties before being considered for admission.
Department of Economics
Lawrence S. Mayer (Ph.D., Ohio State University, 1971):
Cross-lagged panel studies; developmental methodology.
Jeffrey R. Wilson (Ph.D., Iowa State University, 1984):
Categorical data analysis; generalized linear models.
School of Mathematical and Statistical Sciences
Shu-Chuan (Grace) Chen (Ph.D., Penn State University, 2003):
Pattern Recognition; Mixture Models; Bioinformatics.
Randy Eubank (Ph.D., Texas A&M University, 1979):
Nonparametric smoothing, statistical computing.
Sharon L. Lohr (Ph.D., University of Wisconsin, Madison, 1987):
Experimental design; survey samplings.
Ananda Majumdar (Ph.D., University of Connecticut, 2004):
Bayesian hierarchical modeling; Spatial and Spatio-temporal modeling.
Kathryn A. Prewitt (Ph.D., University of California, Davis, 1991):
Time series; nonparametric curve estimation.
Yan Yang (Ph.D., University of Illinois Urbana-Champaign, 2006):
Generalized mixed models; computational methods; risk assessment; biostatistics.
Dennis L. Young (Ph.D., Purdue University, 1970):
Multivariate analysis; robust methods.
Department of Industrial Engineering
Jing Li (Ph.D., University of Michigan, 2007):
Applied statistics, data mining, causal modeling and inference for process control
Douglas C. Montgomery (Ph.D., Virginia Polytechnic Institute and State University, 1969):
Design of experiments; optimization and response surface methodology; empirical stochastic modeling.
Rong Pan (Ph.D., Pennsylvania State University, 2002):
Statistical quality control, reliability engineering, time series analysis and control, and supply chain management
George C. Runger (Ph.D., University of Minnesota, 1981):
Process control and optimization; statistical applications in nontraditional manufacturing.
Department of Supply Chain Management
Daniel G. Brooks (Ph.D., Indiana University, Bloomington, 1978):
Risk assessment; statistical decision theory.
Department of Information Systems
Robert D. St. Louis, Jr. (Ph.D., Purdue University, 1972):
Model management systems; time series analysis.
School of Health Management and Policy
Mark Reiser (Ph.D., University of Chicago, 1980):
Latent structure models; social science methodology.
Division of Mathematical and Natural Sciences
Roger Berger (Ph.D., Purdue University, 1977):
Hypothesis testing; (bio)equivalence; generalized linear models; biostatistics.
Connie Borror (Ph.D., Arizona State University, 1998):
Statistical quality control; reliability engineering; applied statistics.
Arizona State University's main campus is in Tempe, a city of 166,000 in the Phoenix metropolitan area. Strong academic programs and faculty are complemented by the attractions of year-round sunshine, cultural diversity on campus and in the community, and the resources of one of the nation's fastest-growing areas. Libraries, which house over six million items, and free computing facilities throughout campus support the research of students and faculty. Of ASU's 64,000 students, 13,000 are pursuing graduate studies.
Please contact:
Director, Committee on Statistics
Graduate College
Arizona State University
Box 871003
Tempe, AZ 85287-1003
(480) 965-2671
You are cordially invited to arrange a visit to see the campus and meet some of the faculty and students. Limited inexpensive dormitory accommodation is available for applicants who want to visit campus for a few days.
E-mail: statistics@asu.edu.