Ph.D. in Statistics - Introduction

Guidelines and Policies
for the Ph.D. with a Major in Business Administration,
Concentration in Statistics
Department of Statistics

The University of Tennessee, Knoxville does not discriminate on the basis of race, sex, color, religion, national origin, age, handicap, or veteran status in provision of educational opportunities, reemployment opportunities and benefits.

UTK does not discriminate on the basis of sex or handicap in the education programs and activities which it operates pursuant to requirements of Title IX of the Educational Amendments of 1972, Public Law 92-318; and Section 504 of the Rehabilitation Act of 1973, Public Law 93-112, respectively. This policy extends to both employment by and admission to the University.

Inquiries concerning Title IX and Section 504 should be directed to the Office of the Director of Affirmative Action; 403-5 Andy Holt Tower; The University of Tennessee, Knoxville; Knoxville, Tennessee 37996 - 0144; (615) 974-2498. Charges of violation of the above policy should also be directed to the office of the Director of Affirmative Action.

This is a publication of the Department of Statistics, The University of Tennessee, Knoxville.

Ph.D. Program in Business Administration with a concentration in Statistics

The explosion of data in the business world has made the field of Statistics increasingly valuable. Simple tools applied to small data applications no longer suffice. Rather, the norm is massive and complex databases with difference structures. The size and complexity of such data presents both a challenge and opportunity to Statistics. As has always been the case, new applications drive the development of new and novel statistical techniques.

The Statistics, Operations, and Management Science Department at the University of Tennessee is pleased to offer a Statistics concentration Ph.D. in Business Administration to develop researchers who are well-equipped to make a vital contribution in such environments. Our department’s expertise includes data mining and multivariate modeling, time series analysis, process mining and improvement, and design of experiments. Students interested in these areas would find have the opportunity to study with world-renowned researchers.

Persons entering the Ph.D. program at UT will need to have a grounding in the basic business disciplines and prior, advanced education in Statistics. (In some instances, deficiencies in these areas can be removed by additional coursework at UT) The required Ph.D. courses at UT are:

• Computational methods in statistics
• Theory for developing new tools (two courses)
• Doctoral courses in two or more of the following areas: data mining, statistical process control, design of experiments, and multivariate modeling
• Graduate seminar in the chosen research field
• Complimentary course work in one chosen discipline outside statistics, such as Management Science, Marketing, Finance, Economics, and Engineering Sciences. (The area of this coursework should support either the dissertation or the long-term work interests of the student.)

The dissertation must be applications-driven research. Internship is also required of those who have little prior work experience.

The PhD program in Statistics at the University of Tennessee recruits enthusiastic, capable students, pursuing careers that involve research in Statistics and Probability, both the theory and applications.

The Department of Statistics, Operation and Management Sciences currently (2004-2005) has twenty four faculty members and twenty graduate students. Additionally, a continuous flow of visiting graduate students and postdoctoral fellows contributes greatly to the academic environment.

By means of joint faculty appointments and joint research projects we maintain active academic contact with the departments of; Computer Science, Mathematics, Psychology, Sociology, Agriculture, the Center for Quantitative Science in Forestry and Woods, the College of Engineering, the School of Business Administration, the Physics Laboratory, the Applied Computational Biology Division of the Oak Ridge National Laboratory.

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Our View

Statistical thinking is important in all disciplines engaged in empirical inquiry. The purpose of Statistical Science is to develop methods for designing and analyzing such inquiries, and to disseminate this methodology through teaching and scholarly communication.

Development of useful statistical methodology cannot take place in a vacuum. To be scientifically relevant this development should be problem-driven, motivated and guided by applications of scientific importance. Identifying and understanding important applications in all disciplines particularly in Industry and Business which what the department excels in. This requires interaction with other disciplines that acquire and analyze data. Collaborative research is therefore essential to the viability and growth of Statistics.

Computers have tremendously expanded the theoretical statistical thinking in practice. The Bootstrap, parametric and nonparametric clustering, prediction methods like CART and Neural Networks, the Cox model for survival analysis and reliability, data visualization, Bayesian inferences and Markov Chain Monte Carlo, design of experiments would have been unthinkable without the advances in computing.

Demand is rapidly increasing for new data analytic tools, and for individuals trained to invent, evaluate, and apply them. There is a new emerging field called: Data mining, which attracts employers and students. The increasing importance of computers in both data collection and data analysis has made expertise in computing an important prerequisite for creating and applying new and innovative data analysis tools.

At the time of the establishment of the Department of Statistics at the University of Tennessee in 1972, the department had already strong and thriving programs in Industrial statistics. The Statistics Department dedicated itself to further strengthening these areas while developing new expertise within the college of Business and emerging and important area of model selection, Data mining and statistical methodology. Cross-disciplinary research was also emphasized and continues to be a distinguishing feature of the academic program. The recently merger with the Operation and management sciences, provide exciting new research opportunities for both faculty and graduate students.

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