The Master of Science in Statistics degree is designed to provide a suitable mix of theoretical and applied background for students interested in a career in statistics. The curriculum provides students with the necessary technical, analytical and interpretive skills required of professional statisticians while concentrating on education in the fundamentals of statistics and its interdisciplinary nature. Course offerings are structured to give students a variety of choices of specialization in order to pursue a career in academia, government or industry and/or further their pursuit of a Ph.D. degree in statistics. The student must complete and defend a thesis and obtain a passing score on the related oral examination.
The department requires all candidates for the Master of Science in Statistics complete and defend a thesis and obtain a passing score on the related oral examination.
A minimum of 30 credit hours are required for the Master’s degree. A student may transfer up to six semester hours of graduate level courses, earned with a grade of B or better from an institution approved for this purpose by the Graduate School. The department may waive some of the course requirements for those students who have taken equivalent course work at another institution. Students are required to take at least four hours of STAT 7990, and may count at most three additional hours of 69XX or 79XX courses toward the degree.
STAT 7600 Statistical Theory I (3) pr. STAT 3600
STAT 7610 Statistical Theory II (3) pr. STAT 7600
STAT 7800 Linear Models (3) pr. STAT 7600, MATH 2660
STAT 7020 Regression Analysis (3) pr. STAT 7000
STAT 7840 Multivariate Analysis (3) pr. STAT 7000
STAT/MATH 7810 Modern Stochastic Processes I
STAT/MATH 7820 Modern Stochastic Processes II
STAT or STAT/MATH courses number 6000 and above, or applied probability and statistics courses from outside the department with permission of the student’s advisory committee.
STAT 7990, 4 credit hours. Students usually work on a new statistical model/method or discover some novel insight on existing statistical methods.