Applied Statistics, MS

Contact:

Pinyuen Chen, Advisor
pinchen@syr.edu
215 Carnegie
315-443-1577

Faculty

Pinyuen Chen, Peng Gao, Susan H. Gensemer, Vernon L. Greene, Chihwa (Duke) Kao, Hyune-Ju Kim, Yingyi Ma, Jan Ivar Ondrich, Jeffrey M. Stonecash, Raja Velu, Janet Wilmoth, Douglas A. Wolf, Yildiray Yildirim

A graduate program in applied statistics leading to a master’s degree is administered by the interdisciplinary Statistics Program. This program includes professors from computer and information science, education, engineering, management, mathematics, psychology, and the social sciences, among others. This program is distinguished from other graduate programs in statistics by its emphasis on applications. The interdisciplinary program in statistics is based in the College of Arts and Sciences, but welcomes students from all schools and colleges at Syracuse University. Included among these may be students who are pursuing other degrees, but might wish also to pursue the M.S. degree in statistics.

Admission

All applicants are expected to have a basic foundation in statistical training that includes one course in introductory statistics, one course in regression analysis, and four courses in applications areas. Graduate Record Examination scores, or their equivalent, and performance in a student’s undergraduate degree program will be carefully evaluated.

Applicants who are not currently enrolled in any program at Syracuse apply for admission to the Applied Statistics Master’s degree program through http://www.syr.edu/gradschool/em/future_howtoapply.html by March 15. Students who are currently enrolled at Syracuse University should contact Professor Pinyuen Chen at pinchen@syr.edu for further information.

M.S. Degree

The master’s degree in applied statistics requires completion of 33 graduate credits. Each candidate must submit a coherent program of 11 courses beyond the bachelor’s degree, subject to the following requirements.

Within the first semester after admission to the degree program, the students will plan their course of study in consultation with their advisors and submit it for approval to the Statistics Program Director.

In order to graduate, a student must earn (1) at least a 3.0 grade in each of the four core courses, (2) a GPA of 3.0 or better in this program of study leading to the M.S. in applied statistics, and (3) no more than two Cs in his/her statistics program coursework.

The absence of either a comprehensive final examination or a master’s thesis is compensated for by an additional 3 credits of coursework, represented by STT 690 or STT 750 / MAT 750 , whose objective is to apply knowledge of statistics to some real world problem.

Four Core Courses

All candidates for the degree program must complete the following set of four core courses (12 credits):

MAT 521 - Introduction to Probability 3 credit(s) (students with a strong mathematics background are to take MAT 651 ).
MAT 525 - Mathematical Statistics 3 credit(s) (students with a strong mathematics background are to take MAT 652 ).
STT 750 - Statistical Consulting 3 credit(s) or
MAT 750 - Statistical Consulting 3 credit(s) For those students who do not include STT 750 /MAT 750 in their programs of study, STT 690 should be taken and it should have a significant consulting component.

Any one of the following courses in regression Analysis:

MAT 654 - Linear Models 3 credit(s)
PSY 757 - Multiple Correlation and Regression 3 credit(s)
MAS 766 - Linear Statistical Models I: Regression Models 3 credit(s)
APM 630
SOC 714 - Intermediate Social Statistics 3 credit(s)
ECN 621 - Econometrics I 3 credit(s)
PPA 810

Four graduate courses (12 credits) are to be chosen from the following list:

The remaining 9 credits, selected in consultation with the student’s advisor, should:

emphasize statistical applications, or
involve consulting or advisement about statistical applications.

Degree:

Master of Science in Applied Statistics

Applied Statistics, MS

Contact:

Faculty

Admission

M.S. Degree

Four Core Courses

Any one of the following courses in regression Analysis:

Four graduate courses (12 credits) are to be chosen from the following list:

Design of Experiments

Sampling Theory

Multivariate Methods

Nonparametric Methods

Time Series Modeling and Analysis

Stochastic Processes/Markov Processes

Statistical Simulation and Nonstandard Data Analysis

Topics in Statistics

Advanced Probability I and II

Statistical Ranking, Selection, and Multiple Comparisons

Spatial Statistics

Econometrics

Statistical Consulting

The remaining 9 credits, selected in consultation with the student’s advisor, should:

Degree:

Total Credits: 33