Master of Science in Statistics
Preparation
Before entering the program, the student should have completed the following.
 3 semesters of calculus
 1 semester of linear algebra
 2 semesters of calculusbased probability theory
 Working knowledge of a programming language
Students lacking some of the above undergraduate coursework may be admitted conditionally
and may make up this coursework during the first year of the program (these courses
will not be counted toward the degree course requirements).
Required Courses (31+ units)
The student must complete a minimum of 31 units of coursework as described below. Core courses must be completed with no grade less than B in each course.
Core courses 

9 units from the following electives 

All of the following 

Other requirements
Advising.
Upon entry to the program, the student will be assigned to a graduate adviser in
statistics. Thereafter, the adviser will meet with the student each semester and discuss
his or her academic program.
Program of study.
The program of study, to include a plan for removal of any conditions on admission,
must be approved by the graduate adviser.
Plan A (thesis option) and Plan B (nonthesis option).
The thesis option (Plan A) requires approval of the graduate adviser and the statistics
division faculty member who will chair the thesis committee. Students who choose Plan
A must include Stat 799A in the 31unit program and are required to pass a final oral
examination on the thesis, open to the public. In most cases, Plan B will be followed.
Students who choose Plan B are required to complete three additional units of 600
and 700numbered statistics courses, not including Stat 799A, and pass a twopart
comprehensive written examination. Policy and procedures for the Plan B examination are documented and available from
the Department of Mathematics and Statistics.
Degree Learning Outcomes
Listed below are the applicable Degree Learning Outcomes (DLOs) for this degree.
 Describe and formulate statistical hypotheses based on scientific questions at hand.
 Choose and apply correct methods and modeling approaches for data analysis.
 Evaluate multiple approaches for a given problem and data set using statistical or computational tools such as cross validation and/or Monte Carlo simulations.
 Evaluate the fit of a statistical model and improve the fit by methods such as variable transformations and interactions as appropriate.
 Interpret statistical inferences in terms of reallife problems.
 Appraise and apply a new method in the literature for problem solving and data analysis as appropriate.
 Be able to critically evaluate, select, and use appropriate statistical software.
 Communicate and report statistical findings orally and in writing to both statisticians and other quantitatively oriented scientists.