- Master's Degree Programs
- Prospective Master's Students
- FAQs for Current Students
- Statistics Courses
- Graduate Resources
The regular Master's degree (Master of Arts in Statistics) is restricted to students who are already enrolled in a Ph.D. program at the University of Michigan in Ann Arbor. It is a dual degree earned while a student is working towards a Ph.D. in another field, aimed at students who do a significant amount of statistics as part of their thesis research. It is also awarded as an embedded degree to students working towards a Ph.D. in Statistics.
Students interested in a Master’s degree in Statistics who are not currently enrolled in a Ph.D. program at the University of Michigan, or whose Ph.D. thesis at Michigan does not involve a significant statistical component, should apply to the Master’s Program in Applied Statistics.
Applicants must have already been accepted by a Rackham Ph.D. program and should have a reasonable background in calculus, linear algebra, introductory probability, theoretical statistics and applied statistics.
Prospective applicants are encouraged to consult with the Department of Statistics prior to submitting an application. Students are strongly discouraged from completing the course requirements and then applying to the program. Note also that you must be enrolled in the program for one year and complete a writing component before the degree is awarded.
Applicants should reach out to the department to discuss interest in the program and additional application materials. A letter of recommendation from a student’s research advisor in the student’s home Ph.D. department is also required, along with an up-to-date CV and Statement of Purpose.
The Winter application deadline is December 1st and the Fall application deadline is April 15.
The program requires a minimum of 24 credit hours of course work, including two cognate courses, and a writing component. Course selection must be pre-approved by the Program Advisor. Specifically, the requirements are:
- STATS 500 (Statistical Learning I: Regression) and STATS 503 (Statistical Learning II: Multivariate Analysis). More advanced students are encouraged to replace this sequence with STATS 600 (Linear Models) and STATS 601 (Analysis of Multivariate and Categorical Data) [6 credit hours]
- BIOSTAT 601 (Probability) and BIOSTAT 602 (Statistical Inference).More advanced students are encouraged to replace this sequence with STATS 610 (Statistical Inference) and STATS 611 (Large Sample Theory). [at least 6 credit hours]
- At least two elective statistics courses from graduate-level courses offered by the Department of Statistics or other approved courses (see list below). [at least 6 credit hours]
- Two cognate courses from another department. Consult the Program Advisor about acceptable cognate courses. [at least 4 credit hours]
- A writing component that demonstrates mastery of statistical methods in the design of data collection methods and/or modeling and analysis of data in the student's area of research. See more details below.
Students who have already taken courses that are equivalent to the required courses should discuss possible substitutions with the Program Advisor. Note that all students have to complete at least 24 credit hours in the program.
List of Elective Courses
The following courses are acceptable as electives.
- STATS 406: Computational Methods in Statistics and Data Science
- STATS 414: Topics Course (examples of previous offerings: Applied Survival Analysis, Bayesian Analysis)
- STATS 430: Applied Probability
- STATS 451: Introduction to Bayesian Data Analysis
- STATS 506: Computational Methods and Tools in Statistics
- STATS 509: Statistics for Financial Data
- STATS 526: Discrete State Stochastic Processes
- STATS 531: Analysis of Time Series
- STATS 535: Reliability
- STATS 547: Probabilistic Modeling in Bioinformatics
- STAT 551: Bayesian Modeling and Computation
- STATS 560: Introduction to Nonparametric Statistics
- STATS 570: Design of Experiments
- STATS 580: Methods and Theory of Sample Design
- STATS 607: Statistical Computing
- BIOSTAT 615: Statistical Computing
- BIOSTAT 675: Survival Analysis
- BIOSTAT 682: Applied Bayesian Inference
- BIOSTAT 695: Analysis of Categorical Data
- BIOSTAT 696: Spatial Statistics
- Any approved STATS 600-level or above courses.
All STATS courses 600-level or above can be used as elective courses, with the exception that students cannot use 600, 601 as electives if they have taken STATS 500, 503, and that they cannot use 610, 611 as electives if they have taken BIOSTAT 601 and 602.
To obtain credits toward the degree requirement for cross-listed courses, students have to enroll under the STATS course number.
Since the dual degree is primarily designed for students who do a significant amount of statistics for their thesis research, the students in the program are required to have a Ph.D. thesis chapter, or a thesis-based research paper submitted for publication, which demonstrates mastery of statistical methods at the level of a Master's project. The writing component may focus on data collection (design of experiments, survey design), modeling and analysis of data, or both. It must be approved by a member of the student's Ph.D. committee with an appointment in the Statistics department. This committee member will need to sign a form approving the statistical writing component, typically at the time of the defense. The student must also provide a two-page summary of the writing component, to be submitted with the signed approval form, describing the scientific problem under investigation, its importance, and statistical methods used.
In the event that the student's thesis does not have a sufficient statistical component, this requirement may be replaced with two additional elective courses (at least 6 credit hours), making the total credit requirement equivalent to that of the Master's Program in Applied Statistics. Students are strongly advised to consult with the Statistics faculty member on their committee well in advance of the defense to determine the best course of action. Those who do not anticipate having a significant statistical component in their thesis should apply directly to the Master's Program in Applied Statistics.