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Applied Master's Program

The Masters program in Applied Statistics prepares graduates for careers as applied statisticians in industry, government, consulting firms, and research organizations. Course requirements include at least 10 courses for a total of 30 credit hours. While requirements include basic courses in probability and theoretical statistics, the emphasis is on statistical modeling and data analysis. A wide variety of elective and cognate courses are offered in the Department of Statistics and in other departments, including BiostatisticsComputer ScienceEconomicsIndustrial & Operations EngineeringMathematicsSchool of InformationSociology, and the Survey Research Center. Most students take two years (4 semesters) to complete the degree, although it is possible to do it in 3 semesters. Students cumulative GPA must be 3.00 (B) or better to stay in good standing.

Prerequisites

It is strongly recommended that prospective students have a good background in calculus and linear algebra and have taken one course in probability, one course in theoretical statistics and at least one in applied statistics. Students who have not taken these prerequisite courses are generally required to take them in the first year of graduate study, with no credit toward the requirements for the degree.

Courses

The following core, elective, and cognate courses apply to all students.

Students must take each of the following core courses:

  • STATS 500: Statistical Learning I: Regression
  • STATS 503: Statistical Learning II: Multivariate Analysis
  • STATS 504: Principles and Practices in Effective Statistical Consulting
  • STATS 510: Probability and Distribution Theory
  • STATS 511: Statistical Inference

Students must take a minimum of three of the following elective courses:

  • STATS 406: Introduction to Statistical Computing
  • STATS 414: Introduction to Bayesian Data Analysis
  • STATS 430: Applied Probability
  • STATS 506: Computational Methods and Tools in Statistics
  • STATS 509: Statistics for Financial Data
  • STATS 526: Discrete State Stochastic Processes
  • STATS 531: Modeling and Analysis of Time Series Data
  • STATS 535: Reliability
  • STATS 547: Probabilistic Modeling in Bioinformatics
  • 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.

To obtain credits toward the degree requirement for cross-listed courses, students have to enroll under the STATS course number.

Students must take at least 4 credits of cognate courses:

Cognates are courses from outside the Department of Statistics, and must be pre-approved by the program advisor.

Cognate Suggestions

Students can use the flexibility in the choice of elective and cognate courses to align their program of study with their interests. Some suggestions of cognate courses by area are given below, along with electives that are most important to take if you are interested in that area. Students can take cognate courses from multiple areas, or choose other courses to serve as cognates. All cognate choices must be pre-approved by the program advisor.

Econometrics and Forecasting

Statistical techniques play an important role in predicting/forecasting various economic phenomena. Students
interested in this area can take STATS 509 and STATS 531 as electives and take one or more of the following:

  • ECON 501: Applied Microeconomic Theory
  • ECON 502: Applied Macroeconomic Theory
  • ECON 675: Applied Microeconometrics
  • ECON 676: Applied Macroeconometrics
Financial Statistics

Modeling and analysis of financial data is another area that is attracting a lot of attention. Students interested in this area can take STATS 509 and STATS 531 as electives and take one or more of:

  • MATH/IOE 506: Stochastic Analysis for Finance
  • IOE 552: Financial Engineering I
  • IOE 553: Financial Engineering II
  • FIN 513: Financial Analysis
  • FIN 580: Financial Derivatives in Corporate Finance
Industrial Statistics

Students can take courses offered by the Department of Industrial & Operations Engineering to develop expertise in the application of quality and reliability methods in industrial statistics. Suggested courses include STATS 570 and STATS 535 as electives and one or more of:

  • IOE 466: Statistical Quality Control
  • IOE 541: Inventory Analysis and Control
  • IOE 545: Queuing Networks
  • IOE 568: Statistical Learning & Applications in Quality Engineering
Information Sciences

Advances in computing and measurement technologies have led to massive amounts of data being collected routinely. Statistical methods play a fundamental role in the collection, visualization, mining and analysis of large data sets. Students interested in this area can take STATS 406, STATS 506 and/or STATS 607 as electives and take one or more of:

  • EECS 477: Introduction to Algorithms
  • EECS 484: Database Management Systems
  • EECS 485: Web Database Information Systems
  • SI 539: Design of Complex Web Sites
  • SI 572: Database Application Design
  • SI 601: Data Manipulation
  • SI 618: Exploratory Data Analysis
  • SI 649: Information Visualization
  • SI 664: Database Application Design
  • SI 665: Online Searching and Databases
Survey Sampling

The use of sample surveys to obtain information on a myriad of subjects is becoming ever more popular. The demand for statisticians trained in this sub-area is extremely high. The University of Michigan has, in various departments and in the Institute for Social Research, the faculty talent to be able to offer one of the best specializations in the country. For this area, students can enroll in STATS 580 as an elective and take one or more of:

  • SurvMeth 625: Methods of Survey Sampling
  • SurvMeth 630: Questionnaire Design
  • SurvMeth 670: Survey Design Seminar
  • SurvMeth 701: Analysis of Complex Sample Survey Data