Course Director: Dr. Robert Frost
- Course description: This is a time-intensive graduate-level course in mathematical statistics designed to teach the fundamental knowledge of statistical theory required to read and, with further study, contribute to the statistical methodology literature. An in depth overview of statistical estimation and hypothesis testing will be provided, including the method of least squares, maximum likelihood methods, asymptotic methods, and correction for multiple comparisons. The basic elements of statistical design and sample size calculations will be introduced. Resampling strategies will be discussed in the context of the bootstrap, as well as simulation as a tool for statistical research. The emphasis will be on theory used in modern applications in biomedical sciences, including genomics, epidemiology, and clinical and health services research. The statistical program language R will be leveraged for computational examples, problem sets and exams.
- Dartmouth Coursework: No specific Dartmouth courses are required. See syllabus for specific prerequisites.
- QBS 120 is a fast-paced, calculus-based graduate mathematical statistics course with a strong theoretical component. It is assumed that students are comfortable with multivariate calculus, mathematical proofs, linear algebra and R programming. A strong internal motivation to learn the material and complete challenging assignments is essential to success in this class. Students should expect to spend 10-15+ hours per week outside of class. Students are strongly encouraged to review the content and level of theory in the class textbook (Rice, see below) prior to registering for the class. Versions of prior problem sets (and solutions) are also available on request to help students assess the class workload, theoretical component and assumed mathematical and computational background.