The research component of this project will develop new statistical methods for making inferences from a class of data in which measurements over time are associated with several "experimental units" that have a complicated dependence in their structural and/or stochastic properties. Such data arise frequently in diverse applications such as traffic flow, evolutionary biology, growth curve analysis, neuroimaging, and finance. This research will focus on cases where there is a discrete (and often small) set of units whose relationships are scientifically meaningful. Specifically, this project will develop models in three primary areas: (i) spatial models to improve inferences from functional Magnetic Resonance Imaging (fMRI) data, (ii) models of functional connectivity that allow fMRI data to provide information about how the components of brain systems interact, and (iii) new models for risk assessment and dependence analysis in computational finance.
The educational component of this project will focus on teaching statistical methods and reasoning to experimental psychologists. This project will design a course that builds on case studies and new pedagogical approaches to provide such authentic learning for these students. The emphasis will be on applying statistics in real scientific problems drawn from the psychological literature, effective communication and writing skills, and integrating the computer into the course at all levels. The project will produce the following: (i) A suite of interactive software modules written in Java for exploring the key concepts covered in the class, (ii) A complete set of homework projects and computer laboratory exercises based on issues in the psychological literature, (iii) A set of tools for developing interactive lectures, and (iv) An assessment of student performance both in the class and in downstream courses. These products will be packaged to be easily distributable to and useable by other instructors.
