Aaron A. King, Ph.D.

Nelson G. Hairston Collegiate Professor of Ecology, Evolutionary Biology,
Complex Systems, and Mathematics, University of Michigan
External Professor, Santa Fe Institute
Fellow of the American Association for the Advancement of Science

EEB 480. Model-based Statistical Inference for Ecology.

This course is an introduction to the modern theory and practice of scientific data analysis using both standard and innovative approaches. The unifying concepts are those of probability model, information, and inference. Students will learn and use the basic principles of model formulation, estimation, interpretation, criticism, and refinement. The course will make use of lectures, readings, and computer exercises in the R statistical computing environment. Students will obtain hands-on experience in data analysis using data provided by the instructor and students. In particular, students with scientific questions of their own and data sets to analyze will have a chance to work on these in the course. Students will develop and practice good habits in the organization, performance, and presentation of data and data analysis. Although examples will be for the most part drawn from Ecology, students from other disciplines, including Evolutionary Biology, Public Health, and Natural Resources, will learn valuable technique.

Students completing the course will have gained

Course topics include:

Additional topics that may be covered, according to opportunity and interest, include:

Prerequisites: An undergraduate-level grounding in calculus and statistics. Students unfamiliar with numerical computation in any language on any platform should consult with the instructor before registering for the course.

The course syllabus can be found here.

© 2024 Aaron A. King
3038 Biological Sciences Building
1105 North University Avenue
Ann Arbor MI 48109-1085 USA