Ecological Models and Data

Catalog description

Emerging issues in health and environmental sciences require an understanding of how to make inferences and decisions in the face of limited data and uncertainty. In the past few decades, advances in computational sciences have opened new ways of using and interpreting data. These advances are fundamentally changing how biologists approach statistics. This course introduces students to the principles of probability and likelihood that underpin these growing areas of statistics, and teaches students how to apply these principles to statistical computing. Examples come from ecological models and data and reflect general principles of working with discrete and continuous data, maximum likelihood and Bayesian estimation techniques, and using fundamental principles of probability to build and interpret mixed and mixture models. Work will be done using the open-source statistics program, R, and exercises will emphasize building skills by working with peers and by trial-and-error, both of which are key features of the open-source computing community.