EEB 480 - Model-based Statistical Inference for Ecology
Section: 001
Term: FA 2018
Subject: Ecology and Evolutionary Biology (EEB)
Department: LSA Ecology & Evolutionary Biology
Credits:
4
Requirements & Distribution:
BS
Advisory Prerequisites:
Senior natural science concentrator or Graduate student.
BS:
This course counts toward the 60 credits of math/science required for a Bachelor of Science degree.
Repeatability:
May not be repeated for credit.
Primary Instructor:

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 model, information, and inference. Students will learn and use the basic principles of model formulation, estimation, interpretation, criticism, and refinement.

Specific topics covered will include: exploratory data analysis, data visualization, general and generalized linear models, elements of model structure, stochastic simulation, likelihood, maximum-likelihood and Bayesian inference, and hierarchical/mixed-effects models. Additional topics that may — according to opportunity and interest — be covered include resampling, survival analysis, time series analysis, dynamical systems models, spatial analysis, and phylogenetic comparative analysis.

Students will obtain hands-on experience in data analysis using data provided by the instructor and/or by 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. Although examples will be for the most part drawn from Ecology, students from other disciplines, including Evolutionary Biology and Natural Resources, will learn valuable techniques.

Students completing the course will have a critical understanding of the theoretical foundations and the practical methods of modern ecological data analysis. They will be ready to either adapt existing methods or develop and implement new methods to their own scientific questions. Further, they will be prepared to understand and criticize models and statistical inference in the scientific literature.

Students are expected to have an undergraduate-level grounding in calculus, algebra, and statistics. Students unfamiliar with numerical computation in any language on any platform should consult with the instructor before registering for the course.

Course Requirements:

No data submitted

Intended Audience:

No data submitted

Class Format:

The course will make use of lectures, readings, and computer exercises in the R statistical computing environment.

EEB 480 - Model-based Statistical Inference for Ecology
Schedule Listing
001 (LEC)
P
30641
Open
7
 
-
MW 1:00PM - 3:00PM
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