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Analysis and Modeling of Environmental Data

EAS
549
Description

The course will meet the Analytic or Stats requirement, but not both. This course will consist of an overview of standard and innovative techniques in ecological data analysis and modeling. Topics will include: linear regression, mixed effects models (fixed and random effects), maximum likelihood, general linear models and general additive models, survival analysis, time series, spatial analysis and Bayesian and hierarchical Bayesian approaches.  This course is designed for students to work on their own data, or simulated data, related to their research projects or scientific interests. While reviewing the major statistical methods used in ecology, students will work on their projects and will be presenting their work to the class along the semester, these presentations will consist on: initial exploratory data analysis, selection of statistical analysis or modeling approach, implementation, and results.  

Faculty/Instructor
Inés Ibáñez
Credits
Minimum credits
3.00
Maximum credits
3.00
Undergrad
No
Graduate
Yes
Prerequisites
Students are expected to have a (undergrad) background in calculus, algebra, and statistics. Students will need their own laptops for the R and OpenBUGS lab
Offered Fall Semester
No
Offered Winter Semester
Yes
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University of Michigan School for Environment and Sustainability
Dana Building
440 Church Street
Ann Arbor, MI 48109
(734) 764-6453
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