Generalized Linear and Mixed Models in Ecology and Conservation Biology - Online
- Importing and formatting data to achieve meaningful statistical analysis
- Exploring, describing, and manipulating data sets to identify strengths and work around weaknesses
- Selecting regression framework based on the type of data and research objectives
- Understanding statistical methods, assumptions, limits, and applications of regression approaches
- Performing model selection and testing for model fit and performance
- Understanding, interpreting, visualizing, and presenting results with associated uncertainties
Participants must be familiar with basics of programming in R either independently or with the help of pre-course assignments.
January 18 – March 8, 2022
- Available Formats
Graduate (CONS 625, 3 credits)
Professional Training (SMSC 0501, 6 CEUs)
Graduate: See Mason’s graduate tuition rates
Professional Training: $500.00
Apply by November 23, 2021 for first consideration
Payment Deadline: December 7, 2021 (course can fill before deadline)
Meet the Faculty
Smithsonian-Mason School of Conservation
This asynchronous online course (previously called “Statistics for Ecology and Conservation Biology”) provides an overview of modern regression-based statistical analysis techniques relevant to ecological research and applied conservation, starting with basic linear models and moving quickly to generalized linear models (GLMs) and mixed models. The course aims to provide a robust understanding of the wide range of regression approaches available, the assumptions associated with each, and the circumstances under which each should be applied. Models covered enjoy widespread use in ecology and conservation biology and can be applied to a huge diversity of data types, study designs, and research questions. Emphasis is placed not only on proper implementation of models, but also on interpretation and explanation of results, recognizing uncertainty and model limitations.
The total cost for professional training covers:
- Access to all recorded lecture material, analysis demonstration code, exercise data and final exercise solution code
- Access to optional weekly live sessions (Zoom) for review of weekly analysis assignments and general Q & A with the instructor
Acceptance does not guarantee you a seat in the course. Seats are allocated as registration payments are received, and early registration is strongly encouraged to ensure you get a spot.
Email SCBItraining@si.edu for additional information.
“I was pleasantly surprised to enjoy statistics for the first time in my career, and the intensive two-week session is conducive to processing and integrating lessons.”
“I went from only knowing the basic R operations, to being able to apply currently-used predictive models to read data in a matter of two weeks. I would highly recommend this course to anyone who is wanting to create explanatory and predictive models from their data.”