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What You'll Study
  • 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.

Program Details

January 16 – March 12, 2023



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 21, 2022 for first consideration

Payment Deadline: December 5, 2022 (course can fill before deadline)

Meet the Faculty

Joe Kolowski
Joe Kolowski
Research Ecologist and Grad/Professional Training Manager
Smithsonian-Mason School of Conservation
Joe Kolowski manages the Smithsonian-Mason School of Conservation’s graduate- and professional-level capacity building programs. His passion for conservation and applied research spawned an enthusiasm for the integration of research with effective conservation education and training. 


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.

See Detailed Curriculum >>

What’s Included

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 for additional information.

Woman pointing to a student's computer screen

A great course to learn R and apply to various ecological data sets. I highly recommend investing your time in this course early, so you can apply it throughout your research process.

Course participant 2021 Generalized Linear and Mixed Models in Ecology and Conservation Biology - online offering

The online format provided the flexibility to participate in the course, and the amazing recorded lectures, detailed scripts, and zoom sessions make remote learning much easier. I highly recommend the course.

Course participant 2021 Generalized Linear and Mixed Models in Ecology and Conservation Biology - online offering

The lectures were well thought out and helped guide us through specific statistical terms and concepts that we often work with but may not fully understand. By the end of the course my confidence increased and I am able to implement the knowledge gained into my own work as a terrestrial ecologist. The course will help tremendously with R programming, interpreting results, plotting and writing papers, making us all better scientists.

Hannah Madden (online format participant, 2021) Terrestrial Ecologist, Caribbean Netherlands Science Institute
Take the next step toward a once-in-a-lifetime opportunity