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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
Dates

January 19 – March 12, 2021

Location

Virtual

Available Formats

Graduate (CONS 625, 3 credits)
Professional Training (SMSC 0501, 6 CEUs)

Cost

Graduate: See Mason’s graduate tuition rates

Professional Training: $500.00

Deadlines

Apply by December 21, 2020 for first consideration

Payment Deadline: January 4, 2021

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. 

Curriculum

This asynchronous online course 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 SCBItraining@si.edu for additional information.

Woman pointing to a student's computer screen

“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.”

Chase LaDue (in-person course participant) PhD student in Environmental Science and Policy at George Mason University

“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.”

Tabitha King (in-person course participant) Master's Student in Environmental Science and Policy at George Mason University
Take the next step toward a once-in-a-lifetime opportunity