[Ocees] FW: Online SMSC Regression Analysis in R course returns in early 2022

[Alvarado Serrano, Diego]

Talking about upcoming courses, this one is probably of interest to some people in this list (and it's free!)

[cid:image001.png at 01D76452.6055F1A0]

https://opengeohub.org/summer_school_2021<https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fopengeohub.org%2Fsummer_school_2021&data=04%7C01%7Cocees%40listserv.ohio.edu%7C387fff5b313f4d043acd08d9328b4a47%7Cf3308007477c4a70888934611817c55a%7C0%7C0%7C637596396093590582%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=kDMwxXmZx7O7lr3wOvUfXFIRP%2FwuzXenbODlE5xHcaM%3D&reserved=0>

Diego A.

From: OCEES <ocees-bounces at listserv.ohio.edu> On Behalf Of Popescu, Viorel
Sent: Friday, June 18, 2021 1:39 PM
To: ocees at listserv.ohio.edu
Subject: [Ocees] FW: Online SMSC Regression Analysis in R course returns in early 2022

FYI, courtesy of Marissa....

From: Dyck, Marissa <md441717 at ohio.edu<mailto:md441717 at ohio.edu>>
Sent: Friday, June 18, 2021 7:23 PM
To: Popescu, Viorel <popescu at ohio.edu<mailto:popescu at ohio.edu>>
Subject: Fw: Online SMSC Regression Analysis in R course returns in early 2022

See below

May be worth sending out via OCEES courses have filled up fast in the past.


Marissa Dyck

she/her


Popescu lab<https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvioreldpopescu.com%2F&data=04%7C01%7Cocees%40listserv.ohio.edu%7C387fff5b313f4d043acd08d9328b4a47%7Cf3308007477c4a70888934611817c55a%7C0%7C0%7C637596396093590582%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=UfJM5odzZB7FCNMKAXpfY3sTeNskBwfkbIMxlu6TcL0%3D&reserved=0>

PhD Candidate

Department of Biological Sciences

Ohio University

Irvine 320





________________________________
From: NZP-SCBI Training <scbitraining at si.edu<mailto:scbitraining at si.edu>>
Sent: Thursday, June 17, 2021 10:20 AM
Subject: Online SMSC Regression Analysis in R course returns in early 2022


The Smithsonian-Mason School of Conservation (SMSC), a partnership between George Mason University and the Smithsonian Conservation Biology Institute (SCBI), offers focused intensive training geared toward graduate students and professionals working in biodiversity conservation and management. Due to high demand and positive reviews, recently piloted online versions of our courses will be offered again in 2022, starting with our popular course in regression analysis. All courses offer continuing education credits (CEUs) and most can be taken for graduate credit at additional cost. Limited scholarships are available for eligible applicants but those interested in scholarships should apply early. If you have additional questions after reviewing the course web pages, please contact: SCBITraining at si.edu<mailto:SCBITraining at si.edu>



Generalized Linear and Mixed Models in Ecology and Conservation Biology - ONLINE

Jan 18 - March 8, 2022

Website: https://smconservation.gmu.edu/programs/graduate-and-professional/glm_ecology-ol/

Cost: 500.00 USD (Scholarships available for eligible applicants)

Application Deadline: Nov 23, 2021 (Note: the class is expected to fill well before this date, so all are encouraged to apply and register early to secure your seat)



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. Participants will conduct all exercises using R, a free software environment for statistical computing and graphics which has now become the standard in this field. In addition to recorded lectures and detailed demonstration analysis code, each week will include 2 optional live virtual sessions, one for review of weekly assigned problem sets, and the other for discussion and problem set Q&A. All exercises and demos will use real ecological data sets and participants will complete an independent analysis project on a unique assigned dataset during the last week of the course. This course schedule is designed to allow maximum flexibility for those juggling challenging work and family schedules.


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