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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">FYI, courtesy of Marissa….</span><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"><o:p></o:p></span></b></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"><o:p> </o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">From:</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> Dyck, Marissa <md441717@ohio.edu>
<br>
<b>Sent:</b> Friday, June 18, 2021 7:23 PM<br>
<b>To:</b> Popescu, Viorel <popescu@ohio.edu><br>
<b>Subject:</b> Fw: Online SMSC Regression Analysis in R course returns in early 2022<o:p></o:p></span></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal" style="background:white"><span style="font-family:"Calibri",sans-serif;color:black">See below<o:p></o:p></span></p>
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<p class="MsoNormal" style="background:white"><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
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<p class="MsoNormal" style="background:white"><span style="font-family:"Calibri",sans-serif;color:black">May be worth sending out via OCEES courses have filled up fast in the past.<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
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<p><span style="font-family:"Calibri",sans-serif;color:black">Marissa Dyck<o:p></o:p></span></p>
<p><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black">she/her</span><span style="font-family:"Calibri",sans-serif;color:black"><o:p></o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#333333"><a href="https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvioreldpopescu.com%2F&data=04%7C01%7Cocees%40listserv.ohio.edu%7C8ec2d462412d4574561608d9327feb03%7Cf3308007477c4a70888934611817c55a%7C0%7C0%7C637596347241527618%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=9bxFTiYHhBz3xcYhD3SDtDERW8qrxT5C7xz0j4zqhNA%3D&reserved=0" originalsrc="https://vioreldpopescu.com/" shash="c1ohjkbA+aQEv2zpuzyNPJEi85OYlsGE2AVbWXKNe9Qr7Peqieu8tkU/DRlZ62ByAFBsT5xlzKDzHXFCSncjvmxDcY2J5yUcAtzV6zKplCTLInyfa6cZLcmHahR849tk5ik0SepcdhNaS2BE8AGsKl5o+TQZ2XUSeK9mejdem4o=" title=" https://vioreldpopescu.com/">Popescu
lab</a></span><span style="font-family:"Calibri",sans-serif;color:black"><o:p></o:p></span></p>
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<p><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#333333">PhD Candidate</span><span style="font-family:"Calibri",sans-serif;color:black"><o:p></o:p></span></p>
<p><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#333333">Department of Biological Sciences</span><span style="font-family:"Calibri",sans-serif;color:black"><o:p></o:p></span></p>
<p><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#333333">Ohio University</span><span style="font-family:"Calibri",sans-serif;color:black"><o:p></o:p></span></p>
<p><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#333333">Irvine 320</span><span style="font-family:"Calibri",sans-serif;color:black"><o:p></o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black">From:</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black"> NZP-SCBI Training <<a href="mailto:scbitraining@si.edu">scbitraining@si.edu</a>><br>
<b>Sent:</b> Thursday, June 17, 2021 10:20 AM<br>
<b>Subject:</b> Online SMSC Regression Analysis in R course returns in early 2022</span>
<o:p></o:p></p>
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<p class="MsoNormal"> <o:p></o:p></p>
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<p class="xmsonormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">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
<u>but those interested in scholarships should apply early</u>. If you have additional questions after reviewing the course web pages, please contact:
<a href="mailto:SCBITraining@si.edu">SCBITraining@si.edu</a> <o:p></o:p></span></p>
<p class="xxmsonormal" style="margin-right:107.05pt;text-autospace:none"><b><span style="font-size:14.0pt;font-family:"Calibri",sans-serif"> </span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p class="xxmsonormal" style="margin-right:107.05pt;text-autospace:none"><b><span style="font-size:14.0pt;font-family:"Calibri",sans-serif">Generalized Linear and Mixed Models in Ecology and Conservation Biology - ONLINE</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p class="xxmsonormal"><b><span style="font-family:"Calibri",sans-serif;color:black">Jan 18 – March 8, 2022</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p class="xxmsonospacing"><i><span style="font-family:"Calibri",sans-serif">Website:</span></i><span style="font-family:"Calibri",sans-serif">
<a href="https://smconservation.gmu.edu/programs/graduate-and-professional/glm_ecology-ol/">
https://smconservation.gmu.edu/programs/graduate-and-professional/glm_ecology-ol/</a>
</span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p class="xxmsonospacing"><i><span style="font-family:"Calibri",sans-serif">Cost:</span></i><span style="font-family:"Calibri",sans-serif"> 500.00 USD (Scholarships available for eligible applicants)</span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p class="xxmsonospacing"><i><span style="font-family:"Calibri",sans-serif">Application Deadline:</span></i><span style="font-family:"Calibri",sans-serif"> Nov 23, 2021 (Note:
<u>the class is expected to fill well before this date, so all are encouraged to apply and register early to secure your seat</u>)</span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p class="xxmsonospacing"><span style="font-family:"Calibri",sans-serif"> </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p class="xxmsonospacing"><span style="font-family:"Calibri",sans-serif">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.</span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p class="xmsonormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> <o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">FYI, courtesy of Marissa….</span><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"><o:p></o:p></span></b></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"><o:p> </o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">From:</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> Dyck, Marissa <md441717@ohio.edu>
<br>
<b>Sent:</b> Friday, June 18, 2021 7:23 PM<br>
<b>To:</b> Popescu, Viorel <popescu@ohio.edu><br>
<b>Subject:</b> Fw: Online SMSC Regression Analysis in R course returns in early 2022<o:p></o:p></span></p>
</div>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal" style="background:white"><span style="font-family:"Calibri",sans-serif;color:black">See below<o:p></o:p></span></p>
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<p class="MsoNormal" style="background:white"><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
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<div>
<p class="MsoNormal" style="background:white"><span style="font-family:"Calibri",sans-serif;color:black">May be worth sending out via OCEES courses have filled up fast in the past.<o:p></o:p></span></p>
</div>
<div>
<div>
<p class="MsoNormal"><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
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<div id="Signature">
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<div id="divtagdefaultwrapper">
<p><span style="font-family:"Calibri",sans-serif;color:black">Marissa Dyck<o:p></o:p></span></p>
<p><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black">she/her</span><span style="font-family:"Calibri",sans-serif;color:black"><o:p></o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#333333"><a href="https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvioreldpopescu.com%2F&data=04%7C01%7Cocees%40listserv.ohio.edu%7C8ec2d462412d4574561608d9327feb03%7Cf3308007477c4a70888934611817c55a%7C0%7C0%7C637596347241527618%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=9bxFTiYHhBz3xcYhD3SDtDERW8qrxT5C7xz0j4zqhNA%3D&reserved=0" originalsrc="https://vioreldpopescu.com/" shash="c1ohjkbA+aQEv2zpuzyNPJEi85OYlsGE2AVbWXKNe9Qr7Peqieu8tkU/DRlZ62ByAFBsT5xlzKDzHXFCSncjvmxDcY2J5yUcAtzV6zKplCTLInyfa6cZLcmHahR849tk5ik0SepcdhNaS2BE8AGsKl5o+TQZ2XUSeK9mejdem4o=" title=" https://vioreldpopescu.com/">Popescu
lab</a></span><span style="font-family:"Calibri",sans-serif;color:black"><o:p></o:p></span></p>
</div>
<p><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#333333">PhD Candidate</span><span style="font-family:"Calibri",sans-serif;color:black"><o:p></o:p></span></p>
<p><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#333333">Department of Biological Sciences</span><span style="font-family:"Calibri",sans-serif;color:black"><o:p></o:p></span></p>
<p><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#333333">Ohio University</span><span style="font-family:"Calibri",sans-serif;color:black"><o:p></o:p></span></p>
<p><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#333333">Irvine 320</span><span style="font-family:"Calibri",sans-serif;color:black"><o:p></o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black">From:</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black"> NZP-SCBI Training <<a href="mailto:scbitraining@si.edu">scbitraining@si.edu</a>><br>
<b>Sent:</b> Thursday, June 17, 2021 10:20 AM<br>
<b>Subject:</b> Online SMSC Regression Analysis in R course returns in early 2022</span>
<o:p></o:p></p>
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<p class="MsoNormal"> <o:p></o:p></p>
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<p class="xmsonormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">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
<u>but those interested in scholarships should apply early</u>. If you have additional questions after reviewing the course web pages, please contact:
<a href="mailto:SCBITraining@si.edu">SCBITraining@si.edu</a> <o:p></o:p></span></p>
<p class="xxmsonormal" style="margin-right:107.05pt;text-autospace:none"><b><span style="font-size:14.0pt;font-family:"Calibri",sans-serif"> </span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p class="xxmsonormal" style="margin-right:107.05pt;text-autospace:none"><b><span style="font-size:14.0pt;font-family:"Calibri",sans-serif">Generalized Linear and Mixed Models in Ecology and Conservation Biology - ONLINE</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p class="xxmsonormal"><b><span style="font-family:"Calibri",sans-serif;color:black">Jan 18 – March 8, 2022</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p class="xxmsonospacing"><i><span style="font-family:"Calibri",sans-serif">Website:</span></i><span style="font-family:"Calibri",sans-serif">
<a href="https://smconservation.gmu.edu/programs/graduate-and-professional/glm_ecology-ol/">
https://smconservation.gmu.edu/programs/graduate-and-professional/glm_ecology-ol/</a>
</span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p class="xxmsonospacing"><i><span style="font-family:"Calibri",sans-serif">Cost:</span></i><span style="font-family:"Calibri",sans-serif"> 500.00 USD (Scholarships available for eligible applicants)</span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
<p class="xxmsonospacing"><i><span style="font-family:"Calibri",sans-serif">Application Deadline:</span></i><span style="font-family:"Calibri",sans-serif"> Nov 23, 2021 (Note:
<u>the class is expected to fill well before this date, so all are encouraged to apply and register early to secure your seat</u>)</span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
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<p class="xxmsonospacing"><span style="font-family:"Calibri",sans-serif">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.</span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
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