Linear Regression (Excel) and Cellular Respiration for Biology, Chemistry and Mathematics
03 Dec 2021 | Teaching Materials | Contributor(s):
By Irene Corriette1, Beatriz Gonzalez1, Daniela Kitanska2, Henriette Mozsolits2, Sheela Vemu3
1. Santa Fe College 2. Passaic County Community College 3. Waubonsee Community College
Students typically find linear regression analysis of data sets in a biology classroom challenging. These activities could be used in a Biology, Chemistry, Mathematics, or Statistics course. The...
Regression: Tree Rings and Measuring Things
03 Apr 2021 | Teaching Materials | Contributor(s):
By Greg Wiggins1, Suzanne Lenhart2
1. Department of Mathematics, University of Tennessee, Knoxville 2. University of Tennessee
Students develop scatter plots of growth rings by tree diameters and determine the equation of their best fit line.
Striped Bass: A Regulatory Success Story
12 Jul 2019 | Teaching Materials | Contributor(s):
By J. Alexander McCrickard1, Angela Hanretty1, Amanda Thompson1
Virginia Commonwealth University
This module examines the Maryland striped bass moratorium (1985-1989) as a fisheries management success story. Maryland DNR striped bass young of the year data is utilized for least squares linear...
Avian Censys Analysis
23 Jan 2019 | Teaching Materials | Contributor(s):
By Darcy Taniguchi
California State University San Marcos
This Swirl course uses ecological data of bird counts and habitat variables to estimate inter-observer variability and the relationship of birds with habitat variables.
A Very Basic Tutorial for Performing Linear Mixed Effects Analyses: Tutorial 2
20 Oct 2018 | Teaching Materials | Contributor(s):
By Bodo Winter
University of California, Merced
The second of two tutorials that introduce you to linear and linear mixed models. This tutorial serves as a quick boot camp to jump-start your own analyses with linear mixed effects models.
Linear Models and Linear Mixed Effects in R: Tutorial 1
The first of two tutorials that introduce you to linear and linear mixed models.
Advancing population ecology with integral projection models: a practical guide
By Cory Merow1, Johan Dahlgren2, Jessica Metcalf3, Dylan Childs4, Margaret Evans5, Eelke Jongejans6, Sydne Record7, Mark Rees4, Roberto Salguero-Gómez8, Sean McMahon9
1. Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA 2. Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden 3. Department of Zoology, Oxford University, Oxford, UK 4. Department of Animal and Plant Sciences, University of Sheffield, Sheffield, UK 5. Laboratory of Tree-Ring Research and Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA 6. Department of Animal Ecology and Ecophysiology, Institute for Water and Wetland Research, Radboud University Nijmegen, Nijmegen, The Netherlands 7. Harvard University, Harvard Forest, Petersham, MA, USA 8. Centre for Biodiversity and Conservation Science, University of Queensland, St Lucia, Qld, Australia 9. Smithsonian Environmental Research Center, Edgewater, MD, Edgewater, MD, USA
Integral projection models (IPMs) use information on how an individual's state influences its vital rates – survival, growth and reproduction – to make population projections using regression...