|Course Title||Data Analysis II: Ecological Modeling Using R
|Description||TUITION FEES: The Cost To Learner displayed in the Course Details is an approximate amount. Please refer to the 'Class Cost to Learner' displayed in the Class Details for the specific class session you wish to attend. Additional information is available at the NCTC website.
Note: Tuition is not applicable to FWS, BLM and NPS employees
The purpose of this course is to build a suite of tools for the purpose of modeling biological and environmental data. Tools include multiple linear regression, generalized linear models (Logistic, Poisson, and Negative Binomial regression) for modeling presence/absence and count data, trend analysis via linear and generalized linear mixed models, and mean separation techniques. Other topics include variable selection and screening, model comparison techniques using AIC, BIC and cross-validation. Emphasis is placed upon model interpretation as it relates to informing management decisions, understanding model assumptions and critical evaluation of competing models.
Class is highly interactive and is project driven. All material is presented using the R programming language.
• Development of skills for the use of multiple linear regression (variable selection, hypothesis testing, handling of multicollinearity, data transformations, and outlier diagnostics) .
• Development of skills for modeling presence/absence data and presence only data.
• Development of skills for modeling abundance and relative abundance data.
• Exposure to integrating statistical models with GIS technology for the purposes of creating resource selection/species distribution maps.
• Recognize and use appropriate spatial and temporal designs when collecting data and assessing variance. A variety of experimental designs and sampling designs are discussed and analyzed.
• Increased proficiency in the R programming language.
Target Audience:Biologists needing a better quantitative basis to make management decisions, particularly in the area of strategic habitat conservation and climate change response. Skills gained will prepare participants to engage in species habitat modeling.
Prerequisite Courses: Data Analysis I: Statistical Concepts and Procedures Using R
|Delivery Method||Instructor Led
|Course Content Contact||ERIC KELCHLIN; Phone: 304.876.7453; Email: firstname.lastname@example.org;
|Extracted from DOI LEARN on||11/26/2014 4:00:31 AM