Overview
Description
Applied use and interpretation of multivariate and modeling techniques for natural resources and biological studies.
Units
Lecture2
Laboratory Studio1
Catalog Details
Offering
Offered: Every Fall - Odd Years
Terms
fall
Attributes
Standard
Learning Outcomes
- identify and contrast the major classes of statistical models used by ecologists (e.g., Bayesian vs frequentist, likelihood-based, machine learning) and explain how and why ecologists use these models.
- apply analysis tools such as Generalized Linear Models (GLM), Bayesian inference, and Random Forest (RF) on diverse data sets representative of those commonly considered in observational studies in ecology.
- explore data sets quantitatively and graphically and to prepare data appropriately for analysis.
- perform statistical analysis, data visualization, simulation modeling, model validation and programming with the statistical computing language R.
- critically evaluate the strength of inferences drawn from statistical models by understanding and testing major assumptions and using tools such as cross-validation.
- communicate statistical and computational concepts by leading lectures and discussion on advanced topics in data analysis.