Quantitative Methods and Models in R
Progressing from data to valid ecological inference requires the
· comfortable combining models with data to answer ecological
· familiar with frequentist, likelihood, and Bayesian approaches
· more knowledgeable of deterministic models and statistical distributions
· skilled at visualizing, manipulating, and analyzing data using R
How do we achieve this? While only a superficial level of understanding may be required to apply a statistical tool, much greater understanding comes from having to teach one. Thus, each week students will introduce new material from the book, and lab time will be used to work through data examples. Additional discussion time later in the week helps solidify understanding of the material. Students will also be tasked with integrating the course material into a team-based term project, which will undergo peer review towards the end of the term.
What was the most effective part of the course from the viewpoint of previous students?
“Having to teach a section of the book myself.”
“The background/frameworks of different statistical approaches.
“… to take a practical approach to modeling and data.”
“Learning R was fantastic! I wanted to be dangerous!”
This course is offered in the fall of odd numbered years, includes 2 1-hr meeting periods and 1 2-hr lab period (3 CR)