Instructor: Jack Weiss, Curriculum in Ecology, 218 Miller Hall, 962-5930, firstname.lastname@example.org
Prerequisite: Stat 31
Meetings: 4 lectures hours per week split between classroom and computer lab
Meeting Times M 2–4 pm [201A Rosenau]
W 2–4 pm [217 Wilson]
Stauffer, Howard B. 2008. Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists. Wiley, New York.
Software: R, a freeware implementation of the S language. The R home page is http://www.r-project.org. The software can be downloded from http://cran.r-project.org. WinBUGS, a freeware program for Bayesian statistical inference, downloadable from http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml Note: WinBUGS only runs on Windows. An alternative to WinBUGS for Macintosh users is JAGS. http://www-ice.iarc.fr/~martyn/software/jags/
Course Website: http://www.unc.edu/courses/2008fall/ecol/563/001/
Office Hours: I’m available any time on MWF. I try to reserve TTh for my statistical consulting projects and will often be unavailable on these days. My office is in 218 Miller Hall (home of the Institute for the Environment). Miller Hall is on the corner of McCauley and Pittsboro, next to the Carolina Inn.
Evaluation: Weekly computer exercises using R and WinBUGS. Take-home midterm and final exam
Registration Details: Seats are reserved separately for ECOL 563 (10 seats) and BIOL 563 (10 seats). If the course is closed for one of these listings, try registering for the other one.
The prerequisites are modest, a one semester undergraduate or high school course in statistics (the equivalent of UNC Stat 31) and some exposure to the concepts of calculus. If you have never studied statistics, this is probably not the course for you. Having said this I think it is quite possible to obtain the requisite background through self-study before the course begins. I expect you to be familiar with (as in heard of and can quickly relearn) the following ideas:
Just about any elementary statistics text covers this material. The book Introduction to the Practice of Statistics by David S. Moore and George P. McCabe, the text that is used in undergraduate statistics courses at UNC, is an adequate choice as is any other text written at this level. Personal favorites of mine include Statistics with Applications to the Biological and Health Sciences by M. Anthony Schork and Richard D. Remington (any edition) and Biometry by Robert R. Sokal and F. James Rohlf.
No familiarity with statistical software is assumed. We will be using R, a free implementation of the S language, available for free download at http://cran.r-project.org. R runs on all major operating systems, including Windows, Unix, and Mac OS X. R is a state-of-the-art modern statistical package actively supported by the worldwide scientific community. It is not easy to use but it has become the de facto standard for scientific research. Although I've used many statistical packages in my career and even spent a number of years working for the SAS Institute, I now do all my work using R. Our lecture sessions in Rosenau Hall will include hands-on instruction in the use of R. In addition there are many online resources and published textbooks in the use of R. Links to these appear on the course web site.
For Bayesian estimation we will use WinBUGS, downloadable for free from http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml. WinBUGS is the most popular implementation of Markov chain Monte Carlo methods for Bayesian estimation and has a large online support group. WinBUGS only runs on Windows. An alternative to WinBUGS for Macintosh users is JAGS: http://www-ice.iarc.fr/~martyn/software/jags/. The modeling language of JAGS is nearly identical to that of WinBUGS. Both JAGS and WinBUGS can be run directly from R through the use of add-on packages.
This is a course in statistical modeling for ecologists and their kin. We focus on elementary statistical methods, primarily regression, and describe how they can be extended to make them more appropriate for analyzing ecological data. These extensions include using more realistic probability models (beyond the normal distribution) and accounting for situations in which observations are not statistically independent. We cover the topics listed below.
Phone: (919) 962-5930
Address: Curriculum in Ecology, Box 3275, University of North Carolina, Chapel Hill, 27516
Copyright © 2008
Last Revised--Aug 19, 2008