Statistics for Environmental Science—Spring 2010

ECOL 562, ENST 562

Course Details

Instructor: Jack Weiss, Curriculum in Ecology, 201a Coates Hall, 962-5930, jack_weiss@unc.edu

Meetings: Two 75-minute lecture hours per week split between classroom and computer lab

Meeting Times: T 3:30-4:45 pm, 35 Graham Memorial Hall and
Th 3:30-4:45 pm, 201a Rosenau Hall

Text: Various online e-books and articles freely available through the UNC library system

Software: R, a freeware implementation of the S language. The R home page is http://www.r-project.org. The software can be downloaded from http://cran.r-project.org In week 7 of the course we will also use WinBUGS which you will need to download from http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml at that time. You may enjoy using the nice code editor Tinn-R that color-codes R text. It is also freeware and can be downloaded from the following location: http://sourceforge.net/projects/tinn-r/

Course Website: http://www.unc.edu/courses/2010spring/ecol/562/001/

Office Hours: I'm available at any time on M, T, Th, and F. I generally will be out most Wednesdays and so will typically be unavailable that day. My office is 201a Coates Hall (home of the Curriculum for the Environment and Ecology). Coates Hall is located at 223 E. Franklin St. across the street from Morehead Planetarium.

Evaluation: Weekly homework and final exam

Registration Details: Seats are reserved separately for ECOL 562 (10 seats) and ENST 562 (8 seats). If the course is closed for one of these listings, try registering for the other one.

Overview of the Course

An introduction to statistical methods for ecology and environmental science. This is a topics course. Our emphasis here is on breadth rather than depth. (The other graduate course I teach takes an in-depth approach to the topics covered in the first third of this course.) Familiarity with the standard parametric approaches of statistical analysis such as hypothesis testing is assumed. The course is intended to serve as a transition between what is typically taught in an undergraduate statistics course and what is actually needed to successfully analyze data in ecology and environmental sciences. The ideal enrollee is an upper level undergraduate or beginning graduate student who has already taken an introductory statistics course and wishes to see the modern application of statistics to environmental science and ecology.

Prerequisites

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 concepts:

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.

Statistical Software

No familiarity with statistical software is assumed. We will be using R, an 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 most of 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 on the use of R. Links to these appear on the course web site.

Course Content

This is a topics course that tries to cover many of the methods that have proved useful in analyzing environmental data. Our topics will include the following.

Jack Weiss
Phone: (919) 962-5930
E-Mail: jack_weiss@unc.edu
Address: Curriculum for the Environment and Ecology, Box 3275, University of North Carolina, Chapel Hill, 27599
Copyright © 2010
Last Revised--Jan 12, 2010
URL: http://www.unc.edu/courses/2010spring/ecol/562/001/docs/descrip.html