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Analysis of Environmental Data
Adelaide,
29 Jan - 10 Feb, 2007
Analysis of Environmental Data is an intensive 2-week course that will
provide participants with a training in the theory and application of
statistical techniques useful for the analysis of environmental data.
Course content 1. Data management, preparation and exploratory data analysis
2. Introduction to statistical modeling and regression analysis 3. Multivariate methods A provisional lecture list and timetable for the course can be found
here. Each topic will be presented using a 1-hour lecture and 2-hour
practical. The lecture will introduce the theory of each set of methods
and models, discuss their assumptions, and give students the knowledge
to enable them to identify the type of model appropriate for a
particular data analytical problem. The following practical will
reinforce the understanding of the lecture material by giving the
student the opportunity to learn by example and apply the techniques to
datasets to answer real environmental questions. Logistics There will be a lecture and practical each weekday morning and
afternoon, and on at least one of the weekend days, depending on the
enthusiasm of the course takers and my stamina! Other time, including
evenings, is available for working on the open ended projects and your
own data - you are particularly encouraged to bring your own data to
discuss and work on during the course. Last year most participants
worked on their own data well into the night! The course is directed towards advanced undergraduate, graduate,
and working professionals in in the environmental sciences. Prereq: an
undergraduate course in statistics, understanding of basic concepts such
as correlation and regression, and familiarity with PC-based software
for data analysis. You will get training in a number of software packages during the course, including CANOCO, TWINSPAN and C2, although for most of the course we will use the R statistical package. R is an extremely powerful statistical and graphical computing environment that is freely available under the General Public License (GPL). R can be downloaded from http://cran.r-project.org/index.html: versions are available for Windows, Unix and Mac OS. You are strongly encouraged to invest in the book by Peter Dalgaard listed below and to familiarize yourself with the program before the course. In due course I will also post a tutorial for participants to complete before the course so we can "hit the ground running". R does not have the bells and whistles and menu-driven commands of some expensive commercial packages. Because of this some find that R has an initial steep learning curve - but do not be put off – the course will take you through the main features of the program step-by-step and demonstrate that its flexibility, extensive list of statistical methods, and powerful publication-quality graphics will amply repay time spent learning it. Suggested reading Dalgaard, P. (2002) Introductory Statistics with R.
Springer, New York. - Excellent introduciton |
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