Burnham and Anderson (2002) provide the formulas for computing AIC from any
model that generates either a log-liklihood value (Page 61) or a residual error
(Page 63) so you can generate an AIC for pretty much any statistical model. SAS
provides AIC as standard output on some procedures such as P
Paul,
You do realize that Syngenta spends a large amount of money on websites and
other media asserting that atrazine is safe and has no affect on wildlife or
humans at ppb levels. There have also been law suits by 16 midwestern cities in
six states suing Syngenta for water treatment costs to r
Travis,
I would suggest reading over section 18.1.3 in Quinn, G. P and M. J. Keough.
Experimental Design and Data Analysis for Biologists. Cambridge University
Press, Cambridge, UK. It has a nice discussion of the various methodologies
available for testing hypotheses about group differences ba
I would take a look "Statistics without Math" by William E. Magnusson and
Guilherme Mourão. It is published by Sinauer. Many of the chapters are short
and can stand alone.
Sent from the cloud
On Jan 13, 2011, at 8:30 PM, Anna Mosser wrote:
> I'm looking for one or two short readings on scient
There are a couple of confusing points in your response Bob.
1.) Why would you use R^2 rather than dAIC and wi to see how large the
differences between models are?
2.) Doesn't all prediction assume that conditions are "similar enough" that the
prediction conditions are valid?
3.) Related to abo
I can certainly second the recommendation for Scheiner and Gurevitch. However,
I find it even more useful in conjunction with the text "Experimental Design
and Data Analysis for Biologists by Gerry P. Quinn and Michael J. Keough. Also
the answer to the question, What tests are used in Ecology? i
