[R] Terminology and canonical statistical user literature

[partha_bagchi@hgsi.com]

I would recommend Peter Dalgaard's book for an introduction to Statistics 
with R. Also, a resource that maybe more aligned with what you are asking 
for may be the so called ARTIST project. Details at
www.gen.umn.edu/artist/






"Lutz Prechelt" <prechelt at pcpool.mi.fu-berlin.de>
Sent by: r-help-bounces at stat.math.ethz.ch
03/16/2004 06:44 AM

 
        To:     "R Help" <r-help at stat.math.ethz.ch>
        cc: 
        Subject:        [R] Terminology and canonical statistical user literature


Brian Ripley wrote (to somebody asking about "effect sizes"):
> ...
> Given that, I wonder if you are used to standard terminology.

Good point. But I think for many of us there is more behind that.

I personally belong to an (apparently fairly large) group of
R users who may be enthusiastic, but are statistical laymen
due to a lack of formal education in the area.

The half-knowledge that I have is often sufficient to see that
many otherwise nice sources of statistical knowledge are
dangerously incomplete when it comes to explaining the
preconditions required for applying a certain technique
(One example: The extensive NIST handbook at
http://www.itl.nist.gov/div898/handbook/
fails to mention that the Wilcoxon rank sum test assumes a
continuous distribution underlying the sample)
This is not to speak of how to correctly interpret the results.

My situation is this:
- I often have a hard time understanding the R documentation
due to lack of background.
- I am not in a position to obtain a full background like
a statistics student would get it.
- I am very interested in carefully checking/validating my
application of statistical techniques.
- I cannot usually get a consulting statistician to help me.

My question:
Could some of the R gurus maybe agree on a book
(or very small set of books) with the following properties?:
- explains typical approaches of statistical analysis
(like MASS, but not as condensed)
- carefully describes preconditions, how to check them,
robustness if they are violated, interpretation of results
- avoids explaining the innards of the techniques
(and generally uses the perspective of the computer age)
- uses terminology that is easily mapped to R

If yes, I would be very interested in seeing this list.

I understand that one book cannot cover it all,
but maybe there is at least something like "CAS-"
(Conservative Applied Statistics without S) that
is of this type?  :-)

Lutz Prechelt

Prof. Dr. Lutz Prechelt;  prechelt at inf.fu-berlin.de
Institut für Informatik; Freie Universität Berlin
Takustr. 9; 14195 Berlin; Germany
+49 30 838 75115; http://www.inf.fu-berlin.de/inst/ag-se/

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