Dr. Prechelt - It's been my observation that there IS no book of the sort you have asked for. There have been many attempts over the last 75 years to write such a book. Attempts by some very smart and articulate people . . . and no such attempt that I know of has succeeded.
I am forced to conclude that there is something intrinsic to the subject matter which makes it refractory to a good textbook exposition. We can speculate about what that is, but I think the evidence is plain that there is some inherent difficulty. The way which does seem to work in learning this material is a spiral - do an introductory look with a limited set of basic statistical procedures. Don't worry if you didn't understand quite all of the details. Let it sit for a few months; find an applied situation where you just HAVE to make use of what you know. Then, three months later, go back and view all of the same procedures again, from a somewhat more sophisticated or abstract viewpoint, and extend your knowledge to a few more procedures. This approach to learning statistics does seem to work, but it's not a quick process. I don't know of any other. - tom blackwell - u michigan medical school - ann arbor - On Tue, 16 Mar 2004, Lutz Prechelt wrote: > 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; [EMAIL PROTECTED] > Institut f�r Informatik; Freie Universit�t Berlin > Takustr. 9; 14195 Berlin; Germany > +49 30 838 75115; http://www.inf.fu-berlin.de/inst/ag-se/ > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html > ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
