(resending to include r-help)
Monte Milanuk wrote:
Hello,
I'm looking for a textbook that can explain some of the math behind
the intro-to-intermediate stuff like ANOVA, multiple regression, non-
parametric tests, etc.
A little background: I took an intro stats course last year and
would like to further my education. Being as that was the highest
(and only) stats class the local community college offers, it looks
like I'm on my own from here. I've been working through some of the
online 'stats with R' tutorials as well as Dalgaard's ISWR. Where
I'm running into problems is the transition from Bluman's 'A Brief
Introduction to Elementary Statistics' (covers up through paired t-
tests, chi-squared/goodness-of-fit, simple linear regression &
correlation, and just barely mentions ANOVA) with a TI-83+, to even
books like ISWR... when they start getting into the things like one
and two-way ANOVA, multiple regression, model selection, survival,
etc. I start feeling like I have one hand tied behind my back - I
just don't have enough theoretical exposure to really understand what
techniques I would use when, relative to my own projects outside the
book.
Several of the 'intro to stats using R' books and pdf tutorials
mention that they are not really meant as a standalone statistics
text book, but in addition to a traditional stats textbook (Verzani
mentions Kitchen's book specifically). So I guess what I'm looking
for is any other recommendations on intro or intermediate textbooks
that deal primarily with the math/theory behind the processes. If
they were oriented towards R that's be great, but otherwise I guess
I'd be most interested in something relatively platform-agnostic -
I've seen some books that were slanted heavily towards a particular
software package (Minitab) that I cannot afford or justify for
personal use.
Re. ISwR, you might want to take notice that it was originally written
for a course that used Altman's "Practical Statistics for Medical
Research". It is, however, a bit wordy for some and glosses rather too
quickly over the math.
Another popular item for ambitious beginners is Kirkwood and Sterne:
Essential Medical Statistics. Their notation is a bit maddening (for
teachers anyway) but they do cover a lot of ground without digging too
deeply into the math.
If you want more math, beware that what is good, strongly depends on
your prerequisites. Linear model theory, e.g., gets much easier with
matrix calculus and nearly trivial if you know about abstract linear
algebra and projections in N dimensional vector spaces. For relatively
basic levels, look at booke that are popular for first courses in
Engineering: Devore, Johnson+Miller+Freund, and probably more.
--
O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalga...@biostat.ku.dk) FAX: (+45) 35327907
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