[OT][Long][You All Know Everything Dept] Recommendations for Stat book?

From: Kenny Tilton (ktilton_at_nyc.rr.com)
Date: 06/10/04 

Date: Thu, 10 Jun 2004 16:11:36 GMT

This OT post falls in the "Lispniks Know Everything Department". The
last time I mentioned that I got a nice borscht recipe. Now someone is
looking for a good stat book, and reported no luck with NGs specific to
that. Here is a doozy of a spec:

"I am looking for a statistics and probability textbook that would be
small in size, but pithy in
explanation.

I below list a few books that I have looked at and the deficiencies that
I found with them:

Example 1)
"Introduction to Probability and Statistics", Mendenhall, Beaver,
Beaver, 10th edition:

http://www.amazon.com/exec/obidos/tg/detail/-/0534357784/qid=1086880204/sr=1-3/ref=sr_1_3/002-3751143-0849664?v=glance&s=books

I found it to be too light in terms of content. For example, it defined
in 2nd chapter what
population variance and sample variance is, and that in case of one you
divide by n and in case of
the other by n-1. Later in the text, and I quote:

"You may wonder why you need to divide by (n-1) rather than n when
computing sample variance ...
turns out that the sample variance s^2 with (n-1) in the denominator
provides better estimates of
(sigma)^2 than would an estimator calculated with n in the denominator"

And that's it. As a mathematically curious person with college calculus
experience, I find that to
be extremely intellectually unsatisfying. I _COULD_ google for the
missing information, but having
it all in one source would be heavenly. Having thumbed through the rest
of the book, I observed
that pretty much nothing is formally proven or explained. The book comes
in at 750+ pages and is
physically a clunker, which is unjustifiable for the amount of
information it omits. There is an
inordinate amount of time spent on how to use the included Minitab, with
screenshots and all. I
need a textbook, not a software manual.

Example 2)

"Probability and Statistics for Engineering and the Sciences" by Jay L.
Devore 5th edition

http://www.amazon.com/exec/obidos/tg/detail/-/0534372813/002-3751143-0849664?v=glance

Here is an example of the sort of content that I am looking for.
Everything is formally derived,
using univariate calculus when necessary.

Problems:

the book is extremely slow reading, but not due to the included proofs.
In the introduction, the
author mentions how he spent an inordinate amount of time researching
"real-life examples" because
he found that students are more interested and motivated to learn the
subject if they are
presented with something other than "artificial examples with little
variation". I would rather he
had omitted all of that, and made the text a faster read. And the
size/weight of the book is once
again an issue just like the size of the book in example 1.

Most of the learning that I will be doing of this is during my hour-long
commute on the New York
City subway, standing with a laptop and a few other books in my
backpack. Something physically
small and light would be really nice to have."

Me, I think the guy should stick to Harry Potter on the subway, but if
anyone can think of a suitable title I will relay it back.

kenny 

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Relevant Pages

  • Corollary: N-P Silliness in Estimation Theory (was: Re: Unusual formulae for confidence interva
    ... sample variance when divided by (N-1) which was unambiguously ... N-P theorists' pre-occupation of the notion of an UNBIASED estimate. ... the unbiased estimate for sigma was Jack Tomsky, ...
    (sci.stat.math)
  • Re: Difference b/w Standard Deviation and Variance
    ... At least Afonso is trying to talk statistics when he made his error ... __the SAMPLE STANDARD DEVIATION is its squared root. ... IF I WANT, based on the sample items, to have an UNBIASED ESTIMATION of the POPULATION VARIANCE we must divide the sum of the squared deviations not by N but by N-1. ... an insignificant number of authors chose to define ABUSIVELY the sample variance using N-1. ...
    (sci.stat.math)
  • Re: Do you want it?
    ... Jack Tomsky wrote: ... "Now consider the sample variance, ... RF> some estimation criterion. ... RF> For (N-1), # = Unbiased Estimate. ...
    (sci.stat.math)
  • Re: Do you want it?
    ... "Now consider the sample variance, ... RF> some estimation criterion. ... RF> For (N-1), # = Unbiased Estimate. ... mean, M, is known and the unbiased estimate for the covariance ...
    (sci.stat.math)