HDTenEightyP/NetNews huggingface dataset:
Newsgroups: bit.listserv.stat-l
Date:         Mon, 23 Nov 1992 15:38:55 EST
From:         Martin Czigler <mcz@SARNOFF.COM>
Subject:      Proportion of total variance with correlated variates

I'm interested in finding the proportion of total variance due to
specific random factors.  For example, suppose X1 and X2 are normally
distributed with 0 mean, and Y=X1+X2.  If X1 and X2 are uncorrelated,
then the proportion of Y's variance due to X1 is given by
sigma1^2/(sigma1^2+sigma2^2).  However, when the factors are
correlated, the appropriate answer is less clear.  Suppose X1 and X2
have the same variance, and are correlated with a coefficient of -1.
Then Y will be constant, i.e. var[Y] = 0, while both X1 and have a
positive variance.  Is it meaningful to ask what proportion of Y's
variability is due to X1 and X2 individually?

Thanks,

--Martin


Martin Czigler
David Sarnoff Research Center
CN 5300
Princeton, NJ  08543
mcz@windo.sarnoff.com
Martin_Czigler@maca.sarnoff.com
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Date:         Tue, 17 Nov 1992 08:44:58 EST
From:         "D. Glen McPherson" <mcphers@HILBERT.MATHS.UTAS.EDU.AU>
Subject:      Re: guidelines for checking assumptions

The thought that statisticians could come to a consensus about the choice and
application of a set of formal test criteria to check assumptions in any area
of statistical application is idealism in the extreme. However, this should
not preclude us from offering quite specific guidelines to nonstatisticians
who have little experience in how to proceed. The fact that two statisticians
or two statistical reference books recommend different tests or vary in the
manner of interpreting findings is lesser of an evil than a complete misuse
of statistics by someone who fails to check a critical assumption.

If we are to offer guidelines, there are two rules which I suggest must be
obeyed. The first is to make the guidelines exhaustive. Nothing will turn a
user off faster than to reach a point where the guidelines stop giving
guidance. For example, if the guidelines suggest a test for equality of variance
and there is evidence of heterogeneity of variance, there must be advice on
what to do next. This may be specific, may be a reference to another
 publicationor may be the advice 'At this stage seek advice from a consultant
 statistician'.

The second rule is to advise users to follow the usual convention in their own
discipline, if one exists, unless there is clear evidence that such a convention
is unreasonable. Publication of research findings is difficult enough without
introducing methods that are unfamiliar to referees. Where a new method for
checking assumptions is being introduced, it is generally wise for statistical
consultants to play a part in arguing the case for the new method. Perhaps they
may even offer to write the relevant section in the paper.

Regards,  Glen.
--
 ---------------------------------------------------------------------------
|  Glen McPherson            |                                   |  _--_|\  |
|  Department of Mathematics |-----------------------------------| /      \ |
|  University of Tasmania    |               E-Mail:             | \_.--._/ |
|  Australia.                | mcphers@hilbert.maths.utas.edu.au |       *  |
 ---------------------------------------------------------------------------
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Posted-Date: Thu, 19 Nov 92 08:59:48 EST
Newsgroups: bit.listserv.stat-l
Date:         Thu, 19 Nov 1992 08:59:48 EST
From:         Dennis Boos <boos@STAT.NCSU.EDU>
Subject:      neg. binomial

I'm feeling lazy.  A consultee came in
wanting SAS code (say NLIN or IML) for
fitting the negative binomial by maximum
likelihood and then giving a chi-squared
test of fit.  Does anyone out there have this
coded?

The data might be 16 independent counts:

x  0  1  2  3  4  5  6  7  8  9

f  2  3  5  2  3  0  0  0  0  1

Here f is the frequency of count values x.

Thanks.



          Dennis D. Boos

        * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
      * Department of Statistics                                *
    *  North Carolina State U.                                   *
  *  Raleigh, NC 27695-8203                * Raleigh, NC          *
 * boos@stat.ncsu.edu                                             *
* * * * * * * * * * * * * * * *                                  *
                               *                              *
                                 *                       * *
                                   *             * * * *
                                     *        *
                                        * * *


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Date:         Mon, 23 Nov 1992 08:21:00 EST
From:         SMC4@NCH02A.EM.CDC.GOV
Subject:      distribution-free regression, esp. mult

J.S. Maritz in "Distribution-Free Statistical Methods" (Chapman and
Hall, publishers, 1984) seems to cover distribution-free regression, although
I haven't read it very fully recently...

Hope this helps.
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Date:         Tue, 17 Nov 1992 09:40:00 PST
From:         Charles Chamberlin <CHAMBERLINC@AXE.HUMBOLDT.EDU>
Subject:      Re: Normal probability plots

Shapiro,Wilk,and Chen(1968) JASA 63:1343-1372 report an empirical
comparison of alternative methods for testing for normality.  The
methods they compare include the chi-squared test, the Kolmogorov-
Smirnov test, as well as the Shapiro-Wilk test. 45 alternative
distributions in 12 families and five sample sizes were studied.
Overall, the S-W test was most powerful.
It also can be applied to small sample sizes.

-------------------------------------------------------------------------------
Charles E. Chamberlin
Environmental Resources Engineering
Humboldt State University
Arcata, CA  95521
USA
(707) 826-4346
(707) 826-3616 (fax)
ChamberlinC@axe.humboldt.edu
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Date:         Tue, 17 Nov 1992 06:01:38 EST
From:         Stephen Baker <sbaker@UMASSMED.UMMED.EDU>
Subject:      Re: So, why assumptions?
              "ataylor@LALA.ZOO.HAWAII.EDU" at Nov 16, 92 10:59 am

> PS from/to Russ Lenth:
>
> > PS - I'm told that the term "rule of thumb" has to do with the size of
> > stick a husband may use to beat his wife (presumably in places like
> > Iowa where the ERA was just defeated).  I suggest we use more tasteful
> > terminology.
> >
> Interesting point, which I would rather not have learned. Is a phrase
> in poor taste if (almost) no one using it knows where it came from?
>
I had heard that rule of thumb related to literally using the thumb to
measure length as in with a ruler, because the thumb is about 1" from
tip to first knuckle.   I think there is a limit to how far one should
take "politically correctness " and we just went three thumblengths
beyond it.

My own rule of thumb for testing normality for day to day analyses is to
look for symetrical mound shaped histograms.  If there is a lot riding
on it, (e.g. a grant or publication) I do K-S tests.




Stephen P. Baker                               phone:  (508) 856-2625
Lecturer in Biostatistics                              (508) 856-3131 fax
Department of Academic Computing                       (413) 253-3923 home
University of Massachusetts Medical School     bitnet: sbaker@umassmed.bitnet
55 Lake Avenue North                         internet: sbaker@umassmed.ummed.edu
Worcester, MA 01655                                rf:  -. .---- --. - --.-
From: W_URBAN@UNHH.UNH.EDU
Newsgroups: bit.listserv.stat-l
Subject: Teaching basic computations
Date: 18 Nov 92 19:21:05 GMT
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    Overheard in office next door:

    "...now you square the sum...now you divide by N......"

    Is there a PC tutorial for basic computations?

    will@unh.edu
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Date:         Mon, 23 Nov 1992 11:07:42 EST
From:         Walt Pirie <WLTPIRIE@VTVM1.BITNET>
Subject:      Wanted: Rank test

For the problem below, two ideas come to mind. One is Kendall's Test of
Concordance. Some Nonparametric books include it, or look in Kendall's
Rank Correlation Book. The other is the General Linear Models by Ranks
procedure by Hettmansperger and McKean, but I don't know if there is
a general software package for that. It's much too complex to do by hand.





    I can't believe it! I'm too old for this. I had a student gather a
 bit of data thinking that when the time came I would just pick up
 Siegel or some other hoary text and look up the proper significance
 test. Now I can't find any test that addresses the problem.

   A group of 5 men ranks four things; and a group of five women do also:
                  Thing1  Thing2  Thing3  Thing4
       Man 1 :      4        2      1       3
       Man 2 :      4        3      2       1
        .
        .
        .
     Woman 1 :      3        2      4       1
     Woman 2 :      3        4      1       2
        .
        .
      etc.

      Now, I'm not obsessive about being fooled by chance, but I would
  like to know whether the disparity in the rankings between the men and women
  is of suffucient size that it is unlikely to have arisen solely from the
  chance selection of these two samples from populations in which men and
  women rank the four things similarly (or identically or something).

     Can anyone help?

_______________________________________________________________________

GENE V GLASS                                    ATGVG AT ASUACAD.BITNET
COLLEGE OF EDUCATION                            ATGVG AT ASUACVAX.BITNET
ARIZONA STATE UNIVERSITY                        INTERNET ADDRESS:
Tempe, AZ 85287-2411                            ATGVG@ASUVM.INRE.ASU.EDU
602-965-2692  (303-499-2816 from June '92 until Dec. '92)
  (While on sabbatical until Dec. '92, I can be reached at
    GLASS@SPOT.COLORADO.EDU )

|==================================|============================|
|  Walter R. Pirie                 |                            |
|  Department of Statistics        |                            |
|  Virginia Tech                   |                            |
|  Blacksburg, VA 24061-0439       |                            |
|                                  |                            |
|  Tel. 703-231-5441               |                            |
|  Bitnet WLTPIRIE@VTFVM1          |                            |
|  Telnet WLTPIRIE@VTVM1.CC.VT.EDU |                            |
|                                  |                            |
|  Fax  703-231-3863               |                            |
|==================================|============================|
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Date:         Thu, 19 Nov 1992 16:26:43 AST
From:         Rolf Turner <rolf@MATH.UNB.CA>
Subject:      Anscombe's (?) regression examples.

Some time ago I came across a collection of small artificially
constructed bivariate data sets designed to illustrate various
pitfalls of mindlessly regressing y on x without looking at the
data.  They were, I recall, constructed so that one got the same
results from doing a mindless regression on each data set, although
the true implications were mind-bogglingly different.  I believe
that these examples were due to Anscombe.

I thought I had these examples tucked away somewhere; now that I
want to refer to them, I can't find them.  And our library does not
have the sources in which I thought I might look for them.

Can anyone help me out, either by providing the actual data, or
by providing a reference where I can definitely find them?

Thanks for any and all help.


Rolf Turner
Dep't. of Maths and Stats
U.N.B., Fredericton, N.B. Canada E3B 5A3
rolf@math.unb.ca

----------------------------------------------------------------------
|                                                                    |
|          Nice day, isn't it?                                       |
|                                                                    |
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Newsgroups: bit.listserv.stat-l
Date:         Mon, 16 Nov 1992 00:59:00 EST
Comments:     208 Cedar Bldg.
Comments:     University Park, PA USA
Comments:     AC 814-863-2401
From:         Dennis Roberts <DMR@PSUVM.BITNET>
Subject:      So, why assumptions?

Most of the comments re: testing assumptions for various statistical tests
have argued against FORMAL testing but rther, graphical or other less
"formal" ways of checking assumptions.  It seems to me that this means that
some thumb rule that one person adopts to say that "it doesn't violate it
ENOUGH" will be different than some other person's who uses either a
different way of checking or a different thumb rule criterion.  I would like
to suggest that either an assumption is important or it isn't.  Simulations
have shown us some of these cases, some where it does matter and some where
it doesn't.  I think that if we adopt the strategy of informal checking, we
really have not solved the problem ...  it still is too easy to use a method
or adopt some criterion whereby we rarely, if ever, violate the assumption
enough so as to discard the resulting analysis.  While I am not suggesting
that I am a formal devotee of rigid assumption testing, I would offer the
following: if we believe that some assumptions are important, and advocate
checking, then we should AS A STATISTICAL COMMUNITY, decide which
assumptions are worthy of this and agree to some COMMON THUMB RULE that we
can all use in a common way.  Without this, who decides where to draw the
line?  I don't think "common sense" is good enough.
From: maj@waikato.ac.nz
Newsgroups: bit.listserv.stat-l
Subject: Re: Test for assumptions
Date: 16 Nov 92 09:05:28 +1300
Organization: University of Waikato, Hamilton, New Zealand

In article <m0mqRNV-0000W3C@vishnu.reed.edu>, Albyn Jones <jones@REED.EDU> writes:
> re: F-tests for equal variances...
> 
> "To make a preliminary test on variances is rather like
> putting to sea in a rowing boat to to find out whether conditions
> are sufficiently calm for an ocean liner to leave port"
> 
> George Box
> 
> Anova is pretty robust, why use a preliminary test which is less
> robust than the ultimate analysis?  The fact that several statistical
> packages give you Bartlett's test should not be taken as an endorsement
> of the implied procedure.
> 
> Several people have suggested graphical methods ie. informal tests.
> This seems much more sensible.
> 
> albyn jones
> jones@reed.edu

I totally agree with the above. There is also the point that
formal testing of the assumptions of tests leads you to an
infinite regress.
-- 
Murray A. Jorgensen [ maj@waikato.ac.nz ]    University of Waikato
Department of Mathematics and Statistics     Hamilton, New Zealand      
__________________________________________________________________
'Tis the song of the Jubjub! the proof is complete,
if only I've stated it thrice.'
From: feh@duke.edu (Frank Harrell)
Newsgroups: bit.listserv.stat-l
Subject: Bootstrapping non-independent observations
Keywords: bootstrap
Date: 21 Nov 92 14:52:10 GMT

Does anyone know of a good reference that covers bootstrapping
dependent observations? I'm looking for info on the legitimacy of
doing the ordinary bootstrap (pretending independent observations)
when there is a simple or complex correlation structure to the
observations. Is it still possible to get the correct
covariance matrix?


-- 
----------------------------------------------------------------------------
Frank E Harrell Jr			feh@biostat.mc.duke.edu
Associate Professor of Biostatistics
Division of Biometry                    Duke University Medical Center
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Date:         Mon, 23 Nov 1992 08:18:00 EST
From:         SMC4@NCH02A.EM.CDC.GOV
Subject:      Wanted: Ranks Test

This seems like a job for either a kappa statistic (interobserver
agreement/disagreement) or a Spearmann's rank correlation. I don't have the
calculation methods right here, but you can look them up in a number of
different texts.  Come to think of it, SAS PROC CORR can do Spearmann's rank
correlations.

Hope this helps...
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Date:         Mon, 23 Nov 1992 22:10:14 -0500
From:         Len Thomas <lthomas@LAKE.SCAR.UTORONTO.CA>

subject: Bootstrap estimates of power

I have been asked to compute a power/effect size curve for an experiment
which used the Wilcoxon's signed-ranks test.  Since the underlying distribution
from which the samples were drawn is unknown I propose to bootstrap the
samples to estimate power.  Different effect sizes can be simulated by
adding 1 to the value of each sample in one group with a probability set by
an effect size variable, which will range from 0 to 1.

Beeing rather new at this game, I'd appreciate any comments you folks may have.
Are there any published references to bootstrap estimation of power?
I feel fine simulating different effect sizes in this manner when there are
only two groups, but how could I do it if there were 3 or more (i.e. for a
Kruskal-Wallis test)?

Please direct any thoughts to my internet and I'll summarise for the list.

Len Thomas,                              lthomas@lake.scar.utoronto.ca
Division of Life Sciences,
Univerisity of Toronto at Scarborough.
Canada.
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Date:         Tue, 17 Nov 1992 08:24:59 EST
From:         McClelland_R%PSB%BLS@NIH3PLUS.BITNET
Subject:      Re: Rule of thumb

|Date: 11-17-92  8:20am
|From: McClelland_R:PSB:BLS
|  To: {STAT-L@MCGILL1}:BITNET:NIH
|Subj: Re: Rule of thumb
|In-Reply-To: Message from {STAT-L@MCGILL1}:BITNET of 11-16-92
------------------------------------------------------------------

>PS from/to Russ Lenth:

>> PS - I'm told that the term "rule of thumb" has to do with the size
of
>> stick a husband may use to beat his wife (presumably in places like
>> Iowa where the ERA was just defeated).  I suggest we use more
tasteful
>> terminology.
>>
>Interesting point, which I would rather not have learned. Is a phrase
>in poor taste if (almost) no one using it knows where it came from?

Let's try to unwind a bit. It is just a saying.

Rob McClelland
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Date:         Mon, 16 Nov 1992 20:46:31 EST
From:         Walt Pirie <WLTPIRIE@VTVM1.BITNET>
Subject:      Guidelines-assumptions

reading the comment from Glen, below, I couldn't help thinking of
the report about a month ago, from Andy Taylor I believe, of an article
rejected by a psychology journal because the editors and a referee
objected to using exact p-values in a binomial test, instead of the
"more familiar" normal approximation.
Walt


The second rule is to advise users to follow the usual convention in their own
discipline, if one exists, unless there is clear evidence that such a convention
is unreasonable. Publication of research findings is difficult enough without
introducing methods that are unfamiliar to referees. Where a new method for
checking assumptions is being introduced, it is generally wise for statistical
consultants to play a part in arguing the case for the new method. Perhaps they
may even offer to write the relevant section in the paper.

Regards,  Glen.
--
 ---------------------------------------------------------------------------
|  Glen McPherson            |                                   |  _--_|\  |
|  Department of Mathematics |-----------------------------------| /      \ |
|  University of Tasmania    |               E-Mail:             | \_.--._/ |
|  Australia.                | mcphers@hilbert.maths.utas.edu.au |       *  |
 ---------------------------------------------------------------------------

|==================================|============================|
|  Walter R. Pirie                 |                            |
|  Department of Statistics        |                            |
|  Virginia Tech                   |                            |
|  Blacksburg, VA 24061-0439       |                            |
|                                  |                            |
|  Tel. 703-231-5441               |                            |
|  Bitnet WLTPIRIE@VTFVM1          |                            |
|  Telnet WLTPIRIE@VTVM1.CC.VT.EDU |                            |
|                                  |                            |
|  Fax  703-231-3863               |                            |
|==================================|============================|
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Date:         Tue, 17 Nov 1992 08:56:51 ECT
From:         Lary Jones <LJONES@BINGVMB.BITNET>
Subject:      Re: So, why assumptions?
              17 Nov 1992 06:01:38 EST from <sbaker@UMASSMED.UMMED.EDU>

I hesitate to add to a discussion of non-statistical concepts, but here
is my say on phrases such as "rule of thumb."

First of all, while the common understanding of a phrase may provide
guidelines for its usage, there are numerous cases where a phrase may be
appropriately used in one culture and highly insulting in another. The
use of a phrase like "rule of thumb" on STAT-L is probably ok. If it is
insulting to a specific group, and that may not be common knowledge, it
would help if someone could enlighten the group; briefly and with
specific reference. I am ready to change my language given a substantive
reason.

My understanding of "rule of thumb" combines all that has been said. The
first digit of the thumb was a common measure in early cultures.
However, later laws specified that when delivering legal punishment with
a rod or stick, the width of the rod should not exceed the size of the
first digit of the thumb.

What I have just related is based on accumulated knowledge, not research.
OK, I'm game.  Can anybody provide a _verified_ origin to rule of thumb?
You can mail your submissions to me -- personally -- and I will summarize
to those interested ;-)

__________________________________________________________________
Lary Jones...Computing Services...Binghamton Univ...(607) 777-2879
School of Management Research Support ...... LJones@BingVMB.Bitnet
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Date:         Mon, 16 Nov 1992 09:12:49 GMT
From:         Ronan M Conroy <RCONROY@IRLEARN.UCD.IE>
Subject:      Normal probability plots

I frequently use normal probability plots to check distributions
of variables visually. I also have the facility (on Data Desk)
to get a correlation between the data and the normal distribution.
Does anyone have information on whether there's much you can
tell from the correlation? My own experience is that you can get
an r of > 0.9 from the log of a normal distribution, and the
ones that look good by eye usually have an r in the range 0.98
upwards. Twisting the tails of the plot, however, where data are
thin, isn't going to affect the r a whole lot, but is going to
affect your distribution. Maybe r is simply anecdotal...
Any views?

ronan
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