HDTenEightyP/NetNews huggingface dataset:
Original_To:  IN%"ILFC594@VAX2.CONCORDIA.CA"
Original_cc:  BITNET%"stat-l@mcgill1"
Newsgroups: bit.listserv.stat-l
Date:         Tue, 1 Sep 1992 16:01:00 EDT
From:         Chen Xi <CHENX@CUA.BITNET>
Subject: RE: Election polls

From:   IN%"ILFC594@VAX2.CONCORDIA.CA"  "GEOFF SELIG - CONCORDIA U. COMPUTING SE
RVICES"  1-SEP-1992 14:10:03.89
To:     IN%"CHENX@CU4300.CUA.EDU"  "Chen Xi"
CC:
Subj:   RE:  Election polls

 > I am interested in what statisticians think about the current wave of
 > election polls.  Specifically, how should the media and public make sense
 > out of them, particulary in light of the fact that they widely diverge, yet
 > claim to be based on valid random sampling techniques. [...] For example,
 > the latest ABC poll shows Clinton at 56 and Bush at 36, while the Time/CNN
 > poll shows Clinton at 48 and Bush at 40.  Any ideas about the statistical
 > interpretations of such data are greatly appreciated.

%Each poll has a level of reliability (something like +-4% 95 times out of
%100).  What this means is that if we were to repeat the poll 100 times, the
%actual (population) percentage of voters supporting Clinton would lie in
%the range 56% +- 4% (or 52% to 60%) in the first poll and 48% +- 4% (or 44%
%to 52%) in 95 out of the 100 polls taken.
[........]

The published survey result relies on also the experiement design.  For
the consideration of the simplicity, the totally random design is always
in favor.  But, for this kind of the survey, some other kinds of the
design such as stratified design, split design and so on might be helpful.
I do not know what kind of the design methods did ABC or CNN use, they would
not explain this since most people are not interested in them.  But,
as I know, the sample size of such poll is not very large.  The general
sample size is only a few thousands or even lower.  Besides, any kind of the
poll is based on a lot of assumptions, and many of them are biased.  For
example, the timing, the design of the question, and even the qualification
of the survey personnel may affect the survey results.  So, do not trust the
result too much.  For the reliability or confidence interval of the survey
result, sometimes it is  also misleading.  The construction of the confidence
interval relies on the specific distribution assumptions.  If the poll result
is not distributed as Normal or Poisson, the confidence interval itself is
questionable.  Anyway, such a poll result may be good for the general media,
but not good for the scientific research.  If somebody say that they believe
it, you may tell them that they had better say they know it.  Besides, some
news reports are not objective.  I can still remember the anchor of NBC at
the recent Olympics spreaded the rumors that the Chinese swimmer took drugs,
but they were finally tested negative.  If I say that I trust them, I am
cheating on myself.

Chen Xi
Academic Computing Services
The Catholic U. of America
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Newsgroups: bit.listserv.stat-l
Date:         Wed, 2 Sep 1992 01:30:56 EDT
From:         Simon Jackman <SJACKMAN@PUCC.BITNET>
Subject: RE: Election polls

>%Each poll has a level of reliability (something like +-4% 95 times out of
>%100).  What this means is that if we were to repeat the poll 100 times, the
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^
>%actual (population) percentage of voters supporting Clinton would lie in
>%the range 56% +- 4% (or 52% to 60%) in the first poll and 48% +- 4% (or 44%
>%to 52%) in 95 out of the 100 polls taken.

I wonder if Clinton and Bush are frequentists...?   They'd love to be able
to repeat the poll 100 times.  If they had that kind of money I suspect
they'd have pulled out long ago like the other billionaire who decided
that politics wasn't as much fun as it seemed at first.

Ahhh, the states of the world required to have this repeated sampling
gibberish be true.... :-)

Cheers -- Simon Jackman
****************************************************************************
Simon Jackman: Woodrow Wilson School of Public and International Affairs,
               Princeton University, Princeton, NJ 08544  (609) 258-1861
               (visiting)                             fax (609) 258-1985
               and
               Department of Political Science, University of Rochester,
               Rochester, NY
               BitNet:   sjackman@pucc InterNet: sjackman@pucc.princeton.edu
****************************************************************************
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Newsgroups: bit.listserv.stat-l
Date:         Tue, 25 Aug 1992 14:16:52 EDT
From:         Walter Davis <WDAVIS@UNCVM1.BITNET>
Subject:      GAUSS workshop / not SAS-related
X-To:         "Statistics Discussion List" <STAT-L@MCGILL1.BITNET>,
              "SAS(r) Discussion List" <SAS-L@VTVM2.BITNET>

hi all,

apologies for the cross-posting.

I am scheduled to teach a short 2-3 hour introduction to the matrix
programming package GAUSS.  I am wondering if anyone has an outline
for such a course which they would like to share.

thanks in advance,
walt

Walter Davis <WDAVIS@UNCVM1>
Department of Sociology
Institute for Research in Social Science
UNC - Chapel Hill

p.s.  this is not a homework project. :-)
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Return-Path: <fbiondi@convx1.ccit.arizona.edu>
Full-Name: Franco Biondi
Newsgroups: bit.listserv.stat-l
Date:         Tue, 25 Aug 1992 11:03:52 -0700
From:         Franco Biondi <fbiondi@CONVX1.CCIT.ARIZONA.EDU>
Subject: Re: Alleged "cheating"
X-To:         STAT-L%MCGILL1.bitnet@ARIZVM1.ccit.arizona.edu, vokey@HG.ULETH.CA

I would like to thank John R. Vokey for saying:
>What arrogant nonsense.  We don't take any precautions that we are doing the
>work of some professional consultant, either (Although, a really clever
>statistical consultant could fake it.  But then, a really clever consultant
>could do the work herself).  Nor should we.  The student is likely to
>learn a lot more from the posting of a problem than he/she probably would
>by "solving" it on his/her own, or by consulting his/her textbook.  The
>variety of the answers received would be a lesson in itself.  Posting the
>problem to the list is creative problem solving.  If Professor Turner wishes
>not to answer the query, he doesn't have to.  I, for one, applaud the
>initiative of students, indeed all persons who avail themselves of knowledge
>resources.
I don't know how many students subscribe to this list, but it is encouraging
to see that to ask questions is not just another priviledge of the working
professional.

Franco Biondi / Grad. Assoc. / Laboratory of Tree-Ring Research /
University of Arizona / Tucson, AZ 85719 / fbiondi@convx1.ccit.arizona.edu
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Newsgroups: bit.listserv.stat-l
Date:         Wed, 2 Sep 1992 15:46:11 CDT
From:         "Robert P. Parks" <bparks@WUECONA.WUSTL.EDU>
Subject:      Ming's probability problem

Wai Ming posed an interesting probability question.
I am teaching from DeGroot 'Probability and Statistics' and I think he
presents an answer on page 49, which is not the answer that Wai got on his
own.

DeGroot phrases it as a collector's problem - collecting r different
(sport) cards from n packages where each package has exactly one card.  I
think this is identical to the subclone problem -

DeGroot presents the problem as follows:  Let Ai be the event that the i-th
player (clone) is not selected (missing from all n tries).  Then the union of
the r events Ai is the event that at least one is missing.

Much like what Wai did, DeGroot uses a formula:

P(A1 union A2) = P(A1) + P(A2) - P(A1 intersect A2)

except for the general case.  (see pages 39 to 40 of DeGroot).  He then
obtains the probability of missing one card as:

SUM_{j=1}^{r-1} (-1)^{j+1} * rCj * (1 - j/r)^n

where rCj is the combinatorial r choose j.

1 minus this probability is the probability of obtaining all r cards:

SUM_{j=0}^{r-1} (-1)^{j} * rCj * (1 - j/r)^n


This produces, for r=8,

       1 n       1 n       3 n       1 n       5 n       3 n      7 n
1 - 8 (-)  + 28 (-)  - 56 (-)  + 70 (-)  - 56 (-)  + 28 (-)  - 8 (-)
       8         4         8         2         8         4        8


This produces 0 for n<8 and 1 for n->Infinity, as per Wai's checks.

For n=10,50,5 I get

{0.0282, 0.248, 0.531, 0.737, 0.859, 0.926, 0.962, 0.98, 0.99}

Surprizing to me that the probabilities are pretty high, but it is like the
birthday problem in that I guess.

Wai's formula was:

P(8) = [8^n - 8.7^n + 28.6^n - 56.5^n + 70.4^n - 56.3^n + 28.2^n - 8] / 8^n

but something is wrong because with this because, e.g., for n=8, I get

	  7
2.38078 10

from his formula.

Anyway,  I think that it is the collector's problem as discussed in DeGroot.

Bob

*--------------------------------------------------------------------------*
| Robert P. Parks                                    Voice: (314) 935-5665 |
| Department of Economics, Campus Box 1208             Fax: (314) 935-4156 |
| Washington University                                                    |
| One Brookings Drive                                                      |
| St. Louis, Missouri 63130-4899                   bparks@wuecona.wustl.edu|
*--------------------------------------------------------------------------*
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Newsgroups: bit.listserv.stat-l
Date:         Fri, 28 Aug 1992 17:30:30 +0930
From:         Bill Venables <wvenable@STATS.ADELAIDE.EDU.AU>
Subject:      Interim summary: power and sample size calculations

I have already received many excellent leads in my request for references
on power and sample size calculations, and an interim summary is appropriate.

The material suggested is as follows:

Recent Papers:
--------------
Cohen, J. (1992).  A power Primer. Psychological Bulletin, 112, 115-159.
[This issue has not even arrived in Australia yet! WNV]

Books:
------
Cohen, J. (1984). Statistical Power Analysis for Behavioural Sciences,
2nd edition, Academic Press:New York.

Cohen, J. (1988) Statistical power analysis for the behavioural sciences.
Hillsdale, N.J., Lawrence Erlbaum Associates [Appears to be a later
addition of the previous one, with a change of publisher - WNV]

Lipsey, M. W. (1989). Design Sensitivity: Statistical Power for Experimental
Research.

Kraemer, H.C. & Thiemann, S. (1987) How many subjects? Statistical Power
Analysis in Research. ISBN 0-8039-2949-8 $22.95
SAGE Publications, Inc
P.O. Box 5084
Newbury Park, CA 91359-9924
Phone: (805) 499-9774
FAX: (805) 499-0871

Books that cover the subject in passing, well:
----------------------------------------------
Statistical Design and Analysis of Experiments with Applications to
Engineering and Science R. L. Mason, R. F. Gunst, and J. L. Hess
Wiley  1989
sections 12.4-12.5, 13.2-13.3, 14.1-14.3, 15.5-16.1

New Developments in Statistics for Psychology and Social Sciences,
A. D. Lovie (ed.), British Psychological Society & Methuen, London & New York.
[There is an introductory chapter titled "Sample Size and Power" (chapter 7)]

Moore & McCabe's "Introduction to the Practice of Statistics"

Programs:
---------
In addition to a book there is a program by Jack Barnette of the University
of Alabama that demonstrates the principles very well.  The contact address is:
Prof. J. Barnette,
Behavioural Studies,
PO Box 870231,
Tuscaloosa, Alabama USA 35487

Email: JBARNETT@UA1VM.bitnet
-----------------------------------------------------------------------------
My sincere thanks to the respondents, who to date are:

    A. NARAYANAN DECISION & INFO. SYSTEMS--855-2665 <NARAYANA@ucs.indiana.edu>
    ataylor@lala.zoo.Hawaii.Edu (Andy Taylor)
    butler@spdc.ti.com (Stephanie Butler)
    Dennis Roberts <DMR@PSUVM.PSU.EDU>
    Fabio Idrobo <PSY9A3N%BUACCA.bitnet@VM1.MCGILL.CA>
    kjklebe@uccs.edu (Kelli J. Klebe)
    Meredith_Warshaw@frankston.com (Meredith Warshaw)
    r-larkin@uiuc.edu (Ron Larkin)
    Stephen Salbod <SALBOD1%PACEVM.bitnet@CUNYVM.CUNY.EDU>

___________________________________________________________________________
Bill Venables, Dept. of Statistics, | Email: venables@stats.adelaide.edu.au
Univ. of Adelaide, South Australia. | Tel: +61 8 228 5412  Fax: ...232 5670
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X-VMS-To: IN%"Stat-L@McGill1.Bitnet"
Newsgroups: bit.listserv.stat-l
Date:         Thu, 27 Aug 1992 16:23:00 MST
From:         JENSEN@UTAHCCA.BITNET
Subject:      Available Good Stat Software for DOS/Windows for Course Work

Fellow list members,

For those of you who have experience teaching introductory graduate
and (especially) undergraduate level statistics courses incorporating
the use of statistical software for either DOS or WINDOWS, I ask a few
simple but loaded questions:

   What are your preferences for student based statistical software
   to be used in this setting?   More specifically, which statistical
   packages have you found to best aid the teaching process which
   incorporates, among other features, user interactive graphics and
   do not turn students off because of a miserable learning curve
   requiring more time and frustration to learn the software than the
   statistical concepts involved?  Are these packages available in a
   version which can be purchased by students for less than $75?

Right off, let me say that my experience in this realm of student
based packages start with MYSTAT and the student version of SPSS and
end with a demonstration, today, of the student version of Execustat.
In my opinion, with support of some of the faculty here at the Univ
of Utah, MYSTAT and SPSS are horrible packages to use in such cases.
The student version of Execustat has been used in one undergraduate
course here with good reviews from both teacher and students.

I look forward to your responses and will summarize to the list if
there is enough interest.

Jeff Jensen

  |-------------------------------------------------------------------------|
  |  JEFFREY B. JENSEN                 |  Office Phone: (801) 581-7029      |
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Newsgroups: bit.listserv.stat-l
Date:         Thu, 3 Sep 1992 09:07:00 EDT
From:         DOKIEM@DUKEMVS.BITNET
Subject:      ordinal or interval

   The number of possible alternatives that exist for  handling
ordinal data are numerous.  They range from statistical approaches
such as methods for categorical data to measurement approaches.
The choice of solution depends primarily upon what you believe
about the status of your data.
    If you regard your data as imperfect projections of an underlying
dimension, then a measurement approach is called for.  This can range
from optimal scaling to fit a variety of statistical models to an
analysis of the characteristics of the measurement procedure itself.
The latter includes such ideas as item response theory (IRT) and
mixed-variable factor analysis.  The basic idea of item response
theory is that the imperfect categorical responses are  merely
indicators of some underlying latent variable.  Through the
specification of the proper item-latent variable function or regression,
estimates of the underlying variable can be obtained.  The regression
function generally includes parameters that may affect the reported
scores as transformed from the underlying construct.  In a quick but
necessarily incomplete description, it is an attempt to understand the
factors that affect measurement, and control for them.  The
result is an estimate of a continuous variable which not only fits
a grater variety of statistical models, but which is more informative
as to subtle individual differences.  The classic text is Lord (1980),
Applications of item response theory ...
  Of course, before we should take this to seriously, you should read
Lord's entertaining essay in Psychological Bulletin some years back
on "The statistical treatment of football numbers".

                                       Robert Terry
                                       Asst. Professor
                                       Dept. of Psychology
                                       Social and Health Sciences
                                       Duke University
                                       Durham, NC 27708
                                       DOKIEM@DUKEMVS
Newsgroups: bit.listserv.stat-l
From: nichols@spss.com (David Nichols)
Subject: Re: Philosophy and Stats
Organization: SPSS Inc.
Date: Wed, 26 Aug 1992 20:13:19 GMT

This really isn't particularly relevant, but it's one of my favorite
quotes, so I can't resist the chance to go off on a tangent.

As John Wendell mentioned, Einstein had trouble with quantum theory
and made the statement that in his mind God does not play at dice. 
Stephen Hawking's response to this statement was that not only does
God play at dice, but he (his sexism, not mine) sometimes throws them
where you can't see them.

--
 David Nichols           Statistical Support Specialist            SPSS, Inc.
 Phone: (312) 329-3684     Internet: nichols@spss.com     Fax: (312) 329-3657
*******************************************************************************
 Any correlation between my views and those of SPSS is strictly due to chance.
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Encoding: 7 TEXT
X-Mailer: Microsoft Mail V3.0 (beta-2)
Newsgroups: bit.listserv.stat-l
Date:         Thu, 3 Sep 1992 10:42:00 EST
From:         mjk1@CEHIEC1.EM.CDC.GOV
Subject:      Statistical teaching and consultation
X-To:         STAT-L@VM1.MCGILL.CA

I don't know how many people or projects you must support but in addition to
some basic statistical training, you might consider offering, either inthe
class room or through your office, some basic competer programming training.
This would enable others to do the easy tasks saving your staff's time for
some of the more difficult tasks.  ALso, as others get better at programming
they can serve as resources for newer programmers.
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Newsgroups: bit.listserv.stat-l
Date:         Wed, 2 Sep 1992 20:00:11 EDT
From:         J.Laurie.Snell@DARTMOUTH.EDU
Subject:      Wai again
X-To:         "STAT-L%MCGILL1.BITNET"
              <STAT-L%MCGILL1.BITNET@dartcms1.Dartmouth.EDU>

 Wai Ming's formula is indeed correct and agrees with Park's-DeGroot's
calculations except for getting all eight balls in a sample of just eight is,
which is cleaarly 8!/(8^8)=.00240326. This is exactly what Wai's formula
gives, so Parks made a slight mistake in this one case.

Brad Mann and Laurie Snell
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Newsgroups: bit.listserv.stat-l
Date:         Wed, 26 Aug 1992 15:02:44 EDT
From:         J.Laurie.Snell@DARTMOUTH.EDU
Subject:      eight gene problem
X-To:         "STAT-L%McGill1.bitnet"
              <STAT-L%McGill1.BITNET@dartcms1.Dartmouth.EDU>

Along with all the philosophy and sociology of Wai MIng's question we ought
to admit it is an amusing problem.  It is, of course, the coupon collector
problem discussed in Feller.  Feller emphasises the expected time to get all
the coupons etc. but as in Ming's case one is often interested in the
probability that the time is less than a given value.  With Mathematica and
other such computation tools we can usually calculate the probabilities.  For
Ming's problem the answer seems to be .999987.
Here is the way that Brad Mann, a student visiting Dartmouth, computed it.


Suppose there are n different types of genes uniformly distributed, and you
have a sample of M > n of them.  What is the probability of this sample of
size M containing all n types?  Well, this is just the number of  size M
ordered samples of n types which actually have all n types, divided by the
total number of size M ordered samples of n types (some of which may actually
contain less than n types).  This is n! times the number of ways of selecting
n nonempty subsets of a set of size M, divided by n^M.  This is
n!/n^M times the Stirling number of the second kind S2(M,n).  For the case
M=100 and n=8 we get probability .999987.  In fact we get .9899 for M=50 and
n=8.
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Newsgroups: bit.listserv.stat-l
Date:         Thu, 3 Sep 1992 00:00:29 PDT
From:         Rafik Beekun <beekun@UNSSUN.SCS.UNR.EDU>
Subject:      Is there a good book out there on taking polls?

Is there a good book that you have come across that outlines how to
conduct polls?

Thanks.
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X-Organization: Concordia University, Montreal, Quebec
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Newsgroups: bit.listserv.stat-l
Date:         Tue, 1 Sep 1992 14:01:00 EDT
From:         "GEOFF SELIG - CONCORDIA U. COMPUTING SERVICES"
              <ILFC594@VAX2.CONCORDIA.CA>
Subject: Re: Election polls

 > I am interested in what statisticians think about the current wave of
 > election polls.  Specifically, how should the media and public make sense
 > out of them, particulary in light of the fact that they widely diverge, yet
 > claim to be based on valid random sampling techniques. [...] For example,
 > the latest ABC poll shows Clinton at 56 and Bush at 36, while the Time/CNN
 > poll shows Clinton at 48 and Bush at 40.  Any ideas about the statistical
 > interpretations of such data are greatly appreciated.

Each poll has a level of reliability (something like +-4% 95 times out of
100).  What this means is that if we were to repeat the poll 100 times, the
actual (population) percentage of voters supporting Clinton would lie in
the range 56% +- 4% (or 52% to 60%) in the first poll and 48% +- 4% (or 44%
to 52%) in 95 out of the 100 polls taken.

As these two bands (marginally) overlap, we cannot say that the two poll
results are statistically different from each other, even though the
magnitude of the poll result differences appears to imply this!  Thus,
there is no (statistical) difference between Clinton's popularity in the
two polls.

Misinterpreting poll results is one of the ways the (ignorant) media
manipulates the (statistically) naive reader into believing that popularity
differences exist between candidates when they do not.  Although this is
not the case at the moment, we have all read newspaper reports announcing
that candidate X is far ahead of candidate B based on a 3 or 4% lead in the
polls.

Let the reader beware!!!

Geoff Selig        Phone (514)-848-7666 |        _,  /|     The Cat's Meow
Academic Services    FAX (514)-848-7622 |        \ o.O'
Department of Computing Services        |        =(___)=
Concordia University   (H928)           |            U
Montreal, Quebec   H3G 1M8              |  ilfc594@vax2.concordia.ca
                                        |  ilfc594@conu2.BITNET