HDTenEightyP/NetNews huggingface dataset:
Newsgroups: bit.listserv.linkfail
Date:         Mon, 13 Apr 1992 11:38:58 GMT
Reply-To:     Dominique Dumas <BRUCH@FRMOP11.BITNET>
From:         Dominique Dumas <BRUCH@FRMOP11.BITNET>
Subject:      Problem at EBCESCA1 (Spain)

Hello,
Due to system problem at EBCESCA1 (Spain) the link between FRMOP11 and EBCESCA1
was down during all the week-end.
Now it is working sine 11h (local time).
Regards,
Dominique
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Newsgroups: bit.listserv.linkfail
Date:         Tue, 7 Apr 1992 16:12:07 GMT
Reply-To:     "CNUSC operator" <PUPITRE@FRMOP11.BITNET>
From:         "CNUSC operator" <PUPITRE@FRMOP11.BITNET>
Subject:      Computer shutdown/link failure
Comments: To: NAD-F@FRMOP11.BITNET

vms system will be down the 04.08.92 at 9h to 11h(local time)
Sorry for any inconveniences.

CNUSC Operator
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Newsgroups: bit.listserv.linkfail
Date:         Mon, 13 Apr 1992 12:00:42 EDT
Reply-To:     CHOPLIN@FRUTRS51
From:         Herve CHOPLIN - UNIVERSITE FRANCOIS RABELAIS - TOURS
              <CHOPLIN@FRUTRS51.BITNET>
Subject:      FRUTRS51 now available

The air-conditionning failure is removed and the End-Node FRUTRS51 is
now REACHABLE.

Sorry for this inconvenience.

----------------------------------------------------------------------------
Herve CHOPLIN (System Manager and Node Administrator)
UNIVERSITY FRANCOIS RABELAIS
Computing Center (Bat. D)
Parc de Grandmont
37200 TOURS  FRANCE
_._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._.
Phone:    (33).47.36.69.03
E-mail: EARN/BITNET:         CHOPLIN@FRUTRS51.BITNET
        X400:                C=FR;AD=ATLAS;PR=UNIV-TOURS;S=CHOPLIN
        INTERNET:            CHOPLIN@UNIV-TOURS.FR
        VMS PSI_MAIL:        PSI%(2080)3708047411::CHOPLIN
        VMS DecNet (EDUnet): RABLAI::CHOPLIN or 51410::CHOPLIN (node 50.210)
Fax   :   (33).47.36.69.02
****************************************************************************
     LA TOURAINE, JARDIN DE FRANCE - THE TOURAINE, GARDEN OF FRANCE
----------------------------------------------------------------------------
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Newsgroups: bit.listserv.linkfail
Date:         Fri, 10 Apr 1992 11:18:36 GMT
Reply-To:     "CNUSC operator" <PUPITRE@FRMOP11.BITNET>
From:         "CNUSC operator" <PUPITRE@FRMOP11.BITNET>
Subject:      Computer shutdown/link failure
Comments: To: NAD-F@FRMOP11.BITNET

Due to system maintenance, FRMOP11 and FRMOP22
will be down the 14/04/92 from 20:00 to 24:00 (local time).
Sorry for any inconveniences.

CNUSC Operator
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.stat-l
Date:         Wed, 8 Apr 1992 15:55:30 CST
From:         April Milliken-Janne <BIOM030@UNLVM.BITNET>
Subject:      OVERALL F TEST

This was previously sent to EDSTAT-L (sorry to duplicate):

This is rather basic, but not for my clients...maybe you can help me find
a good explanation.  I don't often run across a case where the overall F
value from ANOVA is non-significant, but recently it has occurred quite
often.  The basic question is this:  If the overall F test gives a very
large p-value, can one have any faith in the analysis?  In other words,
if factor A is significant, but the overall F test is non-significant,
should I believe that A is really significant?  To avoid confusion, the
degree of non-significance to which I refer is 0.5 or greater.

I do not think this issue has ever been discussed fully in my course work,
or in the classes for students such as my client.  How should it be taught?

My client seemed to be very upset about it...though I didn't understand his
reasoning;  all he wanted from a statistician was low p-values.  So, I now
ask you to help me out here.  In what terms do I explain to him his next
step?

Thanks,

April Milliken-Janne
BIOM030@UNLVM.UNL.EDU
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Newsgroups: bit.listserv.stat-l
Date:         Mon, 6 Apr 1992 14:44:26 U
From:         dick darlington <dick_darlington@QMRELAY.MAIL.CORNELL.EDU>
Subject:      Interactions

 Interactions
It is well known that when there is collinearity between two variables, they
may each be nonsignificant but dropping either one from the regression can make
the other significant.  But there can be collinearity between a crossproduct
term and one or both of its constituent linear terms.  Thus dropping the linear
term can make the interaction term look significant when the data are actually
highly consistent with the H of no interaction.
     A second reason for retaining linear terms when testing for interaction is
that the significance of the linear terms is affected by arbitrary linear
transformations such as adding a constant; this follows from what Don Ploch
said.  Thus a test on a linear term in the presence of one of its crossproducts
rarely tests an important H.
--Dick Darlington, Psychology, Cornell
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Subject: (no subject given)
Newsgroups: bit.listserv.stat-l
Date:         Wed, 8 Apr 1992 20:18:14 -0400
From:         SULLINS@NAUVAX.BITNET

I need some information on how to interpret MANOVA results when the
multivariate test of an effect is significant but some of the univariate tests
are not.  Also, is it possible to access scores on the "created" multivariate
dependent variable to plot results?  (I have access to both SPSSPC (v.3.0), and
SPSSX (v. 3.0) on a VAX/VMS system).  Are there good references that discuss
these issues?  I have Tabachnick & Fidell's "Using Multivariate Statistics",
but it is not very illuminating in this regard.

Thanks,

Ellen Sullins   602-523-9979
Sullins@nauvax
Sullins@nauvax.ucc.nau.edu
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Newsgroups: bit.listserv.stat-l
Date:         Tue, 14 Apr 1992 13:51:35 -0400
From:         Subodh Kumar <kumar@CS.UNC.EDU>
Subject:      Non-Static Statistics
X-To:         STAT-L%MCGILL1.BITNET@mcnc.org

I have this problem of computing the estimate of average and variance of a
stochastic phenomenon with very little information.

A stream of Sample data arrives at the monitoring station. The problem is that
the storage of all the old data is not allowed. All you have is the current
estimate of variance and average and the current sample (when you get a sample
data) to compute the new average and variance.

* Is there some way to maintain reasonable estimates ?

* Is there a way to give more weightage to the new data than the (very)
  old data ?

* What if last few [about 5-10], but a constant number of, samples are allowed
  to be stored ?

* What if the nature of the probability distribution function is known ?

Subodh Kumar
kumar@cs.unc.edu
Dept. of Computer Science
UNC-Chapel Hill
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Newsgroups: bit.listserv.stat-l
Date:         Fri, 10 Apr 1992 15:30:15 CDT
From:         "James F. Foerster 413-3883" <U12566@UICVM.BITNET>
Subject:      Pooling of Cross sectional and time series data

Is there a good reference on this topic?  A colleague has five "cases"
with 12 time periods, and 13 independent variables.  He proposes to
use stepwise selection with corrections for autocorrelations to
select the "best" model, and will then interpret the coefficients as
elasticities.  (He claims that there are 60 "datapoints".)

This isn't my area, but I have a gut feeling that there are
significant problems.  Am I wrong?
atsanization: Penn State University
Date: Wednesday, 8 Apr 1992 10:41:53 EDT
From: <HXL4@psuvm.psu.edu>
Newsgroups: bit.listserv.stat-l
Subject: advertising statistics

i am doing a project for advertising and i need demographics and psycometric
stats on condom users.  also any info on lifestyle brand condoms and other
brands would be welcomed.  thank you for any consideration you give this
note.
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Newsgroups: bit.listserv.stat-l
Date:         Wed, 8 Apr 1992 23:00:00 EDT
From:         yake <YXHSUX@WMVM1.BITNET>
Subject:      FIML

Does aanyone have experience o  Full Information Maximum Likelihood
(FIML) with 1st autocorrelation?  I need help for resetting the
endogenous variables.

Let the model is
      Demand:     QD=A1+B1*PRICE+B2*X1
      sUPPLY:     QS=A2+C1*PRICE+C2*X2
     EQUILIBRIUM:  Q=QD=QS
     The endogenous variables are Q and PRICE.

Let the corrected model is
     DEMAND       QD'=A1'+B1*PRICE'+B2*X1'
     SUPPLY       QS'=A2'+C1*PRICE'+C2*X2'
     Where
         QD'=QD -lo1*QD
               t       t-1
        QS'=QS -lo2*QS
              t       t-1
        lo1 and lo2 are the coefficients of autocorrelation on the
        demand and supply equations, respectively.

The problem is that the QD' is no longer equal to QS'.  What are the
endogenous variables now?

I appreciate your suggestions.

Yake
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Newsgroups: bit.listserv.stat-l
Date:         Tue, 7 Apr 1992 08:50:39 CST
From:         EJOHNSON@CMSUVMB.BITNET
Subject:      Certification

I hear statisticians talk about being certified. I infer these people have
completed certain core courses or past   a test by a national association of
statisticians. Am I correct, and if so, who should I contact to get more in-
formation?  --Ed Johnson, Central Missouri State
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Newsgroups: bit.listserv.stat-l
Date:         Mon, 6 Apr 1992 21:52:44 EDT
From:         Clark Gaylord <GAYLORD@VTVM2.BITNET>
Subject:      Stepwise vs variable selection

   I just wanted to make a brief point regarding the stepwise / variable
selection question, and I hope that I am not reitterating someone else,
as I'm a little behind on my mail.  This is that stepwise is NOT the only
method for variable selection.  A few of the messages have been saying
how terrible/necessary *variable selection* is and then go on to discuss
the drawbacks of stepwise procedures.  It seems to me that the strongest
criticism of stepwise procedures (provided one does not take p-values
seriously) is that the final list of variables is seldom replicable.  It
has been my experience though that *all* subset methods, with some appropriate
selection criterion, do a fairly good job at giving replicable results.
The trick to remember is that the "final" answer need not be the "real"
answer.
   When performing an all subsets procedure, we still have no hope of
validity in our p-values, so cross-validation or bootstrapping or some similar
method must be employed to make any sense of statistical tests.  Also, the
"best" model should not be considered as the gospel, but rather the best few
should be further investigated with resampling, diagnostics, and, yes,
our brains.  Naturally, when the number of variables is *very* large, the
all subsets (k! models, afterall) method is infeasible, but certainly more
feasible than many years ago, when stepwise became very important.  I do not
think anybody ever intended stepwise procedures to become the gospel on
modelling, they just did not have much of an option when considering the
computational intensity of the alternatives.

Clark K. Gaylord
Dept. of Statistics
Virginia Tech
703/552-2868
e-mail: gaylord@vtvm2 or gaylord@vtvm2.cc.vt.edu
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Newsgroups: bit.listserv.stat-l
Date:         Mon, 13 Apr 1992 09:36:08 CDT
From:         Phil Wood <PSYWOOD@UMCVMB.MISSOURI.EDU>
Subject:      binomial probabilities revisited

A question has been raised about how to compute binomial probabilities.
While Bill V. has suggested that you "might get away" with a normal
approximation (good for large N or moderate p) and others might
suggestion another favorite approximation (such as the arcsin or
Poisson). I find myself wondering why anyone would do approximations
any more in this age of computers.
To be sure, computing the probability directly with factorials is a bit
of a pain, but if you want the exact probability, why not use the F or
incomplete beta distributions and exploit the mathematical relationships
from there? For example:
Assuming N the total sample size, and freq the observed number of "successes"
and crit= the cutoff, you can construct the following F statistic:
F=(freq*(N-crit))/(crit*(N-freq+1)) with df1=2*(n-freq+1) and df2=2*freq
the probability associated with that F is the binomial probability.
Or, if you wanted to be aesthetically pleasing, why not use the incomplete
beta function which is in some statistical packages? For example, in SAS,
probbeta is the name of the function, so you could merely type:
prob_b=probbeta(pi,f1,N-f1+1)
where pi is the theoretical proportion, and f1 is the number of successes
and N is the sample size.
Isn't this easier?
For what it's worth-
Phil Wood
U of MO-Columbia
(I got these relationships from Abramowitz & Stegun's Handbook of Math
Functions, p. 960)
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Newsgroups: bit.listserv.stat-l
Date:         Tue, 7 Apr 1992 19:46:05 EDT
From:         PSY9KRN@BUACCA.BITNET
Subject:      undergraduate courses

Our department of Psychology is examining its academic program, and
the issue of a "statistics" requirement is a key element. Has there been
any survey or report of "stat requirements" for Psychology majors?
Has there been a consensus or recommendation concerning the topics
or competencies that should be taught to psychology majors? Should
there be a 1 semester or 2 semester course requirement? Should the use
of computers be integrated with such a requiremenmt?
Any and all assistance will be gratefully accepted.

David Mostofsky
Dept. Psychology
Boston University
64 Cummington Street
Boston, MA 02215

E-mail     PSY9KRN@BUACCA
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Newsgroups: bit.listserv.stat-l
Date:         Mon, 6 Apr 1992 09:53:41 MDT
From:         vokey@HG.ULETH.CA
Subject:      Interaction in regression models
X-To:         STAT-L@VM1.MCGILL.CA

The primary difficulty in dropping the main effects in the model is that
the interaction is most often coded as product vectors of the main effect
vectors; hence, the interaction is only _carried_ by the interaction vectors,
but is not given directly by them.  Thus, a model containing the interaction
vectors but not the main effect vectors is not indicating that only the
interaction is important.

John R. Vokey
Department of Psychology
University of Lethbridge
Lethbridge, Alberta
CANADA  T1K 3M4
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Newsgroups: bit.listserv.stat-l
Date:         Wed, 15 Apr 1992 14:08:55 CDT
From:         SOCLYMAN@UMCVMB.BITNET
Subject:      Looking for Dick Darlington

This message was cross-posted on edstat-l.  Apologies for doubling your
mail.



Folks,
  I have a colleague who would like to get in touch with Dick
Darlington to clarify some questions he has about the
"usefulness" evaluation of regression results.  I have seen
Prof. Darlington's signature on this or edstat-l.  Professor
Darlington, if you're out there, can I get your e-mail address
please?
  Thanks,
Katherine Lyman
UMissouri-Columbia
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Newsgroups: bit.listserv.stat-l
Date:         Mon, 6 Apr 1992 15:51:38 CST
From:         April Milliken-Janne <BIOM030@UNLVM.BITNET>
Subject:      Transformations

(I ran this in EDSTAT initially and have been receiving a lot of great
answers, so I thought I would look for more ideas and different explanations.
Sorry if you have seen this previously.)

Hello,

Maybe some of you can help me out with the explanation of WHY I really
need transformations.

I am taking a statistical ecology course at the moment.  I have decided to
do a bit of a literature search in order to compare two methods of finding
population mean density on count data, for example, the mean number of
aphids in a soybean field.  The data consist of sample means and sample
variances collected over time.  I have been using Iwao's Patchiness regression
method which deals with a mean crowding calculation regression on xbar.  The
mean crowding calculation contains the variance, and a prediction equation
for the variance is calculated so that a variance*xbar plot can be graphed.

Now, I am comparing this method to Talyor's power law, which also uses sample
means and sample variances.  However, you regress log(variance) on log(xbar)
to find a prediction equation.  Then, you plot the predicted values which
happen to be in a straight line due to the log transformation.  Taylor always
stops right there, but I am interested in putting the data back into original
units.  Suddenly the plot is lousy, and in fact, the method seems to be
worthless.  In other words, initial plots indicate that Taylor has a better
fit, but when in original units, Iwao seems to be the better method.

So, now to my question, since the world is not in log units, why would I ever
be able to argue the use of a transformation?  My conclusions will be based on
original data...shouldn't my analysis also be?  Other disciplines say "You can
prove ANYTHING with statistics"...by transforming our data to make it fit even
BETTER, aren't we guilty as charged?

I would really like to hear some feedback on this issue.  I am still wet behind
the ears (in grad school) and I am quite willing to learn.

Thanks in advance.

April Milliken-Janne                University of Nebraska-Lincoln
BIOM030@UNLVM.UNL.EDU               Department of Biometry
April Milliken-Janne

: I may attend UNL, but my heart is at Kansas State University!
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X-Mailer: ELM [version 2.2 PL11]
Newsgroups: bit.listserv.stat-l
Date:         Sat, 11 Apr 1992 11:10:46 EST
From:         "D. Glen McPherson" <mcphers@HILBERT.MATHS.UTAS.EDU.AU>
Subject:      logistic regression with ungrouped data
X-To:         stat-l@vm1.mcgill.ca

          LOGISTIC REGRESSION WITH UNGROUPED DATA

A typical problem faced by ecologists is to seek to relate the probability
of occurrence of a plant species to enviornmental factors. While logistic
regression offers a vehicle for constructing a relationship, there appear
to be problems with judging the goodness of fit of the model to ungrouped
data. A typical situation is the following. There are 2,500 sites at which
the presence or absence of a species is noted. There are 20 environmental
variables which could potentially affect the probability of occurrence of
the species.

The Likelihood ratio statistic (Deviance, G squared, etc.) has a distribution
which is well approximated by a Chi-squared distribution when based on the
comparison of two models. However, it has been reported (e.g. McCullagh &
Nelder's book on Generalized linear models) that the Chi-squared approximation
is not adequate for the comparison of models with ungrouped data even when
sample sizes are large. (In the illustration quoted above, for example, the
residual degrees of freedom are 2,4800.)

If there is a single explanatory (regressor) variable, it is feasible to
order and group the values of this variable and transform the problem to
one in which there is grouped data. Then, there is the possibility of using
both the Goodness of fit statistic and residual analysis to judge the
adequacy of fit of a model to the data. When there are more than 2 or 3
variables in the regression equation, this is not a feasible option because
the number of groups to be formed is too great. Can anyone provide a method
which can be applied for ungrouped data, or suggest a possible path which
we might investigate?

Many thanks,  Glen.

--
 ---------------------------------------------------------------------------
|  Glen McPherson            |                                   |  _--_|\  |
|  Department of Mathematics |-----------------------------------| /      \ |
|  University of Tasmania    |               E-Mail:             | \_.--._/ |
|  Australia.                | mcphers@hilbert.maths.utas.edu.au |       *  |
 ---------------------------------------------------------------------------
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Newsgroups: bit.listserv.stat-l
Date:         Wed, 15 Apr 1992 12:02:00 EDT
Comments:     208 Cedar Bldg.
Comments:     University Park, PA USA
Comments:     AC 814-863-2401
From:         Dennis Roberts <DMR@PSUVM.BITNET>
Subject:      Correspondence Stat Courses

Clayton Gable rasied the issue of correspondence courses in stat; here is some
information on that. Penn State has the largest correspondence department (call
ed Independent Learning here) in the country, and offers 2 stat courses; both a
re introductory and general. One, STAT 200, is from the stat department is 4 cr
edits. It uses Minitab very minimally, by requiring assignments to submit lists
 of commands students would use. Students are not required to actually do Minit
ab analyses. The other, the one I am the instructor for, EDPSY 400, makes heavy
use of Minitab, and was recently revised to incorporate Minitab. There are 10 a
ssignments, all that require Minitab work,  and 2 tests. This course is 3 credi
ts. If anyone would want more information on what we offer, you could surface m
ail write to Department of Independent Learning, Penn State University, 128 Mit
chell Building, University Park PA, 16802 for a catalog, .... or send me an ema
il message with your SURFACE mailing address and I will make sure the Inde Lear
ning sends you a catalog. I hope this helps.
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Newsgroups: bit.listserv.stat-l
Date:         Sat, 11 Apr 1992 16:03:03 PDT
From:         Barry Wiggs <bwiggs@PULMONARY.UBC.CA>
Subject:      Gauss
X-To:         stat-L%mcgill1.BITNET@cunyvm.cuny.edu

I use Gauss as well and have found it to be just fantastic. It is **MUCH**
more complete and far easier to use than any version of S. The programming
language is sort of a mix between C and Fortran. It might interest some that
another package called MATLAB is virtually 100% compatable with Gauss, what
I mean is that the main commands for matrix operations and basic instructions
are identical. The neat thing is that Matlab is very heavily engineering
oriented and has a vast library of signal processing stuff that I can
quite easily get to work in Gauss. I would suggest as well that potential
users look at the VM (virtual memory) version of Gauss. I quite routinely
work on large datasets (5-15MB) and Gauss-VM just grinds it up quite
happily while SAS, BMDP, SPSS and Systat tend to choke and die. And no, the
VM version on big datasets is not painfully slow. Another really fun
module we use quite heavily is a differential equation solver. You just
type in the equations and out pops the solution as a graph over time!

Barry R. Wiggs
University of British Columbia
Pulmonary Research Laboratory
St. Paul's Hospital
Vancouver, BC, Canada
bwiggs@pulmonary.ubc.ca
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X-Vms-Mail-To: UUCP%"stat-l@vm1.mcgill.ca"
Newsgroups: bit.listserv.stat-l
Date:         Mon, 13 Apr 1992 15:28:26 EST
From:         dixon@SREL.EDU
Subject:      SE's of slopes
X-To:         stat-l@vm1.mcgill.ca

Evan Cooch asked why the standard errors of slopes from two models
are not the same.  Using SAS notation, his first model was:

1) PROC GLM; CLASS TRT;
   MODEL Y = X TRT TRT*X;
   ESTIMATE 'TRT A' X 1 X*TRT 1 0 0;
...

And, if I understand him correctly, his second model was:

2) PROC GLM (or REG);
  BY TRT;
  MODEL Y = X;

The models differ in the specification of the error variances.
Model 2 estimates separate error variances for each trt group;
Model 1 pools those estimates into a common error variance.

Since se(beta) depends on the error variance, you will get different
estimates of se(beta) in the two models, unless each trt group has
exactly the same observed error variance (extremely unlikely).
Since, he finds that the se(beta) are quite different, I suspect
that the trt groups have quite different error variances.  Usually,
I find the approach in model 1 to be the most useful.  Among, other
virtues, it gives a more precise estimate of the error variance, if
sigma^2 is the same in each group.  However, that is questionable here.
I would use the se(beta) from model 2.

Other opinions?

Philip Dixon
Biostatistician
Savannah River Ecology Lab
Dixon@srel.edu