HDTenEightyP/NetNews huggingface dataset:
From: dmurdoch@mast.queensu.ca (Duncan Murdoch)
Subject: Re: unweighted mean of means
Organization: Queen's University
Date: Thu, 3 Sep 1992 14:08:17 GMT

In article <winzar.73.0@newsman> winzar@newsman (Hume Winzar) writes:
>Of course this does not get you arround the systematic bias created by the 
>propensity to answer TRUE more often.

That bias results from a mis-designed questionaire.  If the correct choice 
is randomly chosen (with equal probabilities) and the question worded to 
fit, then any system of picking answers that doesn't depend on knowledge 
about the statement will have equal chances of picking the right and wrong 
answers.  You should also try to make knowledge of the subject matter 
important, e.g. don't do this:

  As everyone knows, 2+2 = 5.    True False

  2+2=4.                         True False

  As everyone knows, unbiased estimates of the variance are essential.
			         True False

Most sponsored surveys are just about this blatant in wording their 
questions to elicit the "correct" answer.  

Duncan Murdoch
dmurdoch@mast.queensu.ca
From: hrubin@pop.stat.purdue.edu (Herman Rubin)
Newsgroups: sci.math.stat
Subject: Re: Non-linear Regression in Chemistry
Date: 3 Sep 92 14:33:41 GMT
Organization: Purdue University Statistics Department

In article <1992Sep3.064230.42744@kuhub.cc.ukans.edu> jeff@kuhub.cc.ukans.edu (Jeff Bangert) writes:
>A chemist has asked me to solve a problem: she has data and a model
>which is to be fit by 'least squares'.  It looks like non-linear
>regression, except that:

>    1. the model has two equations
>    2. both are non-linear
>    3. there are parameters common to the two equations.

>I would like to know:

>    1. is there a 'standard' method for solving this problem?
>    2. is there literature in stat or chemistry which I could read?

To use a reasonable statisical approach, you should set up your probablity
model, and carefully assess your assumptions.  Then set up at least a 
reasonable approximation to the likelihood function, and do whatever
processing your assumptions call for.

I would not expect any package to handle this well.  Numerical analysis
is an art.
-- 
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
Phone: (317)494-6054
hrubin@pop.stat.purdue.edu (Internet, bitnet)  
{purdue,pur-ee}!pop.stat!hrubin(UUCP)
From: ruckman@oasys.dt.navy.mil (Christopher Ruckman)
Newsgroups: sci.math.stat
Subject: Re: Bubble Charting Software needed Mac or PC
Date: 1 Sep 92 10:29:32 GMT
Reply-To: ruckman@oasys.dt.navy.mil (Christopher Ruckman)
Organization: Carderock Division, NSWC, Bethesda, MD

In sci.math.stat, RS0VRD@rohvm1.rohmhaas.com (Ross Druker) writes:
>Deltapoint Pro on Mac and PC can create bubble charts.  Can anyone recommend
>other SW     preferrably Mac (or PC) that can create bubble charts. Thanks.

For DOS+Windows, check out Stanford Graphics from 3D-Visions.  You can
get info by calling 1-800-SAY-GRAF.
From: tanm@seneca.bst.rochester.edu (Martin Tanner)
Newsgroups: sci.math.stat
Subject: postdoc position
Date: 28 Aug 92 20:01:49 GMT
Organization: University of Rochester (Rochester, NY)


Applications are invited for an anticipated federally-funded
postdoctoral position, beginning January, l993.  The postdoctoral 
fellow will participate with the three investigators in developing
new statistical methodology for medical technology assessment 
(ROC analyses), and will be responsible for software development.  
Jointly sponsored by Biostatistics and Preventive Medicine.  
Requirements:  PhD (or nearly) and excellent computer skills.  
Applications from women and minorities are especially encouraged.  
Send cv, preprints/reprints and three letters of recommendation to:
 
Prof. W. J. Hall
Dept of Biostatistics/Box 630
University of Rochester Medical Center
Rochester, NY l4642

Inquiries to:  hall@metro.bst.rochester.edu
Newsgroups: sci.math.stat
From: art@playfair.Stanford.EDU (Art Owen)
Subject: Neural Nets & Statistics
Organization: Stanford University
Date: Tue, 25 Aug 92 17:49:06 GMT

One way for statisticians to think of neural nets
is as "recursive generalized linear models".
If you are familiar with glms, then consider a
glm in which each predictor may in fact be the
result of a glm on another set of predictors.

I wouldn't claim that the above covers all neural
models, but it does cover "feedforward neural nets
with a single hidden layer", which are among the 
ones most often used on statistical problems.

To explain glm's to a connectionist, you can
say they are feedforward nets with "no hidden layer".

Art Owen
From: kloukin@csi.forth.gr (Christos Kloukinas)
Newsgroups: sci.math.stat
Subject: info on statistical packages wanted...
Keywords: statistical package UNIX SUN
Date: 27 Aug 92 07:32:54 GMT
Distribution: sci

Could you please inform me about statistical packages that are available
for Unix - that is SUN SPARCstations - ?
Any kind (commercial , shareware, etc.) would do.
If you are already using one I would appreciate it if you would briefly
(or not so briefly if you like) evaluate it.

Please respond through E-Mail to save bandwidth.
I shall , however, post a summary of the answers I shall get.

My Internet address is : kloukin@csi.forth.gr

Thank you all !

Chris Kloukinas.

P.S. Hope this isn't a FAQ! :)
                Chris K. Kloukinas E-Mail: kloukin@csi.forth.gr
*** The opinions expressed are those of the sender unless otherwise stated. ***
"Unfortunately, the current generation of mail programs do not have checkers
 to see if the sender knows what he is talking about" (A.S. Tanenbaum)
Newsgroups: sci.math.stat
From: shinn@agsm.ucla.edu (Shinn-Tzong Wu)
Subject: Where to find statlib???
Organization: UCLA, Department of Psychology, Drug Abuse Research Center
Date: 28 Aug 92 15:09:16 PDT

Hi, does anyone know which ftp site(s) carrys statlib, I used to have the
address but lost it.  Thanks...


Shinn

shinn@shin.sscnet.ucla.edu
From: thompson@atlas.socsci.umn.edu (T. Scott Thompson)
Newsgroups: sci.math.stat
Subject: Re: Simple Proof that c=median minimizes E[ |X-c| ] needed.
Date: 4 Sep 92 16:02:05 GMT
Article-I.D.: daphne.thompson.715622525
Reply-To: thompson@atlas.socsci.umn.edu
Organization: Economics Department, University of Minnesota

The original poster might also be interested in the fact that
c = median also solves the problem

             min  E( |x-c| - |x| )
              c

The advantage of this characterization over the original one is that
since |x-c| - |x| is a bounded function of x, the expectation 
E( |x-c| - |x| ) always exists, while E( |x-c| ) might be infinite.
--
T. Scott Thompson              email:  thompson@atlas.socsci.umn.edu
Department of Economics        phone:  (612) 625-0119
University of Minnesota        fax:    (612) 624-0209
From: desj@ccr-p.ida.org (David desJardins)
Newsgroups: sci.math.stat
Subject: Re: Probability
Date: 29 Aug 92 16:05:30 GMT
Organization: IDA Center for Communications Research, Princeton

Peter Pui Tak Chiu <ppc1@cec2.wustl.edu> writes:
>Assume there are m kinds of balls in an infinitely large pool (i.e.
>there are infinitely many balls) and the distributions of different
>kinds of balls are equal.  Find the probability of getting all kinds of
>balls if n balls are picked.

>Therefore, the probability is:    (n-1)C(m-1)
>				---------------
>                                 (n+m-1)C(m-1)

Nope.  Suppose N = M.  Obviously the chance of getting one ball of each
color is M!/(M^M).  Your formula reduces to M!(M-1)!/(2M-1)!, which is
obviously not the same thing.

Your result is wrong because the arrangements are not equally likely.
The chance of getting M red balls is 1/(M^M), but the chance of getting
one ball of each color is M!/(M^M).  You have given each of these the
same weight.

Here is a correct approach based on inclusion-exclusion.  Let P(K) be
the probability that all of the balls are selected from a particular set
of K colors, with every color occurring at least once.  Thus P(M) is the
quantity we want.  Let Q(L) be the probability that all of the balls are
selected from a particular set of L colors, but not necessarily with
every color occurring.  Clearly Q(L) = (L/M)^N.

Now, of course,

   Q(L) = \sum_{K=0}^L B(L,K) P(K),

where B(L,K) is the binomial coefficient L-choose-K.  This just says
that if all of the balls are from a set of L colors, then the set of
colors which actually occur must be some K-set contained within the
L-set.  The number of such K-sets is B(L,K), for each K.

The inclusion-exclusion formula expresses P in terms of Q:

   P(K) = \sum_{L=0}^K (-1)^(K-L) B(K,L) Q(L).

It is a direct consequence of the previous formula.  If it's not
familiar to you, you can look it up in any standard combinatorics text,
or just derive it for yourself.  

Thus,

   P(M) = \sum_{L=0}^M (-1)^(M-L) B(M,L) (L/M)^N.

                                        David desJardins
From: chaas@coe.drexel.edu (Chuck Haas)
Newsgroups: sci.math.stat
Subject: Re: Literature wanted
Date: 25 Aug 92 15:52:23 GMT
Organization: Drexel University
X-Useragent: Nuntius v1.1b2

Literature wanted
Newsgroups: sci.math.stat,bit.listserv.stat-l
From: scott@mickey.acs.wmich.edu (Julie Scott)
Subject: New New S
Organization: Western Michigan University Academic Computing Services
Date: Mon, 31 Aug 1992 18:54:28 GMT
Keywords: AT&T,S software

I am interested in obtaining information about a new 1991-92 version of S  
from AT&T.  This is not the version marketed by Stat Sci.  There seems to  
be a discrepancy between what a rep at ATandT told me about new versions  
of this software and what the faculty are telling me.  The rep I talked  
with indicated the last release was 1988, yet I have had repeated  
references to a 1991/92 version available from AT&T.

Does anyone have any information about this?  Is there a good contact at  
AT&T about this, other than a sales rep that may have outdated  
information?

Many thanks,

Julie Scott
Western Michigan University
scott@gw.wmich.edu
From: rbrown@maddog.fammed.wisc.edu (Roger Brown)
Newsgroups: sci.math.stat
Subject: Vector ARIMA software
Date: 26 Aug 92 15:32:48 GMT
Organization: UW-School of Nursing

I would like to thank Brian Young, Dennis Murphy and Doug Bates for
information about access to the old Wisconsin Multiple Time Series
Analysis programs. A number of people wanted me to post the results.

--------------------------------------------------------------------
The Wisconsin Multiple Time Series Analysis Programs evolved into a
commercially available package called SCA. You can contact the SCA
folks at Scientific Computing Associates, P. O. Box 625, De Kalb, IL
60115. Now, if it's multiple time series model reduction you are
interested in, try my IBMOD software in StatLib. . . soon to be
updated.
:-)

Good luck!

--Brian
--------------------------------------------------------------------

The package you are alluding to is now know as SCA, distributed by
Scientific Computing Associates of Lisle, IL. It runs both 
univariate and multivariate time series, and is the ancestor of the
WMTS package developed at UW by Box, Tiao, et al. The marketing
rep is William (Bill) Lattyak, and their number is (708) 960-1698.
If you wish to fax, the number is (708) 960-1815 or 1816. Hope
this is enough to get you started. Tell Bill I said hi  :-).

Dennis Murphy

--------------------------------------------------------------------

Doug Bates provided similar information.

RLB
Newsgroups: sci.math.stat,bit.listserv.stat-l
From: scott@mickey.acs.wmich.edu (Julie Scott)
Subject: Re: New New S
Organization: Western Michigan University Academic Computing Services
Date: Tue, 1 Sep 1992 17:30:27 GMT

Many thanks to the people who have sent me e-mail on the 1992 version of S  
software from AT&T.  For those of you interested in the new version from  
AT&T (latest release April 1992), call 1-800-462-8146.

Julie Scott
Western Michigan University
Newsgroups: sci.math.stat
From: trafton@confidence.Princeton.EDU (Greg Trafton)
Subject: Statistical Tables
Originator: news@nimaster
Organization: Princeton University
Date: Fri, 28 Aug 1992 19:44:37 GMT

Hi, all.  I am currently looking making a handout for some simple
statistical tests, and would like to include a fresh copy of the
tables -- not copied from a book.  Does anyone know where there are
ftp-able (or another public source) with basic statistical tables in
computer format?  (I could, of course, simply type them in, but that
seems like an enourmous waste of time!)  I am specifically looking for
tables for the Mann Whitney "U" test, the Sign test, and chi square...

Thanks in advance!
Greg Trafton
(trafton@clarity.princeton.edu)
From: RS0VRD@rohvm1.rohmhaas.com (Ross Druker)
Newsgroups: sci.math.stat
Subject: Bubble Charting Software needed Mac or PC
Date: 31 Aug 92 17:23:36 GMT
Organization: Rohm and Haas Company

Deltapoint Pro on Mac and PC can create bubble charts.  Can anyone recommend
other SW     preferrably Mac (or PC) that can create bubble charts. Thanks.
Newsgroups: sci.math.stat,bit.listserv.stat-l
From: naftaly@supergas.dazixco.ingr.com (Naftaly Stramer)
Subject: Re: New New S
Reply-To: naftaly@dazixco.ingr.com
Organization: Dazix
Date: Wed, 2 Sep 1992 21:26:58 GMT


In article <1992Sep1.173027.5113@gumby.cc.wmich.edu>, scott@mickey.acs.wmich.edu (Julie Scott) writes:
>Many thanks to the people who have sent me e-mail on the 1992 version of S  
>software from AT&T.  For those of you interested in the new version from  
>AT&T (latest release April 1992), call 1-800-462-8146.
>
>Julie Scott
>Western Michigan University

Is there someting on the Public Domain for the new S software?

---
Naftaly Stramer (303)581-2370, FAX (303)581-9972    DAZIX, An Intergraph Company
Internet: naftaly@dazixco.ingr.com                  5101 Spine Road    
UUCP: ..uunet!ingr!dazixco!naftaly  	            Boulder, CO 80301
Please excuse my English
From: ekdug@b56vxg.kodak.com (James Cox)
Newsgroups: sci.math.stat
Subject: Comparison Of Model With Historical Data
Date: 3 Sep 92 21:49:00 GMT
Organization: Eastman Kodak Company, Rochester NY
News-Software: VAX/VMS VNEWS 1.41


We have purchased a simplified model of a complex kinetic chemical reaction.
This model uses as inputs data from an industrial scale process (and the data
from that process has all the usual errors associated with real-life data:
noise, drift, bias, etc).

The model produces six outputs (concentrations) that can be directly compared
to composition analyses from the plant (which are also subject to all sorts
of errors).  There is one tuning parameter (that has a semi-theoretical basis)
for the model that adjusts the production of one key component (and indirectly
all the other concentrations - there are a total of 19 predicted compositions).

The problem is when we tune the model to match the plant's production of the
key component at one set of (historical) operating conditions, it cannot 
accurately predict even the key component's concentration at other sets of
operating conditions.

Naturally, we think that the model has been oversimplified to the point of 
unusability, while the view of the company that developed the model is that
our plant data is too noisy.  Their suggested approach is to use an average
of the plant data inputs to develop an average tuning parameter value and use
that.  Their justification for this is that the variation in the predicted 
compositions from the model with an "average" tuning parameter should be less 
than the variation in the plant's measured compositions.  (Even if the
variations do turn out this way, I don't think it would take a model with a 
theoretical basis to get this sort of behavior!)

We can analyze the variation in a large set of plant data.  We can also tune 
the model to match the key component concentration for every point in the set
of data.  If we then define a function which is the sum of the differences 
between the six plant concentrations and the six predicted ones, we would know
the variation in the defined function, the variation in the tuning parameter
and the variation in the plant data inputs to the model.  

What we would like to say, based on the above information is something like:
                                                                          	
"Ah hah!  Since the variation in the tuning parameter is larger (or has a 
larger effect on the defined function) than any of the variations in the plant
data, obviously the model is not valid!"

Is there any way to reach this conclusion in a valid statistical way? 
Especially given the fact that we do not know the theoretical relationships 
between the plant data inputs to the model and the model outputs?

Any help will be *most* appreciated (and might contribute to my job security!).
Please reply here or to to james_cox@Kodak.COM (which may or may not actually
get to me.)
From: s135@stat.Berkeley.EDU (Chad Heilig)
Newsgroups: sci.math.stat
Subject: Geometric Standard Deviation
Date: 25 Aug 92 21:55:45 GMT
Organization: Statistics Dept., U. C. Berkeley

Keywords: geometric standard deviation

I need to find a reference to verify the definition of the geometric
standard deviation.  Here is the particular problem:

Given a set of numbers x_1,...,x_n, let y_i = log x_i.
It can be shown that the geometric mean of the x_i is the same as
exp(ybar), where ybar is the arithmetic mean of the y_i.

I want to confirm that the geometric SD of the x_i is also
exp(ysigmahat), where ysigmahat is the standard deviation of the y_i.

I have a hunch that this is the way geometric SD is defined, but
I'd happily yield to corrections.  Moreover, if I am wrong, I'd
like to find a link between the GSD and exp(ysimgahat).

I am using this information to justify one way of estimating
the GM and GSD of a lognormal distribution without having to
explain MLE's and MME's.

Please reply by e-mail to this account; I'll summarize the responses
and re-post later.

Thanks.

Chad
s135@stat.Berkeley.EDU
Newsgroups: sci.math.stat
From: ppc1@cec2.wustl.edu (Peter Pui Tak Chiu)
Subject: Probability
Organization: Washington University, St. Louis Mo.
Date: Sat, 29 Aug 1992 13:54:04 GMT



Hi everyone!


Some of my friends and I have been discussing a question and
we came up w/ an answer and would like to verify if its correctness.

Please give any comments freely!!!

Thanks...

Question:

Assume there are m kinds of balls in an infinitely large pool
(i.e. there are infinitely many balls)
and the distributions of different kinds of balls are equal.
Find the probability of getting all kinds of balls if n balls
are picked.

Solution:

First group the n balls you picked into m groups according to
their kinds using m-1 sticks.  So the number of possible
arrangements for the sticks and balls is (n+m-1)C(m-1).

Then we are to find the number of possible arrangements of:
(m balls of different kinds) + (n-m balls of whatever kinds) + (m-1 sticks)
since the m balls are fixed (to satisfy the constraint that there
exists at least one ball of each kind) and what we are trying to
find is the number of arrangements of n-m balls and m-1 sticks.
So, the possible arrangements is:    ((n-m)+(m-1))C(m-1) = (n-1)C(m-1)

Therefore, the probability is:    (n-1)C(m-1)
				---------------
                                 (n+m-1)C(m-1)
From: bomar@mizar.usc.edu (Jason Bomar)
Newsgroups: sci.math.stat
Subject: info on "trends"
Date: 26 Aug 1992 11:16:42 -0700
Organization: University of Southern California, Los Angeles, CA
NNTP-Posting-Host: mizar.usc.edu

I would like to know if anyone knows how statistically something is determined
to be a trend over time.  Is this a question of regression, analysis of
variane, some sort of time series analysis or what.  I would greatly 
appreciate any help/references that anyone could provide.  I can be contacted 
via e-mail at bomar@mizar.usc.edu.  Thank you very much.
  
Xref: sparky sci.math.stat:1781 comp.sources.wanted:4184
From: stankerr@uiuc.edu (Stan Kerr)
Newsgroups: sci.math.stat
Subject: Re: Where to find statlib???
Date: 3 Sep 92 15:09:29 GMT
Organization: University of Illinois
X-Xxdate: Thu, 3 Sep 92 10:11:48 GMT
X-Useragent: Nuntius v1.1.1d9
X-Xxmessage-Id: <A6CB9564C4012105@opus.cso.uiuc.edu>


In article <1992Aug28.150916.13066@mic.ucla.edu> Shinn-Tzong Wu,
shinn@agsm.ucla.edu writes:
>Hi, does anyone know which ftp site(s) carrys statlib, I used to have the
>address but lost it.  Thanks...

It's statlib@lib.stat.cmu.edu

----------
Stan Kerr    Computing and Communications Services Office   
U of Illinois/Urbana      stankerr@uiuc.edu   (217)333-5217
From: wangj@eniac.seas.upenn.edu (Jie Wang )
Newsgroups: sci.math.stat
Subject: Question on ratio estimate
Date: 3 Sep 92 03:00:14 GMT
Organization: University of Pennsylvania

Hi, there, could someone out there help me with the following
ratio estimate problem ? Any comments and/or references are
appreciated. Now the problem:

Suppose {X(i)} and {Y(i)} are i.i.d. sequences respectively, but
X(i) and Y(i) may be correlated. Let m(x) and m(y) be the expected
values of X(i) and Y(i) repectively. We would like to estimate 
	    m(x)
	R = ----. 	We know that
	    m(y)


	^      [ sum_1_to_n X(i) ] / n
	R(n) = -----------------------  
               [ sum_1_to_n Y(i) ] / n

is a consistent but biased estimator for R. Does anyone know of
any RIGOROUS treatment on the variance of this estimator ?
Particularly, I am interested in some sufficient conditions of X(i),Y(i),
that imply
                           ^
lim_n_to_infinity   n var( R(n) ) 

         E(X**2)    E(Y**2)      E(X*Y)
= R**2 { -------  + ------- - 2--------- }.
         m(x)**2    m(y)**2    m(x)*m(y)


Comments on more general nonlinear estimator are also welcome.

Thanks a lot.

--J. Wang
Newsgroups: sci.math.stat
From: dquah@atlas.socsci.umn.edu (Danny Quah)
Subject: Re: info on "trends"
Summary: trends, stochastic, deterministic, econometrics
Organization: College of Liberal Arts, Univ of Minn
Date: Sat, 29 Aug 1992 00:37:19 GMT

In article <l9nikaINNf99@mizar.usc.edu> bomar@mizar.usc.edu (Jason Bomar) writes:
>I would like to know if anyone knows how statistically something is determined
>to be a trend over time.  Is this a question of regression, analysis of

	In the macroeconomics, time series, and econometrics literature,
exactly this issue of definition/determination of a trend has been debated
with some ferocity.  The keypoints are a little involved to treat fully
with any justice in this forum, but those interested might look at papers
such as:

Beveridge, S. and Nelson, C. Journal of Monetary Economics 1981
	A New Approach to Decomposition of Economic Time Series
	into Permanent and Transitory Components

Nelson, C. and C. Plosser, Journal of Monetary Economics 1982
	Trends and Random Walks in Macroeconomic Time Series

Watson, M., Journal of Monetary Economics 1986
	Univariate Detrending Methods with Stochastic Trends

Stock, J. and M. Watson, Journal of Economic Perspectives, 1988
	Variable Trends in Economic Time Series

Cochrane, J., Journal of Political Economy, 1988
	How big is the random walk component in GNP?

Quah, D., Econometrica, 1992, The Relative Importance of
	Permanent and Transitory Components

	Any one of these papers will also contain references to
many others.  The list above is woefully short, but should serve
as a useful start to follow up on... (my apologies to those who feel
I should have included their papers too as one of the key works... :-))


--
Danny Quah dquah@atlas.socsci.umn.edu
College of Liberal Arts   University of Minnesota, Minneapolis, West Bank
Newsgroups: sci.math.stat
From: menees@utkvx2.utk.edu (Menees, Bill)
Subject: Simple Proof that c=median minimizes E[ |X-c| ] needed.
News-Software: VAX/VMS VNEWS 1.41    
Organization: University of Tennessee Computing Center
Date: Thu, 3 Sep 1992 18:44:00 GMT

	I'm a senior math major taking my first p&s course, and this problem
has come up and it intrigues me.  My prof. has a proof for it, but he said it
was way over my head.  Does anyone know of a proof suitable for a senior 
undergrad?  Thanks in advance.
Xref: sparky sci.math.stat:1762 comp.sources.wanted:4128