HDTenEightyP/NetNews huggingface dataset:
From: Stephen S Davis <sdavis@magnus.acs.ohio-state.edu>
Subject: RE: Comp Stat Tutorial
Organization: The Ohio State University
Date: Fri, 22 Jan 1993 05:23:07 GMT

Last week I ask for suggestions from the groups in the header
regarding recommendations for a stat tutor (PC based).  What follows
is a compilation of replies with a harty thank you.  While most of
the recommendations were for textbooks, I appreciate every reply.

"Distribution-Free Tests" by: Weave H.R. & Worthinton P.L.
"Statistics" by: William Hays
"Intro to Stats" by: Moore & McCabe
"Inferential Stats" by: Rohatgi

PBS, "Against All Odds" program and text by David Moore
ARCHIE file: "STATU123.ZIP"
CLIFF's "Satistics" (I found this in the OSU bookstore ($27))

And a special thanks to Dr. Larry Grimm who sent me a personal letter
with the following recommendations:
"Statistics Tutor" by: Allen & Pittenger
"Statistical Applications for the Behavioral Sciences" by Larry Grimm


Again, thanks for your considerations in this matter, I will be
checking some of these resources out.

BTW - Text for the class is "Multivariate Data Analysis with
Readings" by: Hair, Anderson, Tatham, and Black.  It's a bit wordy
with no review of simple linear regression or hypothesis testing but
it does have a good explaination of variable charateristics and how
they effect technique choices (nice chart).  554 pages/$59.95
(required).  Additionaly, the professor put together specific course
notes that we are following with expanded examples and data runs.

Regards, ssd

Stephen S. Davis                   FUZZNAME: Stephen_Davis@osu.edu
4910 Upper Valley Rd         EMAIL: sdavis@magnus.acs.ohio-state.edu
Dayton, OH 45424             Home (513)879-1869
OSU APRTMNT (614)262-3480    OSU Work, Ed P&L (614)292-4872
From: chaas@coe.drexel.edu (Chuck Haas)
Newsgroups: sci.math.stat
Subject: Re: "Contagious" Distribution
Date: 28 Jan 93 14:11:35 GMT
Organization: Drexel University
X-Xxmessage-Id: <A78D55303C0118D7@n1-24-215-coe-abbotts.coe.drexel.edu>
X-Xxdate: Thu, 28 Jan 93 01:17:36 GMT
X-Useragent: Nuntius v1.1.1d17

See:NL Johnson and S Kotz, Discrete Distributions, Houghton-Mifflin
(1969), which has a chapter on contagious distributions and also
discussion of a number of specific types.  The most common are Negative
Binomial, and Neyman type A.  There are also a wealth of references on
fitting contagious distributions of various types.



Charles N. Haas
Professor, Environmental Engineering
Drexel University, Philadelphia PA 19104
======================================
CHaas@COE.Drexel.Edu
From: pddxt@chalk.cpceng.cpc.gmeds.com (Dino Triantos 575-4386)
Newsgroups: sci.math.stat
Subject: F distribution and relation to Beta Help!
Date: 21 Jan 93 18:14:33 GMT
Reply-To: pddxt@chalk.cpceng.cpc.gmeds.com
Organization: GM/EDS NAO

I'm working on a problem in Mood, Graybill and Boes that asks to find the
relationship between the F and Beta distributions.  The problem asks:  If 
X has an F distribution with m and n degrees of freedom, show that
 
		        mX/n
		 W =   -------       has a Beta distribution.
		      1 + mX/n

Any suggestions or solutions would be greatly appreciated! 

Dinos Triantos
GM/Midsize Automotive Division 
Vehicle Systems Analysis
C-P-C Engr. Center Rm# G241
Fax: (313) 575-7270
E-mail: pddxt@cpceng.cpc.gmeds.com
Newsgroups: sci.math.stat
From: herman@nosc.mil (John W. Herman)
Subject: Re: Robust correlation
Keywords: robust, correlation
Organization: Naval Ocean Systems Center, San Diego, CA
Date: Thu, 21 Jan 1993 16:33:11 GMT

vms@jyu.fi (Veli Matti Suppola) writes:

>A few days ago I posted a question to this newsgroup. The question
>was about robust correlation coefficients.
>I got a few answers but I do know, that many of You know something
>about this subject. 
>Thanks to all of You, who answered my question.
>I am very grateful.
>So, if You know something about robust correlation coefficients,
>please let me know. I mean books, articles and so on.

I'm not sure what you're trying to find.  The Rank Correlation
Coeffecient is a robust approximation to the true correlation.  If you
have symmetrical distributions, you can also look at sign matching.  I'm
sure that both of these have been mentioned to you by other people.
Maybe a description of your problem would help elicit a better
response.
Newsgroups: sci.math.stat
From: naras@cda.mrs.umn.edu (B. Narasimhan)
Subject:  More on Random Number Generators.
Organization: University of Minnesota - Morris
Date: Thu, 21 Jan 1993 17:06:48 GMT

Here is the intro part of a paper by some other physicists.

\centerline{\bf Hidden Errors in Simulations and the Quality 
                of Pseudorandom Numbers}

by I. Vattulainen$^1$, K. Kankaala$^{1,2}$, J. Saarinen$^1$, and
T. Ala-Nissila$^3$.

In a recent letter Ferrenberg {\it et al.} (FLW) [1] present 
intriguing results arising from combinations of some random 
number generators and Monte Carlo acceleration algorithms. 
In particular, they observe systematic errors when the 
Wolff algorithm [2] is used at $T_c$ in the $2-d$ Ising model.
As an explanation of this, they propose that subtle correlations
arise in the random number sequences, in the sense that the
higher order bits of the random numbers are correlated.

We have recently carried out extensive statistical,
bit level, and visual tests for several commonly used
pseudorandom number generators in physics applications [3].
Two of these were used in Ref. 1, namely
R250 [4] and CONG [5].
Using the Wolff algorithm FLW discovered problems
with R250, but not with CONG. Thus, our test results bear
direct relevance to the existence of possible problems, and
differences between these two algorithms.

-- 
B. Narasimhan                             naras@cda.mrs.umn.edu
Division of Science and Math.
The University of Minnesota at Morris
Morris, MN 56267
Newsgroups: sci.math.stat
From: shoe@calvin.edu (Al Shoemaker)
Subject: Re: PC version of BMDP
Keywords: BMDP
Organization: Calvin College
Distribution: usa
Date: Wed, 27 Jan 1993 16:38:23 GMT

Yes, there is a BMDP PC version.  I use it all the time in my consulting
work with physicians.  I use the BMDP-386 version, but there should also
be the older version which should work on any machine.  (I believe you need
4 megabytes for  the 386 version.)
  BMDP is also beta-testing a windows version and they already are sending
out info on it.
   You can contact BMDP at 1-800-238-2637.

   I do not work for BMDP--I simply use their product (along with SPSS).
  
       Al Shoemaker (shoe@calvin.edu)

Newsgroups: sci.math.stat
From: tsu1@vax.oxford.ac.uk
Newsgroups: sci.math.stat
Subject: TIME SERIES FORECASTING/KALMAN FILTER
Date: 26 Jan 93 20:46:59 GMT
Organization: Oxford University VAX 6620


I am working in a project dealing with the forecasting of traffic congestion
in urban road networks, for use in a real time drivers' information system.
I used formulations like

^y(t+1) = a1.y(t) + a2.y(t-1) + ..... +
          b11.q1(t) + b12.q1(t-1) + ..... +
          b21.q2(t) + b22.q2(t-1) + ..... +
            .......
          c1.yh(t+1) + c2.yh(t) + ...
          d11.qh1(t) + d12.qh1(t-1) + ..... +
          d21.qh2(t) + d22.qh2(t-1) + ..... +         (1)
         
          
where ^y(t+1) the predicted level of flow at time t+1 at the link of interest
       y(t)   the measured flow at the link of interest
       qi(t)  the measured flow at link i upstream of the link of interest 
       yh(t)  historical flow for the link of interest at time t
       qhi(t) historical flow for link i at time t
              (historical flow (t) = average flow(t) over the last few days) 

and kalman filter theory to identify the system parameters which are assumed to
be time varying.
I used several formulations (differnt terms of the AR and MA components)  but
I did not find any formulation that consistently performs better.

I then tried to predict the ratio between predicted and historical 
flow ^ry(t+1)=^y(t+1)/yh(t+1) and then from this ratio to predict the ^y(t+1)
using formulations like
  ^ry(t+1) = A1.ry(t) + A2.ry(t-1) + ...
             B11.rq1(t) + ........ 
             B21.rq2(t) + ....

   ^y(t+1) = ^ry(t+1).yh(t+1)

where   rqi(t) = qi(t)/qhi(t)

I found that the latter approach consistently performs better than the former.
There is often a regularity in the patterns observed every day, but given the
fact in both formulations I used historical info I didn't expect significant
differences in the forecasts.
Can anybody help me on how I can a develop a theoretical proof of this outcome?
Do you know of any papers discussing a relevant subject?


Thanks very much,

Petros Vythoulkas
TSU1@vax.oxford.ac.uk
Transport Studies Unit
University of Oxford
From: wbdst+@pitt.edu (William B Dwinnell)
Newsgroups: sci.math.stat
Subject: Re: Desperate situation
Date: 27 Jan 93 03:52:26 GMT
Organization: University of Pittsburgh


Well, there are 2 ways of interpreting "average" in this problem, but 
yours does seem to make more sense.
From: U19163@uicvm.uic.edu (LARRY GRIMM)
Newsgroups: sci.math.stat
Subject: Hist. of Multivariate
Date: 24 Jan 93 19:06:58 GMT
Article-I.D.: uicvm.93024.130658U19163
Organization: University of Illinois at Chicago

Could anyone please provide references (book chapters, journal
articles) that discuss the historical development of multivariate
statistical analyses.  Also, I could use good definitional overviews
of the field.  Thanks.
From: wbdst+@pitt.edu (William B Dwinnell)
Newsgroups: sci.math.stat
Subject: Re: Desperate situation
Date: 26 Jan 93 19:33:56 GMT
Organization: University of Pittsburgh


An average is not an absolute: it must be taken over a domain. Do you 
define the solution as being the number of times played, when 1/2
of all people playing have dies?xxx sorry, died?
Newsgroups: sci.math.stat
From: clint@leland.Stanford.EDU (Clint Cummins)
Subject: Re: Fit a circle to (x,y) datapoints?
Organization: DSG, Stanford University, CA 94305, USA
Date: Tue, 26 Jan 93 22:44:43 GMT

You can't specify y in terms of x, because a circle is not a function
(one x can map to 2 different values of y).  Instead, use a "fake" dependent
variable with the value zero, and on the right hand side, write the function
to be minimized (when squared).  This approach will work for any nonlinear
least squares package.  In your case, let's say the circle's origin is
(A,B) with radius R.  Dependent variable:  ZERO=0;
Function for PROC NLIN:  ZERO = R - SQRT((X-A)**2 + (Y-B)**2)

Clint Cummins
From: hirai@aster.osaka.jip.co.jp (Hirai Kazuto)
Newsgroups: sci.math.stat
Subject: RE: Decision Making References and Bayesian analysis
Date: 27 Jan 93 02:10:31 GMT
Distribution: sci
Organization: /home/hirai/.organization





  I thank Mr. Robert E George and Mr. T. Scott Thompson for kind email
to me.  I'll try to search for the references and to read them.

  Now, in Japanese statisticians there are many discussions about Bayesian
approach to some traditional statistical models or method. How about this
Japanese situation ?

 P.S.    I have not a thorough knowlege of Bayesian analysis.



       
              $B"!(J        ++++++++++++++++++++++++++++++++++++++++++++++++++++    
    JAPAN     $B"#(J          Kazuto Hirai (field:Environmental Science &
              $B"#(J                              Quantitative Analysis )
        $B"#"""#"#(J          mail address: hirai@aster.osaka.jip.co.jp
       $B"!(J $B"!(J              Department of Social System, Osaka Office, 
                          Japan Information Processing Service
                          Co.Ltd.,            Osaka 550, Japan  
 $B""(J:Osaka(Kansai area) 
                        +++++++++++++++++++++++++++++++++++++++++++++++++++++
From: wbdst+@pitt.edu (William B Dwinnell)
Newsgroups: sci.math.stat
Subject: Re: Desperate situation
Date: 26 Jan 93 19:57:09 GMT
Organization: University of Pittsburgh


Yes, a quick and dirty Monte Carlo (actually it was a Las Vegas simulation-
they're not the same, you know!) simlation has borne out my original
numbers. You can expect to die some time arounfd the 3rd or 4th try.
The curve vaguely resembles a log curve. I'm sure there's a way to get
an exact figure through clxxx calculus, I believe using a definite integral
and dividing to get an average is right, byxx but I always hated 
integration- differentiation is so much easier!
From: wbdst+@pitt.edu (William B Dwinnell)
Newsgroups: sci.math.stat
Subject: Re: Desperate situation
Date: 26 Jan 93 21:56:09 GMT
Article-I.D.: blue.2581
Organization: University of Pittsburgh


Rex: How are you defining the "average"? Do you mean the mean number of tries
successfully past by all people trying this? Do you mean the number of
tries at which half the people are dead? Possibly you mean something
else altogether?
Newsgroups: sci.math.stat
From: mcclella@yertle.Colorado.EDU (Gary McClelland)
Subject: Re: F distribution and relation to Beta Help!
Organization: University of Colorado, Boulder
Date: Fri, 22 Jan 1993 16:50:22 GMT

pddxt@chalk.cpceng.cpc.gmeds.com (Dino Triantos 575-4386) writes:

>I'm working on a problem in Mood, Graybill and Boes that asks to find the
>relationship between the F and Beta distributions.  The problem asks:  If 
>X has an F distribution with m and n degrees of freedom, show that
> 
>		        mX/n
>		 W =   -------       has a Beta distribution.
>		      1 + mX/n

>Any suggestions or solutions would be greatly appreciated! 

>Dinos Triantos
>GM/Midsize Automotive Division 
>Vehicle Systems Analysis
>C-P-C Engr. Center Rm# G241
>Fax: (313) 575-7270
>E-mail: pddxt@cpceng.cpc.gmeds.com

Is this a homework problem or will the solution to this
statistical issue help GM build better cars?  The net has
informal prohibitions against providing answers to homework
problems.  The problem you raise is a very standard homework
assignment in undergraduate statistics classes.  When I did it as
a homework problem many years ago I remember that it was a bit
easier to do it as W = 1/(1+(m/n)F).  It won't help you solve it,
but a useful insight is that W is often known as R^2.  Good luck!

gary mcclelland
univ of colorado
mcclella@yertle.colorado.edu
Newsgroups: sci.math.stat
From: ssailer@portal.hq.videocart.com (Steve Sailer)
Subject: Need Help w/Scanner Data Analysis
Organization: VideOcart Inc.
X-Newsreader: Tin 1.1 PL3
Date: Mon, 25 Jan 1993 17:32:51 GMT

I'd appreciate any thoughts you might have on this stumper in
marketing statistics:

Information Resources, Inc. (IRI) publishes statistics describing
the purchasing of tens of thousands of different products sold in
supermarkets. Among the statistics are "% of households buying."
This measure cannot be simply aggregated across time. For
example, if you know that 30% of all households buy cat food in
one 13 week quarter, you would not assume that over the course of
a 52 week year, the % of households buying cat food is 4 time 30%
or 120%. Typically, you would have to pay IRI to retabulate
purchasing for each household over the desired time period.
However, we want to find a another way.

What we need is a rough way to quickly estimate % of households
buying various products during typical 2 week periods, assuming
we already know from IRI the exact statistics for 52 and 13 weeks
periods.

The other statistic of relevance that we already have for the 52
and 13 week periods is "# of purchase occasions among buyers." If
you multiply % of households buying times number of purchase
occasions among those buyers, you get the average number of
purchase occasions among all shoppers. For example, if 30% of the
shoppers bought cat food, and on average those cat food buyers
bought it 3.33 times per quarter, then the overall average
shopper (including both cat lovers and cat haters) bought cat
food 1.0 times per quarter. For this statistic, in contrast to
the other two, it _is_ reasonable to extrapolate the average
annual number of cat food purchases among all shoppers: 1.0 times
4 = 4 (assuming no seasonal fluctuations or long term growth or
decline).

All right, here are some examples to help you think:

                               52 Weeks 13 Weeks  2 Weeks
Cold Cereal - All Brands       -------- --------  -------
  % of Households Buying           94.7%    77.0%       ?
  Purchase Occasions/Buyer         11.7      3.6        ?
                                  -----     ----     ----
  Pur Occ/All Shoppers            11.08     2.77     0.43

Kellogg's Corn Flakes
  % of Households Buying           36.7%    17.6%       ?
  Purchase Occasions/Buyer          2.9      1.5        ?
                                   ----     ----     ----
  Pur Occ/All Shoppers             1.06     0.26     0.04

Dry Dog Food - All Brands
  % of Households Buying           31.3%    21.3%       ?
  Purchase Occasions/Buyer          6.8      2.5        ?
                                   ----     ----     ----
  Pur Occ/All Shoppers             2.13     0.53     0.08


It has been suggested that a negative binomial distribution is
the key to the problem, but I'm not qualified to comment on that.

Once again, we greatly appreciate your help with any suggestions
you might have. Please E-mail or post if you think the question
is of general interest.


-- 
Steve Sailer
VideOcart, Inc.      ssailer@hq.videocart.com
From: uurtamos@helium.gas.uug.arizona.edu (stephen j uurtamo )
Newsgroups: sci.math.stat
Subject: pseudo random numbers
Date: 21 Jan 93 15:33:50 GMT
Distribution: usa
Organization: UofA Mathematics Department


Howdy.  Could any of you give me good references to some good pseudo random
number generators?  They should have the following characteristics:

(1) Be easy to implement in C (Or already exists fast C code).
(2) The period should be scalable to fairly large values (10^20 or so).
(3) The motivation behind the prng should be well documented and rigorous.
    (i.e. linear congruential prng would be documented in papers on finite
    fields.  I actually need the straight skinny on why people think that
    these are any good.)

If any of you know of some pretty good prng with these characteristics (and
can send me references) I would really appreciate it.  I don't recognize most
of the prng mentioned by name in this group, so I'm a little lost. (If this is
in a FAQ then you can shoot me.)

Thanks in advance.

steve
Newsgroups: sci.math.stat
From: tao@mace.Princeton.EDU (Terry Tao)
Subject: Re: Why Algebra...Important
Originator: news@nimaster
Organization: Princeton University
Date: Mon, 25 Jan 1993 16:40:19 GMT

In article <1993Jan25.023340.4349@monu1.cc.monash.edu.au> khchung@monu1.cc.monash.edu.au (Mr KH Chung) writes:
>In article <7459@ucsbcsl.ucsb.edu>, unicef@epilogue.mcl.ucsb.edu (Joel Rising) writes:
>> Math is a language, but a far more powerful one.  To convince youself,  
>> write out some equation, and then try to describe all the relationships it  
>> embodies.  It's not just "x=ky" but also "x is proportional to y" or "as x  
>> goes up one, y goes up by some factor k."  Relate the variables to some  
>> real-world quantity, and the equation becomes even more rich with  
>> information.  Perhaps a whole paragraph could be gleaned from "x=ky."
>
>On the other hand, how do you order a cup of coffee using only mathematical
>notation?
>:-)

Claim: \exists Cup = {(x, y, z) = d((x, y), Cup_0) = r_cup, z_0 < z < z_0 +
h_cup} \cup {(x, y, z_0): d((x, y), Cup_0) < r_cup}:d(Cup, me) < \epsilon_0 \and
\exists Coffee = \{ f(x, y): d((x, y),  Cup_0) < r_cup} s.t. Coffee \in \overbar
{Cup} \and T_coffee > 90 F \and t > t_0 => Hap_me(t) > Hap_me(t_0).

Corollary: f(x, y) \to 0 as t -> \infinity.

:-)

Terry

Xref: sparky sci.math:18818 sci.edu:1500 misc.education:6162 sci.math.num-analysis:3980 sci.math.stat:2881
From: mjs@hubcap.clemson.edu (M. J. Saltzman)
Newsgroups: sci.math,sci.edu,misc.education,sci.math.num-analysis,sci.math.stat
Subject: Conference on Graduate Programs in the Applied Mathematical Sciences
Date: 27 Jan 93 21:06:26 GMT
Organization: Clemson University, Clemson SC


	     Conference on Graduate Programs in the Applied
                       Mathematical Sciences II
                         April  16-18, 1993
                         Clemson University
                            Clemson, SC

Current challenges for graduate education in classical analysis, 
computational and discrete mathematics, operations research and 
statistics will be discussed at this conference hosted by the 
Department of Mathematical Sciences at Clemson University.

Specific conference objectives are to review the current status of 
graduate education in the applied mathematical sciences; to review 
the role of recent developments in the mathematical sciences in 
the solution of modern technological problems and the challenges 
and opportunities these developments present to graduate 
education; to obtain perspectives on current industrial/government 
employment opportunities and placement; and to publicize 
innovative graduate programs to the professional community.

Participants will be selected to provide broad representation from 
all areas of the mathematical sciences, including individuals from 
academic, governmental and industrial institutions. The program 
will consist of a number of panel, audience, and group 
discussions.  This format necessitates a limited number of 
participants.  A conference proceedings will be published.

Preliminary Program
Friday April 16
Morning
   I. Graduate Mathematical Sciences Education: An Historical Perspective.
  II. Current Status of Applied Mathematical Sciences Graduate Education 
      in the U.S.
 III. Challenges for Graduate Education in the Mathematical Sciences.

Afternoon
  IV. Curricula for the 90's and Beyond.
   V. Curriculum Innovations. 

Saturday April 17
Morning
  VI. Computation in the Curriculum.  
 VII. Employment Opportunities for the 90's.  

Afternoon
VIII. Non-Academic Careers for MS and Ph.D. Students. 
  XI. Group Discussions.
      1. Recruiting and Advising Graduate Students.
      2. Department Administration, University Support, Rewards.
      3. Undergraduate Preparation.

Sunday April 18
  X.  Summary and Closing Remarks.

Invited participants include: John Burns, Don Bushaw, Ron Douglas, James 
Dyer, Bob Easterling, Avner Friedman, Carl Harris, Robert Hogg, Bill Lucas,
Robert Lundegard, Tom Magnanti, Joyce McLaughlin, Anna Nagurney, 
Fred Roberts, Mary Wheeler, Doug Zahn.    

This conference is supported by a grant from the National Security 
Agency and additional funding from NSF is expected.  For further 
information contact, R. E. Fennell, Department of Mathematical 
Sciences, Clemson University, Clemson SC 29634-1907, (803) 656-3257, 
email conf@math.clemson.edu  
-- 
		Matthew Saltzman
		Clemson University Math Sciences
		mjs@clemson.edu
Xref: sparky sci.math:18828 sci.math.stat:2886