HDTenEightyP/NetNews huggingface dataset:
Newsgroups: bit.listserv.edstat-l
Date:         Fri, 10 Apr 1992 12:09:13 EDT
Reply-To:     pgill@kean.ucs.mun.ca
From:         pgill@KEAN.UCS.MUN.CA
Subject:      impact of stat
X-To:         edstat-l@ncsuvm.cc.ncsu.edu

Again and again the questions why, what and how statistics should be
taught to undergrduates from various majors comes up on the network.
Arnie asks, "what is the use of teaching a subject if the students
never apply the information and THEY JUST FORGET IT ?"
Same could be said for any subject (say Calculus) supposed to be
supplying the basic skills.  I wonder how many of us can compute the
square root of a simple number (say, 179 to two decimal places
without a calculator ?
Statistics is a tool that is almost always required in almost any
field of study.  The way we are teaching statistics in universities
is certainly is not the best way.  Typically, we teach it a Calculus
way to a large class (100-200 students) solving some problems, giving
multiple choice quizzes and exams.  Hardly any time can be devoted to
discussion.  Students can compute the test statistics, "accept H0",
fit linear regression but can not interpret the computed value of the
slope parameter.... and so on.
(By the way, the above is also true for major Stat students to some
extent.)
And down the line after few years when it comes to applying the Satat
Stat tools, student (who might be doing PhD or masters) finds that the
tools not only are badly rusted (s)he does not even remember which tool
to apply.
The debate is currently going on in various circles.  Some of the recent
commentries are :
1. Statistical Education : Improvements are badly needed by Robert Hogg.
American Statistician Nov 1991, Vol 45, 342-343.
2. Why is introductory statistics difficult to learn .. by Donald Watts
American Statistician Nov 1991, Vol 45, 290-291.
3. Writing as a component of statistics education by Norean Radke-Sharpe
American statistician Nov 1991, Vol 45, 292-293.
4. teaching Statistics : More data, less lecturing.  AMSTAT NEWS
Dec 1991 front page.

Hopefully, we will see some discussion on the network during the summer.
Let us hear from Psychologists, Economists, Biologists, Educationists (?),
Engineers, Biometricians, Biostatisticians ,Academic Statisticians and
anyone else who cares.

P. S. Gill
Dept of Mathematics and statistics
Memorial Univ of Newfoundland
St. John's, Canada
PGILL@KEAN.UCS.MUN.CA
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Newsgroups: bit.listserv.edstat-l
Date:         Fri, 10 Apr 1992 08:05:00 EDT
Reply-To:     BEINS@ITHACA.BITNET
From:         BEINS@ITHACA.BITNET
Subject:      impact of stat

Arnie Kahn noted the tendency of the students in his department to forget
their statistics between sophomore and senior years.  At Ithaca College,
some of our students (more than a few) manage to develop their statistical
skills over the years.

We have a requirement for our BA majors (but not for BS majors--but don't
get me started on that) that they take a three-semester sequence on a
Research Team with a single professor.  We have 11 or 12 teams with
ongoing research, each led by a different professor.  Our decent students
(and not only the superstars) can ultimately handle the statistical
approach on their teams reasonably well.  Depending on the team, the
students use ANOVA, correlations, occasionally something new and mysterious
like log linear analysis.  Mostly they use SPSSX on the mainframe.

Any given student won't be exposed to all the different statistical
techniques, but at least they persist in doing some statistical thinking.

Needless to say, Research Team is a labor intensive course for the faculty
and a notable curricular commitment for the department, but the payoff is
quite striking and students get immersed and personally involved in the
research enterprise.  (Students receive two credits for each semester on
Team; a number of my students have continued as "fourth semester" students
via independent research when they get turned on.)

Barney Beins, Psychology
Ithaca College
Beins@Ithaca.bitnet
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Newsgroups: bit.listserv.edstat-l
Date:         Wed, 15 Apr 1992 13:50:24 CDT
Reply-To:     SOCLYMAN@UMCVMB.BITNET
From:         SOCLYMAN@UMCVMB.BITNET
Subject:      Request for Dick Darlington's e-mail address

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 stat-l.  Could someone
send me Prof. Darlington's e-mail address please?
  Thanks,
Katherine Lyman
UMissouri-Columbia
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X-Mailer: UVa PCMail 1.6.1
Newsgroups: bit.listserv.edstat-l
Date:         Fri, 17 Apr 1992 14:40:18 EDT
Reply-To:     "Patty Hulick" <pah2e@UVA.PCMAIL.VIRGINIA.EDU>
From:         "Patty Hulick" <pah2e@UVA.PCMAIL.VIRGINIA.EDU>
Subject:      regression texts
X-To:         edstat-l@ncsuvm.cc.ncsu.edu

A few weeks back, someone asked if anyone knew of any regression texts which
would be good for such a course.  As a first year Graduate Student in
Quantitative, I am taking such a course.  However, the text we use is the
Cohen and Cohen 1983 text.  For the most part, I like this book but I do
believe it is more of a reference book for those who have some previous
experience with regression (I took an excellent course in Advanced
Quantitative Methods as an undergrad).  The other day I stumbled across what
looks to be a more basic level text--

Regression with Graphics:  A Second Course in Applied Statistics
Lawrence C. Hamilton

(I do not remember if this text has been mentioned or not.)

Of course, the Sage Papers on Regression are quite helpful, especially as
supplements, as well.

Patty
UVA
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Newsgroups: bit.listserv.edstat-l
Date:         Wed, 15 Apr 1992 15:18:40 CDT
Reply-To:     SOCLYMAN@UMCVMB.BITNET
From:         SOCLYMAN@UMCVMB.BITNET
Subject:      D.Darlington's address

Networkers,
  Several people have sent Prof. Darlington's address for which I offer
my thanks.  Several others have reminded me that I could have gotten
this information with a query to the listserver.  I plumb forgot!
Sorry for wasting time/space.
  This apology is cross-posted on stat-l.
K. Lyman
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Subject: (no subject given)
Newsgroups: bit.listserv.edstat-l
Date:         Fri, 10 Apr 1992 18:07:15 -0400
Reply-To:     SULLINS@NAUVAX.BITNET
From:         SULLINS@NAUVAX.BITNET

Thanks to all who responded to my question about interpreting MANOVA. The most
common theme seemed to be to ignore the univariate tests altogether. Regarding
my second question about how to get at scores on the "composite" DV in order
to plot interactions, I received 2 related types of solutions, several requests
for solutions from others in the same situation, and 1 message that (as I
interpret it) says there ain't no such animal.  Most suggested using SPSS
DISCRIM to get the linear discriminant function scores as follow-up to the
MANOVA (Jodie Ullman, Gary Lautenschlager) or to get the scores as canonical
variates (Magda Vuylsteke,Karl Wuensch, Glenn Meyer). David Nichols of SPSS
says "..there is no "created" multivariate dependent variable.  The
multivariate tests are all functions of the eigenvalues of the product of two
matrices.  There is no single new variable there to plot."

So...I'm still not sure exactly how to proceed, and am in the process of
tracking down the references that were suggested by various folks.

By the way, a piece of information I neglected to mention in the original
message is that 2 of my IVs are within-subjects variables --  if the
discriminant and/or canonical analyses **is** what I need, does this pose a
problem?  If not, I'm still not sure of how to write the commands to get the
(alledged) scores.

Here's an example of one of my MANOVA statements:

MANOVA KNAGmn KNAGmp KNAGfn KNAGfp
       KNUNmn KNUNmp KNUNfn KNUNfp
       KNEAmn KNEAmp KNEAfn KNEAfp
    BY Sex (1,2) FrndSex (1,2)
    /WSFactors=Type (2) Valence (2)
    /Measure=KNAG KNUN KNEA
    /Design

This particular analysis yields significant main effects for Type and Valence,
a couple of 2-way interactions, and a 3-way interaction between Type, Valence,
and Sex.  This is only one set of DVs that are analyzed using the same 4 IVs.
The 3-way interaction is significant in them all, so it is important to be
able to describe this interation accurately. Plotting a 3-way interaction for
each univariate DV not only gives too many graphs to make sense of, the
univariate interactions don't even all look the same!  Hence my search for the
elusive multivariate DV scores.  Any further suggestions would still be
appreciated.  Should I discover a good solution, I'll post it to the list.

                                Thanks Again,

                                Ellen Sullins
                                Psych Dept.
                                Norther Arizona University
                                602-523-9979
                                Sullins@NAUVAX (Bitnet)
                                Sullins@NAUVAX.UCC.NAU.EDU (Internet)
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Subject: (no subject given)
Newsgroups: bit.listserv.edstat-l
Date:         Thu, 9 Apr 1992 12:49:06 -0400
Reply-To:     SULLINS@NAUVAX.BITNET
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.edstat-l
Date:         Mon, 13 Apr 1992 15:55:45 CDT
Reply-To:     ECO_GXC@SHSU.BITNET
From:         ECO_GXC@SHSU.BITNET
Subject:      stat text (geo stat)

can anybpody reccommennd a good text on spatial statistics or geo
statistics to use in a first year graduate course in crime pattern
analysis (criminal justice)
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Newsgroups: bit.listserv.edstat-l
Date:         Wed, 8 Apr 1992 18:37:29 EDT
Reply-To:     Lee <WOLFLE@VTVM1.BITNET>
From:         Lee <WOLFLE@VTVM1.BITNET>
Subject:      Statistics in Undergraduate Psychology Programs.

   With regard to David Mostofsky's inquiry about undergraduate
statistics requirements in psychology, allow me to point out
that this question is hardly a new one.
   In the summer of 1952 Dael Wolfle, Claude Buxton, Charles
Cofer, John Gustad, Robbie MacLeod, and Bill McKeachie met in
Ithaca and published their report: _Improving Undergraduate
Instruction in Psychology_ (The Macmillan Company, 1952).
   To summarize their thinking, they believed that an under-
graduate in psychology ought to understand that psychological
variables are continuous rather than falling into distinct
categories; they should become aware of the importance of
precise measurement; they should become aware of sampling
issues, and statistical inference; they should become aware
of the probability aspects of psychological events; they should
become aware of the logic of experimental design.
   Moreover, they should become aware of the notion of multiple
causation, so that they can avoid seeking or accepting easy
explanations or oversimplifications of psychological
processes.
                                  Lee Wolfle
                                  WOLFLE @ VTVM1
                                  wolfle@vtvm1.cc.vt.edu
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.edstat-l
Date:         Tue, 14 Apr 1992 12:20:10 PDT
Reply-To:     USERUMSK@UBCMTSG.BITNET
From:         USERUMSK@UBCMTSG.BITNET
Subject:      sampling-simulation

Dear Networkers;
   I would greatly appreciate your feedback/comments/remarks etc
as well as any reference sources on the following issue.
I am recently involved in a simulation study to investigate the degree
of sampling variability and possible bias of estimates.
I am using a multi-normal random number generator to obtain sample data
with a specified covariance matrix as as input.

  I am considering to choose one of the following sampling methods to
generate a set of sample data (say 2000 samples, each having n=100) and
subsequently calculate sample statistics from each sample.

A) assume an infinite population size and;
  1) generate a sample size of 100 and repeat this 2000 times, or
  2) generate a sample size of say 200000 and randomly select a
     subsample of 100 for 2000 times without replacement, or
  3) the same as 2), but with replacement.

B) assume a finite population size and;
  4) generate a population (1000000 values) and select a random
     sample size of 100, 2000 times without replacement, or
  5) the same as 4), but with replacement, or
  6) generate a population (200000 values) and select a random sample of
     size 100, 2000 times without replacement (thus, every value in
     the population would be selected exactly onec), or
  7) the same as 6), but with replacement.

If I calculate a mean and variance, and confidence interval for the
population value based on 2000 replications, would there be substantial
differences among the 7 options?  If so, how one could justify the use
of particular method in a simulation study??

Thanks for your time in advance.

Hanjoo Eom (e-mail: userumsk@ubcmtsg or hanjoo_eom@mtsg.ubc.ca)
Department of Education
Univ. of British Columbia
Vanc. B.C. Canada
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X-Organization: Indiana University of Pennsylvania
X-Envelope-to: edstat-l@ncsuvm.bitnet
X-VMS-To: NETMAIL%"edstat-l@ncsuvm.bitnet"
X-VMS-Cc: NICHOLAS_KARATJAS
Newsgroups: bit.listserv.edstat-l
Date:         Thu, 9 Apr 1992 10:51:00 EDT
Reply-To:     "NICK KARATJAS" <BFURJPA@IUP.BITNET>
From:         "NICK KARATJAS" <BFURJPA@IUP.BITNET>

I would like to be added to the subricption list for edstat-l.
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Date:         Fri, 10 Apr 1992 11:46:00 EST
Reply-To:     Arnie Kahn <FAC_ASKAHN@JMUVAX1.BITNET>
From:         Arnie Kahn <FAC_ASKAHN@JMUVAX1.BITNET>
Subject:      forwarded mail

This was sent to me but I think it was directed to the list.
Arnie

From:   IN%"pgill@kean.ucs.mun.ca" 10-APR-1992 10:44:55.60
To:     IN%"FAC_ASKAHN@VAX1.ACS.JMU.EDU"  "Arnold S. Kahn", IN%"FAC_COUCH@VAX1.A
   CS.JMU.EDU"  "Jim Couch", IN%"FAC_RGB@VAX1.ACS.JMU.EDU"  "BOB BROOKSHIRE"
CC:
Subj:   impact of stat


Again and again the questions why, what and how statistics should be
taught to undergrduates from various majors comes up on the network.
Arnie asks, "what is the use of teaching a subject if the students
never apply the information and THEY JUST FORGET IT ?"
Same could be said for any subject (say Calculus) supposed to be
supplying the basic skills.  I wonder how many of us can compute the
square root of a simple number (say, 179 to two decimal places
without a calculator ?
Statistics is a tool that is almost always required in almost any
field of study.  The way we are teaching statistics in universities
is certainly is not the best way.  Typically, we teach it a Calculus
way to a large class (100-200 students) solving some problems, giving
multiple choice quizzes and exams.  Hardly any time can be devoted to
discussion.  Students can compute the test statistics, "accept H0",
fit linear regression but can not interpret the computed value of the
slope parameter.... and so on.
(By the way, the above is also true for major Stat students to some
extent.)
And down the line after few years when it comes to applying the Satat
Stat tools, student (who might be doing PhD or masters) finds that the
tools not only are badly rusted (s)he does not even remember which tool
to apply.
The debate is currently going on in various circles.  Some of the recent
commentries are :
1. Statistical Education : Improvements are badly needed by Robert Hogg.
American Statistician Nov 1991, Vol 45, 342-343.
2. Why is introductory statistics difficult to learn .. by Donald Watts
American Statistician Nov 1991, Vol 45, 290-291.
3. Writing as a component of statistics education by Norean Radke-Sharpe
American statistician Nov 1991, Vol 45, 292-293.
4. teaching Statistics : More data, less lecturing.  AMSTAT NEWS
Dec 1991 front page.

Hopefully, we will see some discussion on the network during the summer.
Let us hear from Psychologists, Economists, Biologists, Educationists (?),
Engineers, Biometricians, Biostatisticians ,Academic Statisticians and
anyone else who cares.

P. S. Gill
Dept of Mathematics and statistics
Memorial Univ of Newfoundland
St. John's, Canada
PGILL@KEAN.UCS.MUN.CA
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
X-VMS-To: IN%"edstat-l@ncsuvm.bitnet"
Newsgroups: bit.listserv.edstat-l
Date:         Fri, 10 Apr 1992 12:10:00 EDT
Reply-To:     STEELEKM@APPSTATE.BITNET
From:         STEELEKM@APPSTATE.BITNET
Subject:      impact of stat

Beins commented on the 3-semester research team sequence in
use at Ithaca College as a means to improve retention of
statistical knowledge.

At Appalachian State, we have a new 2-semester research sequence
that is preceded by a stat course in the math department.
The first semester covers descriptive and correlational
methods, the second semester covers experimental methods.
Both semesters involve lots of data collection and analysis
(using SPSS and MYSTAT).

Lots of experience with real data appears to work in
improving retention; but, as at Ithaca, it is
a labor-intensive method.


Ken Steele                          steelekm@conrad.appstate.edu
Dept. of Psychology                 steelekm@appstate.bitnet
Appalachian State University
Boone, NC 28608
USA
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Newsgroups: bit.listserv.edstat-l
Date:         Fri, 10 Apr 1992 15:13:58 U
Reply-To:     dick darlington <dick_darlington@QMRELAY.MAIL.CORNELL.EDU>
From:         dick darlington <dick_darlington@QMRELAY.MAIL.CORNELL.EDU>
Subject:      Impact
X-To:         EDSTAT-L@NCSUVM.CC.NCSU.EDU

 Impact
I used to teach an intro stat course to about 80 psych majors.  They divided
into groups of 5 and each group was allowed to make up 5 questionnaire items.
The whole class then took the questionnaire (anonymously).  Each group got the
responses to its own 5 questions plus 5 general questions I had made up--age,
gender, GPA, etc.  Then each student had to write an individual paper analyzing
that data.  Papers were graded on both the correctness of the statistical
analysis and the interestingness of the paper.  Some were pretty interesting.
E.g. whether a student had tried illegal drugs was predicted by the number of
(legal) drugs in his parents' home but not by his parents' smoking, while the
reverse was true when the dependent variable was the student's use of
marijuana.  And students thought hard about statistical issues they had dozed
through when I just lectured on them.
--Dick Darlington, Psychology, Cornell#000#
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Newsgroups: bit.listserv.edstat-l
Date:         Mon, 6 Apr 1992 18:18:00 PDT
Reply-To:     "Peter A. Lachenbruch" <IARSPAL@UCLAMVS.BITNET>
From:         "Peter A. Lachenbruch" <IARSPAL@UCLAMVS.BITNET>
Subject:      transformations

April Janne asked why we use transformations. I just spent an hour with an
introductory class trying to explain this. My reasons were:

  1. The model we've got gives nonsense predictions - e.g. negative infant
mortality rates, etc.
  2. The variances are unequal at different x values.
  3. The residuals after removing the effects I want to are not normal.

I don't think of this as 'proving anything with statistics' but as helping to
explain features of the data.
Taking logarithms converts an additive model into a multiplicative one, etc.
Reciprocals are good when the data seem to be related to time to an event.
There's a lot of problems coming up with good examples of data sets which show
good behavior for transformations other than the log or reciprocal. Tukey had
one set of heights of volcanoes which looked like a square root worked well.
Rates often respond nicely to log transformations (because the numerators are
like Poisson variables ? I suspect)
Tony
Newsgroups: bit.listserv.edstat-l
Via: sun3.nsfnet-relay.ac.uk; Wed, 8 Apr 1992 17:02:05 +0100
Newsgroups: bit.listserv.edstat-l
Date:         Wed, 8 Apr 1992 17:03:07 BST
Reply-To:     "STATISTICS" <sta010@ABERDEEN.AC.UK>
From:         "STATISTICS" <sta010@ABERDEEN.AC.UK>
Subject:      Statistical Minorities
X-To:         EDSTAT-L@ncsuvm.cc.ncsu.edu

If we want to be statistical, doesn't everybody belong to a minority?
Aristocrats   (Laplace, Condorcet)
Knights       (RA Fisher)
But women- surely they are the majority?

David Kerridge
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Newsgroups: bit.listserv.edstat-l
Date:         Wed, 8 Apr 1992 19:11:40 EDT
Reply-To:     Lee <WOLFLE@VTVM1.BITNET>
From:         Lee <WOLFLE@VTVM1.BITNET>
Subject:      Overall F-test.

   With regard to April Milliken-Janne's inquiry about insignificant
overall F-tests, I would like to suggest something.  Although I am
not particularly conversant with ANOVA designs, I think I know a
little about regression models, and, after all, we are still talking
about the general linear model.
   Consider the following senario: We regress Y on X.  To determine
if the regression is significant, we devide the regression sum-of-
squares by 1 in order to calculate the mean-squares-regression.
It is significant.
   Now lets add nine more variables, none of which explain much
more than miniscule increments to the regression sum of squares.
But now we divide (essentially) the same sum of squares by 10
instead of 1.  Now the F-test is not significant.

   For those who know ANOVA designs much better than I do, please
don't flame me too badly.

                                    Lee Wolfle
                                    WOLFLE @ VTVM1
                                    wolfle@vtvm1.cc.vt.edu
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X-Mailer: ELM [version 2.3 PL11]
Newsgroups: bit.listserv.edstat-l
Date:         Thu, 9 Apr 1992 17:37:49 EDT
Reply-To:     "Dr. Robert Hayden" <hayden@OZ.PLYMOUTH.EDU>
From:         "Dr. Robert Hayden" <hayden@OZ.PLYMOUTH.EDU>
Subject:      women
X-To:         "edstat-l@ncsuvm.cc.ncsu.edu" <edstat-l@ncsuvm.cc.ncsu.edu>

Am I the only one who missed the ORIGINAL comment others are
responding to? Here are my responses.

1.

Gertrude Cox.

2.

One respondent provided references on the history of statistics in
general. Springer has 3 such books with titles chosen to disguise
their nature.

	W.S. Peters, Counting for Something
	J. Gani, The Craft of Probabilistic Modeling
	J. Gani, The Making of Statisticians

Peters is more a history of ideas than personalities. The Gani books
are collections of autobiographical papers by prominent statisticians.
Two books covering the years before 1900 are

	S. M. Stigler, The History of Statistics
	H. Westergaard, Contributions to the History of Statistics

I add this because someone thought such books relevant to the original
enquiry, and that same person asked that the list be extended. I am
NOT adding them because any of these had much to say about women!
(See 1, above.)
--


      _
     | |          Robert W. Hayden
     | |          Department of Mathematics
    /  |          Plymouth State College
   |   |          Plymouth, New Hampshire
   | * |          USA
  /    |          (603) 536-2497
 |     )          (603) 535-2489
 L_____/          hayden@oz.plymouth.edu
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