-Original Message-
Dennis Roberts makes a good point here
>i repeat ... the r value shows the extent to which a straight line (in a 2
>variable problem) can pass through a scatterplot and, be close TO the data
>points
>in that sense, r is an index value for the extent to which a straig
out.
Jay
[EMAIL PROTECTED] wrote:
> Classic study: Correlation between local stork population and local births.
>
> -Original Message-
> From: Stu [mailto:[EMAIL PROTECTED]]
> Sent: Thursday, December 06, 2001 1:08 AM
> To: [EMAIL PROTECTED]
> Subject: Re: When does co
Hi
On 6 Dec 2001, David Heiser wrote:
> Most of the focus is on structural equation modeling (SEM). For
> statisticians, a quick referral to Jim Steiger's article "Driving Fast in
> Reverse" in JASA March 2001, p331-p338 (if you have it around) is a quick
> discourse on SEM and the inherent probl
The issue of causality from the results of fitting a model to data has been
a major topic on SEMNET over the last many years.
If anyone wishes to pursue ideas on this and related issues, subscribe to
[EMAIL PROTECTED]
Most of the focus is on structural equation modeling (SEM). For
statisticians,
Classic study: Correlation between local stork population and local births.
-Original Message-
From: Stu [mailto:[EMAIL PROTECTED]]
Sent: Thursday, December 06, 2001 1:08 AM
To: [EMAIL PROTECTED]
Subject: Re: When does correlation imply causation?
> My favorite original example is
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Is it a fair interpretation of what you are saying to say that the
process of correlating phenomena needs to be distinguished from the
va
i repeat ... the r value shows the extent to which a straight line (in a 2
variable problem) can pass through a scatterplot and, be close TO the data
points
in that sense, r is an index value for the extent to which a straight line
MODEL fits the data ...
knowing how the dots on the scatterpl
Whether we can get causal inferences out of correlation and equations has
been a dispute between two camps:
For causation: Clark Glymour (Philosopher), Pearl (Computer scientist),
James Woodward (Philosopher)
Against: Nancy Cartwright (Economist and philosopher), David Freedman
(Mathematici
> My favorite original example is the correlation between number of
> annual murders in a city and number of books in its libraries.
> Students have no trouble seeing that the two are going to have a
> fairly high correlation coefficient(*), but murders don't make
> people read and books don't mak
Dennis Roberts <[EMAIL PROTECTED]> wrote in sci.stat.edu:
>personally, i think it is dangerous in ANY case to say that r = cause ...
Hear, hear!
My favorite original example is the correlation between number of
annual murders in a city and number of books in its libraries.
Students have no tro
On 5 Dec 2001 08:52:41 -0800, [EMAIL PROTECTED] (Dennis Roberts) wrote:
> correlation NEVER implies causation ...
That is true
- in the strong sense, and
- in formal logic, and
- as a famous quotation among researchers.
(And, reported as wrongly contrasted to 'ANOVA'.)
Or, correlation always
what if we see, from the side, one person on the right swing his/her fist
... and, as the fist arrives at what appears to be the impact point of the
face of a person on the left ... that IMMEDIATELY the person on the left
falls backwards and down
now, we do this over and over again ... and obs
perhaps the problem here is with the word ... "cause"
say i put in a column some temps in F ... then use the F to C formula ...
and get the corresponding C values ...
then i do an r between the two and find 1.0
now, is the formula the "cause" of the r of 1?
maybe we might see it as a cause bu
Dennis warns "the problem with this is ... does higher correlation mean MORE
cause? lower r mean LESS cause?
in what sense can think of cause being more or less? you HAVE to think that
way IF you want to use the r value AS an INDEX MEASURE of cause ..."
Dennis is not going to like this, since he
At 07:36 AM 12/5/01 -0500, Karl L. Wuensch wrote:
> Accordingly, I argue that correlation is a necessary but not a
> sufficient condition to make causal inferences with reasonable
> confidence. Also necessary is an appropriate method of data
> collection. To make such causal infer
correlation NEVER implies causation ...
and i agree with mike totally
At 09:01 AM 12/5/01 -0600, Mike Granaas wrote:
>We really need to emphasize over and over that it is the manner in which
>you collect the data and not the statistical technique that allows one to
>make causal inferences.
>
>M
On Wed, 5 Dec 2001, Karl L. Wuensch wrote:
>
>
> So why is it that many persons believe that one can make causal inferences
>with confidence from the results of two-group t tests and ANOVA but not with the
>results of correlation/regression techniques. I believe that this delusio
ows, but is long.
When Does Correlation Imply
Causation?
It is not rare for researchers and students to confuse (1) correlation as
a statistical technique with (2) nonexperimental data collection methods, which
are also often described as correlational. For example, a doctor
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