On 26 Mar 2003 at 10:55, Rich Ulrich wrote:

> On Wed, 26 Mar 2003 13:18:17 +0100, "david" <[EMAIL PROTECTED]> wrote:
> 
> > Hello
> > I would like to know a measure for testing the binormality of a data set.
> > thanks in advanced
> 
> I don't use the term, binormal.  
> And Google turns up something concerning topology,
> which is not a usual topic in these groups.
> 
> Are you referring to bivariate normality?

What is the wromg with the word binormal?

> 
> If so -- the straight, simple approach, to start, is 
> to test the variables separately, and combine tests,
> preserving power or alpha, depending on which matters
> to you.
> 
> Bivariate normality implies that the linear combinations
> of the two should also be normal.  The obvious 
> combinations are the principal components:  form
> them and test them; and combine tests.

Could it be an idea here to look into exploratory projection
pursuit, which is often looking for the least normal linear 
conbination?

Kjetil Halvorsen

> 
> There are various tests for 'normality.'  
> How the data are generated could suggest the fashion
> in which non-normality is apt to arise.  If the variables
> are two versions of the same measurement, there 
> might be special salience to simple difference score.
> 
> If you have a particular need -- symmetry, continuity
> (lack of ties), absence of outliers, presence of 
> exponential tails, infinite theoretical range --
> you should select or design a test to meet that need.  
> 
> If you have a particular reason to suspect deviations
> that you can characterize -- and they matter -- you 
> should test for those deviations.  
> 
>  - "goodness of fit" depends on what aspects you 
> want to fit, so there is never a single test for something
> like  "normality."
> 
> Hope this helps.
> -- 
> Rich Ulrich, [EMAIL PROTECTED]
> http://www.pitt.edu/~wpilib/index.html
> .
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