🚀 go-pugleaf

sandell@chowning.CNMAT.Berkeley.EDU (Gregory J. Sandell) writes:


>Hello,

>I'm struggling with some results from an auditory perception experiment.   
>Basically, I have perceptual magnitude judgments for 120 sounds, and the  
>judgments are nicely explained by a regression on five acoustical  
>parameters of the sound stimuli.

>However, I'm not content to report merely that I had a sucessful  
>regression and name the five parameters.  I would like to characterize the  
>kinds of interactions that occur among the five parameters (let's call  
>them A, B, C, D and E) over the 120 trials (let's call this variable X).

>For example, there are an infinite number of values that could be plugged  
>into the regression equation to produce any value in the range of X.   
>Perhaps the results are such that the values for A, B and C could all be  
>zero and D and E could carry the whole wieght...or vice versa.  But the  
>fact is, there is only a small set of actual values for A,B,C,D and E  
>(120), and there is never any such case.  A meaningful question is, what  
>types of ratios of values for A,B,C,D,E exist in the actual data?  For  
>example, are there cases where B alone compensates for small values of A?   
>Or whenever C is small is it often true that A is small as well but B, D  
>and E are large?  Those kinds of questions.

>I would be interested in knowing about:

>1. methods for posthoc analysis of regression results

I am somewhat enamoured (sp?) of using residuals for such analysis. For
such analysis I have used the following article and some of its 
citations.
   "Diagnostic value of residual & partial residual plots"
   ER Mansfield & MD Conerly
   _The American Statistician_ _41_,2 (May 1987) pp107-116

>2. books on intuitive approaches to explaining regression results (note:   
 
Another personal favorite is a little off the beaten path.  The following
reference shows how to interpret & implement linguistic (fuzzy) 
interaction terms.
   "Fuzzy set analysis for behavioral & social sciences"
   Michael Smithson
   Springer-Verlag 1987  
-- 
Brian Schott/Decision Sciences Dept.             qmdbms@gsusgi2.gsu.edu
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