🚀 go-pugleaf

In article <1992Oct29.163032.11967@egh-qc.uucp> pete@egh-qc.uucp (Pete Phillips) writes:

>I have recently read a paper where the authors carried out a
>"Multidimensional Space Analyses".

>Essentially the authors were comparing 6 different aspects of a
>product, and used a formula where they took the square root of the sum
>of various parameters squared.  A low index indicated a good product
>and a high index a bad product.

>Are there any good references on this, and is it a well recognised
>method of producing a single index figure from a number of different
>parameters ?

That is, the authors derived the euclidean distance of the product from an "
origin" in the space of the 6 product attributes.  It's just Pythagorus' 
theorem extended to 6 dimensions. (An obvious assumption in such an analysis 
is that the product attributes are orthogonal - otherwise they should have 
calculated Mahalanobis distances which take into account the correlations 
amongst attribute vectors.  Of course the attributes could be Principle 
Components derived from a larger number of attributes.)

Intuitively, a "small" index, or Euclidean distance, would require that the 
parameters were relatively small and/or of equal length.  Whether that 
constitutes a "good" product I've no idea.  But I'd be very interested to 
know of other opinions.

  - - - - - - - - -
 |      _--_|\     |   Hume Winzar
 |     /      \    |   Commerce School,
 |     *_.--._/    |   Murdoch University,
 |           v     |   Perth, Western Australia
  - - - - - - - - -  
                       E_Mail winzar@csuvax1.csu.murdoch.edu.au
                       Phone: (09) 310 7389
                       Fax:   (09) 310 5004

In article <winzar.93.0@newsman>, winzar@newsman (Hume Winzar) writes...
>That is, the authors derived the euclidean distance of the product from an "
>origin" in the space of the 6 product attributes.  It's just Pythagorus' 


So it would seem that closeness to the origin would indicate a
bad product, contrary to the remarks in the original query,
assuming that the six attributes were desirable.

In article <30OCT199207001549@amarna.gsfc.nasa.gov> packer@amarna.gsfc.nasa.gov (Charles Packer) writes:
>In article <winzar.93.0@newsman>, winzar@newsman (Hume Winzar) writes...
>>That is, the authors derived the euclidean distance of the product from an "
>>origin" in the space of the 6 product attributes.  It's just Pythagorus' 

>So it would seem that closeness to the origin would indicate a
>bad product, contrary to the remarks in the original query,
>assuming that the six attributes were desirable.

Yes, if we accept that more is better.  But a high index is more likely if 
one or two attribute scores are great while the others are small.  (Satisfy 
yourself of this by considering just two dimensions.)  So a "bad" product 
may be one which has inconsistent levels of attributes.  I guess we'd really 
have to know what the attributes were, and how they were supposed to go 
together.  Still it sounds like an interesting technique for snowing 
clients, helping them believe we can do magic, and helping them see what 
they want to see.  ... SIGH.

  - - - - - - - - -
 |      _--_|\     |   Hume Winzar
 |     /      \    |   Commerce School,
 |     *_.--._/    |   Murdoch University,
 |           v     |   Perth, Western Australia
  - - - - - - - - -  
                       E_Mail winzar@csuvax1.csu.murdoch.edu.au
                       Phone: (09) 310 7389
                       Fax:   (09) 310 5004