Radford Neal wrote:
>
> In article <[EMAIL PROTECTED]>,
> Paige Miller <[EMAIL PROTECTED]> wrote:
>
> >There are so many different factors that go into the amount of medals
> >won that it seems silly to perform a regression based upon population
> >and GDP to use as predictors. Organization of Olympic Committees,
> >training facility quality, programs for youths, weather, etc. all can
> >affect the number of medals won, and then there is the factor of
> >injuries, which to me seems like it cannot be modelled except as
> >random noise.
>
> I presume that the people making such models are interested in whether
> or not the poor or good performance of a country might be due to
> controllable factors such as organization, training facilities, etc.
> In other words, they want to know if they could be doing better, given
> the resources available. So it makes perfect sense to include
> population and GDP as explanatory variables, but NOT type of
> organization of the Olympic Committee, or type of training facility
> used. However, the climate should indeed be included as an
> explanatory variable, if it is thought that it might be important.
> There will of course be random noise, though I'd think that many
> injury problems might be attributable to the training regime used, or
> to sending athletes to the games who shouldn't have been selected to
> go because of their injuries.
Hey Radford, why wouldn't you want to include "type of training
facility" in the model? If it is a useful predictor variable, then you
have a "controllable factor" -- in other words, it may tell you that
training facility type A results in more medals than type B, so your
country should start building facility type A and stop building
facility type B.
--
Paige Miller
Eastman Kodak Company
[EMAIL PROTECTED]
"It's nothing until I call it!" -- Bill Klem, NL Umpire
"Those black-eyed peas tasted all right to me" -- Dixie Chicks
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