Should anyone care to plot up the data, I hazard
that it will be found that the number of medals is
well described by a Poisson with a different mean
for each country during some nominal interval of
years. It may be that such means are explainable
in terms of factors, but the fluctuations from
year to year are not.
Radford Neal wrote:
>
> >Radford Neal wrote:
> >> 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.
>
> Paige Miller <[EMAIL PROTECTED]> wrote:
> >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.
>
> Well, this depends partly on what you're going to look at after you
> fit your model. If - as in the original post - you're just going to
> look at the residuals and say "country A underperformed - they must be
> doing something wrong", then you don't want to include controllable
> variables in the regression. It would, for example, be silly to say
> "on the other hand, country B did better than expected, no need to
> change anything there", if what the positive residual for country B is
> actually saying is that country B did better than expected given that
> they are using a bad training regime, have a disorganized Olympic
> Committee, and conscript winning athletes into the army for ten years.
>
> On the other hand, if you are going to look at the regression
> coefficients too, then you might hope to conclude things about which
> training facilities are best, etc. However, you will have the usual
> problems in trying to make causal conclusions from observational data.
> You will also have the problem that some training facilities cost more
> than others. It's no use telling the Ugandans that they would have
> won more medals if only they'd had a sophisticated training facility
> that they can't afford.
>
> Radford Neal
--
Bob Wheeler --- (Reply to: [EMAIL PROTECTED])
ECHIP, Inc.
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