as a general strategy ... you apply both models to the observed data ...
look at the (squared) residuals of the fits to the real data points ... and
see which model produces the smaller amount of squared error ...
sometimes this is rather obvious if you look at the data ... for example,
what if you have a relationship graph ... X on the baseline and Y on the
vertical ... and it has a curvilinear look to it ... kind of like a banana
plot ...
you could try fitting straight line to the data ... find the squared
residuals ... the go to a fancier exponential equation ... find the squared
residuals ... and we would see in this case that the fancier model produces
smaller errors, on average ...
now, this does not give you the BEST model perhaps but, it is the strategy
one uses (iterating) to converge on what seems to be the best you (model)
can do
At 05:53 PM 10/19/00 +0900, Choi, Young Sung wrote:
>I am a statistically poor researcher and have a statistical problem.
>
>I have two candidate distributions, A(theta1) and B(theta1, theta2) to model
>my data.
>Then how should I determine the best distribution for my data?
>Suggest me an easy book that explain how to select a distribution when
>making a probability model and how to test the goodness of the selected
>distribution over other ones.
>
>Thanks in advance.
>
>
>
>
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