Why do u need 'significance'?

If one prediction interval is smaller than the other than its smaller and
thus more accurate. I would stop there.

However if u really need to get a p value for this difference I suppose u
could use an F test on the prediction variances? I'm not sure exactly what
your testing though and if this is valid, someone else may be able to
comment.

U may also want to test the predictive ability of the models overall,
perhaps using cross validation.

Chris Howden
Founding Partner
Tricky Solutions
Tricky Solutions 4 Tricky Problems
Evidence Based Strategic Development, IP Commercialisation and Innovation, Data
Analysis, Modelling and Training

(mobile) 0410 689 945
(fax / office)
chris@trickysolutions.com.au

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On 26/04/2012, at 20:09, Eiko Fried <torvon@gmail.com> wrote:

Thanks, Chris. But that leaves me with the same problem: what does the
difference in a prediction interval have to be to conclude that the
prediction interval x1 on y1 in model m1 is significantly better than for
x1 on y2 in model m2?


On 26 April 2012 00:48, Chris Howden <chris@trickysolutions.com.au> wrote:

> U could calculate prediction intervals.  The smaller the interval the
> 'better' the estimation.
>

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