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I have been looking for this kind of stuff on the internet for a while and I cannot find any answer.

In the classical linear regression (without weights), one can compute the standard deviation and derive a prediction interval (+/- 1.96*sd) but I cannot figure out how to deal with it when used with weights. If I choose very low weights I will have a very tight interval which is not logical. However I want to take into account the fact that the model is improving because I put bigger weights on last values.

We have : σ^2 = 1/(n-p) Sum(w[i] * R[i]^2) (taken from R documentation).

I told myself that I should replace "n" by the sum of weights to make something more meaningful but what about "p"?

I am a bit confused about this stuff,

Thanks a lot if you know something about it..

Loïc

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