The usual assumption in regression is that noise is iid, that is has the same
distribution except for a location shift at all values of x. Prediction intervals
adopting this premise are based on an average of the noise over the whole sample
and thus produce the result you see in your figure.
Your
Ronnen Levinson wrote:
(I'm reposting this message because the original has not appeared after
about 2 days. Sorry if it shows up twice.)
Hello.
First, thanks to those who responded to my recent inquiry about using
contour() over arbitrary (x,y) by mentioning the interp() function in
the akima
(I'm reposting this message because the original has not appeared after
about 2 days. Sorry if it shows up twice.)
Hello.
First, thanks to those who responded to my recent inquiry about using
contour() over arbitrary (x,y) by mentioning the interp() function in
the akima package. That worked n
