Hi, folks,
x=1:10
y=rep(2:6,2)
lin=lm(y~x)
x=3:12
new=predict(lin,se.fit=T)
#se.fit: the standard error of the predicted means, namely, the square root
of Var( E[y|x] | x)
# How can I generate the variances of the new observations? Namely the
square root of var(y|x),
## Which I think should be much larger than the values from se.fit=T.
The reason why I need to know the estimations of expectation and also
variance is that Y=ln(Z) and I need to know the expectation of Z.
Thanks
Yi
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