On May 31, 2013, at 17:10 , Stefano Sofia wrote:
> I find difficult to understand why in
> lm(log(Y) ~ X)
> Y is assumed lognormal.
> I know that if Y ~ N then Z=exp(Y) ~ LN, and that if Y ~ LN then Z=log(Y) ~ N.
> In
> lm(log(Y) ~ X)
> I assume Y ~ N(mu, sigma^2), and then exp(Y) would be distri
I find difficult to understand why in
lm(log(Y) ~ X)
Y is assumed lognormal.
I know that if Y ~ N then Z=exp(Y) ~ LN, and that if Y ~ LN then Z=log(Y) ~ N.
In
lm(log(Y) ~ X)
I assume Y ~ N(mu, sigma^2), and then exp(Y) would be distributed by a LN, not l
og(Y).
Where is my mistake?
Moreover, in
gl
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