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A linear regression on dependent and predictor variable was run on simulated data after log transformation.
x <- rnorm(423, mean = 55, sd = 12)
y <- rnorm(423, mean = 1.44, sd = 0.3)
dat <- as.data.frame(cbind(x,y))
mod <- lm (log(y)~log (x), data = dat)
summary(mod)
Question:
Is the x intercept in this summary log 0.186 or 0.186? The slope estimate I think is 0.0424. Can this model written as follows::
ln (y) = ln 0.186 + 0.0424 * log (x)
The intercept and slope are as stated in the R output. R is not trying to trick you! The fitted model is
log(y) = 0.186 + 0.0424 * log(x)
On the unlogged scaled, the fitted model is
y = exp(0.186) * x^0.0424
