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 output


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)


1 Answer 1


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

  • $\begingroup$ Thank you so much! $\endgroup$
    – Rabin KC
    Mar 4, 2022 at 2:44

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