I saw this sentence:
"I use log(income) partly because of skewness in this variable but also because income is better considered on a multiplicative rather than additive scale.
In other words, \$1,000 is worth a lot more to a poor person than a millionaire because \$1,000 is a much greater fraction of the poor person’s wealth"
on page 143 on this link http://www.biostat.jhsph.edu/~iruczins/teaching/jf/ch12.pdf.
But when I check their skewness using
library(e1071) in R (as seen below), I found out that the skewness of income is not that high or low. My question is how do I determine if I need to used log transformation in a regression model?
PS the chicago data is in
> skewness(chicago$race)  0.5570103 > skewness(chicago$race)  0.5570103 > skewness(chicago$fire)  1.271188 > skewness(chicago$theft)  2.955751 > skewness(chicago$age)  -0.9210877 > skewness(chicago$income)  1.155 > skewness(chicago$involact)  0.8079598