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 library(faraway)
> skewness(chicago$race)
[1] 0.5570103
> skewness(chicago$race)
[1] 0.5570103
> skewness(chicago$fire)
[1] 1.271188
> skewness(chicago$theft)
[1] 2.955751
> skewness(chicago$age)
[1] -0.9210877
> skewness(chicago$income)
[1] 1.155
> skewness(chicago$involact)
[1] 0.8079598