Interpreting log-transformed variables in linear regression

Statisticians love variable transformations. log-em, square-em, square-root-em, or even use the all-encompassing Box-Cox transformation, and voilla: you get variables that are “better behaved”. Good behavior to statistician parents means things like kids with normal behavior (=normally distributed) and stable variance. Transformations are often used in order to be able to use popular tools such as linear regression, where the underlying assumptions require “well-behaved” variables. Moving into the world of business, one transformation is more than just a “statistical technicality”: the log transform. It turns out that taking a log function of the inputs (X’s) and/or output (Y) variables in linear … Continue reading Interpreting log-transformed variables in linear regression