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Why do we use log transformations when we perform transformation of variables?
2.1. Using the log transformation to make data conform to normality. If the original data follows a log-normal distribution or approximately so, then the log-transformed data follows a normal or near normal distribution. In this case, the log-transformation does remove or reduce skewness.
Do I need to log transform data?
The reason for log transforming your data is not to deal with skewness or to get closer to a normal distribution; that’s rarely what we care about. Validity, additivity, and linearity are typically much more important.
When do you use log transformation in regression?
A log transformation in a left-skewed distribution will tend to make it even more left skew, for the same reason it often makes a right skew one more symmetric. It will only achieve to pull the values above the median in even more tightly, and stretching things below the median down even harder. In that cases power transformation can be of help.
Why do you need to log transform your data?
The reason for log transforming your data is not to deal with skewness or to get closer to a normal distribution; that’s rarely what we care about. Validity, additivity, and linearity are typically much more important. The reason for log transformation is in many settings it should make additive and linear models make more sense.
Which is an example of a log transform?
A multiplicative model on the original scale corresponds to an additive model on the log scale. For example, a treatment that increases prices by 2%, rather than a treatment that increases prices by $20. The log transformation is particularly relevant when the data vary a lot on the relative scale.
Can a log transformation make data normally distributed?
Well, at least with OLS and other related estimators. If your data are log-normally distributed, then the log transformation makes them normally distributed. Normally distributed data have lots going for them. Statisticians generally find economists over-enthusiastic about this particular transformation of the data.