Why would you log transform a variable?
The Why: Logarithmic transformation is a convenient means of transforming a highly skewed variable into a more normalized dataset. Using the logarithm of one or more variables improves the fit of the model by transforming the distribution of the features to a more normally-shaped bell curve.
What do you achieve when you apply a log transformation to a variable in your data set?
The log transformation is, arguably, the most popular among the different types of transformations used to transform skewed data to approximately 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.
How to interpret log transformations in a linear model?
OK, you ran a regression/fit a linear model and some of your variables are log-transformed. Only the dependent/response variable is log-transformed. Exponentiate the coefficient, subtract one from this number, and multiply by 100. This gives the percent increase (or decrease) in the response for every one-unit increase in the independent variable.
Why do you log independent variables in linear regression?
The reason for logging the variable will determine whether you want to log the independent variable (s), dependent or both. To be clear throughout I’m talking about taking the natural logarithm. Firstly, to improve model fit as other posters have noted.
Which is the only variable that is log transformed?
Only the dependent/response variable is log-transformed. Exponentiate the coefficient, subtract one from this number, and multiply by 100. This gives the percent increase (or decrease) in the response for every one-unit increase in the independent variable.
Why do you take the natural logarithm in linear regression?
To be clear throughout I’m talking about taking the natural logarithm. Firstly, to improve model fit as other posters have noted. For instance if your residuals aren’t normally distributed then taking the logarithm of a skewed variable may improve the fit by altering the scale and making the variable more “normally” distributed.