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How is the natural log transformation used in Stata?
The natural log transformation is often used to model nonnegative, skewed dependent variables such as wages or cholesterol. We simply transform the dependent variable and fit linear regression models like this:
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.
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 we use logs in regression analysis?
In regression analysis the logs of variables are routinely taken, not necessarily for achieving a normal distribution of the predictors and/or the dependent variable but for interpretability.
How to interpret Stata models for log cholesterol?
Unfortunately, the predictions from our model are on a log scale, and most of us have trouble thinking in terms of log wages or log cholesterol. Below, I show you how to use Stata’s margins command to interpret results from these models in the original scale.
Can a prediction interval be transformed back to the log scale?
If it has the nominal coverage on the log scale it will have the same coverage back on the original scale, because of the monotonicity of the transformation. A prediction interval for a future observation also transforms just fine. An interval for a mean on the log scale will not generally be a suitable interval for the mean on the original scale.
How to interpret the results of a Stata model?
The c. prefix tells Stata to treat tenure as a continuous variable, and the ## operator tells Stata to include the main effect ( tenure) and the interaction ( tenure * tenure ). Now we can use margins to help us interpret the results of our model.