I'm hoping to analyze the influence of a set of variables on a continous dependent variable (between 0-1). The independent variables are a mix of both categorical, continuous and discrete features.
The dependent variable is not normally distributed:
In my eyes, this doesn't even qualify for a log-normal distribution?
However, zooming in on the interval from 0-0.01 the picture improves a bit:
The remaining interval (0.01-1.0) is distributed accordingly.
Log transforming the dependent variable in the entire interval yields:
What would be a sensible regression strategy in this case? Not all relationships between individual independent variables and the dependent variable are linear.
Would I better off defining a categorical outcome variable with e.g. 3 groups defined based on the log-transformation? Or should I look into non-linear regression analysis? Recently discovered GAMs...but with my lack of experience in regression in general, this seems a bit daunting.
Any guidance is immensely appreciated!