# How to transform continuous data with extreme bimodal distribution

Is there a way to transform a continuous predictor variable (grant) that has a bimodal distribution into a normal distribution (see density plot below)? I have tried log(x+c), z-score and inverse transformation methods yet I can't shake off this extreme bimodal distribution.

Should I consider treating the continuous variable as categorical?

The sample size of the dataset is 3,000 and the response variable is dichotomous.  • Two spikes will remain two spikes with any monotonic transformation. The good news is that regression does not require any marginal distribution to be normal. The bad news is that regression may not be a good method if the response variable has this kind of distribution. But why do you call this dichotomous? Dichotomous is not another word for bimodal. It means two, and only two, distinct values. Nov 14, 2014 at 18:24
• My outcome is dichotomous/binary with the following frequency 74% and 26% for "no" and "yes" respectively. I also have 36 potential predictor variables about 90% of these variable are categorical. Nov 14, 2014 at 19:26
• Thanks for clarifying: this variable is not the response. (I guess @user777 like me thought that it was.) I would worry less about it then. Just proceed carefully and use lots of graphics to watch for side-effects. Nov 14, 2014 at 20:09
• oh, okay. Should I forget about transforming and categorize it? I would like to perform cluster analysis on the data set. I have also included another distribution plot above based on stats.stackexchange.com/questions/25568/…. Nov 14, 2014 at 20:30
• Incidentally, the fact the people don't (usually!) try to convert binary predictors to normal underlines that normality of predictors is not required. Nov 17, 2014 at 10:39

1) There's no way to transform a discrete random variable to be continuous. If it takes $k$ distinct values, no transformation will leave you with more than $k$ distinct values.