# Regression technque to use for continuous data behaving like ordinal

I am trying to create a model to explain/predict fulfillment ratio of a product by a store i.e orders placed divided by orders delivered.The QQ-plot of the fulfillment ratio is:

The QQ-plot of the predictor variables are:

I tried linear regression once but that is not of much use since the spread is not really continuous or normal. Variable transformation is not also working as a large percentage of the observation is concentrated at the extremes. Any suggestion on which regression technique should i use for predicting fulfillment ratio?

I was considering categorizing the the predictors and dependent variable to convert them from numerical to ordinal form, and then use linear regression. Will that help?

Any suggestion to solve this is most welcome. I cannot share my dataset due to data privacy issues otherwise i would have.

• See the chapter on using ordinal regression for continuous $Y$ in my course notes at biostat.mc.vanderbilt.edu/rms – Frank Harrell May 12 '16 at 14:13