# How can I identify and remove outliers in R

I am performing regression analysis on prices of product that we have purchased, based on size and other attributes.

However there are often buys in odd circumstances which factor into the price, that is not (and cannot be) addressed directly in the features of the analysis.

Each time I run a regression, I will check the 20 with the largest error manually, and 90%+ of the time they will be odd buys like mentioned before, and for my purposes can be completely ignored.

I have been looking into cooks distance to remove these, however I'm not sure how to best set the threshold, or if there is a better method to use.

You should use robust regression e.g. lmrob from R-package robustbase.