After properly transforming all the quantitative and qualitative variables and fixing any missing data, what's left is just to detect any possible outlier and to deal with it by removing it from the data or by imputing it as if it were a missing case.
An outlier, sometimes also referred to as an anomaly, is an observation that is very different from all the others you have observed so far. It can be viewed as an unusual case that stands out, and it could pop up due to a mistake (an erroneous value completely out of scale) or simply a value that occurred (rarely, but it occurred). Though understanding the origin of an outlier could help to fix the problem in the most appropriate way (an error could be legitimately removed; a rare case could be kept or capped or even imputed as a missing case), what is of utmost concern is the effect of one or more outliers on your regression analysis results. Any anomalous data in a regression analysis means a distortion of the regression's coefficients...