Data defines the model by dint of genetic programming, producing the best decile table.

A Method for Moderating Outliers, Instead of Discarding Them
Bruce Ratner, Ph.D.

In statistics an outlier is an observation that lies outside the overall pattern of the rest of the data. Outliers can also occur when comparing relationships between two or more variables. Outliers of this type can be easily identified on a scatterplot. When performing regression modeling a single outlier will often render the resultant model misleading. Discarding outliers is a controversial practice frowned on by many statisticians and data analysts. While mathematical criteria provide an objective and quantitative method for data rejection, they do not make the practice more scientifically or methodologically sound. The purpose of this article is to present a method – the GenIQ Model© – for moderating outliers, instead of discarding them. GenIQ is especially useful for building ordinary least squares and logistic regression models. I illustrate GenIQ as a method for handling outliers with a simple, compelling power point presentation (PPT). 

Do not be diffident, make your request by email for the Method for Moderating Outliers, Instead of Discarding Them PPT. When you do, you will be different, having the know-how to moderate outliers, instead of discarding them.

Hope to hear from you!

For more information about this article, call Bruce Ratner at 516.791.3544 or 1 800 DM STAT-1; or e-mail at
Sign-up for a free GenIQ webcast: Click here.