How to Handle Outliers in Your Data | Unilytics

The Challenge

A common request when analyzing large amounts of data is to evaluate the impact exceptional data has on results. Statistics addresses these needs by offering “median” and “average” when normalizing large numbers of data points.

Median selects a data point in the exact center of all data points to define the “normal” value and, as a result, is unaffected by exceptionally high or low data.

Average, also known as “mean”, on the other hand, sums all of the data points and divides by the number of data points to determine the “normal” value. Average is affected by exceptionally high or low data.

http://unilytics.com/handling-outliers-data/?utm_medium=Social+Media&utm_source=Linkedin&utm_campaign=thought+leadership&utm_content=:Handling+Data+Outliers&UC.cid=SMLITLBl-data-outliers&WT.mc_id=SMLITLBl-data-outliers

The following two tabs change content below.

Eric Axelrod

President & Chief Architect at DIGR
I have helped companies bring new data driven products to market, drive efficiency out of their supply chain, execute strategic plans, and drive top line and bottom line growth by enabling every business function with actionable analytics. I can transform a business which is lacking critical insight into an agile, strategic, data driven organization.

Leave a Reply