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Track: Data Mining

Building Statistical Models with Missing Data

Monday, April 12, 3:35-4:15pm EDT

Though businesses are filled with massive amounts of data, it’s more common than not that a dataset is replete with missing data. This provides an interesting challenge in the process of building a descriptive or predictive model. The presenter will illustrate a variety of statistical techniques designed to handle this situation minimizing the negative impact that the missing information has on the final model.

Kevin Potcner image

Kevin Potcner

Kevin Potcner

Academic Ambassador for JMP Statistical Discovery Software from SAS

Kevin Potcner is an Academic Ambassador for JMP Statistical Discovery Software from SAS. He has 25+ years industry experience applying the statistical sciences across a wide range of industries including medical device, pharmaceutical, biotech, food & beverage, consumer goods, automotive, energy, financial services, among others. Kevin has held faculty positions at The Rochester Institute of Technology, University of Florida, University of San Francisco and is currently an instructor for California State University Data Science program. He holds a BS in Printing Sciences and an MS in Applied Statistics both from The Rochester Institute of Technology.