Working with Damaged Datasets in SAS

Real world data is never as neat and clean as we would like. With so many changes in the federal data landscape recently, methods for working with damaged data have become even more important. This presentation addresses situations where data aren't missing but they aren't the same as before. This includes novel heteroscedasticity, where the data becomes more variable than before due to changes in data collection, and data drift, where newer data doesn't look or act like earlier data due to changes in the underlying population. Practical working examples and source code in SAS are included.   

About the Presenter

David J Corliss, PhD has been working with SAS since 12693. He is a Principal Data Scientist at Grafham Analytics, a data science consultancy and is a frequent presentor and instructor at SAS conferences with expertise in time series data and methods, and ethical best practices in data, analytics, and AI. Dr. Corliss is the founder of Peace-Work (link: www.peace-work.org), a volunteer cooperative of statisticians, data scientists and other researchers applying analytics in issue-driven advocacy.