When dealing with practical problems, engineers and analysts often encounter a large amount of abnormal data "lurking" in large data tables and not easily detected, or they don't know how to deal with such tricky data. For example, data from suppliers, unsupervised data (such as clinical trial data), etc. Explore Pattern is a new data processing platform in JMP15. It reviews the integrity of data by finding abnormal patterns in the data. For example: repeated data, random numbers, linear relationships between columns, specification limit matching and distribution. Laura said that these abnormal data are often generated by the following four situations: copying and pasting, applying random variables to generate data, using formulas between columns to create linear relationships, and changing or intercepting data outside the specification limits. She also demonstrated how to identify these abnormal data in JMP based on these four types of abnormal data.
