Isolating and removing sources of variation in test data

We describe new methods of data analysis that identify and isolate sources of variation in production test data based on its natural taxonomy. We also describe an alternative approach to data analysis that integrates disparate data by removing each primary source of variation. This new approach has several advantages compared to traditional "divide and conquer" techniques (e.g. ANOVA). For example, the new approach enables us to identify secondary sources of variation that are normally obscured by the primary source. The methods are demonstrated on a die-speed measurement collected from seventy lots of two products produced at two fabrication facilities.