Some data analysis methods typically used in econometric studies and in ecology have been evaluated and applied in physics software environments. They concern the evolution of observables through objective identification of change points and trends, and measurements of inequality, diversity and evenness across a data set. Within each one of these analysis areas, several statistical tests and measures have been examined, often comparing multiple implementations of the same algorithm available in R or developed by us.
The presentation will introduce the analysis methods and the details of their statistical formulation, and will review their relation with information theory concepts, such as Shannon entropy. It will report the results of their use in two real-life scenarios, which pertain to diverse application domains: the validation of simulation models and the quantification of software quality. It will discuss the lessons learned, highlighting the capabilities and shortcomings identified in this pioneering study.
|Secondary Keyword (Optional)||Experience/plans from outside experimental HEP/NP|
|Primary Keyword (Mandatory)||Analysis tools and techniques|