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Statistical disclosure control (SDC), also known as statistical disclosure limitation (SDL) or disclosure avoidance, is a technique used in data-driven research to ensure no person or organization is identifiable from the results of an analysis of survey or administrative data, or in the release of microdata.
d-separation; D/M/1 queue; D'Agostino's K-squared test; Dagum distribution; DAP – open source software; Data analysis; Data assimilation; Data binning; Data classification (business intelligence)
In the U.S., public disclosure of an invention results in the loss of patentability of the invention after a period of one year. [3]35 U.S.C. § 102 establishes various statutory bars to invention patentability with regard to invention novelty; these explicit bars preclude patentability as exceptions to a general underlying entitlement.
Many examples and problems come from business and economics. Importance: Greatly extended the scope of applied Bayesian statistics by using conjugate priors for exponential families. Extensive treatment of sequential decision making, for example mining decisions. For many years, it was required for all doctoral students at Harvard Business School.
This is a list of important publications in data science, generally organized by order of use in a data analysis workflow.. Whole game of data science. See the list of important publications in statistics for more research-based and fundamental publications; while this list is more applied, business oriented, and cross-disciplinary.
In statistics, identifiability is a property which a model must satisfy for precise inference to be possible. A model is identifiable if it is theoretically possible to learn the true values of this model's underlying parameters after obtaining an infinite number of observations from it.
For example, if a pharmaceutical company wishes to explore the effect of a medication on the common cold but the data sample only includes men, any conclusions made from that data will be biased towards how the medication affects men rather than people in general. That means the information would be incomplete and not useful for deciding if the ...
For example, by truncating the bottom of a line or bar chart so that differences seem larger than they are. Or, by representing one-dimensional quantities on a pictogram by two- or three-dimensional objects to compare their sizes so that the reader forgets that the images do not scale the same way the quantities do.