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Ron S. Kenett

Researcher at Technion – Israel Institute of Technology

Publications -  248
Citations -  3477

Ron S. Kenett is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Information quality & Risk management. The author has an hindex of 29, co-authored 231 publications receiving 3086 citations. Previous affiliations of Ron S. Kenett include University of Turin & Binghamton University.

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Book

Encyclopedia of statistics in quality and reliability

TL;DR: Encyclopedia statistics in quality and reliability, Encyclopedia statistics inquality and reliability , مرکز فناوری اطلاعات و اشاوρزی .
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Quality 4.0—the challenging future of quality engineering

TL;DR: A framework for a quality discipline supporting the fourth industrial revolution is presented, proposed to call Quality 4.0 and it is claimed that these directions constitute a substantial basis to update the body of knowledge and practices of the quality profession.
Journal Article

Analysis of biochemical genetic data on Jewish populations: II. Results and interpretations of heterogeneity indices and distance measures with respect to standards.

TL;DR: The Ashkenazi, Sephardi and Iraqi Jewish populations are found to be consistently close in genetic constitution and distant from all the other populations, namely the Yemenite and Cochin Jews, the Arabs, and the non-Jewish German and Russian populations.
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Bayesian networks of customer satisfaction survey data

TL;DR: BNs offer advantages in implementing models of cause and effect over other statistical techniques designed primarily for testing hypotheses, and include the ability to conduct probabilistic inference for prediction and diagnostic purposes with an output that can be intuitively understood by managers.
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A Test for Detecting Outlying Cells in the Multinomial Distribution and Two-Way Contingency Tables

TL;DR: In this paper, a test based on the maximum adjusted residual from multinomial models, namely, the M test, is proposed, which can be used to test null hypotheses against one- or two-sided alternatives and to detect outliers simultaneously with the rejection of the null hypothesis.