Six Sigma with R: Statistical Engineering for Process Improvement (Use R!, 36)
Cano, Emilio L. L.; Martinez Moguerza, Javier; Redchuk, Andrés
ISBN 10: 1461436516 ISBN 13: 9781461436515
Edité par Springer, 2012
Neuf(s)
Couverture souple
Vendeur
Ria Christie Collections, Uxbridge, Royaume-Uni
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A propos de cet article
Description :
In. N° de réf. du vendeur ria9781461436515_new
Synopsis :
Six Sigma has arisen in the last two decades as a breakthrough Quality Management Methodology. With Six Sigma, we are solving problems and improving processes using as a basis one of the most powerful tools of human development: the scientific method. For the analysis of data, Six Sigma requires the use of statistical software, being R an Open Source option that fulfills this requirement. R is a software system that includes a programming language widely used in academic and research departments. Nowadays, it is becoming a real alternative within corporate environments.
The aim of this book is to show how R can be used as the software tool in the development of Six Sigma projects. The book includes a gentle introduction to Six Sigma and a variety of examples showing how to use R within real situations. It has been conceived as a self contained piece. Therefore, it is addressed not only to Six Sigma practitioners, but also to professionals trying to initiate themselves in this management methodology. The book may be used as a text book as well.
À propos de l'auteur:
Emilio L. Cano is Adjunct Lecturer at the Department of Mathematics at University of Castilla-La Mancha and Research Assistant Professor at the Department of Statistics and Operations Research at University Rey Juan Carlos.
Javier M. Moguerza is Associate Professor in Statistics and Operations Research at University Rey Juan Carlos and member of the Global Young Academy.
Andres Redchuk is Master Black Belt and Research Assistant Professor at the Department of Statistics and Operations Research at University Rey Juan Carlos.
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Détails bibliographiques
Titre : Six Sigma with R: Statistical Engineering ...
Éditeur : Springer
Date d'édition : 2012
Reliure : Couverture souple
Etat : New