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© 2015

Quality Control with R

An ISO Standards Approach

Book

Part of the Use R! book series (USE R)

Table of contents

  1. Front Matter
    Pages i-xxviii
  2. Fundamentals

    1. Front Matter
      Pages 1-1
    2. Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba
      Pages 3-28
    3. Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba
      Pages 29-92
    4. Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba
      Pages 93-118
    5. Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba
      Pages 119-142
  3. Statistics for Quality Control

    1. Front Matter
      Pages 143-143
    2. Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba
      Pages 145-186
    3. Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba
      Pages 187-199
  4. Delimiting and Assessing Quality

    1. Front Matter
      Pages 201-201
    2. Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba
      Pages 203-220
    3. Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba
      Pages 221-236
  5. Control Charts

    1. Front Matter
      Pages 237-237
    2. Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba
      Pages 239-270
    3. Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba
      Pages 271-284
  6. Back Matter
    Pages 285-349

About this book

Introduction

Presenting a practitioner's guide to capabilities and best practices of quality control systems using the R programming language, this volume emphasizes accessibility and ease-of-use through detailed explanations of R code as well as standard statistical methodologies. In the interest of reaching the widest possible audience of quality-control professionals and statisticians, examples throughout are structured to simplify complex equations and data structures, and to demonstrate their applications to quality control processes, such as ISO standards. The volume balances its treatment of key aspects of quality control, statistics, and programming in R, making the text accessible to beginners and expert quality control professionals alike. Several appendices serve as useful references for ISO standards and common tasks performed while applying quality control with R.
                                                                                     












Keywords

ISO standards R software quality control quality control with R statistical data modeling

Authors and affiliations

  1. 1.Department of Statistics and Operations Campus de FuenlabradaRey Juan Carlos UniversityMadridSpain
  2. 2.Department of Statistics and Operations Campus de FuenlabradaRey Juan Carlos UniversityMadridSpain
  3. 3.ENUSA Industrias AvanzadasMadridSpain

About the authors

Emilio L. Cano is Adjunct Lecturer at the University of Castilla-La Mancha and Research Assistant Professor at Rey Juan Carlos University. He also collaborates with the Spanish Association for Quality as trainer for in-company courses. He has more than 14 years of experience in the private sector as statistician.

Javier M. Moguerza is Associate Professor in Statistics and Operations Research at University Rey Juan Carlos. He publishes mainly in the fields of Mathematical Programming and Machine Learning. Currently, he is leading national and international research ICT projects funded by public and private organizations. He belongs to the Global Young Academy, and has been a member since 2010.

Mariano Prieto Corcoba is Continuous Improvement Manager at ENUSA Industrias Avanzadas. He has 30 years of experience in the fields of nuclear engineering and quality. He collaborates with the Spanish Association for Quality as a trainer in Six Sigma Methodology. Currently he is President of the Subcommittee of Statistical Methods of AENOR, the Spanish Association for Standardization and Certification.
                                          






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