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.

Autorentext
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.

Inhalt
An Intuitive Introduction to Quality Control with R.- An Introduction to R for Quality Control.- The Seven Quality Control Tools in a Nutshell: R and ISO Approaches.- R and ISO Standards for Quality Control.- Modeling Quality with R.- Data Sampling for Quality Control with R.- Acceptance Sampling with R.- Quality Specifications and Process Capability Analysis with R.- Control Charts with R.- Nonlinear Profiles with R.