Statistical Analysis of Profile Monitoring

Statistical Analysis of Profile Monitoring
Author :
Publisher : John Wiley & Sons
Total Pages : 298
Release :
ISBN-10 : 9781118071977
ISBN-13 : 1118071972
Rating : 4/5 (972 Downloads)

Book Synopsis Statistical Analysis of Profile Monitoring by : Rassoul Noorossana

Download or read book Statistical Analysis of Profile Monitoring written by Rassoul Noorossana and published by John Wiley & Sons. This book was released on 2011-09-09 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: A one-of-a-kind presentation of the major achievements in statistical profile monitoring methods Statistical profile monitoring is an area of statistical quality control that is growing in significance for researchers and practitioners, specifically because of its range of applicability across various service and manufacturing settings. Comprised of contributions from renowned academicians and practitioners in the field, Statistical Analysis of Profile Monitoring presents the latest state-of-the-art research on the use of control charts to monitor process and product quality profiles. The book presents comprehensive coverage of profile monitoring definitions, techniques, models, and application examples, particularly in various areas of engineering and statistics. The book begins with an introduction to the concept of profile monitoring and its applications in practice. Subsequent chapters explore the fundamental concepts, methods, and issues related to statistical profile monitoring, with topics of coverage including: Simple and multiple linear profiles Binary response profiles Parametric and nonparametric nonlinear profiles Multivariate linear profiles monitoring Statistical process control for geometric specifications Correlation and autocorrelation in profiles Nonparametric profile monitoring Throughout the book, more than two dozen real-world case studies highlight the discussed topics along with innovative examples and applications of profile monitoring. Statistical Analysis of Profile Monitoring is an excellent book for courses on statistical quality control at the graduate level. It also serves as a valuable reference for quality engineers, researchers and anyone who works in monitoring and improving statistical processes.


Statistical Analysis of Profile Monitoring Related Books

Statistical Analysis of Profile Monitoring
Language: en
Pages: 298
Authors: Rassoul Noorossana
Categories: Technology & Engineering
Type: BOOK - Published: 2011-09-09 - Publisher: John Wiley & Sons

GET EBOOK

A one-of-a-kind presentation of the major achievements in statistical profile monitoring methods Statistical profile monitoring is an area of statistical qualit
Statistical Methods for Environmental Pollution Monitoring
Language: en
Pages: 354
Authors: Richard O. Gilbert
Categories: Technology & Engineering
Type: BOOK - Published: 1987-02-15 - Publisher: John Wiley & Sons

GET EBOOK

This book discusses a broad range of statistical design and analysis methods that are particularly well suited to pollution data. It explains key statistical te
Statistical Methods for Healthcare Performance Monitoring
Language: en
Pages: 292
Authors: Alex Bottle
Categories: Mathematics
Type: BOOK - Published: 2016-08-05 - Publisher: CRC Press

GET EBOOK

Healthcare is important to everyone, yet large variations in its quality have been well documented both between and within many countries. With demand and expen
Statistical Design, Monitoring, and Analysis of Clinical Trials
Language: en
Pages: 380
Authors: Weichung Joe Shih
Categories: Medical
Type: BOOK - Published: 2021-10-26 - Publisher: CRC Press

GET EBOOK

Statistical Design, Monitoring, and Analysis of Clinical Trials, Second Edition concentrates on the biostatistics component of clinical trials. This new edition
Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches
Language: en
Pages: 330
Authors: Fouzi Harrou
Categories: Technology & Engineering
Type: BOOK - Published: 2020-07-03 - Publisher: Elsevier

GET EBOOK

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the adva