Multivariate Statistical Methods In Quality Management

Multivariate statistical methods are an essential component of quality engineering data analysis. This monograph provides a solid background in multivariate statistical fundamentals and details key multivariate statistical methods, including simple multivariate data graphical display and multivariate data stratification. * Graphical multivariate data display * Multivariate regression and path analysis * Multivariate process control charts * Six sigma and multivariate statistical methods

​​​​​The intensive use of automatic data acquisition system and the use of cloud computing for process monitoring have led to an increased occurrence of industrial processes that utilize statistical process control and capability analysis. These analyses are performed almost exclusively with multivariate methodologies. The aim of this Brief is to present the most important MSQC techniques developed in R language. The book is divided into two parts. The first part contains the basic R elements, an introduction to statistical procedures, and the main aspects related to Statistical Quality Control (SQC). The second part covers the construction of multivariate control charts, the calculation of Multivariate Capability Indices.

Die Datenanalyse mit mehreren Variablen gehört zum Alltag der Chemometrie. Die Autoren sind ausgezeichnete Spezialisten auf dem Gebiet der multivariaten Analyse. Sie geben hier in saloppem Stil und aufgelockert durch zahlreiche witzige Cartoons eine verständliche Einführung in dieses Thema. Erläutert werden eine Reihe von Anwendungen bei der Qualitätskontrolle, insbesondere in der Nahrungsmittelindustrie. Mit zahlreichen Kontrollfragen und Übungsaufgaben!

Provides a theoretical foundation as well as practical tools for the analysis of multivariate data, using case studies and MINITAB computer macros to illustrate basic and advanced quality control methods. This work offers an approach to quality control that relies on statistical tolerance regions, and discusses computer graphic analysis highlightin

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In the last decades, the production of goods and the offer of services have become quite complex activities mostly because of the markets globalisation, of the continuous push to the innovation and of the constant requests from more and more demanding markets. The main objective of a company system has become the achievement of the quality for the business management cycle. This cycle goes from the design (Plan) to the production (Do), from the control (Check) to the man agement (Action), as well as to the marketing and distribution. Nowadays, the Total Quality of the company system is evaluated, according to the ISO 9000 regulations, in terms of its capacity to adjust the design and the pro duction to the needs expressed (explicitly or implictly) by the final users of a product/service. In this process, the use of statistical techniques is essential not only in the classical approach of Quality Control of a product but also, and most importantly, in the Quality Design oriented to the satisfaction of customers. Thus, Total Quality refers to the global capacity of a company to fit its system to the real needs of its customers by designing products which are able to match the customers' taste and by implementing a statistical control of both the product and the Customer Satisfaction. In such a process of design and evaluation, several statistical variables are involved and with a different nature (numerical, categorical, ordinal).

Multivariate statistical methods are an essential component of quality engineering data analysis. This monograph provides a solid background in multivariate statistical fundamentals and details key multivariate statistical methods, including simple multivariate data graphical display and multivariate data stratification. * Graphical multivariate data display * Multivariate regression and path analysis * Multivariate process control charts * Six sigma and multivariate statistical methods

The book focuses on problem solving for practitioners and model building for academicians under multivariate situations. This book helps readers in understanding the issues, such as knowing variability, extracting patterns, building relationships, and making objective decisions. A large number of multivariate statistical models are covered in the book. The readers will learn how a practical problem can be converted to a statistical problem and how the statistical solution can be interpreted as a practical solution. Key features: Links data generation process with statistical distributions in multivariate domain Provides step by step procedure for estimating parameters of developed models Provides blueprint for data driven decision making Includes practical examples and case studies relevant for intended audiences The book will help everyone involved in data driven problem solving, modeling and decision making.