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Über dieses Buch

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.














Inhaltsverzeichnis

Frontmatter

Fundamentals

Frontmatter

Chapter 1. An Intuitive Introduction to Quality Control with R

Abstract
This chapter introduces Quality Control by means of an intuitive example. Furthermore, that example is used to illustrate how to use the R statistical software and programming language for Quality Control. A description of R outlining its advantages is also included in this chapter, all in all paving the way to further investigation throughout the book.
Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba

Chapter 2. An Introduction to R for Quality Control

Abstract
This chapter introduces R as statistical software and programming language for quality control. The chapter is organized as a kind of tutorial with lots of examples ready to be run by the reader. Moreover, the code is available at the book’s companion website. Even though the RStudio interface is also introduced in the chapter, any other user interface can be used, including the R default GUI and code editor.
Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba

Chapter 3. The Seven Quality Control Tools in a Nutshell: R and ISO Approaches

Abstract
The aim of this chapter is to smoothly introduce the reader to Quality Control techniques from the so-called Seven Basic Quality Control tools: Cause-and-effect-diagram, check sheet, control chart, histogram, Pareto chart, scatter diagram, and stratification. These are basic but powerful tools when used wisely.
Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba

Chapter 4. R and the ISO Standards for Quality Control

Abstract
This chapter details the way ISO international standards for quality control are developed. Quality Control starts with Quality, and standardization is crucial to deliver products and services where quality satisfies final users, whatever they are customers, organizations, or public bodies. The development process, carried out by Technical Committees (TCs), entails a kind of path until the standard is finally adopted, including several types of intermediate deliverables. The work of such TCs is outlined along with the general structure of ISO, and with a focus on the TC in charge of statistical methods. Finally, the current and potential role that R can play, not only as statistical software, but also as programming language, is shown.
Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba

Statistics for Quality Control

Frontmatter

Chapter 5. Modelling Quality with R

Abstract
This chapter provides the necessary background to understand the fundamental ideas of descriptive and inferential statistics. In particular, the basic ideas and tools used in the description both graphical and numerical, of the inherent variability always present in real world are described. Additionally, some of the most usual statistical distributions used in quality control, for both the discrete and the continuous domains are introduced. Finally, the very important topic of statistical inference contains many examples of specific applications of R to solve these problems. The chapter also summarizes a selection of the ISO standards available to help users in the practice of descriptive and inferential statistic problems.
Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba

Chapter 6. Data Sampling for Quality Control with R

Abstract
Statistical Quality Control tries to predict the behavior of a given process through the collection of a subset of data coming from the performance of the process. This chapter showcases the importance of sampling and describes the most important techniques used to draw representative samples. An example using R on how to plot Operating Characteristic (OC) curves and its application to determine the sample size of groups within a sampling process is shown. Finally, the ISO Standards related to sampling are summarized.
Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba

Delimiting and Assessing Quality

Frontmatter

Chapter 7. Acceptance Sampling with R

Abstract
Undoubtedly, an effective but expensive way of providing conforming items to a customer is making a complete inspection of all items before shipping. In an ideal situation, a process designed to assure zero defects would not need inspection at all. In practice, a compromise between these two extremes is attained, and acceptance sampling is the quality control technique that allows reducing the level of inspection according to the process performance. This chapter shows how to apply acceptance sampling using R and the related ISO standards.
Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba

Chapter 8. Quality Specifications and Process Capability Analysis with R

Abstract
In order to assess quality, specification limits are to be established. In this chapter a method to set specification limits taking into account customers’ and producer’s loss is presented. Furthermore, the specification limits are the voice of the customer, and quality can be assessed by comparing it with the voice of the process, that is, its natural limits. Capability indices and the study of long- and short-term variability do the job.
Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba

Control Charts

Frontmatter

Chapter 9. Control Charts with R

Abstract
Control charts constitute a basic tool in statistical process control. This chapter develops the fundamentals of the most commonly applied control charts. Although the general basic ideas of control charts are common, two main different classes are to be considered: control charts for variables, where continuous characteristics are monitored; and control charts for attributes, where discrete variables are monitored. In addition, as a special type of control charts, time weighed charts are also outlined in the chapter. Finally, to guide users in the practice of control charts, a selection of the available ISO standards is provided.
Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba

Chapter 10. Nonlinear Profiles with R

Abstract
In many situations, processes are often represented by a function that involves a response variable and a number of predictive variables. In this chapter, we show how to treat data whose relation between the predictive and response variables is nonlinear and, as a consequence, cannot be adequately represented by a linear model. This kind of data are known as nonlinear profiles. Our aim is to show how to build nonlinear control limits and a baseline prototype using a set of observed in-control profiles. Using R, we show how to afford situations in which nonlinear profiles arise and how to plot easy-to-use nonlinear control charts.
Emilio L. Cano, Javier M. Moguerza, Mariano Prieto Corcoba

Backmatter

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