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E-grāmata: Quality Control with R: An ISO Standards Approach

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  • Formāts: PDF+DRM
  • Sērija : Use R!
  • Izdošanas datums: 20-Nov-2015
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319240466
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Quality Control with R: An ISO Standards ApproachPalielināt
  • Formāts: PDF+DRM
  • Sērija : Use R!
  • Izdošanas datums: 20-Nov-2015
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319240466
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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.

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.

Recenzijas

                                                                   

Part I Fundamentals
1 An Intuitive Introduction to Quality Control with R
3(26)
1.1 Introduction
3(1)
1.2 A Brief History of Quality Control
3(2)
1.3 What Is Quality Control
5(3)
1.4 The Power of R for Quality Control
8(7)
1.5 An Intuitive Example
15(2)
1.6 A Roadmap to Getting Started with R for Quality Control
17(10)
1.7 Conclusions and Further Steps
27(1)
References
27(2)
2 An Introduction to R for Quality Control
29(64)
2.1 Introduction
29(2)
2.2 R Interfaces
31(2)
2.3 R Expressions
33(1)
2.4 R Infrastructure
34(1)
2.5 Introduction to RStudio
34(16)
2.6 Working with Data in R
50(25)
2.7 Data Import and Export with R
75(10)
2.8 R Task View for Quality Control (Unofficial)
85(4)
2.9 ISO Standards and R
89(2)
References
91(2)
3 The Seven Quality Control Tools in a Nutshell: R and ISO Approaches
93(26)
3.1 Origin
93(1)
3.2 Cause-and-Effect Diagram
93(3)
3.3 Check Sheet
96(4)
3.4 Control Chart
100(2)
3.5 Histogram
102(3)
3.6 Pareto Chart
105(8)
3.7 Scatter Plot
113(1)
3.8 Stratification
114(1)
3.9 ISO Standards for the Seven Basic Quality Control Tools
115(2)
References
117(2)
4 R and the ISO Standards for Quality Control
119(26)
4.1 ISO Members and Technical Committees
119(2)
4.2 ISO Standards and Quality
121(1)
4.3 The ISO Standards Development Process
122(3)
4.4 ISO TC69 Secretariat
125(2)
4.5 ISO TC69/SC1: Terminology
127(1)
4.6 ISO TC69/SC4: Application of Statistical Methods in Process Management
127(1)
4.7 ISO TC69/SC5: Acceptance Sampling
128(2)
4.8 ISO TC69/SC6: Measurement Methods and Results
130(1)
4.9 ISO TC69/SC7: Applications of Statistical and Related Techniques
131(1)
4.10 ISO TC69/SC8: Application of Statistical and Related Methodology for New Technology and Product Development
132(1)
4.11 The Role of R in Standards
132(4)
References
136(9)
Part II Statistics for Quality Control
5 Modelling Quality with R
145(42)
5.1 The Description of Variability
145(18)
5.1.1 Background
145(1)
5.1.2 Graphical Description of Variation
146(10)
5.1.3 Numerical Description of Variation
156(7)
5.2 Probability Distributions
163(11)
5.2.1 Discrete Distributions
163(4)
5.2.2 Continuous Distributions
167(7)
5.3 Inference About Distribution Parameters
174(10)
5.3.1 Confidence Intervals
174(5)
5.3.2 Hypothesis Testing
179(5)
5.4 ISO Standards for Quality Modeling with R
184(2)
References
186(1)
6 Data Sampling for Quality Control with R
187(16)
6.1 The Importance of Sampling
187(1)
6.2 Different Kinds of Sampling
188(5)
6.2.1 Simple Random Sampling
188(3)
6.2.2 Stratified Sampling
191(2)
6.2.3 Cluster Sampling
193(1)
6.2.4 Systematic Sampling
193(1)
6.3 Sample Size, Test Power, OC Curves with R
193(4)
6.4 ISO Standards for Sampling with R
197(1)
References
198(5)
Part III Delimiting and Assessing Quality
7 Acceptance Sampling with R
203(18)
7.1 Introduction
203(1)
7.2 Sampling Plans for Attributes
204(7)
7.3 Sampling Plans for Variables
211(6)
7.4 ISO Standards for Acceptance Sampling and R
217(2)
References
219(2)
8 Quality Specifications and Process Capability Analysis with R
221(18)
8.1 Introduction
221(1)
8.2 Tolerance Limits and Specifications Design
221(4)
8.2.1 The Voice of the Customer
222(1)
8.2.2 Process Tolerance
222(3)
8.3 Capability Analysis
225(9)
8.3.1 The Voice of the Process
225(3)
8.3.2 Process Performance Indices
228(2)
8.3.3 Capability Indices
230(4)
8.4 ISO Standards for Capability Analysis and R
234(1)
References
235(4)
Part IV Control Charts
9 Control Charts with R
239(32)
9.1 Introduction
239(4)
9.1.1 The Elements of a Control Chart
240(1)
9.1.2 Control Chart Design
240(2)
9.1.3 Reading a Control Chart
242(1)
9.2 Control Charts for Variables
243(18)
9.2.1 Introduction
243(2)
9.2.2 Estimation of a for Control Charts
245(1)
9.2.3 Control Charts for Grouped Data
245(11)
9.2.4 Control Charts for Non-grouped Data
256(2)
9.2.5 Special Control Charts
258(3)
9.3 Control Charts for Attributes
261(6)
9.3.1 Introduction
261(1)
9.3.2 Attributes Control Charts for Groups
262(2)
9.3.3 Control Charts for Events
264(3)
9.4 Control Chart Selection
267(2)
9.5 ISO Standards for Control Charts
269(1)
References
270(1)
10 Nonlinear Profiles with R
271(14)
10.1 Introduction
271(1)
10.2 Nonlinear Profiles Basics
272(3)
10.3 Phase I and Phase II Analysis
275(7)
10.3.1 Phase I
276(4)
10.3.2 Phase II
280(2)
10.4 A Simple Profiles Control Chart
282(1)
10.5 ISO Standards for Nonlinear Profiles and R
283(1)
References
284(1)
A Shewhart Constants for Control Charts 285(2)
B ISO Standards Published by the ISO/TC69: Application of Statistical Methods 287(6)
C R Cheat Sheet for Quality Control 293(42)
R Packages and Functions Used in the Book 335(4)
ISO Standards Referenced in the Book 339(2)
Subject Index 341
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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