Ebook: Modern Industrial Statistics: With Applications in R, MINITAB and JMP

Fully revised and updated, this book combines a theoretical background with examples and references to R, MINITAB and JMP, enabling practitioners to find state-of-the-art material on both foundation and implementation tools to support their work. Topics addressed include computer-intensive data analysis, acceptance sampling, univariate and multivariate statistical process control, design of experiments, quality by design, and reliability using classical and Bayesian methods. The book can be used for workshops or courses on acceptance sampling, statistical process control, design of experiments, and reliability.

Graduate and post-graduate students in the areas of statistical quality and engineering, as well as industrial statisticians, researchers and practitioners in these fields will all benefit from the comprehensive combination of theoretical and practical information provided in this single volume.

Modern Industrial Statistics: With applications in R, MINITAB and JMP:

• Combines a practical approach with theoretical foundations and computational support.
• Provides examples in R using a dedicated package called MISTAT, and also refers to MINITAB and JMP.
• Includes exercises at the end of each chapter to aid learning and test knowledge.
• Provides over 40 data sets representing real-life case studies.
• Is complemented by a comprehensive website providing an introduction to R, and installations of JMP scripts and MINITAB macros, including effective tutorials with introductory material: www.wiley.com/go/modern_industrial_statistics.

Content:
Chapter I Part Introduction (page 1):
Chapter 1 The Role of Statistical Methods in Modern Industry and Services (pages 3–12):
Chapter 2 Analyzing Variability: Descriptive Statistics (pages 13–40):
Chapter 3 Probability Models and Distribution Functions (pages 41–112):
Chapter 4 Statistical Inference and Bootstrapping (pages 113–176):
Chapter 5 Variability in Several Dimensions and Regression Models (pages 177–234):
Chapter II Part Introduction (page 235):
Chapter 6 Sampling for Estimation of Finite Population Quantities (pages 237–257):
Chapter 7 Sampling Plans for Product Inspection (pages 258–281):
Chapter III Part Introduction (page 283):
Chapter 8 Basic Tools and Principles of Process Control (pages 285–318):
Chapter 9 Advanced Methods of Statistical Process Control (pages 319–360):
Chapter 10 Multivariate Statistical Process Control (pages 361–378):
Chapter IV Part Introduction (pages 379–380):
Chapter 11 Classical Design and Analysis of Experiments (pages 381–445):
Chapter 12 Quality by Design (pages 446–476):
Chapter 13 Computer Experiments (pages 477–493):
Chapter V Part Introduction (page 495):
Chapter 14 Reliability Analysis (pages 497–533):
Chapter 15 Bayesian Reliability Estimation and Prediction (pages 534–546):