Statistical quality control : using Minitab, R, JMP, and Python

Cover -- Title Page -- Copyright Page -- Contents -- Preface -- About the Companion Website -- Chapter 1 Quality Improvement and Management -- 1.1 Introduction -- 1.2 Statistical Quality Control -- 1.2.1 Quality and the Customer -- 1.2.2 Quality Improvement -- 1.2.3 Quality and Productivity -- 1.3 Implementing Quality Improvement -- 1.3.1 Outcomes of Quality Control -- 1.3.2 Quality Control and Quality Improvement -- 1.3.2.1 Acceptance Sampling Plans -- 1.3.2.2 Process Control -- 1.3.2.3 Removing Obstacles to Quality -- 1.3.2.4 Eliminating Productivity Quotas -- 1.3.3 Implementing Quality Improvement -- 1.4 Managing Quality Improvement -- 1.4.1 Management and Their Responsibilities -- 1.4.2 Management and Quality -- 1.4.3 Risks Associated with Making Bad Decisions -- 1.5 Conclusion -- Chapter 2 Basic Concepts of the Six Sigma Methodology -- 2.1 Introduction -- 2.2 What Is Six Sigma? -- 2.2.1 Six Sigma as a Management Philosophy -- 2.2.2 Six Sigma as a Systemic Approach to Problem Solving -- 2.2.3 Six Sigma as a Statistical Standard of Quality -- 2.2.3.1 Statistical Basis for Six Sigma -- 2.2.4 Six Sigma Roles -- 2.3 Is Six Sigma New? -- 2.4 Quality Tools Used in Six Sigma -- 2.4.1 The Basic Seven Tools and the New Seven Tools -- 2.4.2 Lean Tools -- 2.4.2.1 Eight Wastes -- 2.4.2.2 Visual Management -- 2.4.2.3 The 5S Method -- 2.4.2.4 Value-Stream Mapping -- 2.4.2.5 Mistake-Proofing -- 2.4.2.6 Quick Changeover -- 2.5 Six Sigma Benefits and Criticism -- 2.5.1 Why Do Some Six Sigma Initiatives Fail? -- Review Practice Problems -- Chapter 3 Describing Quantitative and Qualitative Data -- 3.1 Introduction -- 3.2 Classification of Various Types of Data -- 3.3 Analyzing Data Using Graphical Tools -- 3.3.1 Frequency Distribution Tables for Qualitative and Quantitative Data -- 3.3.1.1 Qualitative Data -- 3.3.1.2 Quantitative Data.