Statistical Quality Control: Using MINITAB, R, JMP and Python
- Length: 400 pages
- Edition: 1
- Language: English
- Publisher: Wiley
- Publication Date: 2021-04-27
- ISBN-10: 1119671639
- ISBN-13: 9781119671633
- Sales Rank: #8896875 (See Top 100 Books)
STATISTICAL QUALITY CONTROL
Provides a basic understanding of statistical quality control (SQC) and demonstrates how to apply the techniques of SQC to improve the quality of products in various sectors
This book introduces Statistical Quality Control and the elements of Six Sigma Methodology, illustrating the widespread applications that both have for a multitude of areas, including manufacturing, finance, transportation, and more. It places emphasis on both the theory and application of various SQC techniques and offers a large number of examples using data encountered in real life situations to support each theoretical concept.
Statistical Quality Control: Using MINITAB, R, JMP and Python begins with a brief discussion of the different types of data encountered in various fields of statistical applications and introduces graphical and numerical tools needed to conduct preliminary analysis of the data. It then discusses the basic concept of statistical quality control (SQC) and Six Sigma Methodology and examines the different types of sampling methods encountered when sampling schemes are used to study certain populations. The book also covers Phase 1 Control Charts for variables and attributes; Phase II Control Charts to detect small shifts; the various types of Process Capability Indices (CPI); certain aspects of Measurement System Analysis (MSA); various aspects of PRE-control; and more. This helpful guide also
- Focuses on the learning and understanding of statistical quality control for second and third year undergraduates and practitioners in the field
- Discusses aspects of Six Sigma Methodology
- Teaches readers to use MINITAB, R, JMP and Python to create and analyze charts
- Requires no previous knowledge of statistical theory
- Is supplemented by an instructor-only book companion site featuring data sets and a solutions manual to all problems, as well as a student book companion site that includes data sets and a solutions manual to all odd-numbered problems
Statistical Quality Control: Using MINITAB, R, JMP and Python is an excellent book for students studying engineering, statistics, management studies, and other related fields and who are interested in learning various techniques of statistical quality control. It also serves as a desk reference for practitioners who work to improve quality in various sectors, such as manufacturing, service, transportation, medical, oil, and financial institutions. It‘s also useful for those who use Six Sigma techniques to improve the quality of products in such areas.
Cover Table of Contents Title Page Copyright Page Dedication Page Preface Audience Topics Covered in This Book Approach Hallmark Features Student Resources Instructor Resources Errata Acknowledgments About the Companion Website 1 Quality Improvement and Management 1.1 Introduction 1.2 Statistical Quality Control 1.3 Implementing Quality Improvement 1.4 Managing Quality Improvement 1.5 Conclusion 2 Basic Concepts of the Six Sigma Methodology 2.1 Introduction 2.2 What Is Six Sigma? 2.3 Is Six Sigma New? 2.4 Quality Tools Used in Six Sigma 2.5 Six Sigma Benefits and Criticism Review Practice Problems 3 Describing Quantitative and Qualitative Data 3.1 Introduction 3.2 Classification of Various Types of Data 3.3 Analyzing Data Using Graphical Tools 3.4 Describing Data Graphically 3.5 Analyzing Data Using Numerical Tools 3.6 Some Important Probability Distributions Review Practice Problems 4 Sampling Methods 4.1 Introduction 4.2 Basic Concepts of Sampling 4.3 Simple Random Sampling 4.4 Stratified Random Sampling 4.5 Systematic Random Sampling 4.6 Cluster Random Sampling Review Practice Problems 5 Phase I Quality Control Charts for Variables 5.1 Introduction 5.2 Basic Definition of Quality and Its Benefits 5.3 Statistical Process Control 5.4 Control Charts for Variables 5.5 Shewhartand R Control Charts 5.6 Shewhartand R Control Charts When the Process Mean and Standard Deviation are Known 5.7 Shewhartand R Control Charts for Individual Observations 5.8 Shewhartand S Control Charts with Equal Sample Sizes 5.9 Shewhartand S Control Charts with Variable Sample Sizes 5.10 Process Capability Review Practice Problems 6 Phase I Control Charts for Attributes 6.1 Introduction 6.2 Control Charts for Attributes 6.3 The p Chart: Control Charts for Nonconforming Fractions with Constant Sample Sizes 6.4 The p Chart: Control Chart for Nonconforming Fractions with Variable Samples Sizes 6.5 The np Chart: Control Charts for the Number of Nonconforming Units 6.6 The c Control Chart – Control Charts for Nonconformities per Sample 6.7 The u Chart 7 Phase II Quality Control Charts for Detecting Small Shifts 7.1 Introduction 7.2 Basic Concepts of CUSUM Control Charts 7.3 Designing a CUSUM Control Chart 7.4 Moving Average (MA) Control Charts 7.5 Exponentially Weighted Moving Average (EWMA) Control Charts Review Practice Problems 8 Process and Measurement System Capability Analysis 8.1 Introduction 8.2 Development of Process Capability Indices 8.3 Various Process Capability Indices 8.4 Pre‐control 8.5 Measurement System Capability Analysis Review Practice Problems 9 Acceptance Sampling Plans 9.1 Introduction 9.2 The Intent of Acceptance Sampling Plans 9.3 Sampling Inspection vs. 100% Inspection 9.4 Classification of Sampling Plans 9.5 Acceptance Sampling by Attributes 9.6 Single Sampling Plans for Attributes 9.7 Other Types of Sampling Plans for Attributes 9.8 ANSI/ASQ Z1.4‐2003 Sampling Standard and Plans 9.9 Dodge‐Romig Tables 9.10 ANSI/ASQ Z1.9‐2003 Acceptance Sampling Plans by Variables 9.11 Continuous‐Sampling Plans Review Practice Problems 10 Computer Resources to Support SQC: Minitab, R, JMP, and Python 9.1 Introduction Appendix A: Statistical Tables Appendix B: Answers to Selected Practice Problems Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Bibliography Index End User License Agreement
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