Statistical Quality Control : Using MINITAB, R, JMP and Python

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 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 3.4 Describing Data Graphically 3.4.1 Dot Plots 3.4.2 Pie Charts 3.4.3 Bar Charts 3.4.4 Histograms 3.4.5 Line Graphs 3.4.6 Measures of Association 3.5 Analyzing Data Using Numerical Tools 3.5.1 Numerical Measures 3.5.2 Measures of Centrality 3.5.2.1 Mean 3.5.2.2 Median 3.5.2.3 Mode 3.5.3 Measures of Dispersion 3.5.3.1 Range 3.5.3.2 Variance 3.5.3.3 Standard Deviation 3.5.3.4 Empirical Rule 3.5.3.5 Interquartile Range 3.5.4 Box-and-Whisker Plot 3.6 Some Important Probability Distributions 3.6.1 The Binomial Distribution 3.6.1.1 Binomial Probability Tables 3.6.2 The Hypergeometric Distribution 3.6.2.1 Mean and Standard Deviation of a Hypergeometric Distribution 3.6.3 The Poisson Distribution 3.6.3.1 Mean and Standard Deviation of a Poisson Distribution 3.6.3.2 Poisson Probability Tables 3.6.4 The Normal Distribution Review Practice Problems Chapter 4 Sampling Methods 4.1 Introduction 4.2 Basic Concepts of Sampling 4.2.1 Introducing Various Sampling Designs 4.3 Simple Random Sampling 4.3.1 Estimating the Population Mean and Population Total 4.3.2 Confidence Interval for the Population Mean and Population Total 4.3.3 Determining Sample Size 4.4 Stratified Random Sampling 4.4.1 Estimating the Population Mean and Population Total 4.4.2 Confidence Interval for the Population Mean and Population Total 4.4.3 Determining Sample Size 4.5 Systematic Random Sampling 4.5.1 Estimating the Population Mean and Population Total 4.5.2 Confidence Interval for the Population Mean and Population Total 4.5.3 Determining the Sample Size 4.6 Cluster Random Sampling 4.6.1 Estimating the Population Mean and Population Total 4.6.2 Confidence Interval for the Population Mean and Population Total 4.6.3 Determining the Sample Size Review Practice Problems Chapter 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 5.3.1 Check Sheets 5.3.2 Pareto Chart 5.3.3 Cause-and-Effect (Fishbone or Ishikawa) Diagrams 5.3.4 Defect-Concentration Diagrams 5.3.5 Run Charts 5.4 Control Charts for Variables 5.4.1 Process Evaluation 5.4.2 Action on the Process 5.4.3 Action on the Output 5.4.4 Variation 5.4.4.1 Common Causes (Random Causes) 5.4.4.2 Special Causes (Assignable Causes) 5.4.4.3 Local Actions and Actions on the System 5.4.4.4 Relationship Between Two Types of Variation 5.4.5 Control Charts 5.4.5.1 Preparation for Using Control Charts 5.4.5.2 Benefits of Control Charts 5.4.5.3 Rational Samples for control Charts 5.4.5.3.1 Average Run Length 5.4.5.3.2 Operating Characteristic Curve (OC Curve) 5.5 Shewhart X and R Control Charts 5.5.1 Calculating Sample Statistics 5.5.2 Calculating Control Limits 5.5.3 Interpreting Shewhart X and R Control Charts 5.5.4 Extending the Current Control Limits for Future Control 5.6 Shewhart X and R Control Charts When the Process Mean and Standard Deviation are Known 5.7 Shewhart X and R Control Charts for Individual Observations 5.8 Shewhart X and S Control Charts with Equal Sample Sizes 5.9 Shewhart X and S Control Charts with Variable Sample Sizes 5.10 Process Capability Review Practice Problems Chapter 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 6.3.1 Control Limits for the p Control Chart 6.3.2 Interpreting the Control Chart for Nonconforming Fractions 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 6.5.1 Control Limits for np Control Charts 6.6 The c Control Chart – Control Charts for Nonconformities per Sample 6.7 The u Chart Review Practice Problems Chapter 7 Phase II Quality Control Charts for Detecting Small Shifts 7.1 Introduction 7.2 Basic Concepts of CUSUM Control Charts 7.2.1 CUSUM Control Charts vs. Shewhart X and R Control Charts 7.3 Designing a CUSUM Control Chart 7.3.1 Two-Sided CUSUM Control Charts Using the Numerical Procedure 7.3.2 The Fast Initial Response (FIR) Feature for CUSUM Control Charts 7.3.3 One-Sided CUSUM Control Charts 7.3.4 Combined Shewhart-CUSUM Control Charts 7.3.5 CUSUM Control Charts for Controlling Process Variability 7.4 Moving Average (MA) Control Charts 7.5 Exponentially Weighted Moving Average (EWMA) Control Charts Review Practice Problems Chapter 8 Process and Measurement System Capability Analysis 8.1 Introduction 8.2 Development of Process Capability Indices 8.3 Various Process Capability Indices 8.3.1 Process Capability Index: Cp 8.3.2 Process Capability Index: Cpk 8.3.3 Process Capability Index: Cpm 8.3.4 Process Capability Index: Cpmk 8.3.5 Process Capability Index: Cpnst 8.3.5.1 Comparing Cpnst with Cpk and Cpm 8.3.5.2 Other Features of Cpnst 8.3.6 Process Performance Indices: Pp and Ppk 8.4 Pre-control 8.4.1 Global Perspective on the Use of Pre-control – Understanding the Color-Coding Scheme 8.4.2 The Mechanics of Pre-control 8.4.3 The Statistical Basis for Pre-control 8.4.4 Advantages and Disadvantages of Pre-control 8.4.4.1 Advantages of Pre-control 8.4.4.2 Disadvantages of Pre-control 8.5 Measurement System Capability Analysis 8.5.1 Evaluating Measurement System Performance 8.5.2 The Range Method 8.5.3 The ANOVA Method 8.5.4 Graphical Representation of a Gauge R&R Study 8.5.5 Another Measurement Capability Index Review Practice Problems Chapter 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 9.4.1 Formation of Lots for Acceptance Sampling Plans 9.4.2 The Operating Characteristic (OC) Curve 9.4.3 Two Types of OC Curves 9.4.4 Some Terminology Used in Sampling Plans 9.5 Acceptance Sampling by Attributes 9.5.1 Acceptable Quality Limit (AQL) 9.5.2 Average Outgoing Quality (AOQ) 9.5.3 Average Outgoing Quality Limit (AOQL) 9.5.4 Average Total Inspection (ATI) 9.6 Single Sampling Plans for Attributes 9.7 Other Types of Sampling Plans for Attributes 9.7.1 Double-Sampling Plans for Attributes 9.7.2 The OC Curve 9.7.3 Multiple-Sampling Plans 9.7.4 Average Sample Number 9.7.5 Sequential-Sampling Plans 9.8 ANSI/ASQ Z1.4-2003 Sampling Standard and Plans 9.8.1 Levels of Inspection 9.8.2 Types of Sampling 9.9 Dodge-Romig Tables 9.10 ANSI/ASQ Z1.9-2003 Acceptance Sampling Plans by Variables 9.10.1 ANSI/ASQ Z1.9-2003 – Variability Known 9.10.2 Variability Unknown – Standard Deviation Method 9.10.3 Variability Unknown – Range Method 9.11 Continuous-Sampling Plans 9.11.1 Types of Continuous-Sampling Plans 9.11.2 Dodge ́s Continuous Sampling Plans 9.11.3 MIL-STD-1235B Review Practice Problems Chapter 10 Computer Resources to Support SQC: Minitab, R, JMP, and Python 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 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. "This book introduces Statistical Quality Control and elements of Six Sigma Methodology, both of which have widespread application. Chapter 1 of this book begins with a brief discussion of the different types of data encountered in various fields of statistical applications. Some terminology is also defined. Then, the authors introduce certain graphical and numerical tools needed to do some preliminary analysis of these data. In Chapter 2 the basic concept of statistical quality control (SQC) is discussed. The basic concept of Six Sigma Methodology is also introduced. In Chapter 3, the author briefly covers different types of sampling methods, which are encountered whenever we use sampling schemes to study certain populations. Chapter 4 discusses the Phase 1 Control Charts for variables. Phase 1 Control Charts for attributes is covered in Chapter 5. Next, the Phase II Control Charts to detect small shifts is discussed in Chapter 6. In Chapter 7, the author discusses the various types of Process Capability Indices (CPI). Next, in Chapter 8, the book covers certain aspects of Measurement System Analysis (MSA). The book continues with a discussion of various aspects of PRE-control in Chapter 9, which is an important tool of SQC. Chapter 10 covers various kinds of acceptance sampling schemes which are still used at certain places in the world. Finally, Chapter 11 discusses the latest version 19 of MINITAB and R. Using these software packages, the author covers various SQC techniques. After going through the material presented in this chapter, the reader will be able to analyze, using R and/or MINITAB, all the SQC techniques discussed in this book and implement them in various sectors whenever and wherever high-quality products are desired. Statistical quality control refers to the use of statistical methods in the monitoring and maintaining of the quality of products and services. One method, referred to as acceptance sampling, can be used when a decision must be made to accept or reject a group of parts or items based on the quality found in a sample. A second method, referred to as statistical process control, uses graphical displays known as control charts to determine whether a process should be continued or should be adjusted to achieve the desired quality."-- Proporcionado por el editor "This book introduces Statistical Quality Control and elements of Six Sigma Methodology, both of which have widespread application. Chapter 1 of this book begins with a brief discussion of the different types of data encountered in various fields of statistical applications. Some terminology is also defined. Then, the authors introduce certain graphical and numerical tools needed to do some preliminary analysis of these data. In Chapter 2 the basic concept of statistical quality control (SQC) is discussed. The basic concept of Six Sigma Methodology is also introduced. In Chapter 3, the author briefly covers different types of sampling methods, which are encountered whenever we use sampling schemes to study certain populations. Chapter 4 discusses the Phase 1 Control Charts for variables. Phase 1 Control Charts for attributes is covered in Chapter 5. Next, the Phase II Control Charts to detect small shifts is discussed in Chapter 6. In Chapter 7, the author discusses the various types of Process Capability Indices (CPI). Next, in Chapter 8, the book covers certain aspects of Measurement System Analysis (MSA). The book continues with a discussion of various aspects of PRE-control in Chapter 9, which is an important tool of SQC. Chapter 10 covers various kinds of acceptance sampling schemes which are still used at certain places in the world. Finally, Chapter 11 discusses the latest version 19 of MINITAB and R. Using these software packages, the author covers various SQC techniques. After going through the material presented in this chapter, the reader will be able to analyze, using R and/or MINITAB, all the SQC techniques discussed in this book and implement them in various sectors whenever and wherever high-quality products are desired. Statistical quality control refers to the use of statistical methods in the monitoring and maintaining of the quality of products and services. One method, referred to as acceptance sampling, can be used when a decision must be made to accept or reject a group of parts or items based on the quality found in a sample. A second method, referred to as statistical process control, uses graphical displays known as control charts to determine whether a process should be continued or should be adjusted to achieve the desired quality."-- Provided by publisher **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** __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 __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.