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
	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