Statistical Methods for Quality Improvement

Name: Statistical Methods for Quality Improvement
Brand: Wiley
Price: 123.99 EUR
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Thomas P. Ryan(Autor*in)

Wiley (Verlag)

3. Auflage

Erschienen am 26. September 2011

672 Seiten

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Beschreibung

Praise for the Second Edition
"As a comprehensive statistics reference book for qualityimprovement, it certainly is one of the best booksavailable."
--Technometrics
This new edition continues to provide the most current,proven statistical methods for quality control and qualityimprovement
The use of quantitative methods offers numerous benefits in thefields of industry and business, both through identifying existingtrouble spots and alerting management and technical personnel topotential problems. Statistical Methods for Quality Improvement,Third Edition guides readers through a broad range of tools andtechniques that make it possible to quickly identify and resolveboth current and potential trouble spots within almost anymanufacturing or nonmanufacturing process. The book providesdetailed coverage of the application of control charts, while alsoexploring critical topics such as regression, design ofexperiments, and Taguchi methods.
In this new edition, the author continues to explain how tocombine the many statistical methods explored in the book in orderto optimize quality control and improvement. The book has beenthoroughly revised and updated to reflect the latest research andpractices in statistical methods and quality control, and newfeatures include:
* Updated coverage of control charts, with newly added tools
* The latest research on the monitoring of linear profiles andother types of profiles
* Sections on generalized likelihood ratio charts and the effectsof parameter estimation on the properties of CUSUM and EWMAprocedures
* New discussions on design of experiments that includeconditional effects and fraction of design space plots
* New material on Lean Six Sigma and Six Sigma programs andtraining
Incorporating the latest software applications, the author hasadded coverage on how to use Minitab software to obtain probabilitylimits for attribute charts. new exercises have been addedthroughout the book, allowing readers to put the latest statisticalmethods into practice. Updated references are also provided,shedding light on the current literature and providing resourcesfor further study of the topic.
Statistical Methods for Quality Improvement, ThirdEdition is an excellent book for courses on quality control anddesign of experiments at the upper-undergraduate and graduatelevels. the book also serves as a valuable reference for practicingstatisticians, engineers, and physical scientists interested instatistical quality improvement.

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Inhalt

Intro
Statistical Methods for Quality Improvement
Contents
Preface
Preface to the Second Edition
Preface to the First Edition
PART I FUNDAMENTAL QUALITY IMPROVEMENT AND STATISTICAL CONCEPTS
1 Introduction
1.1 Quality and Productivity
1.2 Quality Costs (or Does It?)
1.3 The Need for Statistical Methods
1.4 Early Use of Statistical Methods for Improving Quality
1.5 Influential Quality Experts
1.6 Summary
References
2 Basic Tools for Improving Quality
2.1 Histogram
2.2 Pareto Charts
2.3 Scatter Plots
2.3.1 Variations of Scatter Plots
2.4 Control Chart
2.5 Check Sheet
2.6 Cause-and-Effect Diagram
2.7 Defect Concentration Diagram
2.8 The Seven Newer Tools
2.8.1 Affinity Diagram
2.8.2 Interrelationship Digraph
2.8.3 Tree Diagram
2.8.4 Prioritization Matrix
2.8.5 Matrix Diagram
2.8.6 Process Decision Program Chart
2.8.7 Activity Network Diagram
2.9 Software
2.10 Summary
References
Exercises
3 Basic Concepts in Statistics and Probability
3.1 Probability
3.2 Sample Versus Population
3.3 Location
3.4 Variation
3.5 Discrete Distributions
3.5.1 Binomial Distribution
3.5.2 Beta-Binomial Distribution
3.5.3 Poisson Distribution
3.5.4 Geometric Distribution
3.5.5 Negative Binomial Distribution
3.5.6 Hypergeometric Distribution
3.6 Continuous Distributions
3.6.1 Normal Distribution
3.6.2 t Distribution
3.6.3 Exponential Distribution
3.6.4 Lognormal Distribution
3.6.5 Weibull Distribution
3.6.6 Extreme Value Distribution
3.6.7 Gamma Distribution
3.6.8 Chi-Square Distribution
3.6.9 Truncated Normal Distribution
3.6.10 Bivariate and Multivariate Normal Distributions
3.6.11 F Distribution
3.6.12 Beta Distribution
3.6.13 Uniform Distribution
3.7 Choice of Statistical Distribution
3.8 Statistical Inference
3.8.1 Central Limit Theorem
3.8.2 Point Estimation
3.8.2.1 Maximum Likelihood Estimation
3.8.3 Confidence Intervals
3.8.4 Tolerance Intervals
3.8.5 Hypothesis Tests
3.8.5.1 Probability Plots
3.8.5.2 Likelihood Ratio Tests
3.8.6 Bonferroni Intervals
3.9 Enumerative Studies Versus Analytic Studies
References
Exercises
PARTII CONTROL CHARTS AND PROCESS CAPABILITY
4 Control Charts for Measurements With Subgrouping (for One Variable)
4.1 Basic Control Chart Principles
4.2 Real-Time Control Charting Versus Analysis of Past Data
4.3 Control Charts: When to Use, Where to Use, How Many to Use
4.4 Benefits from the Use of Control Charts
4.5 Rational Subgroups
4.6 Basic Statistical Aspects of Control Charts
4.7 Illustrative Example
4.7.1 R-Chart
4.7.2 R-Chart with Probability Limits
4.7.3 S-Chart
4.7.4 S-Chart with Probability Limits
4.7.5 S2-Chart
4.7.6 X-Chart
4.7.7 Recomputing Control Limits
4.7.8 Applying Control Limits to Future Production
4.7.9 Combining an X- and an S-Chart
4.7.10 Standards for Control Charts
4.7.11 Deleting Points
4.7.12 Target Values
4.8 Illustrative Example with Real Data
4.9 Determining the Point of a Parameter Change
4.10 Acceptance Sampling and Acceptance Control Chart
4.10.1 Acceptance Control Chart
4.10.1.1 Acceptance Chart with X Control Limits
4.10.1.2 Acceptance Charts Versus Target Values
4.11 Modified Limits
4.12 Difference Control Charts
4.13 Other Charts
4.14 Average Run Length (ARL)
4.14.1 Weakness of the ARL Measure
4.15 Determining the Subgroup Size
4.15.1 Unequal Subgroup Sizes
4.16 Out-of-Control Action Plans
4.17 Assumptions for the Charts in This Chapter
4.17.1 Normality
4.17.2 Independence
4.18 Measurement Error
4.18.1 Monitoring Measurement Systems
4.19 Software
4.20 Summary
Appendix
4.A Derivation of Control Chart Constants
4.B ARL Calculations
References
Exercises
5 Control Charts for Measurements Without Subgrouping (for One Variable)
5.1 Individual Observations Chart
5.1.1 Control Limits for the X-Chart
5.1.2 X-Chart Assumptions
5.1.3 Illustrative Example: Random Data
5.1.4 Example with Particle Counts
5.1.5 Illustrative Example: Trended Data
5.1.6 Trended Real Data
5.2 Transform the Data or Fit a Distribution?
5.3 Moving Average Chart
5.4 Controlling Variability with Individual Observations
5.5 Summary
Appendix
References
Exercises
6 Control Charts for Attributes
6.1 Charts for Nonconforming Units
6.1.1 np-Chart
6.1.2 p-Chart
6.1.3 Stage 1 and Stage 2 Use of p-Charts and np-Charts
6.1.4 Alternative Approaches
6.1.4.1 Arcsin Transformations
6.1.4.2 Q-Chart for Binomial Data
6.1.4.3 Regression-Based Limits
6.1.4.4 ARL-Unbiased Charts
6.1.4.5 Unit and Group-Runs Chart
6.1.4.6 Monitoring a Multinomial Process
6.1.5 Using Software to Obtain Probability Limits for p- and np-Charts
6.1.6 Variable Sample Size
6.1.7 Charts Based on the Geometric and Negative Binomial Distributions
6.1.8 Overdispersion
6.2 Charts for Nonconformities
6.2.1 c-Chart
6.2.2 Transforming Poisson Data
6.2.3 Illustrative Example
6.2.4 Regression-Based Limits
6.2.5 Using Software to Obtain Probability Limits for c-Charts
6.2.6 u-Chart
6.2.6.1 Regression-Based Limits
6.2.6.2 Using Computer Software to Obtain u-Chart Probability Limits
6.2.7 Overdispersion
6.2.8 D-Chart
6.2.8.1 Probability-Type D-Chart Limits
6.3 Summary
References
Exercises
7 Process Capability
7.1 Data Acquisition for Capability Indices
7.1.1 Selection of Historical Data
7.2 Process Capability Indices
7.2.1 Cp
7.2.2 Cpm
7.2.3 Cpk
7.2.3.1 CPU and CPL as Process Capability Indices
7.2.4 Cpmk
7.2.5 Other Capability Indices
7.3 Estimating the Parameters in Process Capability Indices
7.3.1 X-Chart
7.3.2 X-Chart
7.3.3 Case Study
7.4 Distributional Assumption for Capability Indices
7.5 Confidence Intervals for Process Capability Indices
7.5.1 Confidence Interval for Cp
7.5.2 Confidence Interval for Cpk
7.5.3 Confidence Interval for Cpm
7.5.4 Confidence Interval for Cpmk
7.5.5 Confidence Intervals Computed Using Data in Subgroups
7.5.6 Nonparametric Capability Indices and Confidence Limits
7.5.6.1 Robust Capability Indices
7.5.6.2 Capability Indices Based on Fitted Distributions
7.5.6.3 Data Transformation
7.5.6.4 Capability Indices Computed Using Resampling Methods
7.6 Asymmetric Bilateral Tolerances
7.6.1 Examples
7.7 Capability Indices That Are a Function of Percent Nonconforming
7.7.1 Examples
7.8 Modified k Index
7.9 Other Approaches
7.10 Process Capability Plots
7.11 Process Capability Indices Versus Process Performance Indices
7.12 Process Capability Indices with Autocorrelated Data
7.13 Software for Process Capability Indices
7.14 Summary
References
Exercises
8 Alternatives to Shewhart Charts
8.1 Introduction
8.2 Cumulative Sum Procedures: Principles and Historical Development
8.2.1 CUSUM Procedures Versus X-Chart
8.2.2 Fast Initial Response CUSUM
8.2.3 Combined Shewhart-CUSUM Scheme
8.2.4 CUSUMs with Estimated Parameters
8.2.5 Computation of CUSUM ARLs
8.2.6 Robustness of CUSUM Procedures
8.2.7 CUSUM Procedures for Individual Observations
8.3 CUSUM Procedures for Controlling Process Variability
8.4 Applications of CUSUM Procedures
8.5 Generalized Likelihood Ratio Charts: Competitive Alternative to CUSUM Charts
8.6 CUSUM Procedures for Nonconforming Units
8.7 CUSUM Procedures for Nonconformity Data
8.8 Exponentially Weighted Moving Average Charts
8.8.1 EWMA Chart for Subgroup Averages
8.8.2 EWMA Misconceptions
8.8.3 EWMA Chart for Individual Observations
8.8.4 Shewhart-EWMA Chart
8.8.5 FIR-EWMA
8.8.6 Designing EWMA Charts with Estimated Parameters
8.8.7 EWMA Chart with Variable Sampling Intervals
8.8.8 EWMA Chart for Grouped Data
8.8.9 EWMA Chart for Variances
8.8.10 EWMA for Attribute Data
8.9 Software
8.10 Summary
References
Exercises
9 Multivariate Control Charts for Measurement and Attribute Data
9.1 Hotelling's T2 Distribution
9.2 A T2 Control Chart
9.2.1 Robust Parameter Estimation
9.2.2 Identifying the Sources of the Signal
9.2.3 Regression Adjustment
9.2.4 Recomputing the UCL
9.2.5 Characteristics of Control Charts Based on T2
9.2.6 Determination of a Change in the Correlation Structure
9.2.7 Illustrative Example
9.3 Multivariate Chart Versus Individual X-Charts
9.4 Charts for Detecting Variability and Correlation Shifts
9.4.1 Application to Table 9.2 Data
9.5 Charts Constructed Using Individual Observations
9.5.1 Retrospective (Stage 1) Analysis
9.5.2 Stage 2 Analysis: Methods for Decomposing Q
9.5.2.1 Illustrative Example
9.5.3 Other Methods
9.5.4 Monitoring Multivariate Variability with Individual Observations
9.6 When to Use Each Chart
9.7 Actual Alpha Levels for Multiple Points
9.8 Requisite Assumptions
9.9 Effects of Parameter Estimation on ARLs
9.10 Dimension-Reduction and Variable Selection Techniques
9.11 Multivariate CUSUM Charts
9.12 Multivariate EWMA Charts
9.12.1 Design of a MEWMA Chart
9.12.2 Searching for Assignable Causes
9.12.3 Unequal Sample Sizes
9.12.4 Self-Starting MEWMA Chart
9.12.5 Combinations of MEWMA Charts and Multivariate Shewhart Charts
9.12.6 MEWMA Chart with Sequential Sampling
9.12.7 MEWMA Chart for Process Variability
9.13 Effect of Measurement Error
9.14 Applications of Multivariate Charts
9.15 Multivariate Process Capability Indices
9.16 Summary
Appendix
References
Exercises
10 Miscellaneous Control Chart Topics
10.1 Pre-control
10.2 Short-Run SPC
10.3 Charts for Autocorrelated Data
10.3.1 Autocorrelated Attribute Data
10.4 Charts for Batch Processes
10.5 Charts for Multiple-Stream Processes
10.6 Nonparametric Control Charts
10.7 Bayesian Control Chart Methods
10.8 Control Charts for Variance Components
10.9 Control Charts for Highly Censored Data
10.10 Neural Networks
10.11 Economic Design of Control Charts
10.11.1 Economic-Statistical Design
10.12 Charts with Variable Sample Size and/or Variable Sampling Interval
10.13 Users of Control Charts
10.13.1 Control Chart Nonmanufacturing Applications
10.13.1.1 Health Care
10.13.1.2 Financial
10.13.1.3 Environmental
10.13.1.4 Clinical Laboratories
10.13.1.5 Analytical Laboratories
10.13.1.6 Civil Engineering
10.13.1.7 Education
10.13.1.8 Law Enforcement/Investigative Work
10.13.1.9 Lumber
10.13.1.10 Forest Operations
10.13.1.11 Athletic Performance
10.13.1.12 Animal Production Systems
10.14 Software for Control Charting
Bibliography
Exercises
PART III BEYOND CONTROL CHARTS: GRAPHICAL AND STATISTICAL METHODS
11 Graphical Methods
11.1 Histogram
11.2 Stem-and-Leaf Display
11.3 Dot Diagrams
11.3.1 Digidot Plot
11.4 Boxplot
11.5 Normal Probability Plot
11.6 Plotting Three Variables
11.7 Displaying More Than Three Variables
11.8 Plots to Aid in Transforming Data
11.9 Summary
References
Exercises
12 Linear Regression
12.1 Simple Linear Regression
12.2 Worth of the Prediction Equation
12.3 Assumptions
12.4 Checking Assumptions Through Residual Plots
12.5 Confidence Intervals and Hypothesis Test
12.6 Prediction Interval for Y
12.7 Regression Control Chart
12.8 Cause-Selecting Control Charts
12.9 Linear, Nonlinear, and Nonparametric Profiles
12.10 Inverse Regression
12.11 Multiple Linear Regression
12.12 Issues in Multiple Regression
12.12.1 Variable Selection
12.12.2 Extrapolation
12.12.3 Multicollinear Data
12.12.4 Residual Plots
12.12.5 Regression Diagnostics
12.12.6 Transformations
12.13 Software For Regression
12.14 Summary
References
Exercises
13 Design of Experiments
13.1 A Simple Example of Experimental Design Principles
13.2 Principles of Experimental Design
13.3 Statistical Concepts in Experimental Design
13.4 t-Tests
13.4.1 Exact t-Test
13.4.2 Approximate t-Test
13.4.3 Confidence Intervals for Differences
13.5 Analysis of Variance for One Factor
13.5.1 ANOVA for a Single Factor with More Than Two Levels
13.5.2 Multiple Comparison Procedures
13.5.3 Sample Size Determination
13.5.4 Additional Terms and Concepts in One-Factor ANOVA
13.6 Regression Analysis of Data from Designed Experiments
13.7 ANOVA for Two Factors
13.7.1 ANOVA with Two Factors: Factorial Designs
13.7.1.1 Conditional Effects
13.7.2 Effect Estimates
13.7.3 ANOVA Table for Unreplicated Two-Factor Design
13.7.4 Yates's Algorithm
13.8 The 23 Design
13.9 Assessment of Effects Without a Residual Term
13.10 Residual Plot
13.11 Separate Analyses Using Design Units and Uncoded Units
13.12 Two-Level Designs with More Than Three Factors
13.13 Three-Level Factorial Designs
13.14 Mixed Factorials
13.15 Fractional Factorials
13.15.1 2k-1 Designs
13.15.2 2k-2 Designs
13.15.3 More Highly Fractionated Two-Level Designs
13.15.4 Fractions of Three-Level Factorials
13.15.5 Incomplete Mixed Factorials
13.15.6 Cautions
13.16 Other Topics in Experimental Design and Their Applications
13.16.1 Hard-to-Change Factors
13.16.2 Split-Lot Designs
13.16.3 Mixture Designs
13.16.4 Response Surface Designs
13.16.5 Designs for Measurement System Evaluation
13.16.6 Fraction of Design Space Plots
13.16.7 Computer-Aided Design and Expert Systems
13.16.8 Sequential Experimentation
13.16.9 Supersaturated Designs and Analyses
13.16.10 Multiple Responses
13.17 Summary
References
Exercises
14 Contributions of Genichi Taguchi and Alternative Approaches
14.1 "Taguchi Methods"
14.2 Quality Engineering
14.3 Loss Functions
14.4 Distribution Not Centered at the Target
14.5 Loss Functions and Specification Limits
14.6 Asymmetric Loss Functions
14.7 Signal-to-Noise Ratios and Alternatives
14.8 Experimental Designs for Stage One
14.9 Taguchi Methods of Design
14.9.1 Inner Arrays and Outer Arrays
14.9.2 Orthogonal Arrays as Fractional Factorials
14.9.3 Other Orthogonal Arrays Versus Fractional Factorials
14.9.4 Product Arrays Versus Combined Arrays
14.9.5 Application of Product Array
14.9.5.1 Cautions
14.9.6 Desirable Robust Designs and Analyses
14.9.6.1 Designs
14.9.6.2 Analyses
14.9.6.3 Experiment to Compare Product Array and Combined Array
14.10 Determining Optimum Conditions
14.11 Summary
References
Exercises
15 Evolutionary Operation
15.1 EVOP Illustrations
15.2 Three Variables
15.3 Simplex EVOP
15.4 Other EVOP Procedures
15.5 Miscellaneous Uses of EVOP
15.6 Summary
Appendix
15.A Derivation of Formula for Estimating s
References
Exercises
16 Analysis of Means
16.1 ANOM for One-Way Classifications
16.2 ANOM for Attribute Data
16.2.1 Proportions
16.2.2 Count Data
16.3 ANOM When Standards Are Given
16.3.1 Nonconforming Units
16.3.2 Nonconformities
16.3.3 Measurement Data
16.4 ANOM for Factorial Designs
16.4.1 Assumptions
16.4.2 An Alternative Way of Displaying Interaction Effects
16.5 ANOM When at Least One Factor Has More Than Two Levels
16.5.1 Main Effects
16.5.2 Interaction Effects
16.6 Use of ANOM with Other Designs
16.7 Nonparametric ANOM
16.8 Summary
Appendix
References
Exercises
17 Using Combinations of Quality Improvement Tools
17.1 Control Charts and Design of Experiments
17.2 Control Charts and Calibration Experiments
17.3 Six Sigma Programs
17.3.1 Components of a Six Sigma Program
17.3.2 Six Sigma Applications and Programs
17.3.3 Six Sigma Concept for Customer Satisfaction
17.3.4 Six Sigma Training
17.3.5 Lean Six Sigma
17.3.6 Related Programs/Other Companies
17.3.6.1 SEMATECH's Qual Plan
17.3.6.2 AlliedSignal's Operational Excellence Program
17.4 Statistical Process Control and Engineering Process Control
References
Answers to Selected Exercises
Appendix: Statistical Tables
Author Index
Subject Index
WILEY SERIES IN PROBABILITY AND STATISTICS

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