Statistical Methods for Quality Improvement

ISBN-10: 0471197750

ISBN-13: 9780471197751

Edition: 2nd 2000 (Revised)

Authors: Thomas P. Ryan
List price: $166.00
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Description: This survey of statistical quality control reviews basic probability, statistics and basic control chart principles, and provides insight into statistically designed experiments. It reviews the use of statistics in quality control.

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Book details

List price: $166.00
Edition: 2nd
Copyright year: 2000
Publisher: John Wiley & Sons, Incorporated
Publication date: 2/14/2000
Binding: Hardcover
Pages: 592
Size: 6.75" wide x 9.75" long x 1.25" tall
Weight: 2.178
Language: English

Preface
Preface to the First Edition
Fundamental Quality Improvement and Statistical Concepts
Introduction
Quality and Productivity
Quality Costs (or Does It?)
The Need for Statistical Methods
Early Use of Statistical Methods for Improving Quality
Influential Quality Experts
Summary
References
Basic Tools for Improving Quality
Histogram
Pareto Charts
Scatter Plots
Control Chart
Check Sheet
Cause-and-Effect Diagram
Defect Concentration Diagram
The Seven New Tools
Summary
References
Basic Concepts in Statistics and Probability
Probability
Sample Versus Population
Location
Variation
Discrete Distributions
Continuous Distributions
Choice of Statistical Distribution
Statistical Inference
Enumerative Studies versus Analytic Studies
References
Exercises
Control Charts and Process Capability
Control Charts for Measurements with Subgrouping (for One Variable)
Basic Control Chart Principles
Real-Time Control Charting Versus Analysis of Past Data
Control Charts: When to Use, Where to Use, How Many to Use
Benefits from the Use of Control Charts
Rational Subgroups
Basic Statistical Aspects of Control Charts
Illustrative Example
Illustrative Example with Real Data
Determining the Time of a Parameter Change
Acceptance Sampling and Acceptance Control Chart
Modified Limits
Difference Control Charts
Other Charts
Average Run Length
Determining the Subgroup Size
Out-of-Control Action Plans
Assumptions for the Charts in This Chapter
Measurement Error
Summary
Appendix
References
Exercises
Control Charts for Measurements without Subgrouping (for One Variable)
Individual Observations Chart
Moving Average Chart
Controlling Variability with Individual Observations
Summary
Appendix
References
Exercises
Control Charts for Attributes
Charts for Nonconforming Units
Charts for Nonconformities
Summary
References
Exercises
Process Capability
Data Acquisition for Capability Indices
Process Capability Indices
Estimating the Parameters in Process Capability Indices
Distributional Assumption for Capability Indices
Confidence Intervals for Process Capability Indices
Asymmetric Bilateral Tolerances
Capability Indices That Are a Function of Percent Nonconforming
Modified k Index
Other Approaches
Process Capability Plots
Process Capability Indices Versus Process Performance Indices
Process Capability Indices with Autocorrelated Data
Summary
References
Exercises
Alternatives to Shewhart Charts
Introduction
Cumulative Sum Procedures: Principles and Historical Development
CUSUM Procedures for Controlling Process Variability
CUSUM Procedures for Nonconforming Units
CUSUM Procedures for Nonconformity Data
Exponentially Weighted Moving Average Charts
Summary
References
Exercises
Multivariate Control Charts for Measurement Data
Hotelling's T[superscript 2] Distribution
A T[superscript 2] Control Chart
Multivariate Chart Versus Individual X Charts
Charts for Detecting Variability and Correlation Shifts
Charts Constructed Using Individual Observations
When to Use Each Chart
Actual Alpha Levels for Multiple Points
Requisite Assumptions
Effects of Parameter Estimation on ARLs
Dimension Reduction Techniques
Multivariate CUSUM Charts
Multivariate EWMA Charts
Applications of Multivariate Charts
Multivariate Process Capability Indices
Summary
Appendix
References
Exercises
Miscellaneous Control Chart Topics
Pre-Control
Short-Run SPC
Charts for Autocorrelated Data
Charts for Batch Processes
Charts for Multiple-Stream Processes
Nonparametric Control Charts
Bayesian Control Chart Methods
Control Charts for Variance Components
Neural Networks
Economic Design of Control Charts
Charts with Variable Sample Size and/or Variable Sampling Interval
Users of Control Charts
Software for Control Charting
Bibliography
Exercises
Beyond Control Charts: Graphical and Statistical Methods
Other Graphical Methods
Stem-and-Leaf Display
Dot Diagrams
Boxplot
Normal Probability Plot
Plotting Three Variables
Displaying More Than Three Variables
Multi-Vari Chart
Plots to Aid in Transforming Data
Summary
References
Exercises
Linear Regression
Simple Linear Regression
Worth of the Prediction Equation
Assumptions
Checking Assumptions through Residual Plots
Confidence Intervals and Hypothesis Tests
Prediction Interval for Y
Regression Control Chart
Cause-Selecting Charts
Inverse Regression
Multiple Linear Regression
Issues in Multiple Regression
Software for Regression
Summary
References
Exercises
Design of Experiments
A Simple Example of Experimental Design Principles
Principles of Experimental Design
Statistical Concepts in Experimental Design
t Tests
Analysis of Variance for One Factor
Regression Analysis of Data from Designed Experiments
ANOVA for Two Factors
The 2[superscript 3] Design
Assessment of Effects without a Residual Term
Residual Plot
Separate Analyses Using Design Units and Uncoded Units
Two-Level Designs with More Than Three Factors
Three-Level Factorial Designs
Mixed Factorials
Fractional Factorials
Other Topics in Experimental Design and Their Applications
Summary
References
Exercises
Contributions of Genichi Taguchi and Alternative Approaches
"Taguchi Methods"
Quality Engineering
Loss Functions
Distribution Not Centered at the Target
Loss Functions and Specification Limits
Asymmetric Loss Functions
Signal-to-Noise Ratios and Alternatives
Experimental Designs for Stage 1
Taguchi Methods of Design
Determining Optimum Conditions
Summary
References
Exercises
Evolutionary Operation
EVOP Illustrations
Three Variables
Simplex EVOP
Other EVOP Procedures
Miscellaneous Uses of EVOP
Summary
Appendix
References
Exercises
Analysis of Means
ANOM for One-Way Classifications
ANOM for Attribute Data
ANOM When Standards Are Given
ANOM for Factorial Designs
ANOM When at Least One Factor Has More Than Two Levels
Use of ANOM with Other Designs
Nonparametric ANOM
Summary
Appendix
References
Exercises
Using Combinations of Quality Improvement Tools
Control Charts and Design of Experiments
Control Charts and Calibration Experiments
Six Sigma Programs
Statistical Process Control and Engineering Process Control
References
Answers to Selected Exercises
Statistical Tables
Author Index
Subject Index
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