Springer Handbook of Engineering Statistics, 1st Edition

  • Published By:
  • ISBN-10: 1846282888
  • ISBN-13: 9781846282881
  • Grade Level Range: College Freshman - College Senior
  • 1120 Pages | eBook
  • Original Copyright 2006 | Published/Released December 2006
  • This publication's content originally published in print form: 2006

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About

Overview

The Springer Handbook of Engineering Statisticsgathers together the full range of statistical techniques required by engineers from all fields to gain sensible statistical feedback on how their processes or products are functioning and to give them realistic predictions of how these could be improved. This title includes: Contributions from leading experts in statistics and their application to engineering from industrial control to academic medicine and financial risk management giving all-round authoritative coverage; a distinguished international Editorial Board ensuring clarity and precision throughout; and wide-ranging selection of statistical techniques showing the proper way to use each to enable the reader to choose the method most appropriate for his or her purposes. The handbook will be essential reading for all engineers and engineering-connected managers who are serious about keeping their methods and products at the cutting edge of quality and competitiveness.

Table of Contents

Front Cover.
Half Title Page.
Other Frontmatter.
Title Page.
Copyright Page.
Dedication.
Preface.
List of Authors.
Contents.
List of Tables.
List of Abbreviations.
1: Fundamental Statistics and Its Applications.
2: Basic Statistical Concepts.
3: Statistical Reliability with Applications.
4: Weibull Distributions and Their Applications.
5: Characterizations of Probability Distributions.
6: Two-Dimensional Failure Modeling.
7: Prediction Intervals for Reliability Growth Models with Small Sample Sizes.
8: Promotional Warranty Policies: Analysis and Perspectives.
9: Stationary Marked Point Processes.
10: Modeling and Analyzing Yield, Burn-In and Reliability for Semiconductor Manufacturing: Overview.
11: Process Monitoring and Improvement.
12: Statistical Methods for Quality and Productivity Improvement.
13: Statistical Methods for Product and Process Improvement.
14: Robust Optimization in Quality Engineering.
15: Uniform Design and Its Industrial Applications.
16: Cuscore Statistics: Directed Process Monitoring for Early Problem Detection.
17: Chain Sampling.
18: Some Statistical Models for the Monitoring of High-Quality Processes.
19: Monitoring Process Variability Using EWMA.
20: Multivariate Statistical Process Control Schemes for Controlling a Mean.
21: Reliability Models and Survival Analysis.
22: Statistical Survival Analysis with Applications.
23: Failure Rates in Heterogeneous Populations.
24: Proportional Hazards Regression Models.
25: Accelerated Life Test Models and Data Analysis.
26: Statistical Approaches to Planning of Accelerated Reliability Testing.
27: End-to-End (E2E) Testing and Evaluation of High-Assurance Systems.
28: Statistical Models in Software Reliability and Operations Research.
29: An Experimental Study of Human Factors in Software Reliability Based on a Quality Engineering Approach.
30: Statistical Models for Predicting Reliability of Software Systems in Random Environments.
31: Regression Methods and Data Mining.
32: Measures of Influence and Sensitivity in Linear Regression.
33: Logistic Regression Tree Analysis.
34: Tree-Based Methods and Their Applications.
35: Image Registration and Unknown Coordinate Systems.
36: Statistical Genetics for Genomic Data Analysis.
37: Statistical Methodologies for Analyzing Genomic Data.
38: Statistical Methods in Proteomics.
39: Radial Basis Functions for Data Mining.
40: Data Mining Methods and Applications.
41: Modeling and Simulation Methods.
42: Bootstrap, Markov Chain and Estimating Function.
43: Random Effects.
44: Cluster Randomized Trials: Design and Analysis.
45: A Two-Way Semilinear Model for Normalization and Analysis of Microarray Data.
46: Latent Variable Models for Longitudinal Data with Flexible Measurement Schedule.
47: Genetic Algorithms and Their Applications.
48: Scan Statistics.
49: Condition-Based Failure Prediction.
50: Statistical Maintenance Modeling for Complex Systems.
51: Statistical Models on Maintenance.
52: Applications in Engineering Statistics.
53: Risks and Assets Pricing.
54: Statistical Management and Modeling for Demand of Spare Parts.
55: Arithmetic and Geometric Processes.
56: Six Sigma.
57: Multivariate Modeling with Copulas and Engineering Applications.
58: Queuing Theory Applications to Communication Systems: Control of Traffic Flows and Load Balancing.
59: Support Vector Machines for Data Modeling with Software Engineering Applications.
60: Optimal System Design.
Acknowledgements.
About the Authors.
Detailed Contents.
Subject Index.
Front Cover.
Half Title Page.
Other Frontmatter.
Title Page.
Copyright Page.
Dedication.
Preface.
List of Authors.
Contents.
List of Tables.
List of Abbreviations.
1: Fundamental Statistics and Its Applications.
2: Basic Statistical Concepts.
3: Statistical Reliability with Applications.
4: Weibull Distributions and Their Applications.
5: Characterizations of Probability Distributions.
6: Two-Dimensional Failure Modeling.
7: Prediction Intervals for Reliability Growth Models with Small Sample Sizes.
8: Promotional Warranty Policies: Analysis and Perspectives.
9: Stationary Marked Point Processes.
10: Modeling and Analyzing Yield, Burn-In and Reliability for Semiconductor Manufacturing: Overview.
11: Process Monitoring and Improvement.
12: Statistical Methods for Quality and Productivity Improvement.
13: Statistical Methods for Product and Process Improvement.
14: Robust Optimization in Quality Engineering.
15: Uniform Design and Its Industrial Applications.
16: Cuscore Statistics: Directed Process Monitoring for Early Problem Detection.
17: Chain Sampling.
18: Some Statistical Models for the Monitoring of High-Quality Processes.
19: Monitoring Process Variability Using EWMA.
20: Multivariate Statistical Process Control Schemes for Controlling a Mean.
21: Reliability Models and Survival Analysis.
22: Statistical Survival Analysis with Applications.
23: Failure Rates in Heterogeneous Populations.
24: Proportional Hazards Regression Models.
25: Accelerated Life Test Models and Data Analysis.
26: Statistical Approaches to Planning of Accelerated Reliability Testing.
27: End-to-End (E2E) Testing and Evaluation of High-Assurance Systems.
28: Statistical Models in Software Reliability and Operations Research.
29: An Experimental Study of Human Factors in Software Reliability Based on a Quality Engineering Approach.
30: Statistical Models for Predicting Reliability of Software Systems in Random Environments.
31: Regression Methods and Data Mining.
32: Measures of Influence and Sensitivity in Linear Regression.
33: Logistic Regression Tree Analysis.
34: Tree-Based Methods and Their Applications.
35: Image Registration and Unknown Coordinate Systems.
36: Statistical Genetics for Genomic Data Analysis.
37: Statistical Methodologies for Analyzing Genomic Data.
38: Statistical Methods in Proteomics.
39: Radial Basis Functions for Data Mining.
40: Data Mining Methods and Applications.
41: Modeling and Simulation Methods.
42: Bootstrap, Markov Chain and Estimating Function.
43: Random Effects.
44: Cluster Randomized Trials: Design and Analysis.
45: A Two-Way Semilinear Model for Normalization and Analysis of Microarray Data.
46: Latent Variable Models for Longitudinal Data with Flexible Measurement Schedule.
47: Genetic Algorithms and Their Applications.
48: Scan Statistics.
49: Condition-Based Failure Prediction.
50: Statistical Maintenance Modeling for Complex Systems.
51: Statistical Models on Maintenance.
52: Applications in Engineering Statistics.
53: Risks and Assets Pricing.
54: Statistical Management and Modeling for Demand of Spare Parts.
55: Arithmetic and Geometric Processes.
56: Six Sigma.
57: Multivariate Modeling with Copulas and Engineering Applications.
58: Queuing Theory Applications to Communication Systems: Control of Traffic Flows and Load Balancing.
59: Support Vector Machines for Data Modeling with Software Engineering Applications.
60: Optimal System Design.
Acknowledgements.
About the Authors.
Detailed Contents.
Subject Index.