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HELPS YOU FULLY LEVERAGE STATISTICAL METHODS TO IMPROVE
INDUSTRIAL PERFORMANCE
Industrial Statistics guides you through ten practical
statistical methods that have broad applications in many different
industries for enhancing research, product design, process design,
validation, manufacturing, and continuous improvement. As you
progress through the book, you'll discover some valuable methods
that are currently underutilized in industry as well as other
methods that are often not used correctly.
With twenty-five years of teaching and consulting experience,
author Anand Joglekar has helped a diverse group of companies
reduce costs, accelerate product development, and improve
operations through the effective implementation of statistical
methods. Based on his experience working with both clients and
students, Dr. Joglekar focuses on real-world problem-solving. For
each statistical method, the book:
Presents the most important underlying concepts clearly and
succinctly
Minimizes mathematical details that can be delegated to a
computer
Illustrates applications with numerous practical examples
Offers a "Questions to Ask" section at the end of each chapter
to assist you with implementation
The last chapter consists of 100 practical questions followed by
their answers. If you're already familiar with statistical methods,
you may want to take the test first to determine which methods to
focus on.
By helping readers fully leverage statistical methods to improve
industrial performance, this book becomes an ideal reference and
self-study guide for scientists, engineers, managers and other
technical professionals across a wide range of industries. In
addition, its clear explanations and examples make it highly suited
as a textbook for undergraduate and graduate courses in
statistics.
Autorentext
ANAND M. JOGLEKAR, PhD, is a leading statistics educator and consultant. In 1990, Dr. Joglekar founded Joglekar Associates, a firm dedicated to helping industrial organizations reach their goals through the effective implementation of statistical methods. He has taught statistical methods to thousands of industry participants through in-house seminars and seminars sponsored by associations such as the LifeScience Alley®, Institute of Food Technologists, and American Association of Cereal Chemists. Among his many publications, Dr. Joglekar is the author of Statistical Methods for Six Sigma in R&D and Manufacturing (also from Wiley).
Zusammenfassung
HELPS YOU FULLY LEVERAGE STATISTICAL METHODS TO IMPROVE INDUSTRIAL PERFORMANCE
Industrial Statistics guides you through ten practical statistical methods that have broad applications in many different industries for enhancing research, product design, process design, validation, manufacturing, and continuous improvement. As you progress through the book, you'll discover some valuable methods that are currently underutilized in industry as well as other methods that are often not used correctly.
With twenty-five years of teaching and consulting experience, author Anand Joglekar has helped a diverse group of companies reduce costs, accelerate product development, and improve operations through the effective implementation of statistical methods. Based on his experience working with both clients and students, Dr. Joglekar focuses on real-world problem-solving. For each statistical method, the book:
Presents the most important underlying concepts clearly and succinctly
Minimizes mathematical details that can be delegated to a computer
Illustrates applications with numerous practical examples
Offers a "Questions to Ask" section at the end of each chapter to assist you with implementation
The last chapter consists of 100 practical questions followed by their answers. If you're already familiar with statistical methods, you may want to take the test first to determine which methods to focus on.
By helping readers fully leverage statistical methods to improve industrial performance, this book becomes an ideal reference and self-study guide for scientists, engineers, managers and other technical professionals across a wide range of industries. In addition, its clear explanations and examples make it highly suited as a textbook for undergraduate and graduate courses in statistics.
Inhalt
PREFACE.
1.1. Capital Market Returns.
1.2. Sample Statistics.
1.3. Population Parameters.
1.4. Confidence Intervals and Sample Sizes.
1.5. Correlation.
1.6. Portfolio Optimization.
1.7. Questions to Ask.
2.1. What is a t-Test and what is Wrong with It?
2.2. Confidence Interval is Better Than a t-Test.
2.3. How Much Data to Collect?
2.4. Reducing Sample Size.
2.5. Paired Comparison.
2.6. Comparing Two Standard Deviations.
2.7. Recommended Design and Analysis Procedure.
2.8. Questions to Ask.
3.1. Why Design Experiments?
3.2. Factorial Designs.
3.3. Success Factors.
3.4. Fractional Factorial Designs.
3.5. Plackett-Burman Designs.
3.6. Applications.
3.7. Optimization Designs.
3.8. Questions to Ask.
4.1. The Key to Robustness.
4.2. Robust Design Method.
4.3. Signal-to-Noise Ratios.
4.4. Achieving Additivity.
4.5. Alternate Analysis Procedure.
4.6. Implications for R&D.
4.7. Questions to Ask.
5.1. Understanding Specifications.
5.2. Empirical Approach.
5.3. Functional Approach.
5.4. Minimum Life Cycle Cost Approach.
5.5. Questions to Ask.
6.1. Single-Sample Attribute Plans.
6.2. Selecting AQL and RQL.
6.3. Other Acceptance Sampling Plans.
6.4. Designing Validation Studies.
6.5. Questions to Ask.
7.1. Statistical Logic of Control Limits.
7.2. Selecting Subgroup Size.
7.3. Selecting Sampling Interval.
7.4. Out-of-Control Rules.
7.5. Process Capability and Performance Indices.
7.6. At-A-Glance-Display.
7.7. Questions to Ask.
8.1. Manufacturing Application.
8.2. Variance Components Analysis.
8.3. Planning for Quality Improvement.
8.4. Structured Studies.
8.5. Questions to Ask.
9.1. Acceptance Criteria.
9.2. Designing Cost-Effective Sampling Schemes.
9.3. Designing a Robust Measurement System.
9.4. Measurement System Validation.
9.5. Repeatability and Reproducibility (R&R) Study.
9.6. Questions to Ask.
10.1. Empirical Models.
10.2. Mechanistic Models.
10.3. Mechanistic Model for Coat Weight CV.
10.4. Questions to Ask.
11.1. Questions.
11.2. Answers.
APPENDIX: TABLES.
REFERENCES.
INDEX.