Industrial Statistics

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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.

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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).

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PREFACE.

1. BASIC STATISTICS: HOW TO REDUCE FINANCIAL RISK?

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.

2. WHY NOT TO DO THE USUAL t-TEST AND WHAT TO REPLACE IT WITH?

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.

3. DESIGN OF EXPERIMENTS: IS IT NOT GOING TO COST TOO MUCH AND TAKE TOO LONG?

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.

4. WHAT IS THE KEY TO DESIGNING ROBUST PRODUCTS AND PROCESSES?

4.1. The Key to Robustness.

4.2. Robust Design Method.

4.3. Signal-to-Noise Ratios.

4.5. Alternate Analysis Procedure.

4.6. Implications for R&D.

5. SETTING SPECIFICATIONS: ARBITRARY OR IS THERE A METHOD TO IT?

5.1. Understanding Specifications.

5.2. Empirical Approach.

5.3. Functional Approach.

5.4. Minimum Life Cycle Cost Approach.

6. HOW TO DESIGN PRACTICAL ACCEPTANCE SAMPLING PLANS AND PROCESS VALIDATION STUDIES?

6.1. Single-Sample Attribute Plans.

6.2. Selecting AQL and RQL.

6.3. Other Acceptance Sampling Plans.

6.4. Designing Validation Studies.

7. MANAGING AND IMPROVING PROCESSES: HOW TO USE AN AT-A-GLANCE-DISPLAY?

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.

8. HOW TO FIND CAUSES OF VARIATION BY JUST LOOKING SYSTEMATICALLY?

8.1. Manufacturing Application.

8.2. Variance Components Analysis.

8.3. Planning for Quality Improvement.

8.4. Structured Studies.

9. IS MY MEASUREMENT SYSTEM ACCEPTABLE AND HOW TO DESIGN, VALIDATE, AND IMPROVE IT?

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.

10. HOW TO USE THEORY EFFECTIVELY?

10.1. Empirical Models.

10.2. Mechanistic Models.

10.3. Mechanistic Model for Coat Weight CV.

11.1. Questions.