Industrial Statistics Practical Methods and Guidance for Improved Performance

by Joglekar, Anand M.

• ISBN13: 9780470497166
• ISBN10: 0470497165
• Edition: 1st
• Format: Hardcover
• Copyright: 2010-04-05
• Publisher: Wiley

Summary

This useful guide concisely communicates ten practical statistical methods that are widely applicable to research, product design, process design, validation, manufacturing, and continuous improvement in many different industries. The coverage presents the key ideas behind each statistical method in clear, concise, and accessible writing, with minimal mathematical detail. Focusing on practical aspects for problem-solving, the text illustrates applications with numerous examples, providing a accessible resource to scientists, engineers, and other technical personnel in R&D and manufacturing, as well as quality professionals, analytical chemists, and technical managers in industry, universities, and academia.

Author Biography

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 RD and Manufacturing (also from Wiley).

Table of Contents

Preface	p. xi
Basic Statistics: How to Reduce Financial Risk?	p. 1
Capital Market Returns	p. 2
Sample Statistics	p. 5
Population Parameters	p. 9
Confidence Intervals and Sample Sizes	p. 13
Correlation	p. 16
Portfolio Optimization	p. 18
Questions to Ask	p. 24
Why Not to do the Usual t-Test and What to Replace it With?	p. 27
What is a t-Test and what is Wrong with It?	p. 29
Confidence Interval is Better Than a t-Test	p. 32
How Much Data to Collect?	p. 35
Reducing Sample Size	p. 39
Paired Comparison	p. 41
Comparing Two Standard Deviations	p. 44
Recommended Design and Analysis Procedure	p. 46
Questions to Ask	p. 46
Design of Experiments: Is it Not Going to Cost Too Much and Take Too Long?	p. 48
Why Design Experiments?	p. 49
Factorial Designs	p. 53
Success Factors	p. 59
Fractional Factorial Designs	p. 63
Plackett-Burman Designs	p. 66
Applications	p. 67
Optimization Designs	p. 71
Questions to Ask	p. 75
What is the Key to Designing Robust Products and Processes?	p. 77
The Key to Robustness	p. 78
Robust Design Method	p. 83
Signal-to-Noise Ratios	p. 87
Achieving Additivity	p. 89
Alternate Analysis Procedure	p. 92
Implications for R&D	p. 98
Questions to Ask	p. 100
Setting Specifications: Arbitrary or is There a Method to it?	p. 101
Understanding Specifications	p. 103
Empirical Approach	p. 106
Functional Approach	p. 107
Minimum Life Cycle Cost Approach	p. 114
Questions to Ask	p. 119
How to Design Practical Acceptance Sampling Plans and Process Validation Studies?	p. 121
Single-Sample Attribute Plans	p. 123
Selecting AQL and RQL	p. 129
Other Acceptance Sampling Plans	p. 133
Designing Validation Studies	p. 135
Questions to Ask	p. 136
Managing and Improving Processes: How to Use an At-a-Glance-Display?	p. 138
Statistical Logic of Control Limits	p. 139
Selecting Subgroup Size	p. 145
Selecting Sampling Interval	p. 147
Out-of-Control Rules	p. 150
Process Capability and Performance Indices	p. 151
At-A-Glance-Display	p. 155
Questions to Ask	p. 157
How to Find Causes of Variation By Just Looking Systematically?	p. 159
Manufacturing Application	p. 161
Variance Components Analysis	p. 164
Planning for Quality Improvement	p. 166
Structured Studies	p. 168
Questions to Ask	p. 172
Is My Measurement System Acceptable and How to Design, Validate, and Improve it?	p. 174
Acceptance Criteria	p. 175
Designing Cost-Effective Sampling Schemes	p. 178
Designing a Robust Measurement System	p. 180
Measurement System Validation	p. 183
Repeatability and Reproducibility (R&R) Study	p. 185
Questions to Ask	p. 188
How to Use Theory Effectively?	p. 190
Empirical Models	p. 192
Mechanistic Models	p. 197
Mechanistic Model for Coat Weight CV	p. 201
Questions to Ask	p. 205
Questions and Answers	p. 207
Questions	p. 208
Answers	p. 232
Appendix: Tables	p. 251
References	p. 259
Index	p. 261
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