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Pt. I Introduction 1
1 The Scientific Method and Statistics 3
2 Concepts of Quality Control 23
Pt. II Basic Tools 47
3 Theoretical Background: Probability and Statistics 49
4 Descriptive Tools 91
5 Probability Plots 119
6 Inferential Statistics, Prediction and Statistical Decision Rules 139
Pt. III Good Experiments Make for Good Statistics 179
7 Strategies for Experimentation with Multiple Factors 181
8 Basic Two-Level Factorial Experiments 199
9 Additional Tools for Design and Analysis of Two-Level Factorials 249
10 Regression Analysis 291
11 Multiple Level Factorial Experiments 341
12 Screening Designs 399
Pt. IV Optimization Experiments 463
13 Response Surface Methodology 465
14 Response Surface Model Fitting 493
15 Mixture Experiments 519
Pt. V Variability and Quality 583
16 Characterizing Variability in Data 585
17 Shewhart Control Charts 637
18 Off-Line Quality Control and Robust Design 705
App. A 747
App. B 757
Index 805
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Through years of teaching experience, John S. Lawson and John Erjavec have learned that it doesn't take much theoretical background before engineers can learn practical methods of data collections, analysis, and interpretation that will be useful in real life and on the job. With this premise in mind, the authors wrote ENGINEERING AND INDUSTRIAL STATISTICS, which includes the basic topics of engineering statistics but puts less emphasis on the theoretical concepts and elementary topics usually found in an introductory statistics book. Instead, the authors put more emphasis on techniques that will be useful for engineers. With fewer details of traditional probability and inference and more emphasis on the topics useful to engineers, the book is flexible for instructors and interesting for students.
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