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Summary
Summary
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.
Table of Contents
Part I Introduction |
1 The Scientific Method And Statistics |
2 Concepts Of Quality Control |
Part II Basic Tools |
3 Theoretical Background: Probability And Statistics |
4 Descriptive Tools |
5 Probability Plots |
6 Inferential Statistics, Prediction And Statisticsal Decision Rules |
Part III Good Experiments Make For Good Statistics |
7 Strategies For Experimentation With Multiple Factors |
8 Basic Two-Level Factorial Experiments |
9 Additional Tools For Design And Analysis Of Two-Level Factorials |
10 Regression Analysis |
11 Multiple Level Factorial Experiments |
12 Screening Designs |
Part IV Optimization Experiments |
13 Response Surface Methodology |
14 Response Surface Model Fitting |
15 Mixture Experiments |
Part V Variability And Quality |
16 Characterizing Variability In Data |
17 Shewhart Control Charts |
18 Off-Line Quality Control And Robust Design |
Appendix A |
Appenidix B |
Index |