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Full Description
Thoroughly updated throughout, this second edition will continue to be about the practicable methods of statistical applications for engineers, and as well for scientists and those in business. It remains a what-I-wish-I-had-known-when-starting-my-career compilation of techniques.
Contrasting a mathematical and abstract orientation of many statistics texts, which expresses the science/math values of researchers, this book has its focus on the application to concrete examples and the interpretation of outcomes. Supporting application propriety, this book also presents the fundamental concepts, provides supporting derivation, and has frequent do and not-do notes.
Key Features:
Contains details of the computation for the examples.
Includes new examples and exercises.
Includes expanded topics supporting data analysis.
The book is for upper-level undergraduate or graduate students in engineering, the hard sciences, or business programs. The intent is that the text would continue to be useful in professional life, and appropriate as a self-learning tool after graduation - whether in graduate school or in professional practice.
Errata can be found here
Contents
1. INTRODUCTION. 2. PROBABILITY. 3. DISTRIBUTIONS. 4. DESCRIPTIVE STATISTICS. 5. STATISTICAL INFERENCE AND ESTIMATIONS. 6. HYPOTHESIS FORMULATION AND TESTING. 7. NONPARAMETRIC TESTS. 8. REPORTING AND PROPAGATING ERROR IN CALCULATIONS. 9. RISK. 10. STOCHASTIC SIMULATION. 11. IMPACT OF RIGHT OR WRONG DECISIONS 12. ANALYSIS OF VARIANCE. 13. STEADY AND TRANSIENT STATE IDENTIFICATION. 14. LINEAR REGRESSION. 15. NONLINEAR REGRESSION. 16. EXPERIMENTAL DESIGN FOR LINEAR STEADY-STATE MODELS - SCREENING DESIGNS. 17. CONCEPTS FOR PHENOMENOLOGICAL MODEL VALIDATION. 18. EXPERIMENTAL DESIGN FOR NONLINEAR MODELS 19. STATISTICAL PROCESS CONTROL. 20. RELIABILITY. 21. CLASSIFICATION ALGORITHMS.
