ISBN-10:
1933264721
ISBN-13:
9781933264721
Pub. Date:
09/28/2009
Publisher:
Lawyers & Judges Publishing Company, Inc.
Uncertainty Analysis for Forensic Science / Edition 2

Uncertainty Analysis for Forensic Science / Edition 2

by Raymond M. Brach, Patrick F. Dunn

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Product Details

ISBN-13: 9781933264721
Publisher: Lawyers & Judges Publishing Company, Inc.
Publication date: 09/28/2009
Pages: 189
Product dimensions: 6.00(w) x 8.90(h) x 0.60(d)

About the Author

Raymond M. Brach, Ph.D., P.E., is a consultant in the field of accident reconstruction and a professor emeritus from the Department of Aerospace and Mechanical Engineering, University of Notre Dame. He was granted a Ph.D. in Engineering Mechanics from the University of Wisconsin, Madison, and a BS and MS in Mechanical Engineering from Illinois Institute of Technology, Chicago, Illinois. His specialized areas of teaching and research include mechanical design, mechanics, vibrations, acoustics, applications of statistics and quality control, vehicle dynamics, accident reconstruction and microparticle dynamics. He is a licensed professional engineer in the State of Indiana and is a Fellow member of SAE. In addition to over 100 research papers and numerous invited lectures, he has authored Mechanical Impact Dynamics, published by Wiley Interscience in 1991 (also, Revised Edition, www.brachengineering.com, 2007) and Vehicle Accident Analysis and Reconstruction Methods, SAE, 2005, coauthored with R. Matthew Brach.

Patrick F. Dunn, Ph.D., P.E, is a professor of aerospace and mechanical engineering at the University of Notre Dame, where he has been a faculty member since 1985. Prior to 1985, he was a mechanical engineer at Argonne National Laboratory from 1976 to 1985 and a postdoctoral fellow at Duke University from 1974 to 1976. He received his B.S., M.S. and Ph.D. degrees in engineering from Purdue University (1970, 1971 and 1974). He is the author of over 160 scientific journal and refereed symposia publications and a licensed Professional Engineer in Indiana and Illinois. He is a Fellow of the American Society of Mechanical Engineers, and an Associate Fellow of the AmericanInstitute of Aeronautics and Astronautics. He is the recipient of departmental, college and university teaching awards. Professor Dunn's scientific expertise is in fluid mechanics and microparticle behavior in flows. He is an experimentalist with over 40 years of experience involving measurement uncertainty. He is the author of the textbook Measurement and Data Analysis for Engineering and Science (1st edition by McGraw-Hill, 2005; 2nd edition by Taylor & Francis/CRC Press, 2010) involving uncertainty and other issues related to measurements.

Table of Contents

Chapter 1. Introduction

Chapter 2. Units, Dimensions, Significant Figures and Calculations
2.1 Chapter Overview
2.2 A Brief History of English and Metric Systems
2.3 Systems of Units
2.4 SI Standards
2.5 Conversions between Technical English and SI
2.6 Prefixes
2.7 Significant Figures
References

Chapter 3. Probability and Statistics
3.1 Chapter Overview
3.2 Basic Concepts in Probability
3.3 Sample versus Population
3.4 Plotting Statistical Information
3.5 The Probability Density Function
3.6 Central Moments
3.7 The Probability Distribution Function
3.8 Various Probability Density Functions
3.9 Normalized Variables
3.10 Student's t Distribution
3.11 The Standard Deviation of the Means
3.12 Pooling Samples
3.13 The Chi-Square Distribution
References

Chapter 4. Uncertainty Analysis
4.1 Uncertainty
4.2 Comparing Theory and Measurement
4.3 Uncertainty as an Estimated Variance
4.4 Systematic and Random Errors
4.5 Measurement Process Errors
4.6 Quantifying Uncertainties
4.7 Measurement Uncertainty Analysis
4.8 General Uncertainty Analysis
4.9 Detailed Uncertainty Analysis4.10 Uncertainty Analysis Summary
4.11 Finite-Difference Uncertainties
4.12 Interval Uncertainty
References

Chapter 5. Uncertainty Analysis Using Statistics
5.1 Chapter Overview
5.2 Mathematical Relationships
5.3 Equations that Relate Statistical Variables Linearly
5.4 Statistical Variables Related Nonlinearly: Approximate Relationships
5.5 Monte Carlo Analysis

5.6 Mean and Variance of Related Variables
References

Chapter 6. Sensitivity and Design ofExperiments
6.1 Chapter Overview
6.2 Background on the Design of Experiments
6.3 Basics of Design of Experiments
6.4 Factorial Design and Main Effects
6.5 Response Surface and Sensitivity
6.6 Significance of Results of a Factorial Design
6.7 Full Factorial Designs
6.8 Fractional Factorial Designs
6.9 Calculation of the Sums of Squares of Main Effects and Interactions
References

Appendix A. Unit Conversions for Common Units
Appendix B. Normal Probability Graphs
About the Authors
Index

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