Synopses & Reviews
Douglas C. Montgomery, Professor of Engineering and Statistics at Arizona State University, received his B.S., M.S., and Ph.D. degrees from Virginia Polytechnic Institute, all in engineering, from 1969 to 1984 he was a faculty member of the School of Industrial & Systems Engineering at the Georgia Institute of Technology; from 1984 to 1988 he was at the University of Washington, where he held the John M. Fluke Distinguished chair of Manufacturing Engineering, was Professor of Mechanical Engineering, and Director of the Program in industrial Engineering.
Dr. Montgomery has research and teaching interests in industrial statistics including statistical quality control techniques, design of experiments, regression analysis and empirical model building, and the application of operations research methodology to problems in manufacturing systems. He has authored and coauthored many technical papers in these fields and is an author of twelve other books. Dr. Montgomery is a Fellow of the American Society for Quality, a Fellow of the American Statistical Association, a Fellow of the Royal Statistical Society, a Fellow of the Institute of Industrial Engineers, and an Elected member of the International Statistical Institute. He is a Stewart Medallist of the American Society for Quality, and has also received the Brumbaugh Award, the William G. Hunter Award, and the Shewell Award(twice) from the ASQ. He is a recipient of the Ellis R. Ott Award. He is a former editor of the Journal of Quality Technology, the current editor of Quality and Reliability Engineering International, and serves on the editorial boards of several journals.
Review
‘Readers will find the content of the book to be useful in engineering problem solving and decision making.’ (Zentralblatt MATH,1102,May 2007)
Synopsis
Real Engineering Situations, Real Engineering DataWith Montgomery and Runger’s best-selling engineering statistics text, you can learn how to apply statistics to realengineering situations. The text shows you how to use statistical methods to design and develop new products, and new manufacturing systems and processes. You’ll gain a better understanding of how these methods are used in everyday work, and get a taste of practical engineering experience through real-world, engineering-based examples and exercises.
Now revised, this Fourth Edition of Applied Statistics and Probability for Engineersfeatures many new homework exercises, including a greater variation of problems and more computer problems.
Key Features
- The text treats all topics in a way that reflects today's engineering realities. In the probability chapters, the authors emphasize engineering-specific examples, rather than counting methods or artificial applications such as gambling.
- Examples and exercises throughout the text use real data and real engineering situations.
- Coverage of probability is lively and interesting. It is complete but concise so as not to take over the content of the entire text.
- Thorough coverage of regression modeling, design of engineering experiments, and statistical process control from experts in these topics makes the book especially useful as a reference.
Synopsis
This best-selling engineering statistics text provides a practical approach that is more oriented to engineering and the chemical and physical sciences than many similar texts. It's packed with unique problem sets that reflect realistic situations engineers will encounter in their working lives.
Each copy of the book includes an e-Text on CD - that is a complete electronic version of book. This e-Text features enlarged figures, worked-out solutions, links to data sets for problems solved with a computer, multiple links between glossary terms and text sections for quick and easy reference, and a wealth of additional material to create a dynamic study environment for students.
Suitable for a one- or two-term Jr/Sr course in probability and statistics for all engineering majors.
Table of Contents
The Role of Statistics in Engineering.
Probability.
Discrete Random Variables and Probability Distributions.
Continuous Random Variables and Probability Distributions.
Joint Probability Distributions.
Random Sampling and Data Description.
Point Estimation of Parameters.
Statistical Intervals for a Single Sample.
Tests of Hypotheses for a Single Sample.
Statistical Inference for Two Samples.
Simple Linear Regression and Correlation.
Multiple Linear Regression.
Design and Analysis of Single-Factor Experiments: The Analysis of Variance.
Design of Experiments with Several Factors.
Nonparametric Statistics.
Statistical Quality Control.
Appendix A: Statistical Tables and Charts.
Appendix B: Bibliography.
Appendix C: Answers to Selected Exercises.
Glossary.
Index.