Focusing on how statistical tools are integrated into the engineering problem-solving process, this book provides modern coverage of engineering statistics. It presents a wide range of techniques and methods that engineers will find useful in professional practice. All major aspects of engineering statistics are covered, including descriptive statistics, probability and probability distributions, building regression models, designing and analyzing engineering experiments, and more.
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Book Description
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From the Back Cover
The statistics every engineer should know
Statistics is essential for solving many types of engineering problems. Focusing on the statistical techniques most often used in engineering practice, Montgomery, Runger, and Hubele’s Engineering Statistics, Fourth Edition presents a wide range of techniques and methods that you’ll be able to call upon in your professional capacities.
Through its three previous editions, Engineering Statistics set the standard for statistical texts serving engineers. This Fourth Edition follows the classic’s winning approach, and provides a host of new features and improvements that you’ll appreciate. Developed initially with sponsorship from the National Science Foundation, this revision covers all the major aspects of engineering statistics including:
- Descriptive statistics
- Probability and probability distributions
- Statistical tests and confidence intervals for one and two samples
- Building regression models
- Designing and analyzing engineering experiments
- Statistical quality control
The new edition also features numerous enhancements that help you grasp and absorb the material. Emphasizing data analysis and statistical inference, the text makes use of new, cross-sections of real engineering situations and real data sets and takes a step-by-step approach so that you gain insight into the underlying structure of the problems as you master the problem-solving techniques. Notes which help you interpret results are a particularly useful feature of the revised text.
With comprehensive integration of the PC-based statistical software Minitab and online support through WileyPLUS, this newest edition of Engineering Statistics will serve as a practical introduction for students and a reliable reference for every stage of your engineering career.
About the Author
Douglas C. Montgomery, Regents’ Professor of Industrial Engineering and Statistics at Arizona State University
George C. Runger, Ph.D., is a Professor in the department of Industrial Engineering at
Norma Faris Hubele, Professor of Engineering and Statistics at
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Most Helpful Customer Reviews
10 of 10 people found the following review helpful:
3.0 out of 5 stars
Logically organized but steep learning curve,
By
This review is from: Engineering Statistics (Hardcover)
This was the prescribed textbook for my statistics course. The book was not my choice, but I thought to myself, stats is stats, so any stats textbook is a good stats textbook. Let me go through the good points first, then I will talk about the issues that bugged me.
From content alone, I think this book is the first book that completely covers the critical aspects of statistics. I have never come across any text that explains the relationship between Type I and Type II errors as detailed and meticulously as this book. Other areas, such as ANOVA and decision making, are also very well covered. It might take a couple of readings to fully understand it, but in the end they did the best they could to explain these otherwise very difficult topics. I also liked the on-line support provided for the instructors. Contents and solutions could be easily obtained in digital format, and it greatly reduced the time for preparing lecture notes on Powerpoint. However, I found this textbook a little difficult to teach from, particularly in their treatment of hypothesis testing and confidence intervals. This book introduces confidence interval is a special case of hypothesis testing, so both topics are lumped into one big unit. Though in retrospect I think this way is much more coherent, it also presents an almost vertical learning curve for the students. I had to go back to the "old" way of separating the two topics when I gave my lectures. The other thing that bugged me about this book is the number of errors. Whole terms in formulae were missing, and some terms were not defined in previous equations at all. I hope they would get rid of them for the next edition. There was also another section, where the authors went off tangent on a topic. I was left wondering why they went on and on for pages until I saw an ad spot for some of their other textbooks. The last thing was with the exercises and solutions. It was good that all the solutions are available to the instructors on-line, but sometimes I find that they are unintentionally vague. Some questions (at least the ones I assigned) were not very well explained, and students ended up taking a route, often one with the least resistance, that bore no resemblance to the material covered. I really want to like this book, because I for one think that the organization of topics make logical sense, but as teaching material, I think the students will not appreciate the steep learning curve that is required for this text. Statistics (for engineering) is one of those courses where the mathematical foundations required to appreciate the concepts are simply beyond the understanding of the average student. The authors tried their best to bring some satisfaction in this department, but still not quite.
3 of 3 people found the following review helpful:
3.0 out of 5 stars
Pretty good overall, but lots of errors in the key,
This review is from: Engineering Statistics (Hardcover)
This would be a good book if only there weren't so many errors in the key to the exercise problems. I mean, really, there are A LOT of errors. Sometimes if you have an answer that differs from the answer in the key you can't be sure if you really made a mistake or if the answer in the key is incorrect.
3.0 out of 5 stars
Many mistakes in the solutions.,
By Ryan Fraser (Washington, USA) - See all my reviews
This review is from: Engineering Statistics (Hardcover)
I am sure the book is decent, I tried using it but found the teachers lectures much less confusing. Unfortunately she assigned many problems from the book and many of the answers were incorrect making it difficult to be confident in your work. In addition some answers were just missing or mislabeled. More of a side note than a complaint they used the tables in appendix A to solve problems rather than the true values using a calculator which can create some confusion if you are unaware of this.
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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binomial proportion, residuals from this experiment, propellant burning rate problem, compute trial control limits, probability that this shift, mean burning rate, shampoo data, wire bond pull strength data, plasma etch experiment, reboil temperature, digidot plot, hardwood concentration, arsenic flow rate, strength regression model, collecting engineering data, shampoo quality, complete defining relation, estimate the process mean, mean breaking strength, determine the following probabilities, requested probability, building empirical models, compressive strength data, roadway area, more suitable fit Key Phrases - Capitalized Phrases (CAPs): (learn more)
Use the P-value, Residual Error, Analysis of Variance Source, Testing Hypotheses, Predictor Coef, Reconsider Exercise, Western Electric, Test Testing, Residual Figure, Continuation of Exercise, Observed Frequency, San Diego, Variances Known, Los Angeles, Repeat Exercise, P-value Intercept, San Francisco, John Wiley, Term Effect Coef, P-value Model, Solid State Technology, Fractional Factorial Design Discussed, Use Appendix, Rockwell C-scale, Wafer Meas Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
binomial proportion, residuals from this experiment, propellant burning rate problem, compute trial control limits, probability that this shift, mean burning rate, shampoo data, wire bond pull strength data, plasma etch experiment, reboil temperature, digidot plot, hardwood concentration, arsenic flow rate, strength regression model, collecting engineering data, shampoo quality, complete defining relation, estimate the process mean, mean breaking strength, determine the following probabilities, requested probability, building empirical models, compressive strength data, roadway area, more suitable fit Key Phrases - Capitalized Phrases (CAPs): (learn more)
Use the P-value, Residual Error, Analysis of Variance Source, Testing Hypotheses, Predictor Coef, Reconsider Exercise, Western Electric, Test Testing, Residual Figure, Continuation of Exercise, Observed Frequency, San Diego, Variances Known, Los Angeles, Repeat Exercise, P-value Intercept, San Francisco, John Wiley, Term Effect Coef, P-value Model, Solid State Technology, Fractional Factorial Design Discussed, Use Appendix, Rockwell C-scale, Wafer Meas Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
21
books
cite this book:
See all 21 books citing this book
- Statistical Design and Analysis of Experiments, with Applications to Engineering and Science by Robert L. Mason on page 62, and page 63
- Introduction to Statistical Quality Control by Douglas C. Montgomery in Front Matter, and Back Matter
- Random Data: Analysis & Measurement Procedures by Julius S. Bendat on page 117
- Rapid Prototyping: Principles and Applications by Rafiq I. Noorani on page 351
- Risk-Based Reliability Analysis and Generic Principles for Risk Reduction by Michael T. Todinov in Back Matter
See all 21 books citing this book
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