# Best Reference Books – Engineering Statistics

«
»
We have compiled a list of Best Reference Books on Engineering Statistics Subject. These books are used by students of top universities, institutes and colleges.

Here is the full list of best reference books on Engineering Statistics.

We have put a lot of effort into researching the best books for reference on this subject and came out with a recommended list of best books. The table below contains a review of these books and links to the Amazon website to directly purchase these books. As an Amazon Associate, we earn from qualifying purchases, but this does not impact our reviews, comparisons, and listing of these books; the table serves as a ready reckoner list of these best books.

 1. “Engineering Statistics” by Douglas C Montgomery “Engineering Statistics” Book Review: The book is a modern presentation of engineering statistics. It explains the use of statistical tools into the engineering problem-solving process. The book consists of topics like descriptive statistics, probability and probability distributions, statistical test and confidence intervals for one and two samples, building regression models, designing and analyzing engineering experiments, and statistical process control. This text contains many examples and exercises for better understanding and self assessment of the readers.
2. “Mathematical Statistics with Applications” by Dennis Wackerly Book Review: This book presents a strong foundation in the field of statistical theory thereby covering the importance of theory in solving many real world problems. The book makes use of many practical applications and contains numerous exercises that deal with the nature of statistics and determines its role in scientific research. The book contains chapters on graphical methods, numerical methods, probability and interference, binomial and geometric probability distribution, normal, gamma, beta and multivariate probability distributions and many more. advertisement
3. “Mathematical Statistics and Data Analysis” by John A Rice Book Review: This book deals with the mathematical statistics course. The book contains numerous topics along with data analysis and practices most of the concepts of statistics with the help of computer. The author basically focuses on the data analysis, examines real problems with real data and encourages theoretical concepts. The book also contains statistical information, graphical displays and many realistic applications.
4. “Statistical Inference” by Roger Berger and George Casella Book Review: This book demonstrates the building of theory of statistics with the help of first principles of probability theory. The author makes use of techniques, definitions and statistical concepts in order to develop statistical inference theory. This book is very useful at the graduate level and is useful for students studying statistics and who have a strong mathematical background. The book also stresses on the practical usage of statistical concepts along with the understanding of basic statistical concepts.
5. “Springer Handbook of Engineering Statistics” by Pham Hoang “Springer Handbook of Engineering Statistics” Book Review: This book will be helpful for the engineers, statisticians, researchers, teachers, and students of all fields. The book presents many statistical techniques so that the readers can gain sensible statistical feedback. The book features topics like fundamental statistics process monitoring and improvement, reliability modeling and survival analysis, regression methods, data mining, statistical methods and modeling, and a wide range of applications including six sigma. The book provides the reader sufficient knowledge about the products and how their products can be improved. advertisement

6. “Modern Engineering Statistics” by Thomas P Ryan “Modern Engineering Statistics” Book Review: The book gives a statistical approach to engineering applications. It maintains an excellent balance between methodology and applications of engineering statistics. The book explains basic concepts before moving to complex statistical techniques. Each chapter is ended with its summary. The book consists of many examples, exercises, case studies and illustrations. It gives a clear explanation about the relationship between hypothesis tests and confidence intervals. Tools like ‘Minitab’ and ‘JMP’ are used to illustrate statistical analyses.

7. “Modern Statistical and Mathematical Methods in Reliability” by Wilson Alyson “Modern Statistical and Mathematical Methods in Reliability” Book Review: The book is based on ‘Reliability Theory’. All the research activities and applications of reliability theory are mentioned in this book. It consists of topics like reliability modeling, network and system reliability, Bayesian methods, survival analysis, degradation and maintenance modeling, and software reliability. The book is inspired from the papers presented at The Fourth International Conference on Mathematical Methods in Reliability in Santa Fe, New Mexico. advertisement
8. “Computational Methods for Reliability and Risk Analysis” by Enrico Zio

9. “Life-time Data: Statistical Models and Methods” by Jayant V Deshpande “Life-time Data: Statistical Models and Methods” Book Review: The book is basically for students, post graduating in statistics, engineering statistics and medical statistics courses. The concept and role of ageing in choosing appropriate models for lifetime data is discussed in detail. It consists of topics like ageing, tests for exponentiality, competing risks and repairable systems. The book features ‘Public Domain R-software’. Working and use of the preceding software is clearly explained in this book.

10. “Paperback : Si Version ENGINEERING STATISTICS” by Runger “Paperback: Si Version Engineering Statistics” Book Review: The book gives updated information about engineering statistics. It gives an explanation about integration of statistical tools in the process of problem solving in engineering. All the major topics related to engineering statistics like descriptive statistics, probability and probability distributions, statistical test and confidence intervals for one and two samples, building regression models, designing and analyzing engineering experiments, and statistical process control are discussed in detail. 