Soong T.T. Fundamentals of Probability and Statistics for Engineers
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John Wiley & Sons, 2004. — 408 p.This book was written for an introductory one-semester or two-quarter course in probability and statistics for students in engineering and applied sciences. No previous knowledge of probability or statistics is presumed but a good understanding of calculus is a prerequisite for the material.The development of this book was guided by a number of considerations observed over many years of teaching courses in this subject area, including the following:As an introductory course, a sound and rigorous treatment of the basic principles is imperative for a proper understanding of the subject matter and for confidence in applying these principles to practical problem solving. A student, depending upon his or her major field of study, will no doubt pursue advanced work in this area in one or more of the many possible directions. How well is he or she prepared to do this strongly depends on his or her mastery of the fundamentals.
It is important that the student develop an early appreciation for applications. Demonstrations of the utility of this material in nonsuperficial applications not only sustain student interest but also provide the student with stimulation to delve more deeply into the fundamentals.
Most of the students in engineering and applied sciences can only devote one semester or two quarters to a course of this nature in their programs. Recognizing that the coverage is time limited, it is important that the material be self-contained, representing a reasonably complete and applicable body of knowledge.The choice of the contents for this book is in line with the foregoing observations. The major objective is to give a careful presentation of the fundamentals in probability and statistics, the concept of probabilistic modeling, and the process of model selection, verification, and analysis. In this text, definitions and theorems are carefully stated and topics rigorously treated but care is taken not to become entangled in excessive mathematical details.Practical examples are emphasized; they are purposely selected from many different fields and not slanted toward any particular applied area. The same objective is observed in making up the exercises at the back of each chapter.Because of the self-imposed criterion of writing a comprehensive text and presenting it within a limited time frame, there is a tight continuity from one topic to the next. Some flexibility exists in Chapters 6 and 7 that include discussions on more specialized distributions used in practice. For example, extreme-value distributions may be bypassed, if it is deemed necessary, without serious loss of continuity. Also, Chapter 11 on linear models may be deferred to a follow-up course if time does not allow its full coverage.
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Prentice Hall, 1989. — 495 p. The book discusses the topics which are among the most important for a scientist who will be doing numerical computation. This includes many standard items such as solution of systems of linear equations, interpolation, fast Fourier transform, evaluation of integrals and differential equations, optimization and simulation. The book is written for...
Third Edition. - Wiley, 2003. - 822 p.
This is an introductory textbook for a first course in applied statistics and probability for undergraduate students in engineering and the physical or chemical sciences. These individuals play a significant role in designing and developing new products and manufacturing systems and processes, and they also improve existing systems....
Third Edition — Elsevier Academic Press, 2004. — 624 p.
The third edition of this book continues to demonstrate how to apply probability theory to gain insight into real, everyday statistical problems and situations. As in the previous editions, carefully developed coverage of probability motivates probabilistic models of real phenomena and the statistical procedures that...
Springer, 2006. — 281 p.
The purpose of this book is to present concepts in a statistical treatment of risks. Such knowledge facilitates the understanding of the influence of random phenomena and gives a deeper knowledge of the possibilities offered by and algorithms found in certain software packages. Since Bayesian methods are frequently used in this field, a reasonable...
Second Edition. - SIAM, 2004. - 448 p.
The book presents the basic theory of finite difference schemes applied to the numerical solution of partial differential equations. It is designed to be used as an introductory graduate text for students in applied mathematics, engineering, and the sciences, and with that in mind, presents the theory of finite difference schemes in a way...
8th Edition. — Prentice-Hall, 2007. — 816 p.
With its unique balance of theory and methodology, this classic text provides a rigorous introduction to basic probability theory and statistical inference, motivated by interesting, relevant applications. Offers extensively updated coverage, new problem sets, and chapter-ending material to enhance the book’s relevance to today’s...