Many books are already available on the general topic of 'probability and statistics for engineers and scientists', so why choose this one? This textbook differs in that it has been prepared very much with students and their needs in mind. Having been classroom tested over many years, it is a true "learner's book" made for students who require a deeper understanding of probability and statistics and the process of model selection, verification and analysis. Emphasising both sound development of the principles and their engineering applications, this book offers purposely selected practical examples from many different fields. 

This textbook: 

• Presents a sound treatment of the fundamentals in probability and statistics.
• Explains the concept of probabilistic modelling and the process of model selection, verification and analysis.
• Provides self-contained material with smooth and logical transition from chapter to chapter.
• Includes relevant and motivational applications in every chapter with numerous examples and problems.
• Demonstrates practical problem solving throughout the book with stimulating exercises, including answers to selected problems.
• Includes an accompanying online Solutions Manual for instructors with complete step-by-step solutions to all problems. (URL)

Fundamentals In Applied Probability And Statistics For Engineersprovides invaluable support for all engineering students involved in applications of probability, random variables and statistical inference. This book is also an ideal reference for lecturers, educators and newcomers to the field who wish to increase their knowledge of fundamental concepts. Engineering consulting firms will also find the explanations and examples useful.

Written for a one- or two-semester introductory course for engineering or other applied science students, this text requires no previous knowledge of probability and statistics, but does assume a good understanding of calculus. Soong (State U. of New York at Buffalo) presents fundamentals related to the construction of probability models and the analysis of models, discussing the basics of probability and random variables, the nature and applications of probability distributions, parameter estimation techniques, and statistical tests for model verification. The tools developed in the text are finally applied to the study of linear regression models.
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This textbook differs from others in the field in that it has been prepared very much with students and their needs in mind, having been classroom tested over many years.  It is a true “learner’s book” made for students who require a deeper understanding of probability and statistics. It presents the fundamentals of the subject along with concepts of probabilistic modelling, and the process of model selection, verification and analysis.  Furthermore, the inclusion of more than 100 examples and 200 exercises (carefully selected from a wide range of topics), along with a solutions manual for instructors, means that this text is of real value to students and lecturers across a range of engineering disciplines.

Key features:

• Presents the fundamentals in probability and statistics along with relevant applications.
• Explains the concept of probabilistic modelling and the process of model selection, verification and analysis.
• Definitions and theorems are carefully stated and topics rigorously treated.
• Includes a chapter on regression analysis.
• Covers design of experiments.
• Demonstrates practical problem solving throughout the book with numerous examples and exercises purposely selected from a variety of engineering fields.
• Includes an accompanying online Solutions Manual for instructors containing complete step-by-step solutions to all problems.

Preface.

1. Introduction.

Part A: Probability and Random Variables.

2. Basic Probability Concepts.

3. Random Variables and Probability Distributions.

4. Expectations And Moments.

5. Functions of Random Variables.

6. Some Important Discrete Distributions.

7. Some Important Continuous Distributions.

Part B: Statistical Inference, Parameter Estimation, and Model Verification.

8. Observed Data and Graphical Representation.

9. Parameter Estimation.

10. Model Verification.

11. Linear Models and Linear Regression.

Appendix A: Tables.

Appendix B: Computer Software.

Appendix C: Answers to Selected Problems.

Subject Index.