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.

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.

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