This is an excellent textbook that covers the three subjects of its title at an undergraduate upper level in one single volume. The first half of the book is devoted to developing the basic concepts and results of probability theory. The second half is equally divided into two introductory chapters. One is dedicated to statistical inference, including sections on linear regression, Bayesian statistics and nonparametric methods. The remaining chapter deals with basic models in stochastic processes. The book is well organized and neatly written. It clearly reflects the long experience of the author teaching one-semester courses on all these subjects. He succeeds in making the exposition entertaining by presenting plenty of very interesting and illuminating examples. (MATHEMATICAL REVIEWS, 2006)


This book is an amazingly interesting and not-boring textbook to introduce the main ideas and techniques of probability, statistics, and stochastic processes to undergraduate students. Positioning itself between such a "hard" and "soft" approach as, for instance, Hogg and Craig (1978) and Tanis (1987), this book gives a sound quotient of theoretical inference and practical hands-on usage of statistical concepts and methods. The book is very well structured and contains plenty of small graphs and tables, framed propositions and definitions, corollaries, and formulae that can smooth the progress of reading and understanding. Numerous examples explain theoretical tools via their applications in physics, biology, economics, other areas of natural, social, and computer sciences, and various other human activity. (TECHNOMETRICS, 2006)