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Additional chapter and new material included throughout second edition
"This book is well-written and the presentation is clear and concise. The text is intended for a one-semester course for undergraduates, but it can also serve as a basis for a high-school course. The level of the book is very elementary and in most of the chapters of the book only basic calculus is required. . . No measure theory is required. In the huge variety of examples rather explicit calculations are presented: moments of several distributions, confidence intervals, testing some parameters or proportions, linear regression and more. Every subsection is rounded off by numerous exercises.”
—Zentralblatt MATH (review of the first edition)
This second edition of Probability with Statistical Applications offers a practical introduction to probability for undergraduates at all levels with different backgrounds and views towards applications. Calculus is a prerequisite for understanding the basic concepts, however the book is written with a sensitivity to students’ common difficulties with calculus that does not obscure the thorough treatment of the probability content. The first six chapters of this text neatly and concisely cover the material traditionally required by most undergraduate programs for a first course in probability.
The comprehensive text includes a multitude of new examples and exercises, and careful revisions throughout. Particular attention is given to the expansion of the last three chapters of the book with the addition of two entirely new chapters on “Finding and Comparing Estimators” and “Multiple Linear Regression.” The classroom-tested material presented in this second edition textbook forms the basis for a second course introducing mathematical statistics.
Content Level »Upper undergraduate
Keywords »Binomial and Poisson random variables - Cramer--Rao lower bound - Estimation and hypothesis testing - Estimators - Limit theorems - Linear regression - Miximum likelihood - Moment generating functions - Rao--Blackwell theorem - Transformations of random variable and random vectors