Introduction to probability and statistical inference / George G. Roussas.

Includes bibliographical references and index.

1. Some motivating examples and some fundamental concepts --
2. The concept of probability and some basic results --
3. Numerical characteristics of a random variable, some special random variables --
4. Joint and conditional P.D.F.'s, conditional expectation and variance, moment generating function, covariance and correlation coefficient --
5. Independence of random variables and some applications --
6. Transformation of random variables --
7. Some modes of convergence of random variables, applications --
8. An overview of statistical inference --
9. Point estimation --
10. Confidence intervals and confidence regions --
11. Testing hypotheses --
12. More about testing hypotheses --
13. A simple linear regression model --
14. Two models of analysis of variance --
15. Some topics in nonparametric inference--
Index.

This book provides a plethora of examples for each topic discussed, giving the reader more experience in applying statistical methods to different situations. * Content, examples, an enhanced number of exercises, and graphical illustrations where appropriate to motivate the reader and demonstrate the applicability of probability and statistical inference in a great variety of human activities* Reorganized material in the statistical portion of the book to ensure continuity and enhance understanding* A relatively rigorous, yet accessible and always within the prescribed prerequisites, mathematical discussion of probability theory and statistical inference important to students in a broad variety of disciplines* Relevant proofs where appropriate in each section, followed by exercises with useful clues to their solutions* Brief answers to even-numbered exercises at the back of the book and detailed solutions to all exercises available to instructors in an Answers Manual.