A course in probability and statistics

This author's modern approach is intended primarily for graduatelevel mathematical statistics or statistical inference courses. The author takes a finite-dimensional functional modeling viewpoint (in contrast to the conventional parametric approach) to strengthen the connection between statistical theory and statistical methodology. Includes:-- Pared-down and simplified treatment of probability that places emphasis on statistical inference-- Creative, thought-oriented exercises, some based on real data-- Unusually thorough treatment of conditioning Ch. 1. Random Variables And Their Distributions -- Ch. 2. Expectation -- Ch. 3. Special Continuous Models -- Ch. 4. Special Discrete Models -- Ch. 5. Dependence -- Ch. 6. Conditioning -- Ch. 7. Normal Models -- Ch. 8. Introduction To Linear Regression -- Ch. 9. Linear Analysis -- Ch. 10. Linear Regression -- Ch. 11. Orthogonal Arrays -- Ch. 12. Binomial And Poisson Models -- Ch. 13. Logistic Regression And Poisson Regression -- Appendix A: Properties Of Vectors And Matrices. Charles J. Stone. Includes Index.