A course in probability and statistics

This author's modern approach is intended primarily for honors undergraduates or undergraduates with a good math background taking a mathematical statistics or statistical inference course. 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. 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.