The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in linear models, and many examples using real data. Probability & Statistics, Fourth Edition, was written for a one- or two-semester probability and statistics course. This course is offered primarily at four-year institutions and taken mostly by sophomore and junior level students majoring in mathematics or statistics. Calculus is a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus.
1. Introduction to Probability 2. Conditional Probability 3. Random Variables and Distributions 4. Expectation 5. Special Distributions 6. Large Random Samples 7. Estimation 8. Sampling Distributions of Estimators 9. Testing Hypotheses 10. Categorical Data and Nonparametric Methods 11. Linear Statistical Models