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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. Calculus is assumed as a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus.

Probability and Statistics: Pearson New International Edition

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