Probability and Statistics, 4th edition
Published by Pearson (December 27th 2010) - Copyright © 2012
You'll get a bound printed text.
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.
Table of contents
1. Introduction to Probability
2. Conditional Probability
3. Random Variables and Distributions
5. Special Distributions
6. Large Random Samples
8. Sampling Distributions of Estimators
9. Testing Hypotheses
10. Categorical Data and Nonparametric Methods
11. Linear Statistical Models
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