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.
- Introduction to Probability
- Conditional Probability
- Random Variables and Distributions
- Expectation
- Special Distributions
- Large Random Samples
- Estimation
- Sampling Distributions of Estimators
- Testing Hypotheses
- Categorical Data and Nonparametric Methods
- Linear Statistical Models
- Simulation