Probability And Statistics – M. DeGroot, M. Schervish – 4th Edition


The revision of this well-respected text presents a balanced approach of the classical and Bayesian and now includes a chapter on simulation ( Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in linear models, and many examples using real data.

& , Fourth Edition, was written for a one- or two-semester and statistics course. This course is offered primarily at four-year institutions and taken mostly by sophomore and junior level majoring in or statistics. Calculus is a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus.

Table of Content

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
12. Simulation