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

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

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

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  • 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
  • Citation

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