## Probability and StatisticsThe revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a new chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), expanded coverage of residual analysis in linear models, and more examples using real data.
Introduction to Probability; Conditional Probability; Random Variables and Distribution; Expectation; Special Distributions; Estimation; Sampling Distributions of Estimators; Testing Hypotheses; Categorical Data and Nonparametric Methods; Linear Statistical Models; Simulation
For all readers interested in probability and statistics. |

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### Contents

Introduction to Probability | 1 |

Conditional Probability | 49 |

Random Variables and Distributions | 97 |

Copyright | |

11 other sections not shown