Probability and Statistics (Classic Version), 4th edition

Published by Pearson (July 1, 2022) © 2023

  • Morris H. DeGroot Carnegie-Mellon University
  • Mark J. Schervish Carnegie-Mellon University
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Buy nowOpens in a new tab

In this eTextbook — More ways to learn

  • More flexible. Start learning right away, on any device.
  • More supportive. Get AI explanations and practice questions (select titles).
  • More interactive. Bring learning to life with audio, videos, and diagrams.
  • More memorable. Make concepts stick with highlights, search, notes, and flashcards.
  • More understandable. Translate text into 100+ languages with one tap.
$59.94
monthly payments or
pay undefined one-time
Instant access

In this eTextbook — More ways to learn

  • More flexible. Start learning right away, on any device.
  • More supportive. Get AI explanations and practice questions (select titles).
  • More interactive. Bring learning to life with audio, videos, and diagrams.
  • More memorable. Make concepts stick with highlights, search, notes, and flashcards.
  • More understandable. Translate text into 100+ languages with one tap.

Title overview

Probability & Statistics, 4th Edition is a revision of the well-respected text. It 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 a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus.

This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price.

Table of contents

Brief Contents

  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

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