INTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS AND ...

INTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS

Fifth Edition

INTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS

Fifth Edition

Sheldon M. Ross

University of Southern California, Los Angeles, USA

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ISBN: 978-0-12-394811-3

Library of Congress Cataloging-in-Publication Data Ross, Sheldon M.

Introduction to probability and statistics for engineers and scientists / Sheldon M. Ross, Department of Industrial Engineering and Operations Research, University of California, Berkeley. Fifth edition.

pages cm. Includes index. ISBN 978-0-12-394811-3 1. Probabilities. 2. Mathematical statistics. I. Title. TA340.R67 2014 519.5?dc23

2014011941 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library

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For Elise

Preface

The fifth edition of this book continues to demonstrate how to apply probability theory to gain insight into real, everyday statistical problems and situations. As in the previous editions, carefully developed coverage of probability motivates probabilistic models of real phenomena and the statistical procedures that follow. This approach ultimately results in an intuitive understanding of statistical procedures and strategies most often used by practicing engineers and scientists.

Thisbookhasbeenwrittenfor anintroductory course in statisticsor inprobability and statistics for students in engineering, computer science, mathematics, statistics, and the natural sciences. As such it assumes knowledge of elementary calculus.

ORGANIZATION AND COVERAGE

Chapter 1 presents a brief introduction to statistics, presenting its two branches of descriptive and inferential statistics, and a short history of the subject and some of the people whose early work provided a foundation for work done today.

The subject matter of descriptive statistics is then considered in Chapter 2. Graphs and tables that describe a data set are presented in this chapter, as are quantities that are used to summarize certain of the key properties of the data set.

To be able to draw conclusions from data, it is necessary to have an understanding of the data's origination. For instance, it is often assumed that the data constitute a "random sample" from some population. To understand exactly what this means and what its consequences are for relating properties of the sample data to properties of the entire population, it is necessary to have some understanding of probability, and that is the subject of Chapter 3. This chapter introduces the idea of a probability experiment, explains the concept of the probability of an event, and presents the axioms of probability.

Our study of probability is continued in Chapter 4, which deals with the important concepts of random variables and expectation, and in Chapter 5, which considers some special types of random variables that often occur in applications. Such random variables as the binomial, Poisson, hypergeometric, normal, uniform, gamma, chi-square, t, and F are presented.

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