Introduction to Probability and Statistics for Engineers and Scientists

Introduction to Probability and Statistics for Engineers and Scientists

3rd Edition - July 21, 2004

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  • Author: Sheldon Ross
  • eBook ISBN: 9780080470313

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Description

Introduction to Probability and Statistics for Engineers and Scientists, Third Edition, provides an introduction to applied probability and statistics for engineering or science majors . This updated text emphasizes the manner in which probability yields insight into statistical problems, ultimately resulting in an intuitive understanding of the statistical procedures most often used by practicing engineers and scientists. The Third Edition includes new exercises, examples, homework problems, updated statistical material, and more. New exercises and data examples include: the one-sided Chebyshev inequality for data; logistics distribution and logistic regression; estimation and testing in proofreader problems; and product form estimates of life distributions. Real data sets are incorporated in a wide variety of exercises and examples throughout the book, and the enclosed CD-ROM includes unique, easy-to-use software that automates the required computations. This book is intended primarily for undergraduates in engineering and the sciences, and would be of particular interest to students in Industrial Engineering, Operations Research, Statistics, Mathematics, Computer Science, Electrical Engineering, Civil Engineering, Chemical Engineering, and Quantitative Business. It could also be of value in a graduate introductory course in probability and statistics.

Key Features

New in this edition:
* New exercises and data examples including:
- The One-sided Chebyshev Inequality for Data
- The Logistics Distribution and Logistic Regression
- Estimation and Testing in proofreader problems
- Product Form Estimates of Life Distributions
- Observational Studies
* Updated statistical material
* New, contemporary applications

Hallmark features:
* Reflects Sheldon Ross's masterfully clear exposition
* Contains numerous examples, exercises, and homework problems
* Unique, easy-to-use software automates required computations
* Applies probability theory to everyday statistical problems and situations
* Careful development of probability, modeling, and statistical procedures leads to intuitive understanding
* Instructor's Solutions Manual is available to adopters

Readership

Primary audience would be undergraduates in engineering and the sciences. Of particular interest to students in Industrial Engineering, Operations Research, Statistics, Mathematics, Computer Science, Electrical Engineering, Civil Engineering, Chemical Engineering, and Quantitative Business. It could also be used in a graduate introductory course in probability and statistics.

Table of Contents

  • Preface
    Introduction to Statistics
    Descriptive Statistics
    Elements of Probability
    Random Variables and Expectation
    Special Random Variables
    Distributions of Sampling Statistics
    Parameter Estimation
    Hypothesis Testing
    Regression
    Analysis of Variance
    Goodness of Fit Tests and Categorical Data Analysis
    Nonparametric Hypothesis Tests
    Quality Control
    LifeTesting
    Appendix of Tables

Product details

  • No. of pages: 640
  • Language: English
  • Copyright: © Academic Press 2004
  • Published: July 21, 2004
  • Imprint: Academic Press
  • eBook ISBN: 9780080470313

About the Author

Sheldon Ross

Sheldon Ross
Dr. Sheldon M. Ross is a professor in the Department of Industrial and Systems Engineering at the University of Southern California. He received his PhD in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction to Probability Models, Stochastic Processes, and Introductory Statistics. Professor Ross is the founding and continuing editor of the journal Probability in the Engineering and Informational Sciences. He is a Fellow of the Institute of Mathematical Statistics, a Fellow of INFORMS, and a recipient of the Humboldt US Senior Scientist Award.

Affiliations and Expertise

Professor, Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, USA

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