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5th Edition - August 14, 2014

**Author:** Sheldon M. Ross

Hardback ISBN:

9 7 8 - 0 - 1 2 - 3 9 4 8 1 1 - 3

eBook ISBN:

9 7 8 - 0 - 1 2 - 3 9 4 8 4 2 - 7

Introduction to Probability and Statistics for Engineers and Scientists, Fifth Edition is a proven text reference that provides a superior introduction to applied probability and… Read more

Immediately download your ebook while waiting for your print delivery. No promo code is needed.

Introduction to Probability and Statistics for Engineers and Scientists, Fifth Edition is a proven text reference that provides a superior introduction to applied probability and statistics for engineering or science majors. The book lays emphasis in 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.

Real data from actual studies across life science, engineering, computing and business are incorporated in a wide variety of exercises and examples throughout the text. These examples and exercises are combined with updated problem sets and applications to connect probability theory to everyday statistical problems and situations. The book also contains end of chapter review material that highlights key ideas as well as the risks associated with practical application of the material. Furthermore, there are new additions to proofs in the estimation section as well as new coverage of Pareto and lognormal distributions, prediction intervals, use of dummy variables in multiple regression models, and testing equality of multiple population distributions.

This text is intended for upper level undergraduate and graduate students taking a course in probability and statistics for science or engineering, and for scientists, engineers, and other professionals seeking a reference of foundational content and application to these fields.

- Clear exposition by a renowned expert author
- Real data examples that use significant real data from actual studies across life science, engineering, computing and business
- End of Chapter review material that emphasizes key ideas as well as the risks associated with practical application of the material
- 25% New Updated problem sets and applications, that demonstrate updated applications to engineering as well as biological, physical and computer science
- New additions to proofs in the estimation section
- New coverage of Pareto and lognormal distributions, prediction intervals, use of dummy variables in multiple regression models, and testing equality of multiple population distributions.

Scientists and engineers, upper level undergraduates and graduate students in engineering and the sciences

- Dedication
- Preface
- Organization and Coverage
- Supplemental Materials
- Acknowledgments

- Chapter 1. Introduction to Statistics
- 1.1 Introduction
- 1.2 Data Collection and Descriptive Statistics
- 1.3 Inferential Statistics and Probability Models
- 1.4 Populations and Samples
- 1.5 A Brief History of Statistics
- Problems

- Chapter 2. Descriptive Statistics
- 2.1 Introduction
- 2.2 Describing Data Sets
- 2.3 Summarizing Data Sets
- 2.4 Chebyshev’s Inequality
- 2.5 Normal Data Sets
- 2.6 Paired Data Sets and the Sample Correlation Coefficient
- Problems

- Chapter 3. Elements of Probability
- 3.1 Introduction
- 3.2 Sample Space and Events
- 3.3 Venn Diagrams and the Algebra of Events
- 3.4 Axioms of Probability
- 3.5 Sample Spaces Having Equally Likely Outcomes
- 3.6 Conditional Probability
- 3.7 Bayes’ Formula
- 3.8 Independent Events
- Problems

- Chapter 4. Random Variables and Expectation
- 4.1 Random Variables
- 4.2 Types of Random Variables
- 4.3 Jointly Distributed Random Variables
- 4.4 Expectation
- 4.5 Properties of the Expected Value
- 4.6 Variance
- 4.7 Covariance and Variance of Sums of Random Variables
- 4.8 Moment Generating Functions
- 4.9 Chebyshev’s Inequality and the Weak Law of Large Numbers
- Problems

- Chapter 5. Special Random Variables
- 5.1 The Bernoulli and Binomial Random Variables
- 5.2 The Poisson Random Variable
- 5.3 The Hypergeometric Random Variable
- 5.4 The Uniform Random Variable
- 5.5 Normal Random Variables
- 5.6 Exponential Random Variables
- 5.7 The Gamma Distribution
- 5.8 Distributions Arising from the Normal
- 5.9 The Logistics Distribution
- Problems

- Chapter 6. Distributions of Sampling Statistics
- 6.1 Introduction
- 6.2 The Sample Mean
- 6.3 The Central Limit Theorem
- 6.4 The Sample Variance
- 6.5 Sampling Distributions from a Normal Population
- 6.6 Sampling from a Finite Population
- Problems

- Chapter 7. Parameter Estimation
- 7.1 Introduction
- 7.2 Maximum Likelihood Estimators
- 7.3 Interval Estimates
- 7.4 Estimating the Difference in Means of Two Normal Populations
- 7.5 Approximate Confidence Interval for the Mean of a Bernoulli Random Variable
- 7.6 Confidence Interval of the Mean of the Exponential Distribution
- 7.7 Evaluating a Point Estimator
- 7.8 The Bayes Estimator
- Problems

- Chapter 8. Hypothesis Testing
- 8.1 Introduction
- 8.2 Significance Levels
- 8.3 Tests Concerning the Mean of a Normal Population
- 8.4 Testing The Equality of Means of Two Normal Populations
- 8.5 Hypothesis Tests Concerning the Variance of a Normal Population
- 8.6 Hypothesis Tests in Bernoulli Populations
- 8.7 Tests Concerning the Mean of a Poisson Distribution
- Problems

- Chapter 9. Regression
- 9.1 Introduction
- 9.2 Least Squares Estimators of the Regression Parameters
- 9.3 Distribution of the Estimators
- 9.4 Statistical Inferences about the Regression Parameters
- 9.5 The Coefficient of Determination and the Sample Correlation Coefficient
- 9.6 Analysis of Residuals: Assessing the Model
- 9.7 Transforming to Linearity
- 9.8 Weighted Least Squares
- 9.9 Polynomial Regression
- 9.10 Multiple Linear Regression
- 9.11 Logistic Regression Models for Binary Output Data
- Problems

- Chapter 10. Analysis of Variance
- 10.1 Introduction
- 10.2 An Overview
- 10.3 One-Way Analysis of Variance
- 10.4 Two-Factor Analysis of Variance: Introduction and Parameter Estimation
- 10.5 Two-Factor Analysis of Variance: Testing Hypotheses
- 10.6 Two-Way Analysis of Variance with Interaction
- Problems

- Chapter 11. Goodness of Fit Tests and Categorical Data Analysis
- 11.1 Introduction
- 11.2 Goodness of Fit Tests When All Parameters are Specified
- 11.3 Goodness of Fit Tests When Some Parameters are Unspecified
- 11.4 Tests of Independence in Contingency Tables
- 11.5 Tests of Independence in Contingency Tables Having Fixed Marginal Totals
- 11.6 The Kolmogorov–Smirnov Goodness of Fit Test for Continuous Data
- Problems

- Chapter 12. Nonparametric Hypothesis Tests
- 12.1 Introduction
- 12.2 The Sign Test
- 12.3 The Signed Rank Test
- 12.4 The Two-Sample Problem
- 12.5 The Runs Test for Randomness
- Problems

- Chapter 13. Quality Control
- 13.1 Introduction
- 13.2 Control Charts for Average Values: The X¯ Control Chart
- 13.3 S-Control Charts
- 13.4 Control Charts for the Fraction Defective
- 13.5 Control Charts for Number of Defects
- 13.6 Other Control Charts for Detecting Changes in the Population Mean
- Problems

- Chapter 14. Life Testing
- 14.1 Introduction
- 14.2 Hazard Rate Functions
- 14.3 The Exponential Distribution In Life Testing
- 14.4 A Two-Sample Problem
- 14.5 The Weibull Distribution in Life Testing
- Problems

- Chapter 15. Simulation, Bootstrap Statistical Methods, and Permutation Tests
- 15.1 Introduction
- 15.2 Random Numbers
- 15.3 The Bootstrap Method
- 15.4 Permutation Tests
- 15.4.1 Normal Approximations in Permutation Tests
- 15.5 Generating Discrete Random Variables
- 15.6 Generating Continuous Random Variables
- 15.7 Determining the Number of Simulation Runs in a Monte Carlo Study
- Problems

- Appendix of Tables
- Index

- No. of pages: 686
- Language: English
- Published: August 14, 2014
- Imprint: Academic Press
- Hardback ISBN: 9780123948113
- eBook ISBN: 9780123948427

SR

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