Introduction to Probability and Statistics for Engineers and Scientists
- 3rd Edition - July 6, 2004
- Imprint: Academic Press
- Author: Sheldon M. Ross
- Language: English
This updated classic provides a superior introduction to applied probability and statistics for engineering or science majors. Author Sheldon Ross shows how probability yields in… Read more
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This updated classic provides a superior introduction to applied probability and statistics for engineering or science majors. Author Sheldon Ross shows how probability yields insight into statistical problems, resulting in an intuitive understanding of the statistical procedures most often used by practicing engineers and scientists. Real data sets are incorporated in a wide variety of exercises and examples.
The Third Edition includes new exercises, examples, and applications, updated statistical material, and more.
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
* 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
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.
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
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
- Edition: 3
- Published: July 6, 2004
- Imprint: Academic Press
- Language: English
SR
Sheldon M. 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