Introduction to Probability and Statistics for Engineers and Scientists, 5th Edition, (PDF) is a proven textbook reference that offers a superior introduction to applied probability and statistics for engineering or science majors. The ebook lays focus in the manner in which probability yields insight into statistical problems, gradually resulting in an intuitive understanding of the statistical procedures most commonly used by practicing engineers and scientists.
Real data from actual studies across life engineering, science, computing, and business are included 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 ebook also contains an end of chapter review material that stresses key ideas as well as the risks associated with practical application of the material. Moreover, there are new additions to proofs in the estimation section along with 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 aimed at 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 well-known expert author
- New additions to proofs in the estimation section
- 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 the 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 coverage of Pareto and lognormal distributions, prediction intervals, use of dummy variables in multiple regression models, and testing equality of multiple population distributions.
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