Probability And Statistics For Engineering And The Sciences

Probability and Statistics for Engineering and the Sciences

This comprehensive introduction to probability and statistics will give you the solid grounding you need no matter what your engineering specialty. Through the use of lively and realistic examples, the author helps you go beyond simply learning about statistics--you'll also learn how to put the statistical methods to use. In addition, rather than focusing on rigorous mathematical development and potentially overwhelming derivations, Probability and Statistics for Engineering and the Sciences emphasizes concepts, models, methodology, and applications that facilitate your understanding.About This EditionThis market-leading text provides a comprehensive introduction to probability and statistics for engineering students in all specialties. Proven, accurate, and lauded for its excellent examples, Probability and Statistics for Engineering and the Sciences evidences Jay Devore's reputation as an outstanding author and leader in the academic community. Devore emphasizes concepts, models, methodology, and applications as opposed to rigorous mathematical development and derivations. Aided by his lively and realistic examples, students go beyond simply learning about statistics; they also learn how to put statistical methods to use.New FeaturesMore than 40 new examples and 100 new problems were carefully researched and written using the most up-to-date real data. Chapter 1, "Overview and Descriptive Statistics," contains a new subsection, "The Scope of Modern Statistics," which describes and exemplifies how statistics is used in modern disciplines. A significantly revised and simplified Chapter 8, "Tests of Hypotheses Based on a Single Sample," also has a new subsection entitled "More on Interpreting P-values." Wherever possible throughout the book, the language has been tightened and simplified to improve clarity.Additional Features"Simulation Experiments" help students gain an understanding of sampling distributions and the insight they provide, especially when a derivation is too complex to carry out. Strong computer coverage, especially with ANOVA and regression, is supported by an abundance of computer output from SAS