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Probability and Statistics for Engineering and the Sciences

Author(s): Jay L. Devore

ISBN: 9789353506247

9th Edition

Copyright: 2016

India Release: 2020

₹795

Binding: Paperback

Pages: 768

Trim Size: 254 x 203 mm

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Probability and Statistics for Engineering and the Sciences, 9E, metric edition is a calculus-based textbook that provides an introduction to probability and statistics while showcasing the application of theories, methodologies, and models in modern scientific and engineering careers. The book uses lively examples, engineering activities, practice problems, and simulations based on actual data to teach statistical concepts without delving too much into mathematics. The comprehensive textbook includes graphics and screen shots from SAS, MINITAB, and Java Applets to provide readers with a practical understanding of statistical models.

  • Use of computer output from SAS and MINITAB, as well as Java Applets, aids student understanding of ANOVA and regression and demonstrates statistics visually.
  • Sample exams and a glossary of symbols and acronyms help students master concepts and ace exams.
  • Variation plays a critical role in statistics and is emphasized throughout the text. Pooled t procedures for analysis are included.
  • "Simulation Experiments" in the text help students understand sampling distributions.
  • Real data and actual problems are used as examples and exercises throughout the text.
  • Chapters focus on delivering a deep, intuitive understanding of the concepts, and examples and exercises link the concepts to contemporary workplace issues.
  • Hypothesis testing based on P-values now replaces the former rejection region approach throughout the text.

1. Overview and descriptive statistics.

2. Probability.

3. Discrete random variables and probability distributions.

4. Continuous random variables and probability distributions.

5. Joint probability distributions and random samples.

6. Point estimation.

7. Statistical intervals based on a single sample.

8. Tests of hypothesis based on a single sample.

9. Inferences based on two samples.

10. The analysis of variance.

11. Multifactor analysis of variance.

12. Simple linear regression and correlation.

13. Nonlinear and multiple regression.

14. Goodness-of-fit tests and categorical data analysis.

15. Distribution-free procedures.

16. Quality control methods.

Jay L. Devore

California Polytechnic State University, San Luis Obispo

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