Statistics in Engineering
eBook - ePub

Statistics in Engineering

With Examples in MATLAB® and R, Second Edition

  1. 792 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Statistics in Engineering

With Examples in MATLAB® and R, Second Edition

About this book

Engineers are expected to design structures and machines that can operate in challenging and volatile environments, while allowing for variation in materials and noise in measurements and signals. Statistics in Engineering, Second Edition: With Examples in MATLAB and R covers the fundamentals of probability and statistics and explains how to use these basic techniques to estimate and model random variation in the context of engineering analysis and design in all types of environments.

The first eight chapters cover probability and probability distributions, graphical displays of data and descriptive statistics, combinations of random variables and propagation of error, statistical inference, bivariate distributions and correlation, linear regression on a single predictor variable, and the measurement error model. This leads to chapters including multiple regression; comparisons of several means and split-plot designs together with analysis of variance; probability models; and sampling strategies. Distinctive features include:

  • All examples based on work in industry, consulting to industry, and research for industry.
  • Examples and case studies include all engineering disciplines.
  • Emphasis on probabilistic modeling including decision trees, Markov chains and processes, and structure functions.
  • Intuitive explanations are followed by succinct mathematical justifications.
  • Emphasis on random number generation that is used for stochastic simulations of engineering systems, demonstration of key concepts, and implementation of bootstrap methods for inference.
  • Use of MATLAB and the open source software R, both of which have an extensive range of statistical functions for standard analyses and also enable programing of specific applications.
  • Use of multiple regression for times series models and analysis of factorial and central composite designs.
  • Inclusion of topics such as Weibull analysis of failure times and split-plot designs that are commonly used in industry but are not usually included in introductory textbooks.
  • Experiments designed to show fundamental concepts that have been tested with large classes working in small groups.
  • Website with additional materials that is regularly updated.

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Yes, you can access Statistics in Engineering by Andrew Metcalfe,David Green,Tony Greenfield,Mayhayaudin Mansor,Andrew Smith,Jonathan Tuke in PDF and/or ePUB format, as well as other popular books in Mathematics & Teaching Mathematics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half Title
  3. Title Page
  4. Copyright Page
  5. Table of Contents
  6. Preface
  7. 1 Why understand statistics?
  8. 2 Probability and making decisions
  9. 3 Graphical displays of data and descriptive statistics
  10. 4 Discrete probability distributions
  11. 5 Continuous probability distributions
  12. 6 Correlation and functions of random variables
  13. 7 Estimation and inference
  14. 8 Linear regression and linear relationships
  15. 9 Multiple regression
  16. 10 Statistical quality control
  17. 11 Design of experiments with regression analysis
  18. 12 Design of experiments and analysis of variance
  19. 13 Probability models
  20. 14 Sampling strategies
  21. Appendix A - Notation
  22. Appendix B - Glossary
  23. Appendix C - Getting started in R
  24. Appendix D - Getting started in MATLAB
  25. Appendix E - Experiments
  26. References
  27. Index