This book gives a comprehensive introduction to the exciting area of sampling-based methods."An Introduction to Statistical Computing" introduces the classical topics of random number generation and Monte Carlo methods. Sampling-based simulation techniques are now an invaluable tool for exploring statistical models. lessĪ comprehensive introduction to sampling-based methods in statistical computingThe use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems. This includes material on developing MATLAB(R) m-files and VBA macros.
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Users will find use of software packages, specifically MATLAB(R), Excel(R) with VBA and Mathcad(R). Excellent new examples and case studies span asll areas of engineering disciplines the students using this text will be able to apply their new skills to their chosen field. The expanded breadth of engineering disciplines covered is especially evident in the problems, which now cover such areas as biotechnology and biomedical engineering. Numerous new or revised problems drawn from actual engineering practice, many of which are based on exciting new areas such as bioengineering. "Getting Started with MATLAB" abd "Getting Started with Mathcad" which make excellent references.
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Much more than a summary, the Epilogue deepens understanding of what has been learned and provides a peek into more advanced methods. Each part closes with an Epilogue containing Trade-Offs, Important Relationships and Formulas, and Advanced Methods and Additional References. This prepares the student for upcoming problems in a motivating and engaging manner.
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Chapra and Canale's unique approach opens each part of the text with sections called Motivation, Mathematical Background, and Orientation. The sixth edition retains the successful instructional techniques of earlier editions. The text features a broad array of applications that span all engineering disciplines.
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Instructors love "Numerical Methods for Engineers" because it makes teaching easy! Students love it because it is written for them-with clear explanations and examples throughout. Including a final chapter introducing challenging topics such as floating-point precision and algorithm stability, and with extensive online resources to support advanced study, this textbook represents a targeted package for students requiring a solid foundation in Python programming. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving onto the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualisation, this textbook also discusses the use of IPython notebooks to build rich-media, shareable documents for scientific analysis. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to quickly gain proficiency. Learn to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions drawn from both science and engineering.