Numerical and Statistical Methods for Bioengineering
Applications in MATLAB

The first MATLAB-based numerical methods textbook specifically for bioengineers, including topics on hypothesis testing and examples exclusively from bioengineering applications.

Michael R. King (Author), Nipa A. Mody (Author)

9780521871587, Cambridge University Press

Hardback, published 4 November 2010

594 pages, 102 b/w illus.
25 x 19 x 3 cm, 1.4 kg

'I think this book is a winner … [it] is really easy to read and places frameworks for numerical analysis into realistic bioengineering concepts that students will find familiar and relevant. This is most evident in the excellent boxed examples, but also in many of the homework problems. I also really liked the 'key points to consider' at the end of the chapters - these are useful reminders for the students. Finally, the book presents bioinformatics in a manageable fashion that should help demystify this subject for interested students.' K. Jane Grande-Allen, Rice University

The first MATLAB-based numerical methods textbook for bioengineers that uniquely integrates modelling concepts with statistical analysis, while maintaining a focus on enabling the user to report the error or uncertainty in their result. Between traditional numerical method topics of linear modelling concepts, nonlinear root finding, and numerical integration, chapters on hypothesis testing, data regression and probability are interweaved. A unique feature of the book is the inclusion of examples from clinical trials and bioinformatics, which are not found in other numerical methods textbooks for engineers. With a wealth of biomedical engineering examples, case studies on topical biomedical research, and the inclusion of end of chapter problems, this is a perfect core text for a one-semester undergraduate course.

1. Types and sources of numerical error
2. Systems of linear equations
3. Statistics and probability
4. Hypothesis testing
5. Root finding techniques for nonlinear equations
6. Numerical quadrature
7. Numerical integration of ordinary differential equations
8. Nonlinear data regression and optimization
9. Basic algorithms of bioinformatics
Appendix A. Introduction to MATLAB
Appendix B. Location of nodes for Gauss-Legendre quadrature.

Subject Areas: Chemical engineering [TDCB], Biomedical engineering [MQW]