Numerical and Statistical Methods for Bioengineering: Applications in MATLAB
Cambridge University Press, Nov 4, 2010 - Technology & Engineering - 594 pages
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.
What people are saying - Write a review
We haven't found any reviews in the usual places.
2 Systems of linear equations
3 Probability and statistics
5 Rootfinding techniques for nonlinear equations
6 Numerical quadrature
7 Numerical integration of ordinary differential equations
algorithm approximation augmented matrix binary binomial bisection method blood calculated called cells coefficient column vectors compared concentration confidence interval convergence curve ð Þ ¼ data points data set defined degrees of freedom derivative determine drug elements equal estimate Euler’s evaluated example ffiffiffi Figure first-order formula frequency function values Gaussian Gaussian elimination Gaussian quadrature guess value independent initial guess iteration ligand linear equations magnitude MATLAB MATLAB program matrix minimization minimum multiple naphthalene Newton’s method nodes normal distribution null hypothesis numerical solution objective function observed obtain optimization paired parameters performed pivot platelet plot polynomial interpolant population mean problem protein quadrature random variable regression result root round-off errors sample mean Section sequence significand significant digits Simpson’s solve specified standard deviation step subinterval Table Taylor series test statistic trapezoidal rule truncation error type I error zero Þ¼