Home Technique • Download Numerical and Statistical Methods for Bioengineering: by Michael R. King PDF

## Download Numerical and Statistical Methods for Bioengineering: by Michael R. King PDF

By Michael R. King

The 1st MATLAB-based numerical tools textbook for bioengineers that uniquely integrates modelling suggestions with statistical research, whereas holding a spotlight on allowing the person to file the mistake or uncertainty of their end result. among conventional numerical technique issues of linear modelling ideas, nonlinear root discovering, and numerical integration, chapters on speculation trying out, info regression and likelihood are interweaved. a special characteristic of the ebook is the inclusion of examples from medical trials and bioinformatics, which aren't present in different numerical tools textbooks for engineers. With a wealth of biomedical engineering examples, case stories on topical biomedical learn, and the inclusion of finish of bankruptcy difficulties, it is a excellent middle textual content for a one-semester undergraduate direction.

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Extra info for Numerical and Statistical Methods for Bioengineering: Applications in MATLAB

Sample text

This cannot be correct. This error occurs due to the large value of n and therefore the smallness of r=100n. 171 × 10–16, respectively. Since floating-point numbers have at most 16 significant digits, adding two numbers that have a magnitude difference of 1016 results in the loss of significant digits or information as described in this section. To illustrate this point further, if the calculation were performed for a compounding frequency of every nanosecond, the maturity amount is calcuated by MATLAB to be \$250 000 – the principal amount!

In the code below, the variables are converted to the single data type by using the single conversion function. 24%. The magnitude of this error is not insignificant considering that the digital floating-point representation of single numbers is accurate to the eighth decimal point. 33 × 10–12 %. Double precision allows for much more accurate representation of floating-point numbers and should be preferred whenever possible. In the real world, when performing experiments, procuring data, or solving numerical problems, you usually will not know the true or exact solution; otherwise you would not be performing these tasks in the first place!

Then, μ ðs ¼ 1:0 g=lÞ ¼ 0:6666=hr; μ ðs ¼ 1:1 g=lÞ ¼ 0:6875=hr; μ ðs ¼ 0:9 g=lÞ ¼ 0:6428=hr: Using a forward difference approximation to determine μ0 , we obtain 0:6875 À 0:6666 ¼ 0:209 l=ðg hrÞ; relative error Er ¼ 5:94%: 0:1 Using a backward difference approximation to determine μ0 , we obtain μ0 ﬃ 0:6666 À 0:6428 ¼ 0:238 l=ðg hrÞ; relative error Er ¼ 7:11%: 0:1 Using a central difference approximation to determine μ0 , we obtain μ0 ﬃ 0:6875 À 0:6428 ¼ 0:2235 l=ðg hrÞ; relative error Er ¼ 0:585%: 2 Â 0:1 Note the smaller error in the approximation of μ0 when using the central difference formula.