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Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting | 
 enlarge | Manufacturer: Oxford University Press, USA
Category: EBooks
List Price: $43.45
Buy New: $34.76
You Save: $8.69 (20%)
Avg. Customer Rating:  1 reviews
Sales Rank: 20577
Format: Kindle Book
Media: Kindle Edition
Edition: 1
Number Of Items: 1
Pages: 352
Dewey Decimal Number: 570.15118
ASIN: B000WEGCZQ
Publication Date: May 27, 2004
Availability: Usually ships in 24 hours
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Product Description
Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.
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| Customer Reviews:
Found the book I needed! May 12, 2005
11 out of 11 found this review helpful
I've been looking for a book like this for 5 years to help me understand better nonlinear regression. The only choices before this were the same old basic stats books, or the other extreme of stats books for mathematicians. As an engineer, I needed a book that does APPLIED, not THEORETICAL, nonlinear regression. This book gives examples and speaks normal English, unlike Seber and Wild's book, which is virtually devoid of examples and drowns one with matrix math instead. Seber and Wild's book is more like a dictionary of stats equations. For Motulsky's book, here are 3 examples of things you won't find in most other statistics books: 1) the difference between confidence bands and prediction bands for Y(p. 32), showing that the former doesn't include a majority of the points, while the latter does; 2) How to compute egg- and elliptical-shaped joint confidence regions for 2 parameters (p. 114-121); 3) Good explanation of 3 different ways to compute parameter conf. regions (asymptotic, Monte Carlo, model comparison). Just one of these 3 makes it worth the price of the book for me.
Motulsky has so many different helpful topics in here, that he obviously has run into many of the problems that nonlinear regression people will see. The book is helpful regardless of the software you use, because I do a lot of coding in Matlab. I may make this book a standard text for a new engineering statistics class I am preparing.
The main shortcoming of the book for me (remember that I'm an engineer) is lack of the exact equations 1) for confidence bands and prediction bands for Y (p.32), 2) for asymptotic conf. interval for parameter (p. 98), and 3) for standard error vs. standard deviation. For the first 2, the author could reference eqn. numbers in Seber and Wild or Bates so his text flow would not be interrupted. For those of us who publish, we need to know what's going on in the black box. The standard error/standard dev. formulas would make the explanation given easier to follow.
In summary, this is the book that will help walk you through many of the problems and concerns in nonlinear regression. I'm glad someone who understands my situation finally wrote it!
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