# Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting

Oxford University Press, USA, May 27, 2004 - Computers - 351 pages
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. The book will likely be purchased by a high proportion of biological laboratories, for frequent reference. The author gets about 3000 visits per month to his curvefit website, with the average visitor viewing 9 pages.

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I was looking for the definition of R squared and I found this book gives a detailed and accurate explanation.

### Contents

 Contents 12 A Fitting data with nonlinear regression 13 Preparing data for nonlinear regression 19 Nonlinear regression choices 25 The results of nonlinear regression 32 Troubleshooting bad fits 38 B Fitting data with linear regression 47 Models 58
 Using twoway ANOVA to compare curves 166 Using a paired t test to test for a treatment effect in a series 171 Using an unpaired t test to test for a treatment effect in a series 181 H Fitting radioligand and enzyme kinetics data 187 Calculations with radioactivity 194 Analyzing competitive binding data 211 Homologous competitive binding curves 222 Analyzing kinetic binding data 233

 Global models 67 How nonlinear regression works 80 How nonlinear regression minimizes the sumofsquares 91 E Confidence intervals of the parameters 97 Generating confidence intervals by Monte Carlo simulations 104 Comparing the three methods for creating confidence intervals 118 Using simulations to understand confidence intervals and plan 128 F Comparing models 134 Comparing models using Akaikes Information Criterion AIC 143 How should you compare models AICc or F test? 149 Testing whether a parameter differs from a hypothetical value 157 G How does a treatment change the curve? 160
 Analyzing enzyme kinetic data 245 Fitting doseresponse curves 256 The operational model of agonist action 266 Doseresponse curves in the presence of antagonists 276 Complex doseresponse curves 290 J Fitting curves with GraphPad Prism 296 Prisms nonlinear regression dialog 302 Classic nonlinear models built into Prism 312 Importing equations and equation libraries 322 Linear regression with Prism 334 Graphing a family of theoretical curves 344 Copyright

### About the author (2004)

Arthur Christopoulos is at University of Melbourne.