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

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Oxford University Press, USA, May 27, 2004 - Computers - 351 pages
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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
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About the author (2004)

Arthur Christopoulos is at University of Melbourne.

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