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

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.

Table of Contents:
Fitting data with nonlinear regression 1: An example of nonlinear regression 2: Preparing data for nonlinear regression 3: Nonlinear regression choices 4: The first five questions to ask about nonlinear regression results 5: The results of nonlinear regression 6: Troubleshooting "bad fits" Fitting data with linear regression 7: Choosing linear regression 8: Interpreting the results of linear regression Models 9: Introducing models 10: Tips on choosing a model 11: Global models 12: Compartmental models and defining a model with a differential equation How nonlinear regression works 13: Modeling experimental error 14: Unequal weighting of data points 15: How nonlinear regression minimized the sum-of-squares Confidence intervals of the parameters 16: Asymptotic standard errors and confidence intervals 17: Generating confidence intervals by Monte Carlo simulations 18: Generating confidence intervals via model comparison 19: comparing the three methods for creating confidence intervals 20: Using simulations to understand confidence intervals and plan experiments Comparing models 21: Approach to comparing models 22: Comparing models using the extra sum-of-squares F test 23: Comparing models using Akaike's Information Criterion 24: How should you compare modes-AICe or F test? 25: Examples of comparing the fit of two models to one data set 26: Testing whether a parameter differs from a hypothetical value How does a treatment change the curve? 27: Using global fitting to test a treatment effect in one experiment 28: Using two-way ANOVA to compare curves 29: Using a paired t test to test for a treatment effect in a series of matched experiments 30: Using global fitting to test for a treatment effect in a series of matched experiments 31: Using an unpaired t test to test for a treatment effect in a series of unmatched experiments 32: Using global fitting to test for a treatment effect in a series of unmatched experiments Fitting radioligand and enzyme kinetics data 33: The law of mass action 34: Analyzing radioligand binding data 35: Calculations with radioactivity 36: Analyzing saturation radioligand binding data 37: Analyzing competitive binding data 38: Homologous competitive binding curves 39: Analyzing kinetic binding data 40: Analyzing enzyme kinetic data Fitting does-response curves 41: Introduction to dose-response curves 42: The operational model of agonist action 43: Dose-response curves in the presence of antagonists 44: Complex dose-response curves Fitting curves with GraphPad Prism 45: Nonlinear regression with Prism 46: Constraining and sharing parameters 47: Prsim's nonlinear regression dialog 48: Classic nonlinear models built-in to Prism 49: Importing equations and equation libraries 50: Writing user-defined models in Prism 51: Linear regression with Prism 52: Reading unknowns from standard curves 53: Graphing a family of theoretical curves 54: Fitting curves without regression