Fitting Models to Biological Data Using Linear and Nonlinear Regression
A Practical Guide to Curve Fitting
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Product details:
- Publisher OUP USA
- Date of Publication 5 February 2004
- ISBN 9780195171792
- Binding Hardback
- No. of pages352 pages
- Size 179x246x22 mm
- Weight 717 g
- Language English
- Illustrations 150 line illus 0
Categories
Short 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 successfulIntuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.
MoreLong 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. 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.
MoreTable of Contents:
Fitting data with nonlinear regression
An example of nonlinear regression
Preparing data for nonlinear regression
Nonlinear regression choices
The first five questions to ask about nonlinear regression results
The results of nonlinear regression
Troubleshooting "bad fits"
Fitting data with linear regression
Choosing linear regression
Interpreting the results of linear regression
Models
Introducing models
Tips on choosing a model
Global models
Compartmental models and defining a model with a differential equation
How nonlinear regression works
Modeling experimental error
Unequal weighting of data points
How nonlinear regression minimized the sum-of-squares
Confidence intervals of the parameters
Asymptotic standard errors and confidence intervals
Generating confidence intervals by Monte Carlo simulations
Generating confidence intervals via model comparison
comparing the three methods for creating confidence intervals
Using simulations to understand confidence intervals and plan experiments
Comparing models
Approach to comparing models
Comparing models using the extra sum-of-squares F test
Comparing models using Akaike's Information Criterion
How should you compare modes-AICe or F test?
Examples of comparing the fit of two models to one data set
Testing whether a parameter differs from a hypothetical value
How does a treatment change the curve?
Using global fitting to test a treatment effect in one experiment
Using two-way ANOVA to compare curves
Using a paired t test to test for a treatment effect in a series of matched experiments
Using global fitting to test for a treatment effect in a series of matched experiments
Using an unpaired t test to test for a treatment effect in a series of unmatched experiments
Using global fitting to test for a treatment effect in a series of unmatched experiments
Fitting radioligand and enzyme kinetics data
The law of mass action
Analyzing radioligand binding data
Calculations with radioactivity
Analyzing saturation radioligand binding data
Analyzing competitive binding data
Homologous competitive binding curves
Analyzing kinetic binding data
Analyzing enzyme kinetic data
Fitting does-response curves
Introduction to dose-response curves
The operational model of agonist action
Dose-response curves in the presence of antagonists
Complex dose-response curves
Fitting curves with GraphPad Prism
Nonlinear regression with Prism
Constraining and sharing parameters
Prsim's nonlinear regression dialog
Classic nonlinear models built-in to Prism
Importing equations and equation libraries
Writing user-defined models in Prism
Linear regression with Prism
Reading unknowns from standard curves
Graphing a family of theoretical curves
Fitting curves without regression
