Motulsky / Christopoulos - Fitting Models to Biological Data Using Linear and Nonlinear Regression

Fitting Models to Biological Data Using Linear and Nonlinear Regression

A Practical Guide to Curve Fitting

Medium: Buch

ISBN: 978-0-19-517180-8

Verlag: Oxford University Press, USA

Erscheinungstermin: 01.12.2003

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- 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

Fitting Models to Biological Data Using Linear and Nonlinear Regression

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