Fitting Models to Biological Data Using Linear and Nonlinear Regression http://www.fishpond.co.nz/Books/Fitting-Models-to-Biological-Data-Using-Linear-and-Nonlinear-Regression-Harvey-Motulsky-Arthur-Christopoulos/9780195171808

A Practical Guide to Curve Fitting

By Harvey Motulsky, Arthur Christopoulos

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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