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Fitting Models to Biological Data Using Linear and Nonlinear Regression

A Practical Guide to Curve Fitting

Fitting Models to Biological Data Using Linear and Nonlinear Regression
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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

Oxford University Press, USA; May 2004
352 pages; ISBN 9780198038344
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ISBNs
0198038348
9780195171792
9780198038344