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

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Overview


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.
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Product Details

  • ISBN-13: 9780195171792
  • Publisher: Oxford University Press, USA
  • Publication date: 12/28/2003
  • Pages: 352
  • Product dimensions: 9.60 (w) x 6.80 (h) x 0.80 (d)

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

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