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

Motulsky, Harvey

 
9780195171808: Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

L'autore

Arthur Christopoulos is at University of Melbourne.

Contenuti

  • 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

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Altre edizioni note dello stesso titolo

9780195171792: Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting

Edizione in evidenza

ISBN 10:  0195171799 ISBN 13:  9780195171792
Casa editrice: OUP USA, 2004
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