Buch, Englisch, 352 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 857 g
A Practical Guide to Curve Fitting
Buch, Englisch, 352 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 857 g
ISBN: 978-0-19-517179-2
Verlag: Oxford University Press
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.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
- 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
