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 relavtively focused need of an extraordinarily broad range of scientists.
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 relavtively focused need of an extraordinarily broad range of scientists.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
(Professor in the Department of Pharmacology, University of Melbourne, Australia)
Inhaltsangabe
* 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
* 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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