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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
| Erscheinungsjahr: | 2003 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| ISBN-13: | 9780195171808 |
| ISBN-10: | 0195171802 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Motulsky, Harvey
Christopoulos, Arthur |
| Hersteller: | Oxford University Press, USA |
| Maße: | 244 x 170 x 19 mm |
| Von/Mit: | Harvey Motulsky (u. a.) |
| Erscheinungsdatum: | 01.12.2003 |
| Gewicht: | 0,615 kg |
- 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
| Erscheinungsjahr: | 2003 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| ISBN-13: | 9780195171808 |
| ISBN-10: | 0195171802 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Motulsky, Harvey
Christopoulos, Arthur |
| Hersteller: | Oxford University Press, USA |
| Maße: | 244 x 170 x 19 mm |
| Von/Mit: | Harvey Motulsky (u. a.) |
| Erscheinungsdatum: | 01.12.2003 |
| Gewicht: | 0,615 kg |
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