## Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve FittingMost 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. |

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I was looking for the definition of R squared and I found this book gives a detailed and accurate explanation.

### Contents

Contents | 12 |

A Fitting data with nonlinear regression | 13 |

Preparing data for nonlinear regression | 19 |

Nonlinear regression choices | 25 |

The results of nonlinear regression | 32 |

Troubleshooting bad fits | 38 |

B Fitting data with linear regression | 47 |

Models | 58 |

Using twoway ANOVA to compare curves | 166 |

Using a paired t test to test for a treatment effect in a series | 171 |

Using an unpaired t test to test for a treatment effect in a series | 181 |

H Fitting radioligand and enzyme kinetics data | 187 |

Calculations with radioactivity | 194 |

Analyzing competitive binding data | 211 |

Homologous competitive binding curves | 222 |

Analyzing kinetic binding data | 233 |

Global models | 67 |

How nonlinear regression works | 80 |

How nonlinear regression minimizes the sumofsquares | 91 |

E Confidence intervals of the parameters | 97 |

Generating confidence intervals by Monte Carlo simulations | 104 |

Comparing the three methods for creating confidence intervals | 118 |

Using simulations to understand confidence intervals and plan | 128 |

F Comparing models | 134 |

Comparing models using Akaikes Information Criterion AIC | 143 |

How should you compare models AICc or F test? | 149 |

Testing whether a parameter differs from a hypothetical value | 157 |

G How does a treatment change the curve? | 160 |

Analyzing enzyme kinetic data | 245 |

Fitting doseresponse curves | 256 |

The operational model of agonist action | 266 |

Doseresponse curves in the presence of antagonists | 276 |

Complex doseresponse curves | 290 |

J Fitting curves with GraphPad Prism | 296 |

Prisms nonlinear regression dialog | 302 |

Classic nonlinear models built into Prism | 312 |

Importing equations and equation libraries | 322 |

Linear regression with Prism | 334 |

Graphing a family of theoretical curves | 344 |

### Other editions - View all

Fitting Models to Biological Data Using Linear and Nonlinear Regression: A ... Harvey Motulsky No preview available - 2004 |

### Common terms and phrases

affinity AICc analyze ANOVA assumption best-fit curve best-fit value binding curve binding data Bmax bottom plateau calculations chapter choose column compare models competitive binding compute concentration of radioligand confidence interval constant value curve fitting data points data sets define degrees of freedom determine dissociation dose dose-response curve EC5o enter enzyme enzyme kinetics equal equation example experimental exponential decay F test fit the data fraction free concentration full agonist Gaussian distribution global fitting graph Hill slope HillSlope homologous competition initial values kinetics labeled ligand law of mass linear regression logarithm logEC50 mass action mean method nonlinear regression program nonspecific binding null hypothesis number of data number of parameters one-site operational model parameter values Prism radioactivity radioligand binding rate constant receptor replicates response saturation binding Scatchard plot scatter slope factor specific binding standard errors statistical substrate sum-of-squares total binding two-site units variable Vmax zero