## Modelling Binary DataSince the original publication of the bestselling Modelling Binary Data, a number of important methodological and computational developments have emerged, accompanied by the steady growth of statistical computing. Mixed models for binary data analysis and procedures that lead to an exact version of logistic regression form valuable additions to the statistician's toolbox, and author Dave Collett has fully updated his popular treatise to incorporate these important advances. Modelling Binary Data, Second Edition now provides an even more comprehensive and practical guide to statistical methods for analyzing binary data. Along with thorough revisions to the original material-now independent of any particular software package- it includes a new chapter introducing mixed models for binary data analysis and another on exact methods for modelling binary data. The author has also added material on modelling ordered categorical data and provides a summary of the leading software packages. All of the data sets used in the book are available for download from the Internet, and the appendices include additional data sets useful as exercises. |

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### Contents

1 | |

Statistical inference for binary data | 19 |

Models for binary and binomial data | 45 |

Bioassay and some other applications | 103 |

Model checking | 129 |

Overdispersion | 195 |

Modelling data from epidemiological studies | 215 |

Mixed models for binary data | 269 |

Some additional topics | 325 |

Computer software for modelling binary data | 335 |

Values of logitp and probitp | 351 |

Some derivations | 353 |

Additional data sets | 361 |

369 | |

Back Cover | 379 |

Exact Methods | 303 |

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### Common terms and phrases

ˆpi ˆβ ˆψ approximate binary data binary observations binary response variable binomial distribution cancer case-control study change in deviance Chapter confidence interval confounding variables constructed variable corresponding data from Example data set deviance on fitting dose ED50 value equation exact explanatory variables exposure factor fitted model fitted probabilities fitting a linear germination given in Table included index plot indicator variables interaction ith observation likelihood function likelihood residuals linear logistic model linear model link function log(Acid logistic regression lines logistic regression model logistic transform logit pi logit(ˆp matched set methods mice model fitted model that contains nodal involvement null hypothesis observed proportions obtained odds ratio outliers overdispersion P-value packages parameter estimates particular patients probit procedure quadratic random effect random variable response probability response variable result standard error standardised deviance residuals success probability sufficient statistics term tumour variance zero