## Applied Regression Analysis and Generalized Linear ModelsCombining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book. Accompanying website resources containing all answers to the end-of-chapter exercises. Answers to odd-numbered questions, as well as datasets and other student resources are available on the author′s website. NEW! Bonus chapter on Bayesian Estimation of Regression Models also available at the author′s website. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

Exercises | |

Exercises | |

Summary | |

Exercises | |

Exercises | |

Exercises | |

Exercises | |

Exercises | |

Summary | |

Appendix | |

Author Index | |

Exercises | |

### Other editions - View all

### Common terms and phrases

analysis ANOVA applied approach assumptions average calculate cell Chapter coding column combination components computed conditional confidence constant constructed contrast correlation corresponding covariance degrees of freedom depend described deviation discussion distribution dummy regressors employed Equation error variance estimator examine example Exercise expectation explanatory variables factor Figure fixed function given hypothesis illustrated income independent individual interaction interpretation interval least least-squares levels linear models logit main effects marginal matrix means measured methods missing multiple nonlinear normal Note observations occupations parameters partial plot population positive possible present prestige probability procedure produces random reasonable regression coefficients regressors relationship relatively represent residuals response variable sample selection shown shows simple slope span specific standard statistical studentized sum of squares Table transformation usual values vector weight