## 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. Available with Perusall—an eBook that makes it easier to prepare for class Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more. |

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### Contents

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Exercises | |

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Processes | |

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Summary | |

Appendix | |

Estimator | |

Nonlinearity | |

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Summary | |

Recommended Reading | |

Author Index | |

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### Common terms and phrases

additive alternative analysis ANOVA applied approach approximate assumptions average bootstrap calculate Chapter column combination complete components computed conditional confidence constant constructed correlation covariance degrees of freedom depends described deviation discussion distribution dummy effects employed Equation error error variance estimator examine example Exercise expected explanatory variables factor Figure fixed function given hypothesis illustrated income independent individual inference interaction interpretation interval introduced least least-squares linear models logit matrix mean measured methods multiple nonlinear normal Note observations obtained occupations parameters partial plot population possible present prestige probability procedure produces random reasonable regression coefficients regression model regressors relationship relatively represent residuals response variable sample selection shown shows simple slope social span standard statistical sum of squares Table transformation usual values variance vector weight