## 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. |

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### Contents

Summary | |

Linear LeastSquares Regression | |

2 | |

Statistical Inference for Regression | |

DummyVariable Regression | |

2 | |

Recommended Reading | |

5 | |

Recommended Reading | |

2 | |

Missing at Random | |

Infant Mortality | |

Summary | |

1 | |

Analysis of Variance | |

Coefficients | |

2 | |

4 | |

Statistical Theory for Linear Models | |

The Vector Geometry of Linear Models | |

Multiple Regression | |

Plots | |

Fit | |

5 | |

7 | |

Exercises | |

Exercises | |

2 | |

Exercises | |

3 | |

5 | |

2 | |

3 | |

Appendix | |

6 | |

Author Index | |

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### Common terms and phrases

ANOVA ANOVA model assumptions asymptotic autocorrelations average bootstrap Chapter collinearity column components computed confidence intervals constant correlation covariance matrix data analysis data set degrees of freedom dummy regressors Duncan’s error variance estimating equations example Exercise explanatory variables factor Figure fitted values Ftest function GLMs graph imputation income independent interaction intercept kernel leastsquares estimator leastsquares regression likelihood linear models linear regression localpolynomial logit model loglikelihood main effects maximumlikelihood estimator mean methods missing data mixedeffects models ML estimates model matrix multivariate nonlinear nonparametric regression normally distributed null hypothesis occupational prestige orthogonal outliers parameters partial relationship polynomial population probit procedure produces quantitative random effects regression analysis regression coefficients regression equation regression model represent residual sum response variable scatterplot Section simple regression slope span standard deviation standard errors statistical models studentized residuals subspace sum of squares Table timeseries transformation twoway vector weight Xvalues