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. |
Contents
Exercises | |
Exercises | |
Summary | |
Exercises | |
Exercises | |
Exercises | |
Exercises | |
Summary | |
Exercises | |
Exercises | |
Summary | |
Appendix | |
Author Index | |
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Common terms and phrases
ANOVA model assumptions asymptotic autocorrelations average binomial bootstrap cell Chapter collinearity column component-plus-residual plots computed confidence intervals constant correlation covariance matrix data analysis data set degrees of freedom dummy regressors Duncan’s estimating equations example Exercise explanatory variables F-statistic F-test factor Figure fitted values function GLMs graph hat-values income independent infant mortality intercept least-squares coefficients least-squares estimator least-squares regression likelihood likelihood-ratio test linear models linear regression log-likelihood logit model main effects maximum-likelihood estimators methods missing data model matrix multiple regression multivariate nonlinear nonparametric regression normally distributed null hypothesis occupational prestige orthogonal outliers partial relationship Poisson polytomous population power transformation procedure produces regression coefficients regression equation regression model represent residual deviance residual sum response variable sampling variance scatterplot Section selection shows simple regression slope span standard deviation standard errors statistical models studentized residuals subspace sum of squares Table vector X-values