Applied Regression Analysis, Linear Models, and Related Methods

Aimed at researchers and students who want to use linear models for data analysis, John Fox's book provides an accessible, in-depth treatment of regression analysis, linear models, and closely related methods. Fox incorporates nearly 200 graphs and numerous examples and exercises that employ real data from the social sciences. He begins the book with a concise consideration of the role of statistical data analysis in social research. He next covers graphical methods for examining and transforming data, linear least-squares regression, dummy-variables regression, and analysis of variance. Fox also explores diagnostic methods for discovering whether a linear model fit to data adequately represents the data; extensions to linear least squares, including logit and probit models, time-series regression, nonlinear regression, robust regression, and nonparametric regression; and empirical methods for assessing sampling variation, including the bootstrap and cross-validation. More difficult material is segregated in separate sections and chapters and several appendixes are also included presenting background information. Scholars, professionals, researchers, and students in research methods, evaluation, education, sociology, and psychology will appreciate the enhanced and thorough treatment that regression analysis, linear models, and other related methods have received by author John Fox.