Applied Regression Analysis, Linear Models and Related Methods

An accessible, detailed and up-to-date treatment of regression analysis, linear models and closely related methods is provided in this book.

Incorporating nearly 200 graphs and numerous examples and exercises that employ real data from the social sciences, the book begins with a consideration of the role of statistical data analysis in social research.

It then moves on to cover the following topics: graphical methods for examining and transforming data; linear least-squares regression; dummy-variables regression; analysis of variance; 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.