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This fifth edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The coverage offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique.

It is no exaggeration to say that virtually all quantitative research in the social sciences is done with correlation and regression analysis (CRA) and their siblings and offspring. In this book, the editors have ordered the growing research literature on the use of CRA according to its natural steps into four volumes

Regression Analysis for Social Sciencespresents methods of regression analysis in an accessible way, with each method having illustrations and examples. A broad spectrum of methods are included: multiple categorical predictors, methods for curvilinear regression, and methods for symmetric regression. This book can be used for courses in regression analysis at the advanced undergraduate and beginning graduate level in the social and behavioral sciences. Most of the techniques are explained step-by-step enabling students and researchers to analyze their own data. Examples include data from the social and behavioral sciences as well as biology, making the book useful for readers with biological and biometrical backgrounds. Sample command and result files for SYSTAT are included in the text. Key Features * Presents accessible methods of regression analysis * Includes a broad spectrum of methods * Techniques are explained step-by-step * Provides sample command and result files for SYSTAT

Introduction -- The basics -- Essential tools for regression analysis -- What does "holding other factors constant" mean? -- Standard errors, hypothesis tests, p-values, and aliens -- What could go wrong? -- Strategies for other regression objectives -- Methods to address biases from non-random explanatory variables -- Other methods besides ordinary least squares -- Time-series models -- Some really interesting research -- How to conduct a research project -- Summarizing thoughts

SummaryThe sums of squares associated with the independent variables in a multiple regression equation depend on the order in which these variables are introduced. Two methods have been proposed in the literature to avoid this inconvenience: "forward selection" or "backward elimination".With forward selection the independent variables are introduced in successive stages. The order is not predetermined but at each stage that variable is taken as the next one which produces the highest reduction in the residual sum of squares of the dependent variable.With backward elimination on the other hand, we start with the complete regression equation and eliminate the independent variables from it in the order in which they produce the smallest increases in the residual sum of squares.This paper describes a simple and convenient computational lay‐out which can be used for both procedures. In forward selection we start with the matrix of product sums, and in bacward elimination we work from the inverse matrix.In addition these techniques are applied to a variety of practical examples in order to see what results they lead to and what pitfalls may be encountered.