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by John Fox

ISBN13: 978-0761930426

ISBN10: 0761930426

Edition: 2ND 08

Copyright: 2008

Publisher: Sage Publications, Inc.

Published: 2008

International: No

ISBN10: 0761930426

Edition: 2ND 08

Copyright: 2008

Publisher: Sage Publications, Inc.

Published: 2008

International: No

Linear models, their variants, and extensions are among the most useful and widely used statistical tools for social research. The Second Edition of Applied Regression Analysis and Generalized Linear Models provides an accessible, in-depth, modern treatment of regression analysis, linear models, and closely related methods. Author John Fox makes the text as user-friendly as possible: With the exception of three chapters, several sections, and a few shorter passages, the prerequisite for reading the book is a course in basic applied statistics that covers the elements of statistical data analysis and inference. Even relatively advanced topics (such as methods for handling missing data and bootstrapping) are presented in a manner consistent with this prerequisite. Key Features of the Second Edition Covers regression modelsa??such as generalized linear models, limited-dependent-variable-models, mixed models and Cox regressiona??and methods that are increasingly being used in social science research Contains a more robust Web site with extensive appendices of background material (matrices, linear algebra, vector geometry; calculus; probability and estimation); data sets used in the book and for data analytic exercises; and the data-analytic exercises themselves. Incorporates real data from the social sciences that is similar to data readers are likely to encounter. This book should be of interest to students and researchers in the social sciences, as well as other disciplines that employ linear models for data analysis, and in courses on applied regression and linear models where the subject matter of applications is not of special concern.

Author Bio

John Fox is Professor of Sociology at McMaster University in Hamilton, Ontario, Canada. He was previously Professor of Sociology and of Mathematics and Statistics at York University in Toronto, where he also directed the Statistical Consulting Service at the Institute for Social Research. Professor Fox earned a Ph.D. in Sociology from the University of Michigan in 1972. He has delivered numerous lectures and workshops on statistical topics, at such places as the summer program of the Inter-University Consortium for Political and Social Research and the annual meetings of the American Sociological Association. His recent and current work includes research on statistical methods (for example, work on three-dimensional statistical graphs) and on Canadian society (for example, a study of political polls in the 1995 Quebec sovereignty referendum). He is author of many articles, in such journals as Sociological Methodology, The Journal of Computational and Graphical Statistics, The Journal of the American Statistical Association, The Canadian Review of Sociology and Anthropology, and The Canadian Journal of Sociology. He has written several other books, including Applied Regression Analysis, Linear Models, and Related Methods (Sage, 1997), Nonparametric Simple Regression (Sage, 2000), and Multiple and Generalized Nonparametric Regression (Sage, 2000).

Preface 1 - Statistical Models and Social Science I - DATA CRAFT 2 - What is Regression Analysis? 3 - Examing Data 4 - Transforming Data II - LINEAR MODELS AND LEAST SQUARES 5 - Linear Least-Squares Regression 6 - Statistical Inference for Regression 7 - Dummy-Variale Regression 8 - Analysis of Variance 9 - Statistical Theory for Lienar Models* 10 - The Vector Geometry of Linear Models* III - LINEAR-MODEL DIAGNOSTICS 11 - Unusual and Influential Data 12 - Normaility, Constant Variance, Linearity 13 - Collinearity and its Purported Remedies IV - GENERALIZED LIENAR MODELS 14 - Logit and Probit Models 15 - Generalized Linear Models V - EXTENDING LINEAR AND GENERALIZED LINEAR MODELS 16 - Time-Series Regression 17 - Nonlinear Regression 18 - Nonparametric Regression 19 - Robust Regression* 20 - Missing Data in Regression Models 21 - Bootstrapping Regression Models 22 - Model Selection, Averaging, and Validation Appendices References

ISBN10: 0761930426

Edition: 2ND 08

Copyright: 2008

Publisher: Sage Publications, Inc.

Published: 2008

International: No

Linear models, their variants, and extensions are among the most useful and widely used statistical tools for social research. The Second Edition of Applied Regression Analysis and Generalized Linear Models provides an accessible, in-depth, modern treatment of regression analysis, linear models, and closely related methods. Author John Fox makes the text as user-friendly as possible: With the exception of three chapters, several sections, and a few shorter passages, the prerequisite for reading the book is a course in basic applied statistics that covers the elements of statistical data analysis and inference. Even relatively advanced topics (such as methods for handling missing data and bootstrapping) are presented in a manner consistent with this prerequisite. Key Features of the Second Edition Covers regression modelsa??such as generalized linear models, limited-dependent-variable-models, mixed models and Cox regressiona??and methods that are increasingly being used in social science research Contains a more robust Web site with extensive appendices of background material (matrices, linear algebra, vector geometry; calculus; probability and estimation); data sets used in the book and for data analytic exercises; and the data-analytic exercises themselves. Incorporates real data from the social sciences that is similar to data readers are likely to encounter. This book should be of interest to students and researchers in the social sciences, as well as other disciplines that employ linear models for data analysis, and in courses on applied regression and linear models where the subject matter of applications is not of special concern.

Author Bio

Table of Contents

Preface 1 - Statistical Models and Social Science I - DATA CRAFT 2 - What is Regression Analysis? 3 - Examing Data 4 - Transforming Data II - LINEAR MODELS AND LEAST SQUARES 5 - Linear Least-Squares Regression 6 - Statistical Inference for Regression 7 - Dummy-Variale Regression 8 - Analysis of Variance 9 - Statistical Theory for Lienar Models* 10 - The Vector Geometry of Linear Models* III - LINEAR-MODEL DIAGNOSTICS 11 - Unusual and Influential Data 12 - Normaility, Constant Variance, Linearity 13 - Collinearity and its Purported Remedies IV - GENERALIZED LIENAR MODELS 14 - Logit and Probit Models 15 - Generalized Linear Models V - EXTENDING LINEAR AND GENERALIZED LINEAR MODELS 16 - Time-Series Regression 17 - Nonlinear Regression 18 - Nonparametric Regression 19 - Robust Regression* 20 - Missing Data in Regression Models 21 - Bootstrapping Regression Models 22 - Model Selection, Averaging, and Validation Appendices References

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