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Applied Regression Analysis and Generalized Linear Models by John Fox and John Fox Jr. (2008, Hardcover)

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About this product

Product Identifiers

PublisherSAGE Publications, Incorporated

ISBN-100761930426

ISBN-139780761930426

eBay Product ID (ePID)63139905

Product Key Features

Number of Pages688 Pages

LanguageEnglish

Publication NameApplied Regression Analysis and Generalized Linear Models

SubjectProbability & Statistics / Regression Analysis, Probability & Statistics / General, Research, Statistics

Publication Year2008

TypeTextbook

Subject AreaMathematics, Social Science

AuthorJohn Fox, John Fox Jr.

FormatHardcover

Dimensions

Item Height1.7 in

Item Weight48.1 Oz

Item Length10 in

Item Width7 in

Additional Product Features

Edition Number2

Intended AudienceCollege Audience

LCCN2007-047617

Reviewshelps to bridge the divide between introductory and intermediate to advanced methods courses. The book is written in a clear, concise manner and organized in such a way as to help facilitate comprehension of the material...Together [with] theR and S-plus Companion to Applied Regression [has] made a fantastic contribution to the world of quantitative social science methology., This is an excellent text on regression applications and methods, written with authority, lucidity, and eloquence., helps to bridge the divide between introductory and intermediate to advanced methods courses. The book is written in a clear, concise manner and organized in such a way as to help facilitate comprehension of the material...Together [with] the R and S-plus Companion to Applied Regression [has] made a fantastic contribution to the world of quantitative social science methology., helps to bridge the divide between introductory and intermediate to advanced methods courses. The book is written in a clear, concise manner and organized in such a way as to help facilitate comprehension of the material...Together [with] the R and S-plus Companion to Applied Regression [has] made a fantastic contribution to the world of quantitative social science methology., helps to bridge the divide between introductory and intermediate to advanced methods courses. The book is written in a clear, concise manner and organized in such a way as to help facilitate comprehension of the material...Together [with] the R and S-plus Companion to Applied Regression[has] made a fantastic contribution to the world of quantitative social science methology.

Dewey Edition22

IllustratedYes

Dewey Decimal300.1/519536

Table Of ContentPreface1 - Statistical Models and Social ScienceI - DATA CRAFT2 - What is Regression Analysis?3 - Examining Data4 - Transforming DataII - LINEAR MODELS AND LEAST SQUARES5 - Linear Least-Squares Regression6 - Statistical Inference for Regression7 - Dummy-Variable Regression8 - Analysis of Variance9 - Statistical Theory for Linear Models10 - The Vector Geometry of Linear ModelsIII - LINEAR-MODEL DIAGNOSTICS11 - Unusual and Influential Data12 - Diagnosing Non-Normality, Nonconstant Error Variance, and Nonlinearity13 - Collinearity and its Purported RemediesIV - GENERALIZED LINEAR MODELS14 - Logit and Probit Models15 - Generalized Linear ModelsV - EXTENDING LINEAR AND GENERALIZED LINEAR MODELS16 - Time-Series Regression17 - Nonlinear Regression18 - Nonparametric Regression19 - Robust Regression20 - Missing Data in Regression Models21 - Bootstrapping Regression Models22 - Model Selection, Averaging, and ValidationA NotationReferences

SynopsisThe Second Edition extends coverage to regression models such as: generalized linear models; limited-dependent-variable-models; mixed models and Cox regression among others., Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Second Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material throughout the book. Key Updates to the Second Edition : Provides greatly enhanced coverage of generalized linear models, with an emphasis on models for categorical and count data Offers new chapters on missing data in regression models and on methods of model selection Includes expanded treatment of robust regression, time-series regression, nonlinear regression, and nonparametric regression Incorporates new examples using larger data sets Includes an extensive Web site at http://www.sagepub.com/foxthat presents appendixes, data sets used in the book and for data-analytic exercises, and the data-analytic exercises themselves Intended Audience: This core text will be a valuable resource for graduate students and researchers in the social sciences (particularly sociology, political science, and psychology) and other disciplines that employ linear and related models for data analysis., 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 EditionCovers regression models'such as generalized linear models, limited-dependent-variable-models, mixed models and Cox regression'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., Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Second Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material throughout the book. Key Updates to the Second Edition Provides greatly enhanced coverage of generalized linear models, with an emphasis on models for categorical and count data Offers new chapters on missing data in regression models and on methods of model selection Includes expanded treatment of robust regression, time-series regression, nonlinear regression, and nonparametric regression Incorporates new examples using larger data sets Includes an extensive Web site at http: //www.sagepub.com/fox that presents appendixes, data sets used in the book and for data-analytic exercises, and the data-analytic exercises themselves Intended Audience: This core text will be a valuable resource for graduate students and researchers in the social sciences (particularly sociology, political science, and psychology) and other disciplines that employ linear and related models for data analysis.

LC Classification NumberHA31.3.F69 2008