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An Introduction to Generalized Linear Models (Quantitative Applications in the Social Sciences): 145 - Softcover
Synopsis
Do you have data that is not normally distributed and don′t know how to analyze it using generalized linear models (GLM)? Beginning with a discussion of fundamental statistical modeling concepts in a multiple regression framework, the authors extend these concepts to GLM (including Poisson regression. logistic regression, and proportional hazards models) and demonstrate the similarity of various regression models to GLM. Each procedure is illustrated using real life data sets, and the computer instructions and results will be presented for each example. Throughout the book, there is an emphasis on link functions and error distribution and how the model specifications translate into likelihood functions that can, through maximum likelihood estimation be used to estimate the regression parameters and their associated standard errors. This book provides readers with basic modeling principles that are applicable to a wide variety of situations.
Key Features:
- Provides an accessible but thorough introduction to GLM, exponential family distribution, and maximum likelihood estimation
- Includes discussion on checking model adequacy and description on how to use SAS to fit GLM
- Describes the connection between survival analysis and GLM
This book is an ideal text for social science researchers who do not have a strong statistical background, but would like to learn more advanced techniques having taken an introductory course covering regression analysis.
"synopsis" may belong to another edition of this title.
About the Author
Prof. Ho′s research concerns with the development and application of quantitative methods in the neural and behavioral sciences. His current research interests include effective connectivity analysis in fMRI experiments, social network analysis, statistical approach for testing mathematical axioms, diagnostics in nonlinear SEM.
"About this title" may belong to another edition of this title.
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An Introduction to Generalized Linear Models
Published by
SAGE Publications, Incorporated, 2005
ISBN 10: 0761920846
ISBN 13: 9780761920847
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An Introduction to Generalized Linear Models (Quantitative Applications in the Social Sciences): 145
Published by
SAGE Publications Inc, United States, Thousand Oaks, 2005
ISBN 10: 0761920846
ISBN 13: 9780761920847
Used
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Paperback. Condition: Very Good. Do you have data that is not normally distributed and dont know how to analyze it using generalized linear models (GLM)? Beginning with a discussion of fundamental statistical modeling concepts in a multiple regression framework, the authors extend these concepts toGLM (including Poisson regression. logistic regression, and proportional hazards models) and demonstrate the similarity of various regression models to GLM. Each procedure is illustrated using real life data sets, and the computer instructions and results will be presented for each example. Throughout the book, there is an emphasis on link functions and error distribution and how the model specifications translate into likelihood functions that can, through maximum likelihood estimation be used to estimate the regression parameters and their associated standard errors. This book provides readers with basic modeling principles that are applicable to a wide variety of situations. Key Features: - Provides an accessible but thorough introduction to GLM, exponential family distribution, and maximum likelihood estimation - Includes discussion on checking model adequacy and description on how to use SAS to fit GLM - Describes the connection between survival analysis and GLM This book is an ideal text for social science researchers who do not have a strong statistical background, but would like to learn more advanced techniques having taken an introductory course covering regression analysis. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged. Seller Inventory # GOR008611437
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Introduction to Generalized Linear Models
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