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An introduction to generalized linear models

by George Henry Dunteman | 02 November 2005
PAPERBACK
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 toáGLM (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 GLMáThis 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.
€22.39
67 Reward Points
Currently out of stock
Delivery in 5-7 Days
Eligible for free delivery

Any purchases for more than €10 are eligible for free delivery anywhere in the UK or Ireland!

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 toáGLM (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 GLMáThis 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.
€22.39
67 Reward Points
Currently out of stock
Delivery in 5-7 Days
Eligible for free delivery

Any purchases for more than €10 are eligible for free delivery anywhere in the UK or Ireland!


Product Details

ISBN - 9780761920847
Format - PAPERBACK
Publisher -
Published - 02/11/2005
Categories - All, Books, History and Politics, Politics Philosophy, Popular Sociology, All, Books, Business Computers, Computers, Information Management
No. of Pages - 72
Weight - 122
Edition -
Series - - Not Available
Page Size - 22
Language - en-US
Readership Age - Not Available
Table of Contents - Not Available

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