Log-Linear Models and Logistic Regression (Springer Texts in Statistics) de Christensen, Ronald: good (1997)

Descripci�n

Fast Shipping - Good and clean conditions used book. Pages and cover are intact. Limited notes marks and highlighting may be present. May show signs of normal shelf wear and bends on edges. Item may be missing CDs or access codes. May include library marks.

N� de ref. del art�culo GWV.0387982477.G

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Detalles bibliogr�ficos

T�tulo: Log-Linear Models and Logistic Regression (Springer Texts in Statistics)
Editor: Springer
Fecha de publicaci�n: 1997
Idioma: Ingl�s
ISBN 10: 0387982477
ISBN 13: 9780387982472
Encuadernaci�n: Encuadernaci�n de tapa dura
Edici�n: 2� Edici�n
Estado: good

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Sinopsis

The primary focus here is on log-linear models for contingency tables, but in this second edition, greater emphasis has been placed on logistic regression. The book explores topics such as logistic discrimination and generalised linear models, and builds upon the relationships between these basic models for continuous data and the analogous log-linear and logistic regression models for discrete data. It also carefully examines the differences in model interpretations and evaluations that occur due to the discrete nature of the data. Sample commands are given for analyses in SAS, BMFP, and GLIM, while numerous data sets from fields as diverse as engineering, education, sociology, and medicine are used to illustrate procedures and provide exercises. Throughoutthe book, the treatment is designed for students with prior knowledge of analysis of variance and regression.

Rese�a del editor

The primary focus here is on log-linear models for contingency tables, but in this second edition, greater emphasis has been placed on logistic regression. The book explores topics such as logistic discrimination and generalised linear models, and builds upon the relationships between these basic models for continuous data and the analogous log-linear and logistic regression models for discrete data. It also carefully examines the differences in model interpretations and evaluations that occur due to the discrete nature of the data. Sample commands are given for analyses in SAS, BMFP, and GLIM, while numerous data sets from fields as diverse as engineering, education, sociology, and medicine are used to illustrate procedures and provide exercises. Throughoutthe book, the treatment is designed for students with prior knowledge of analysis of variance and regression.

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