Logistic Regression Using SAS

Name: Logistic Regression Using SAS
Brand: SAS Publishing
SKU: 9781599946412
Price: 66.55 EUR
Availability: InStock

Theory and Application, Second Edition

Автор: D. Allison Paul

Език:

Корици: С меки корици

Издател: SAS Publishing

Наличност: Външен склад

Изпращаме след 9-15 дни

66.55 € 130.17 лв

If you are a researcher or student with experience in multiple linear regression and want to learn a...

Информация за книгата

Автор

D. Allison Paul

Език

Английски език

Корици

Книга - С меки корици

Издадена

2012

страници

348

EAN

9781599946412

ISBN

9781599946412

Enbook ID

08765929

Издател

SAS Publishing

Теглоt

890

Размери

280 x 212 x 23

Пълно описание

If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul Allison's Logistic Regression Using SAS: Theory and Application, Second Edition, is for you! Informal and nontechnical, this book both explains the theory behind logistic regression, and looks at all the practical details involved in its implementation using SAS. Several real-world examples are included in full detail. This book also explains the differences and similarities among the many generalizations of the logistic regression model. The following topics are covered: binary logistic regression, logit analysis of contingency tables, multinomial logit analysis, ordered logit analysis, discrete-choice analysis, and Poisson regression. Other highlights include discussions on how to use the GENMOD procedure to do loglinear analysis and GEE estimation for longitudinal binary data. Only basic knowledge of the SAS DATA step is assumed. The second edition describes many new features of PROC LOGISTIC, including conditional logistic regression, exact logistic regression, generalized logit models, ROC curves, the ODDSRATIO statement (for analyzing interactions), and the EFFECTPLOT statement (for graphing non-linear effects). Also new is coverage of PROC SURVEYLOGISTIC (for complex samples), PROC GLIMMIX (for generalized linear mixed models), PROC QLIM (for selection models and heterogeneous logit models), and PROC MDC (for advanced discrete choice models).

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