Logistic Regression Using SAS

Name: Logistic Regression Using SAS
Brand: SAS Publishing
SKU: 9781599946412
Price: 281.79 PLN
Availability: InStock

Theory and Application, Second Edition

Autor: D. Allison Paul

Język:

Oprawa: Miękka

Wydawca: SAS Publishing

Dostępność: Dostępna u dostawcy

Wysyłamy za 9-15 dni

281.79 zł

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

Informacje o książce

Autor

D. Allison Paul

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2012

strony

348

EAN

9781599946412

ISBN

9781599946412

Enbook ID

08765929

Wydawca

SAS Publishing

Waga

890

Wymiary

280 x 212 x 23

Pełny opis

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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Logistic Regression Using SAS

Theory and Application, Second Edition

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