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SAS for mixed models /

The subject of mixed linear models is taught in graduate-level statistics courses and is familiar to most statisticians. During the past 10 years, use of mixed model methodology has expanded to nearly all areas of statistical applications. It is routinely taught and applied even in disciplines outsi...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Littell, Ramon C., Milliken, George A., 1943- (Autor), Stroup, Walter W. (Walter Whitney) (Autor), Wolfinger, Russell Dean (Autor), Schabenberger, Oliver (Autor)
Autor Corporativo: Safari, an O'Reilly Media Company
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cary, NC : SAS Institute, 2007.
Edición:Second edition.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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245 1 0 |a SAS for mixed models /  |c by Ramon C. Littell, George A. Milliken, Walter W. Stroup, Russell D. Wolfinger, Oliver Schabenberger. 
250 |a Second edition. 
264 1 |a Cary, NC :  |b SAS Institute,  |c 2007. 
300 |a 1 online resource (828 pages) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
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347 |a text file 
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504 |a Includes bibliographical references, and index. 
505 0 |a Introduction -- Randomized block designs -- Random effects models -- Multi-factor treatment designs with multiple error terms -- Analysis of repeated measures data -- Best linear unbiased prediction -- Analysis of covariance -- Random coefficient models -- Heterogeneous variance models -- Mixed model diagnostics -- Spatial variability -- Power calculations for mixed models -- Some Bayesian approaches to mixed models -- Generalized linear mixed models -- Nonlinear mixed models -- Case studies -- Appendix 1. Linear mixed model theory -- Appendix 2. Data sets. 
520 |a The subject of mixed linear models is taught in graduate-level statistics courses and is familiar to most statisticians. During the past 10 years, use of mixed model methodology has expanded to nearly all areas of statistical applications. It is routinely taught and applied even in disciplines outside traditional statistics. Nonetheless, many persons who are engaged in analyzing mixed model data have questions about the appropriate implementation of the methodology. Also, even users who studied the topic 10 years ago may not be aware of the tremendous new capabilities available for applications of mixed models. Like the first edition, this second edition presents mixed model methodology in a setting that is driven by applications. The scope is both broad and deep. Examples are included from numerous areas of application and range from introductory examples to technically advanced case studies. The book is intended to be useful to as diverse an audience as possible, although persons with some knowledge of analysis of variance and regression analysis will benefit most. 
542 |f Copyright: SAS Institute  |g 2006 
550 |a Made available through: Safari, an O'Reilly Media Company. 
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700 1 |a Stroup, Walter W.  |q (Walter Whitney),  |e author. 
700 1 |a Wolfinger, Russell Dean.  |e author. 
700 1 |a Schabenberger, Oliver.  |e author. 
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