| Titre : | SAS FOR LINEAR MODELS, 1 |
| Autre titre: | SAS POUR LES MODELES LINEAIRES |
| Auteurs : | RAMON C. LITTELL ; WALTER W. STROUP ; RUDOLF J. FREUND |
| Type de document : | Livre |
| Mention d'édition : | FOURTH EDITION |
| Editeur : | NORTH CAROLINA : SAS INSTITUTE INC., 2002 |
| ISBN/ISSN/EAN : | 978-0-471-22174-6 |
| Format : | 466 P. |
| Langues: | Anglais |
| Concepts : |
MODELE LINEAIRE
REGRESSION LOGICIEL STATISTIQUE ANALYSE DE VARIANCE |
| Résumé : | This clear and comprehensive guide provides everything you need for powerful linear model analysis. Using a tutorial approach and plenty of examples, the authors lead you through methods related to analysis of variance with fixed and random effects. You will learn to use the appropriate SAS procedure for most experiment designs (including completely random, randomized blocks, and split plot) as well as factorial treatment designs and repeated measures. SAS* for Linear Models, Fourth Edition, also includes analysis of covariance, multivariate linear models, a nd generalized linear models for non-normal data This edition has been substantially updated to reflect the evolution of contemporary software and statistical analysis methods. Recognizing their considerable impact on linear model analysis, this book covers MIXED and GENMOD procedures in detail. Also included in this edition are updated examples, new software-related features, and other new material. The book contains new chapters on generalized linear models, analysis of covariance, and repeated measures, plus new information about unbalanced mixed-model analyses.. Find inside : - Regression models - Balanced ANOVA with both fixed- and random-effects models - Unbalanced data with both fixed- and random-effects models - Covariance models - Generalized linear models - Multivariate models - Reoeated measures This is a book for the statistically sophisticated SAS software user. The coverage is quite broad, starting with a brief review of basic regression ideas and extending through mixed models and generalized linear models, including Poisson models, logistic models, models that use quasi-likelihood and generalized estimating equations. Advanced concepts are presented in a user-friendly way and interesting relevant examples are presented. David A. Dickey Professor of Statistics North Carolina State University |
Exemplaires (1)
| Cote | Support | Section | Disponibilité |
|---|---|---|---|
| RES 12757 | Livre | Réserve | Disponible |
