Univariate and Multivariate General Linear Models

Regular price €192.20
Quantity:
Ships in 10-20 days
Delivery/Collection within 10-20 working days
Shipping & Delivery
A01=Kevin Kim
A01=Neil Timm
advanced statistical modeling with SAS
ANOVA Table
applied statistics
Author_Kevin Kim
Author_Neil Timm
Box Cox Power Transformation
Category=PBT
chi
code
Compound Symmetry
design
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
experimental design
FGLS Estimate
Full Rank Model
Growth Curve Model
hierarchical modeling
hypothesis
hypothesis testing
linear regression model
MANCOVA Model
MANOVA techniques
matrix
Missing Data
mixed
MMM Analysis
multiple imputation
Multiple Linear Regression
multivariate analysis
Multivariate Normal
Multivariate Normal Random Variables
Multivariate Normality Tests
normality
PROC CATMOD
Proc GLM
Proc IML
Random Coefficient Model
Random Independent Variables
REML Estimate
sas
SAS Code
SAS IML
SAS statistical procedures
Simultaneous Confidence Sets
Split Plot Design
square
structural equation modeling
SUR Model
univariate normality
Unweighted Test

Product details

  • ISBN 9781584886341
  • Weight: 1210g
  • Dimensions: 152 x 229mm
  • Publication Date: 11 Oct 2006
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
Secure checkout Fast Shipping Easy returns

Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data sets from a variety of disciplines, such as the social and behavioral sciences.

With revised examples that include options available using SAS 9.0, this expanded edition divides theory from applications within each chapter. Following an overview of the GLM, the book introduces unrestricted GLMs to analyze multiple regression and ANOVA designs as well as restricted GLMs to study ANCOVA designs and repeated measurement designs. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis, and multivariate GLMs that cover multivariate regression analysis, MANOVA, MANCOVA, and repeated measurement data analyses. The book also analyzes double multivariate linear, growth curve, seeming unrelated regression (SUR), restricted GMANOVA, and hierarchical linear models.

New to the Second Edition

  • Two chapters on finite intersection tests and power analysis that illustrates the experimental GLMPOWER procedure
  • Expanded theory of unrestricted general linear, multivariate general linear, SUR, and restricted GMANOVA models to comprise recent developments
  • Expanded material on missing data to include multiple imputation and the EM algorithm
  • Applications of MI, MIANALYZE, TRANSREG, and CALIS procedures

    A practical introduction to GLMs, Univariate and Multivariate General Linear Models demonstrates how to fully grasp the generality of GLMs by discussing them within a general framework.
  • Kevin Kim, Neil Timm

    More from this author