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Univariate and Multivariate General Linear Models : Theory and
 
 
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Univariate and Multivariate General Linear Models : Theory and [Paperback]

Neil H. Timm (Author), Tammy A. Mieczkowski (Author)

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Book Description

September 15, 1997
Researchers and students of applied statistics and the social and behavioral sciences will find this book indispensable for understanding both general linear model theory and application. The model is discussed using a general framework, enabling students to appreciate fully the generality of the model. The authors present and explain in detail actual SAS code for analyzing general linear model applications. Additional topics include the following: ANOVA and MANOVA models with and without restrictions, the GMANOVA model, the mixed MANOVA-GMANOVA model, the sum-of-profiles model, the SUR model, the CGMANOVA model, HLM, mixed models, and the analysis of repeated measures with missing data.

Supports releases 6.09 and higher of SAS software.


Editorial Reviews

About the Author

Neil H. Timm

Neil H. Timm is a professor at the School of Education at the

University of Pittsburgh. A prolific author of statistical papers

and presentations in multivariate analysis and linear models, Neil

brings you the benefit of over 20 years experience using SAS software.

He has acted as Statistical Consultant for numerous companies as well

as an editor for various esteemed statistical journals. His

impressive background also includes experience as an instructor of

many workshops, training sessions, and symposiums in univariate

and multivariate linear models.

Tammy A. Mieczkowski

Tammy A. Mieczkowski is a graduate student researcher in the

School of Medicine at the University of Pittsburgh, and she has over

5 years experience using SAS software. With a masters degree in

Applied Statistics, she is currently pursuing a Ph.D. in Research

Methodology. She is published in a number of medical journals

and is co-developer of several education monographs.


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Inside This Book (learn more)
First Sentence:
Data analysis in the social and behavioral sciences and numerous other disciplines is associated with the model statisticians call the general linear model (GLM). Read the first page
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Roy's Greatest Root, Hotelling-Lawley Trace, Statistic Value, Pillai's Trace, Squares Square, Sum of Mean Source, Estimate Error Parameter, Corrected Total, Parameter Estimates Parameter Standard, Res Log Likelihood, Akaike's Information Criterion, Covariance Parameter Estimates, Adj R-sq, Dep Mean, Market Value, Tests of Fixed Effects Source, Value Prob, Variable Variable, Matrix Transformation, Cov Parm Subject Estimate Std Error, Linear Growth, Matrix Characteristic Percent Characteristic Vector, Pillars Trace, Std Error of Parameter Estimate Parameter, System Weighted
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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