Regression and ANOVA

An Integrated Approach Using SAS Software

Keith E. Muller, Bethel A. Fetterman

566 pages, parution le 08/10/2003

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Résumé

The information contained in this book has served as the basis for a graduate-level biostatistics class at the University of North Carolina at Chapel Hill. The book focuses in the General Linear Model (GLM) theory, stated in matrix terms, which provides a more compact, clear, and unified presentation of regression of ANOVA than do traditional sums of squares and scalar equations.

The book contains a balanced treatment of regression and ANOVA yet is very compact. Reflecting current computational practice, most sums of squares formulas and associated theory, especially in ANOVA, are not included. The text contains almost no proofs, despite the presence of a large number of basic theoretical results. Many numerical examples are provided, and include both the SAS code and equivalent mathematical representation needed to produce the outputs that are presented.

All exercises involve only “real” data, collected in the course of scientific research. The book is divided into sections covering the following topics:

Basic Theory
Multiple Regression
Model Building and Evaluation
ANOVA
ANCOVA

Preface
Examples and Limits of the GLM
Statement of the Model, Estimation, and Testing
Some Distributions for the GLM
Multiple Regression: General Considerations
Testing Hypotheses in Multiple Regression
Correlations
GLM Assumption Diagnostics
GLM Computation Diagnostics
Polynomial Regression
Transformations
Selecting the Best Model
Coding Schemes for Regression
One-Way ANOVA
Complete, Two-Way Factorial ANOVA
Special Cases of Two-Way ANOVA and Random Effects Basics
The Full Model in Every Cell (ANCOVA as a Special Case)
Understanding and Computing Power for the GLM

Appendix A Matrix Algebra for Linear Models
Appendix B Statistical Tables
Appendix C Study Guide for Linear Model Theory
Appendix D Homework and Example Data
Appendix E Introduction to SAS/IML
Appendix F A Brief Manual to LINMOD
Appendix G SAS/IML Power Program User's Guide
Appendix H Regression Model Selection Data
References
Index

L'auteur - Keith E. Muller

Ph.D., is Associate Professor of Biostatistics at the University of North Carolina at Chapel Hill. He teaches classes and seminars in the theory and practice of univariate and multivariate linear models with Gaussian errors. A SAS user since 1978, he is best known for his contributions to theory and practice of sample size and power calculations, including SAS/IML programs for power in repeated measures.

L'auteur - Bethel A. Fetterman

M.S., is Director of Clinical Data Processing and Analysis at PharmaLinkFHI in Research Triangle Park, North Carolina. She is currently on leave from the doctoral program in Biostatistics at the University of North Carolina at Chapel Hill. A SAS user since 1989, she uses SAS software in designing, managing, analyzing, and reporting clinical trials of new pharmaceutical.

Caractéristiques techniques

	PAPIER
Éditeur(s)	Wiley
Auteur(s)	Keith E. Muller, Bethel A. Fetterman
Parution	08/10/2003
Nb. de pages	566
Format	21 x 27,5
Couverture	Broché
Poids	1310g
Intérieur	Noir et Blanc
EAN13	9780471469438
ISBN13	978-0-471-46943-8