Regression and ANOVA: An Integrated Approach Using SAS Software / Edition 1 available in Paperback
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The information contained in this book has served as the basis fora graduate-level biostatistics class at the University of NorthCarolina at Chapel Hill. The book focuses in the General LinearModel (GLM) theory, stated in matrix terms, which provides a morecompact, clear, and unified presentation of regression of ANOVAthan do traditional sums of squares and scalar equations.The book contains a balanced treatment of regression and ANOVA yetis very compact. Reflecting current computational practice, mostsums of squares formulas and associated theory, especially inANOVA, are not included. The text contains almost no proofs,despite the presence of a large number of basic theoreticalresults. Many numerical examples are provided, and include both theSAS code and equivalent mathematical representation needed toproduce the outputs that are presented.All exercises involve only "real" data, collected in the course ofscientific research. The book is divided into sections covering thefollowing topics:* Basic Theory* Multiple Regression* Model Building and Evaluation* ANOVA* ANCOVA
|Product dimensions:||8.20(w) x 10.90(h) x 1.40(d)|
About the Author
Keith E. Muller, Ph.D., is Associate Professor ofBiostatistics at the University of North Carolina at Chapel Hill.He teaches classes and seminars in the theory and practice ofunivariate and multivariate linear models with Gaussian errors. ASAS user since 1978, he is best known for his contributions totheory and practice of sample size and power calculations,including SAS/IML programs for power in repeated measures.
Bethel A. Fetterman, M.S., is Director of Clinical DataProcessing and Analysis at PharmaLinkFHI in Research Triangle Park,North Carolina. She is currently on leave from the doctoral programin Biostatistics at the University of North Carolina at ChapelHill. A SAS user since 1989, she uses SAS software in designing,managing, analyzing, and reporting clinical trials of newpharmaceutical.
Table of Contents
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.
GLM Assumption Diagnostics.
GLM Computation Diagnostics.
Selecting the Best Model.
Coding Schemes for Regression.
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.