Description
Book SynopsisThe 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
Trade Review“…very useful to applied scientists and for graduate level courses in areas of non-mathematical statistics…” (
Zentralblatt Math, Vol.1039, No.8, 2004)
Table of ContentsPreface.
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.
