Regression Analysis


  • Rudolf Freund, Texas A&M University, U.S.A.
  • William Wilson, University of North Florida, Jacksonville, Florida, U.S.A.
  • Ping Sa, University of North Florida

The book provides complete coverage of the classical methods of statistical analysis. It is designed to give students an understanding of the purpose of statistical analyses, to allow the student to determine, at least to some degree, the correct type of statistical analyses to be performed in a given situation, and have some appreciation of what constitutes good experimental design.
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Because of the universal appeal of statistics and statistical methodology, most graduate programs include as part of their curriculum one or two course in statistical methods. In addition, undergraduate students majoring in mathematics or statistics are required or encouraged to take courses in statistical methods. This book is intended to serve as a text for such courses. The book requires no mathematics beyond algebra, however, mathematically oriented students will still find the material in the text challenging.


Book information

  • Published: March 2006
  • ISBN: 978-0-12-088597-8


" well-written , well-organized, and succeeds in making regression analysis understandable, without being overly technical." Donice McCune, Stephen F. Austin University "I would say that this book is excellent from both a pedagogical perspective and a learning perspective (by the student). The instructor will enjoy discussing various concepts and then illustrating the concepts through the thorough examples. 6. This textbook will help give the students additional mathematical maturity for handling other statistics courses, especially applied courses like Analysis of Variance." Steven Garren, James Madison University

Table of Contents

1. The Analysis of Means: A Review of Basics and an Introduction to Linear Models 2. Simple Linear Regression:Linear Regression with One Independent Variable 3. Multiple Regression 4. Problems with Observations 5. Multicollinearity 6. Problems with the Model 7. Curve Fitting8. Introduction to Nonlinear Models 9. Indicator Variables 10. Categorical Response Variables 11. Generalized Linear ModelsAppendix A: Statistical TablesAppendix B: A Brief Introduction to MatricesAppendix C: Estimation ProceduresReferences