Regression Analysis and its Application

Richard F. Gunst is Professor of Statistics at Southern Methodist University in Dallas, Texas. He received his Ph.D. (1972) in mathematical statistics from Southern Methodist University. He has been a statistical consultant on statistical modeling and on the design and analysis of experiments to many industrial companies, notably many major automotive and petroleum firms. His research areas include linear and nonlinear regression modeling, statistical experimental design, spatial statistical modeling, and general statistical methods. He has had major industrial and governmental research funding, including grants from the Department of Energy, NASA, the Air Force Office of Scientific Research, and the Department of Veterans Affairs. He has published 3 books on statistical design, modeling, and analysis and over 75 peer-reviewed research articles. Dr. Gunst is a co-recipient of the 1974 and 1985 W.J Youden Award from Technometrics, the 1994 Frank Wilcoxon Award from Technometrics, the Most Outstanding Statistical Application Award from the American Statistical Association (ASA), and the 2005 Sheth Foundation Award from the Journal of the Academy of Marketing Science. He is a Fellow of the ASA.Robert L. Mason is Institute Analyst at Southwest Research Instituteâ in San Antonio, Texas. He received his Ph.D. (1971) in mathematical statistics from Southern Methodist University. He is a nationally known industrial statistician and has had a distinguished career in statistics. He also is an Adjunct Professor in Statistics at the University of Texas at San Antonio. His major work experience has been in applying statistical methods to solve data analysis and experimental design problems for commercial and government clients in the engineering and physical sciences. He is the co-author of five books in statistical design, data analysis, and process control, and has published over 100 papers in refereed statistical and scie

• 1. Introduction 2. Initial Data Exploration 3. Single-Variable Least Squares 4. Mul Tip Le-V Ariable Preliminaries 5. Multiple-Variable Least Squares 6. Inference 7. Residual Analysis 8. Variable Selection Techniques 9. Multicollinearity Effects 10. Biased Regression Estimators

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