Concise, mathematically clear, and comprehensive treatment of the subject. * Expanded coverage of diagnostics and methods of model fitting. * Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models. * More than 200 problems throughout the book plus outline solutions for the exercises. * This revision has been extensively class-tested. "With excellent motivating and presenting style, this book is suitable for a beginning graduate level regression course." ( Journal of Statistical Computation and Simulation , July 2005) "...revises and expands the standard text, providing extensive coverage of state-of-the-art theory..." ( Zentralblatt Math , Vol. 1029, 2004) "...largely rewritten...very useful for self-study...an excellent choice for a course in linear models and researchers who are interested in recent literature in the fields..." ( Technometrics , Vol. 45, No. 4, November 2003) “...rewritten to reflect current thinking, such as the major advances in computing during the past 25 years.” ( Quarterly of Applied Mathematics , Vol. LXI, No. 3, September 2003) An extensive treatment of a key method in the statistician’s toolbox For more than two decades, the First Edition of Linear Regression Analysis has been an authoritative resource for one of the most common methods of handling statistical data. There have been many advances in the field over the last twenty years, including the development of more efficient and accurate regression computer programs, new ways of fitting regressions, and new methods of model selection and prediction. Linear Regression Analysis, Second Edition, revises and expands this standard text, providing extensive coverage of state-of-the-art theory and applications of linear regression analysis. Requiring no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models, this new edition features: Up-to-date accounts of computational methods and algorithms currently in use without getting entrenched in minor computing details - A careful and detailed survey of the research literature, making this a highly useful reference - Expanded coverage of diagnostics, and more discussion of methods of model fitting, model selection and prediction - More than 200 problems throughout the book plus outline solutions Concise, mathematically clear, and comprehensive, Linear Regression Analysis, Second Edition, serves as both a reliable reference for the practitioner and a valuable textbook for the student. An extensive treatment of a key method in the statistician s toolbox For more than two decades, the First Edition of Linear Regression Analysis has been an authoritative resource for one of the most common methods of handling statistical data. There have been many advances in the field over the last twenty years, including the development of more efficient and accurate regression computer programs, new ways of fitting regressions, and new methods of model selection and prediction. Linear Regression Analysis, Second Edition, revises and expands this standard text, providing extensive coverage of state-of-the-art theory and applications of linear regression analysis. Requiring no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models, this new edition features: Up-to-date accounts of computational methods and algorithms currently in use without getting entrenched in minor computing details - A careful and detailed survey of the research literature, making this a highly useful reference - Expanded coverage of diagnostics, and more discussion of methods of model fitting, model selection and prediction - More than 200 problems throughout the book plus outline solutions Concise, mathematically clear, and comprehensive, Linear Regression Analysis, Second Edition, serves as both a reliable reference for the practitioner and a valuable textbook for the student. George A. F. Seber, PhD, Dip. Counsel. is an Emeritus Professor in the Department of Statistics at the University of Auckland in New Zealand. A Fellow of the New Zealand Royal Society and recipient of the Hector Medal in Science by the NZ Royal Society, he is the author or coauthor of 15 statistical books and an extensive book on counselling.  His research interests have included linear and nonlinear modeling, multivariate analysis,  statistical ecology, genetics, epidemiology, and adaptive sampling. Alan J. Lee, PhD, is a Professor of Statistics in the Department of Statistics at the University of Auckland.His research interests include statistical modeling, semi-parametric inference, circular statistics and social statistics. Along with Dr Nicholas Fisher, he is a principal in the consultancy Valumetrics Australia.