Linear Regression Analysis

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 detailsA careful and detailed survey of the research literature, making this a highly useful referenceExpanded coverage of diagnostics, and more discussion of methods of model fitting, model selection and predictionMore 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.

Spis treści:
Preface.
Vectors of Random Variables.
Multivariate Normal Distribution.
Linear Regression: Estimation and Distribution Theory.
Hypothesis Testing.
Confidence Intervals and Regions.
Straight–Line Regression.
Polynomial Regression.
Analysis of Variance.
Departures from Underlying Assumptions.
Departures from Assumptions: Diagnosis and Remedies.
Computational Algorithms for Fitting a Regression.
Prediction and Model Selection.
Appendix A. Some Matrix Algebra.
Appendix B. Orthogonal Projections.
Appendix C. Tables.
Outline Solutions to Selected Exercises.
References.
Index.

Nota biograficzna:
GEORGE A. F. SEBER, PhD, is Professor Emeritus of Statistics at the University of Auckland, New Zealand.
ALAN J. LEE, PhD, is the Chairman of the Department of Statistics at the University of Auckland.

Okładka tylna:
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 detailsA careful and detailed survey of the research literature, making this a highly useful referenceExpanded coverage of diagnostics, and more discussion of methods of model fitting, model selection and predictionMore than 200 problems throughout the book plus outline solutions
Concise, mathematically clear, and comprehensive, Linear Regression Analysis, Second Edition, serves as both a reliable referenc e for the practitioner and a valuable textbook for the student.