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Other titles in the Wiley Series in Probability and Statistics series:

Linear Regression Analysis (Wiley Series in Probability and Statistics)

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Linear Regression Analysis (Wiley Series in Probability and Statistics) Cover

 

Synopses & Reviews

Publisher Comments:

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.

Book News Annotation:

Updating and expanding the original text, this new edition covers the current theory and applications of linear regression analysis. It provides a survey of the research literature and outlines the solutions to approximately 200 sample problems. Particular attention is given to diagnostics, model fitting, model selection, and prediction. Seber and Lee have each taught statistics at the University of Auckland. Annotation (c)2003 Book News, Inc., Portland, OR (booknews.com)

Synopsis:

Intended Audience: Practicing statisticians; high-end graduate-level regression courses; libraries.

Synopsis:

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.

Synopsis:

* This revision has been extensively class-tested.

Synopsis:

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.

Synopsis:

Includes bibliographical references (p. 531-548) and index.

About the Author

"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)

Table of Contents

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.

Product Details

ISBN:
9780471415404
Author:
Seber, George A. F.
Publisher:
Wiley
Author:
Lee, Alan J.
Author:
Seber, G. A. F.
Location:
Hoboken, NJ
Subject:
General
Subject:
Regression analysis
Subject:
Probability & Statistics - General
Subject:
Probability & Statistics - Regression Analysis
Subject:
Statistics
Subject:
Mathematics - General
Copyright:
Edition Number:
2
Edition Description:
WOL online Book (not BRO)
Series:
Wiley Series in Probability and Statistics
Series Volume:
329
Publication Date:
20030205
Binding:
HARDCOVER
Grade Level:
General/trade
Language:
English
Illustrations:
Yes
Pages:
582
Dimensions:
9.72x6.06x1.45 in. 2.02 lbs.

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Related Subjects

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Science and Mathematics » Mathematics » General

Linear Regression Analysis (Wiley Series in Probability and Statistics) New Hardcover
0 stars - 0 reviews
$182.25 In Stock
Product details 582 pages Wiley-Interscience - English 9780471415404 Reviews:
"Synopsis" by , Intended Audience: Practicing statisticians; high-end graduate-level regression courses; libraries.
"Synopsis" by , 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.

"Synopsis" by , * This revision has been extensively class-tested.
"Synopsis" by , 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.

"Synopsis" by , Includes bibliographical references (p. 531-548) and index.
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