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Linear Regression Analysis Hardcover – February 5, 2003

ISBN-13: 978-0471415404 ISBN-10: 0471415405 Edition: 2nd

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Product Details

  • Hardcover: 582 pages
  • Publisher: Wiley; 2 edition (February 5, 2003)
  • Language: English
  • ISBN-10: 0471415405
  • ISBN-13: 978-0471415404
  • Product Dimensions: 6.5 x 1.5 x 9.4 inches
  • Shipping Weight: 2.1 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (4 customer reviews)
  • Amazon Best Sellers Rank: #1,145,368 in Books (See Top 100 in Books)

Editorial Reviews

Review

"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)

From the Back Cover

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.


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Most Helpful Customer Reviews

10 of 10 people found the following review helpful By daniel parry on June 19, 2000
Format: Hardcover
Seber pays meticulous attention to detail. Not only does he present a comprehensive text on regression, but his precise, straight-forward language separates this work from other books on the topic. This is a "must-have" for any serious statisticians library.
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5 of 6 people found the following review helpful By 02 on March 24, 2010
Format: Hardcover
I'm using the book as a textbook this semester for a PhD course in Statistics. There are too many mistakes and typos in the 2nd edition of the book. The readers should be very carefull. I think the 2nd edition of the book is too sloppy. The authors should proofread the book thoroughly and correct all mistakesa and typos before it goes to print again.
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0 of 1 people found the following review helpful By Bingying on June 21, 2013
Format: Hardcover Verified Purchase
The book is very suitable for graduate students major in statistics. The delivery is quick and the book is in good condition. I am satisfied
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2 of 10 people found the following review helpful By Mackson Sylvestre on March 18, 2010
Format: Hardcover Verified Purchase
Book is in very good condition and was received within the expected time range.
The book itself is a good theoretical book for linear regression and statistical modeling.
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