9780256117363-0256117365-Applied Linear Statistical Models

Applied Linear Statistical Models

ISBN-13: 9780256117363
ISBN-10: 0256117365
Edition: 4
Author: William Wasserman, John Neter, Michael Kutner, Christopher Nachtsheim
Publication date: 1996
Publisher: McGraw-Hill/Irwin
Format: Hardcover 1408 pages
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Book details

ISBN-13: 9780256117363
ISBN-10: 0256117365
Edition: 4
Author: William Wasserman, John Neter, Michael Kutner, Christopher Nachtsheim
Publication date: 1996
Publisher: McGraw-Hill/Irwin
Format: Hardcover 1408 pages

Summary

Applied Linear Statistical Models (ISBN-13: 9780256117363 and ISBN-10: 0256117365), written by authors William Wasserman, John Neter, Michael Kutner, Christopher Nachtsheim, was published by McGraw-Hill/Irwin in 1996. With an overall rating of 4.1 stars, it's a notable title among other Statistics (Education & Reference) books. You can easily purchase or rent Applied Linear Statistical Models (Hardcover) from BooksRun, along with many other new and used Statistics books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.35.

Description

There are two approaches to undergraduate and graduate courses in linear statistical models and experimental design in applied statistics. One is a two-term sequence focusing on regression followed by ANOVA/Experimental design. Applied Linear Statistical Models serves that market. It is offered in business, economics, statistics, industrial engineering, public health, medicine, and psychology departments in four-year colleges and universities, and graduate schools. Applied Linear Statistical Models is the leading text in the market. It is noted for its quality and clarity, and its authorship is first-rate. The approach used in the text is an applied one, with an emphasis on understanding of concepts and exposition by means of examples. Sufficient theoretical foundations are provided so that applications of regression analysis can be carried out comfortably. The fourth edition has been updated to keep it current with important new developments in regression analysis.

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