Applied Regression Analysis: A Research Tool (1998. Corr. 2nd Printing)

John O Rawlings,Sastry G Pantula,David A Dickey

ISBN 10: 0387984542 / ISBN 13: 9780387984544

Published by Springer April 1998, 1998

About this Item

used hardcover copy in illustrated boards, no jacket, as issued. light shelfwear, corners perhaps slightly bumped. pages and binding are clean, straight and tight. there are no marks to the text or other serious flaws.

Seller Inventory # 1554608

Bibliographic Details

• Title: Applied Regression Analysis: A Research Tool (1998. Corr. 2nd Printing)
• Publisher: Springer April 1998
• Publication Date: 1998
• Language: English
• ISBN 10: 0387984542
• ISBN 13: 9780387984544
• Binding: Hardcover
• Edition: 2nd Edition
• Condition: VG
• Seller catalogs:
  • MATH statistics

About this title

Synopsis

Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool.
Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to statistical methods and a thoeretical linear models course.
Applied Regression Analysis emphasizes the concepts and the analysis of data sets. It provides a review of the key concepts in simple linear regression, matrix operations, and multiple regression. Methods and criteria for selecting regression variables and geometric interpretations are discussed. Polynomial, trigonometric, analysis of variance, nonlinear, time series, logistic, random effects, and mixed effects models are also discussed. Detailed case studies and exercises based on real data sets are used to reinforce the concepts. The data sets used in the book are available on the Internet.

About the Author

John O. Rawlings, Professor Emeritus in the Department of Statistics at North Carolina State University, retired after 34 years of teaching, consulting, and research in statistical methods. He was instrumental in developing, and for many years taught, the course on which this text is based. He is a Fellow of the American Statistical Society.