Applied Regression Analysis: A Research Tool (2nd Edn) (Springer Texts in Statistics)

Rawlings, John O.

ISBN 10: 0387984542 / ISBN 13: 9780387984544

Published by Springer, 1998

About this Item

This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1350grams, ISBN:9780387984544.

Seller Inventory # 9790570

Bibliographic Details

• Title: Applied Regression Analysis: A Research Tool (2nd Edn) (Springer Texts in Statistics)
• Publisher: Springer
• Publication Date: 1998
• Language: English
• ISBN 10: 0387984542
• ISBN 13: 9780387984544
• Binding: Hardcover
• Edition: 2nd Edition
• Condition: Fair
• Weight: 1350 grams
• Seller catalogs:
  • Mathematics

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.