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Applied Regression Analysis : A Research Tool

Applied Regression Analysis : A Research Tool - 2nd edition

ISBN13: 978-0387984544

Cover of Applied Regression Analysis : A Research Tool 2ND 98 (ISBN 978-0387984544)
ISBN13: 978-0387984544
ISBN10: 0387984542
Cover type: Hardback
Edition/Copyright: 2ND 98
Publisher: Springer-Verlag New York
Published: 1998
International: No

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Applied Regression Analysis : A Research Tool - 2ND 98 edition

ISBN13: 978-0387984544

John O. Rawlings, Sastry G. Pantula and David A. Dickey

ISBN13: 978-0387984544
ISBN10: 0387984542
Cover type: Hardback
Edition/Copyright: 2ND 98
Publisher: Springer-Verlag New York

Published: 1998
International: No
Summary

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.This book 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 appied regression course to graduate students. This book seves 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.This book 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.

Author Bio

Rawlings, John O. : North Carolina State University

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 Association and the Crop Science Society of America.

Pantula, Sastry G. : North Carolina State University

Sastry G. Pantula is Professor and Directory of Graduate Programs in the Department of Statistics at North Carolina State University. He is a member of the Academy of Outstanding Teachers at North Carolina State University.

Dickey, David A. : North Carolina State University

David A. Dickey is Professor of Statistics at North Carolina State University. He is a member of the Academy of Outstanding Teachers at North Carolina State University.

Table of Contents

Preface
1 Review of Simple Regression
2 Introduction to Matrices
3 Multiple Regression in Matrix Notation
4 Analysis of Variance and Quadratic Forms
5 Case Study: Five Independent Variables
6 Geometry of Least Squares
7 Model Development: Variable Selection
8 Polynomial Regression
9 Class Variables in Regression
10 Problem Areas in Least Squares
11 Regression Diagnostics
12 Transformation of Variables
13 Collinearity
14 Case Study: Collinearity Problems
15 Models Nonlinear in the Parameters
16 Case Study: Response Curve Modeling
17 Analysis of Unbalanced Data
18 Mixed Effects Models
19 Case Study: Analysis of Unbalanced Data
Appendix Tables
References
Author Index
Subject Index

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