# Applied Regression Analysis

## A Research Tool

• John O. Rawlings
• Sastry G. Pantula
• David A. Dickey
Textbook

Part of the Springer Texts in Statistics book series (STS)

1. Front Matter
Pages i-xviii
2. Pages 1-36
3. Pages 37-74
4. Pages 75-99
5. Pages 101-159
6. Pages 161-181
7. Pages 183-204
8. Pages 205-234
9. Pages 235-268
10. Pages 269-323
11. Pages 325-340
12. Pages 341-396
13. Pages 397-432
14. Pages 433-462
15. Pages 463-484
16. Pages 485-514
17. Pages 515-544
18. Pages 545-572
19. Pages 573-592
20. Pages 593-619
21. Back Matter
Pages 621-659

### Introduction

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.

### Keywords

Analysis of variance Excel Regression analysis SAS STATISTICA Time series linear regression

### Editors and affiliations

• John O. Rawlings
• 1
• Sastry G. Pantula
• 1
• David A. Dickey
• 1
1. 1.Department of StatisticsNorth Carolina State UniversityRaleighUSA

### Bibliographic information

• DOI https://doi.org/10.1007/b98890
• Copyright Information Springer-Verlag New York, Inc. 1998
• Publisher Name Springer, New York, NY
• eBook Packages
• Print ISBN 978-0-387-98454-4
• Online ISBN 978-0-387-22753-5
• Series Print ISSN 1431-875X
• Buy this book on publisher's site