Regression Analysis and its Application
A Data-Oriented Approach
1st Edition
Published on 27. April 2018
424 pages
978-1-351-41929-1 (ISBN)
Description
Regression Analysis and Its Application: A Data-Oriented Approach answers the need for researchers and students who would like a better understanding of classical regression analysis. Useful either as a textbook or as a reference source, this book bridges the gap between the purely theoretical coverage of regression analysis and its practical application.
The book presents regression analysis in the general context of data analysis. Using a teach-by-example format, it contains ten major data sets along with several smaller ones to illustrate the common characteristics of regression data and properties of statistics that are employed in regression analysis. The book covers model misspecification, residual analysis, multicollinearity, and biased regression estimators. It also focuses on data collection, model assumptions, and the interpretation of parameter estimates.
Complete with an extensive bibliography, Regression Analysis and Its Application is suitable for statisticians, graduate and upper-level undergraduate students, and research scientists in biometry, business, ecology, economics, education, engineering, mathematics, physical sciences, psychology, and sociology. In addition, data collection agencies in the government and private sector will benefit from the book.
The book presents regression analysis in the general context of data analysis. Using a teach-by-example format, it contains ten major data sets along with several smaller ones to illustrate the common characteristics of regression data and properties of statistics that are employed in regression analysis. The book covers model misspecification, residual analysis, multicollinearity, and biased regression estimators. It also focuses on data collection, model assumptions, and the interpretation of parameter estimates.
Complete with an extensive bibliography, Regression Analysis and Its Application is suitable for statisticians, graduate and upper-level undergraduate students, and research scientists in biometry, business, ecology, economics, education, engineering, mathematics, physical sciences, psychology, and sociology. In addition, data collection agencies in the government and private sector will benefit from the book.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Product notice
Reflowable
ISBN-13
978-1-351-41929-1 (9781351419291)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
10/2019
1st Edition
CRC Press
€97.00
Shipment within 15-20 days
Book
07/1980
1st Edition
CRC Press
€197.50
Shipment within 15-20 days
Persons
Richard F. Gunst is Professor of Statistics at Southern Methodist University in Dallas, Texas. He received his Ph.D. (1972) in mathematical statistics from Southern Methodist University. He has been a statistical consultant on statistical modeling and on the design and analysis of experiments to many industrial companies, notably many major automotive and petroleum firms. His research areas include linear and nonlinear regression modeling, statistical experimental design, spatial statistical modeling, and general statistical methods. He has had major industrial and governmental research funding, including grants from the Department of Energy, NASA, the Air Force Office of Scientific Research, and the Department of Veterans Affairs. He has published 3 books on statistical design, modeling, and analysis and over 75 peer-reviewed research articles. Dr. Gunst is a co-recipient of the 1974 and 1985 W.J Youden Award from Technometrics, the 1994 Frank Wilcoxon Award from Technometrics, the Most Outstanding Statistical Application Award from the American Statistical Association (ASA), and the 2005 Sheth Foundation Award from the Journal of the Academy of Marketing Science. He is a Fellow of the ASA.
Robert L. Mason is Institute Analyst at Southwest Research Institutea in San Antonio, Texas. He received his Ph.D. (1971) in mathematical statistics from Southern Methodist University. He is a nationally known industrial statistician and has had a distinguished career in statistics. He also is an Adjunct Professor in Statistics at the University of Texas at San Antonio. His major work experience has been in applying statistical methods to solve data analysis and experimental design problems for commercial and government clients in the engineering and physical sciences. He is the co-author of five books in statistical design, data analysis, and process control, and has published over 100 papers in refereed statistical and scie
Robert L. Mason is Institute Analyst at Southwest Research Institutea in San Antonio, Texas. He received his Ph.D. (1971) in mathematical statistics from Southern Methodist University. He is a nationally known industrial statistician and has had a distinguished career in statistics. He also is an Adjunct Professor in Statistics at the University of Texas at San Antonio. His major work experience has been in applying statistical methods to solve data analysis and experimental design problems for commercial and government clients in the engineering and physical sciences. He is the co-author of five books in statistical design, data analysis, and process control, and has published over 100 papers in refereed statistical and scie
Content
1. Introduction 2. Initial Data Exploration 3. Single-Variable Least Squares 4. Mul Tip Le-V Ariable Preliminaries 5. Multiple-Variable Least Squares 6. Inference 7. Residual Analysis 8. Variable Selection Techniques 9. Multicollinearity Effects 10. Biased Regression Estimators
