Regression Analysis and its Application: A Data-Oriented Approach

Front Cover
CRC Press, Jul 1, 1980 - Mathematics - 424 pages

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.

 

Selected pages

Contents

INTRODUCTION
1
INITIAL DATA EXPLORATION
20
EXERCISES
50
EXERCISES
90
APPENDIX
160
EXERCISES
166
RESIDUAL ANALYSIS
220
VARIABLE SELECTION TECHNIQUES
262
19
312
BIASED REGRESSION ESTIMATORS
315
APPENDIX A DATA SETS ANALYZED IN THIS TEXT
352
Final High School Average Grades and First Year
359
APPENDIX B DATA SETS FOR FURTHER STUDY
368
BIBLIOGRAPHY
389
INDEX
398
Copyright

MULTICOLLINEARITY EFFECTS
290

Other editions - View all

Regression Analysis and its Application: A Data-Oriented Approach
Richard F. Gunst,Robert L. Mason
Limited preview - 2018
Regression Analysis and its Application: A Data-Oriented Approach
Richard F. Gunst,Robert L. Mason
Limited preview - 2018
Regression Analysis and its Application: A Data-Oriented Approach
Richard F. Gunst,Robert L. Mason
No preview available - 1980
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Common terms and phrases

ANOVA assumptions best subset bles calculated Chapter coefficient estimates coefficient of determination column confidence interval covariance data analyst data base Data Set Data Set A.1 deleted residuals dictor variables estimated coefficients estimated regression coefficients F statistic Figure fitted model grade 13 averages handgun high school homicide rate HUMID indicate INFD latent roots latent vectors least squares estimators least squares parameter linear linear regression magnitude matrix measure misspecification multicollinear predictor variables multiple-variable normal deviate observations outliers partial residual points population predicted responses prediction equation principal component probability distribution quadratic random variable raw residuals regression analysis regression model residual plots response and predictor response variable sample Section single-variable specification squared residuals squares parameter estimates standardized studentized residuals suicide rates sum of squares techniques tion transformation TSS(adj unit length varia variances versus X₁ zero

References to this book

About the author (1980)

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 Instituteâ 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 scientific journals. His honors include the Founders Award (1992) of the American Statistical Association (ASA), the W. J. Youden Award (1985, 1974) of the American Society for Quality (ASQ), the McFarland Award (2003) of the Society of Automotive Engineers, and the Jacobson Award (1985) of the American Society of Mechanical Engineers. He is a past President and past Vice-President of the ASA. In addition, he is an elected Fellow of the ASA and of the ASQ.

Bibliographic information

TitleRegression Analysis and its Application: A Data-Oriented Approach
Volume 34 of Statistics: A Series of Textbooks and Monographs
AuthorsRichard F. Gunst, Robert L. Mason
Editionillustrated
PublisherCRC Press, 1980
ISBN0824769937, 9780824769932
Length424 pages
Subjects › 

Computers / General
Mathematics / Number Systems
  
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