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Springer Texts in Statistics Ser.: Applied Regression Analysis : A Research Tool by David A. Dickey, John O. Rawlings and Sastry G. Pantula (1998, Hardcover)

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About this product

Product Identifiers

PublisherSpringer New York

ISBN-100387984542

ISBN-139780387984544

eBay Product ID (ePID)603418

Product Key Features

Number of PagesXviii, 660 Pages

LanguageEnglish

Publication NameApplied Regression Analysis : a Research Tool

SubjectEnvironmental Science (See Also Chemistry / Environmental), Probability & Statistics / General, Probability & Statistics / Regression Analysis, Applied

Publication Year1998

FeaturesRevised

TypeTextbook

Subject AreaMathematics, Science

AuthorDavid A. Dickey, John O. Rawlings, Sastry G. Pantula

SeriesSpringer Texts in Statistics Ser.

FormatHardcover

Dimensions

Item Height0.6 in

Item Weight117.5 Oz

Item Length10 in

Item Width7 in

Additional Product Features

Edition Number2

Intended AudienceScholarly & Professional

LCCN97-048858

Dewey Edition19

ReviewsFrom the reviews: IEEE ELECTRICAL INSULATION MAGAZINE "Virtually all data taken require some form of modeling and curve fitting. This excellent book will give the reader a very clear understanding of the techniques used for fitting most types of data; and, because it covers all the significant areas, it can serve as a reference source. Students and especially researchers involved with data taking and modeling will greatly benefit from this book.", From the reviews:IEEE ELECTRICAL INSULATION MAGAZINE"Virtually all data taken require some form of modeling and curve fitting. This excellent book will give the reader a very clear understanding of the techniques used for fitting most types of data; and, because it covers all the significant areas, it can serve as a reference source. Students and especially researchers involved with data taking and modeling will greatly benefit from this book."

Number of Volumes1 vol.

IllustratedYes

Dewey Decimal519.5/36

Edition DescriptionRevised edition

Table Of ContentReview of Simple Regression.- to Matrices.- Multiple Regression in Matrix Notation.- Analysis of Variance and Quadratic Forms.- Case Study: Five Independent Variables.- Geometry of Least Squares.- Model Development: Variable Selection.- Polynomial Regression.- Class Variables in Regression.- Problem Areas in Least Squares.- Regression Diagnostics.- Transformation of Variables.- Collinearity.- Case Study: Collinearity Problems.- Models Nonlinear in the Parameters.- Case Study: Response Curve Modeling.- Analysis of Unbalanced Data.- Mixed Effects Models.- Case Study: Analysis of Unbalanced Data.

SynopsisLeast squares methods is an essential statistical technique in many areas including the life sciences, environmental sciences, and agriculture. This book provides the background necssary to understand the important concepts of regression analysis without becoming excessively mathematical., 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., 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. 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. 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. 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.

LC Classification NumberQA273.A1-274.9