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Exploring the Essential Features of “John Rawlings – Applied Regression Analysis”
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.
Editorial Reviews
Review
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.”
From the Back Cover
Aimed at the scientist who wishes to gain a working knowledge of regression analysis, the basic purpose of this volume is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is neither a cookbook nor a theoretical book. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching applied regression courses to graduate students in the sciences. Applied Regression Analysis serves as an excellent text for a service course on regression for nonstatisticians and also as a reference for researchers. It also provides a bridge between a two-semester introduction to statistical methods and a theoretical linear models course.
About the Author
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 Society.
Product details
Publisher : Springer; 2nd edition (April 23, 1998)
Language : English
Top reviews from the United States
4.0 out of 5 stars four stars
Reviewed in the United States 🇺🇸 on November 23, 2014
Verified Purchase
four stars
Nathan L. Brouwer
5.0 out of 5 stars A well-written advanced text
Reviewed in the United States 🇺🇸 on April 25, 2010
I’m a biology graduate student taking a mathematical stats course in the stats department that is way over my head. This is not the main text for the class, but I’m using it extensively to fill in the gaps in my knowledge. The book gives a detailed explanation of all core regression and ANOVA concepts in matrix notation, but does not get bogged down in proofs, lemmas, or theorems. It takes time and effort to work through the matrix algebra but everything is presentable in an accessible way, a surprising contrast to most statistical texts I’ve consulted. If you’re lost in the middle of an intermediate to advanced applied stats class, this book will likely help you make sense of the muddle.
Sunny
5.0 out of 5 stars Excellent Applied Regression Text
Reviewed in the United States 🇺🇸 on April 10, 2008
This text covers all aspects of applied regression. In addition, it covers required background, theory, and several examples. It is written clearly and develops logically. I used this text for a course, and I highly recommend this text.
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