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Applied Regression Analysis

  • Kartonierter Einband
  • 680 Seiten
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Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts ... Weiterlesen
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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 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.


From the reviews:


"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."


1. Review of Simple Regression; 2. Introduction to Matrices; 3. Multiple Regression in Matrix Notation; 4. Analysis of Variance and Quadratic Forms; 5. Case Study: Five Independent Variables; 6. Geometric Interpretation of Least Squares; 7. Model Development: Variable Selection; 8. Polynomial Regression; 9. Class Variables in Regression; 10. Problem Areas in Least Squares; 11. Regression Diagnostics; 12. Transformation of Variables; 13. Collinearity; 14. Case Study: Collinearity Problems; 15. Models Nonlinear in the Parameters; 16. Case Study: Response Curve Modeling; 17. Analysis of Unbalanced Data; 18. Mixed Effects Models; 19: Case Study: Analysis of Unbalanced Data.


Titel: Applied Regression Analysis
Untertitel: A Research Tool
EAN: 9781475771558
ISBN: 147577155X
Format: Kartonierter Einband
Herausgeber: Springer New York
Genre: Mathematik
Anzahl Seiten: 680
Gewicht: 1257g
Größe: H254mm x B178mm x T36mm
Jahr: 2013
Auflage: 2nd ed. 1998. Softcover reprint of the original 2n

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