Regression Diagnostics

An Introduction

Author: John Fox

Publisher: SAGE

ISBN: 9780803939714

Category: Mathematics

Page: 92

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With Regression Diagnostics, researchers now have an accessible explanation of the techniques needed for exploring problems that compromise a regression analysis and for determining whether certain assumptions appear reasonable. The book covers such topics as the problem of collinearity in multiple regression, dealing with outlying and influential data, non-normality of errors, non-constant error variance and the problems and opportunities presented by discrete data. In addition, sophisticated diagnostics based on maximum-likelihood methods, scores tests, and constructed variables are introduced.
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Regression diagnostics

identifying influential data and sources of collinearity

Author: David A. Belsley,Edwin Kuh,Roy E. Welsch

Publisher: Wiley-Interscience

ISBN: 9780471058564

Category: Mathematics

Page: 292

View: 5356

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The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "The title of the book more or less sums up the contents. It appears to me to represent a real breakthrough in the art of dealing in 'unconventional' data. . . . I found the whole book both readable and enjoyable. It is suitable for data analysts, academic statisticians, and professional software writers." -Journal of the Royal Statistical Society "The book assumes a working knowledge of all of the principal results and techniques used in least squares multiple regression, as expressed in vector and matrix notation. Given this background, the book is clear and easy to use. . . . The techniques are illustrated in great detail with practical data sets from econometrics." -Short Book Reviews, International Statistical Institute Regression Diagnostics: Identifying Influential Data and Sources of Collinearity provides practicing statisticians and econometricians with new tools for assessing quality and reliability of regression estimates. Diagnostic techniques are developed that aid in the systematic location of data points that are unusual or inordinately influential; measure the presence and intensity of collinear relations among the regression data; and help to identify variables involved in each and pinpoint estimated coefficients potentially most adversely affected. The book emphasizes diagnostics and includes suggestions for remedial action
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Robust Diagnostic Regression Analysis

Author: Anthony Atkinson,Marco Riani

Publisher: Springer Science & Business Media

ISBN: 1461211603

Category: Mathematics

Page: 328

View: 5690

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Graphs are used to understand the relationship between a regression model and the data to which it is fitted. The authors develop new, highly informative graphs for the analysis of regression data and for the detection of model inadequacies. As well as illustrating new procedures, the authors develop the theory of the models used, particularly for generalized linear models. The book provides statisticians and scientists with a new set of tools for data analysis. Software to produce the plots is available on the authors website.
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Transformation and Weighting in Regression

Author: Raymond J. Carroll

Publisher: Routledge

ISBN: 1351407260

Category: Mathematics

Page: 264

View: 7166

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This monograph provides a careful review of the major statistical techniques used to analyze regression data with nonconstant variability and skewness. The authors have developed statistical techniques--such as formal fitting methods and less formal graphical techniques-- that can be applied to many problems across a range of disciplines, including pharmacokinetics, econometrics, biochemical assays, and fisheries research. While the main focus of the book in on data transformation and weighting, it also draws upon ideas from diverse fields such as influence diagnostics, robustness, bootstrapping, nonparametric data smoothing, quasi-likelihood methods, errors-in-variables, and random coefficients. The authors discuss the computation of estimates and give numerous examples using real data. The book also includes an extensive treatment of estimating variance functions in regression.
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An R and S-Plus Companion to Applied Regression

Author: John Fox,Georges Monette

Publisher: SAGE

ISBN: 9780761922803

Category: Mathematics

Page: 312

View: 4749

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"This book fits right into a needed niche: rigorous enough to give full explanation of the power of the S language, yet accessible enough to assign to social science graduate students without fear of intimidation. It is a tremendous balance of applied statistical "firepower" and thoughtful explanation. It meets all of the important mechanical needs: each example is given in detail, code and data are freely available, and the nuances of models are given rather than just the bare essentials. It also meets some important theoretical needs: linear models, categorical data analysis, an introduction to applying GLMs, a discussion of model diagnostics, and useful instructions on writing customized functions. " —JEFF GILL, University of Florida, Gainesville
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Linear Regression Diagnostics

Author: Roy E. Welsch,Edwin Kuh,Nber Computer Research Center

Publisher: Franklin Classics Trade Press

ISBN: 9780353267718

Category: History

Page: 84

View: 9511

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This work has been selected by scholars as being culturally important and is part of the knowledge base of civilization as we know it. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. To ensure a quality reading experience, this work has been proofread and republished using a format that seamlessly blends the original graphical elements with text in an easy-to-read typeface. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
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Regression Analysis

A Constructive Critique

Author: Richard A. Berk

Publisher: SAGE

ISBN: 0761929045

Category: Mathematics

Page: 259

View: 9757

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Regression Analysis: A Constructive Critique identifies a wide variety of problems with regression analysis as it is commonly used and then provides a number of ways in which practice could be improved. Regression is most useful for data reduction, leading to relatively simple but rich and precise descriptions of patterns in a data set. The emphasis on description provides readers with an insightful rethinking from the ground up of what regression analysis can do, so that readers can better match regression analysis with useful empirical questions and improved policy-related research. "An interesting and lively text, rich in practical wisdom, written for people who do empirical work in the social sciences and their graduate students." --David A. Freedman, Professor of Statistics, University of California, Berkeley
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Regression Diagnostics in Practice

Experiences from Modelling Jet Engine Costs

Author: Jeffrey Bruce Garfinkle,J. L. Birkler

Publisher: N.A

ISBN: N.A

Category: Airplanes

Page: 11

View: 7558

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This paper describes how regression diagnostics were used to help develop revised cost-estimating relationships for jet engines. The goal was to derive meaningful, yet easy to use models based on an updated collection of few observations and many variables. First, specific criteria were established for selecting explanatory variables. A variety of numerical and graphical techniques were then used to critique candidate models by examining residuals and evaluating the influence of individual engines. The final models are not only intuitively satisfying, but generally provide better predictions and are easier to use than earlier models. Additionally, the user is provided with a greater understanding of the design and sensitivity of the models, and therefore a better understanding of the actual estimates.
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