[PDF] Multicollinearity In Regression Analysis eBook

Author : P. Cheeseman
Publisher : Springer Science & Business Media
Page : 475 pages
File Size : 27,61 MB
Release : 2012-12-06
Category : Mathematics
ISBN : 1461226600

GET BOOK

This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. These biennial workshops have succeeded in bringing together researchers from Artificial Intelligence and from Statistics to discuss problems of mutual interest. The exchange has broadened research in both fields and has strongly encour aged interdisciplinary work. The theme ofthe 1993 AI and Statistics workshop was: "Selecting Models from Data". The papers in this volume attest to the diversity of approaches to model selection and to the ubiquity of the problem. Both statistics and artificial intelligence have independently developed approaches to model selection and the corresponding algorithms to implement them. But as these papers make clear, there is a high degree of overlap between the different approaches. In particular, there is agreement that the fundamental problem is the avoidence of "overfitting"-Le., where a model fits the given data very closely, but is a poor predictor for new data; in other words, the model has partly fitted the "noise" in the original data.

Author : David G. Kleinbaum
Publisher : Duxbury
Page : 906 pages
File Size : 34,70 MB
Release : 2008
Category : Multivariate analysis
ISBN : 9780495384984

GET BOOK

This bestseller will help you learn regression-analysis methods that you can apply to real-life problems. It highlights the role of the computer in contemporary statistics with numerous printouts and exercises that you can solve using the computer. The authors continue to emphasize model development, the intuitive logic and assumptions that underlie the techniques covered, the purposes, advantages, and disadvantages of the techniques, and valid interpretations of those techniques.

Author : Samprit Chatterjee
Publisher : John Wiley & Sons
Page : 403 pages
File Size : 38,34 MB
Release : 2006-10-20
Category : Mathematics
ISBN : 0470055456

GET BOOK

The essentials of regression analysis through practical applications Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgement. Regression Analysis by Example, Fourth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. This new edition features the following enhancements: Chapter 12, Logistic Regression, is expanded to reflect the increased use of the logit models in statistical analysis A new chapter entitled Further Topics discusses advanced areas of regression analysis Reorganized, expanded, and upgraded exercises appear at the end of each chapter A fully integrated Web page provides data sets Numerous graphical displays highlight the significance of visual appeal Regression Analysis by Example, Fourth Edition is suitable for anyone with an understanding of elementary statistics. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique. The methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Author : David A. Belsley
Publisher : Wiley-Interscience
Page : 0 pages
File Size : 40,81 MB
Release : 1991-02-05
Category : Mathematics
ISBN : 9780471528890

GET BOOK

Integrating the research from the author's previous work, Regression Diagnostics, and significant revision and updating, this monograph presents a self-contained treatment of the problems of ill-conditioning and data weaknesses as they affect the least-squares estimation of the linear model, along with extensions to nonlinear models and simultaneous-equations estimators. Also features a substantial amount of new information, including background material and data sets and numerous related elements previously scattered throughout the literature.

Author : Michael Patrick Allen
Publisher : Springer Science & Business Media
Page : 227 pages
File Size : 49,74 MB
Release : 2007-11-23
Category : Social Science
ISBN : 0585256578

GET BOOK

By assuming it is possible to understand regression analysis without fully comprehending all its underlying proofs and theories, this introduction to the widely used statistical technique is accessible to readers who may have only a rudimentary knowledge of mathematics. Chapters discuss: -descriptive statistics using vector notation and the components of a simple regression model; -the logic of sampling distributions and simple hypothesis testing; -the basic operations of matrix algebra and the properties of the multiple regression model; -testing compound hypotheses and the application of the regression model to the analyses of variance and covariance, and -structural equation models and influence statistics.

Author : Hyoshin Kim
Publisher : Infinite Study
Page : 15 pages
File Size : 17,99 MB
Release :
Category : Mathematics
ISBN :

GET BOOK

This paper proposes an a-level estimation algorithm for ridge fuzzy regression modeling, addressing the multicollinearity phenomenon in the fuzzy linear regression setting.

Author : Roberto Pedace
Publisher : John Wiley & Sons
Page : 380 pages
File Size : 19,31 MB
Release : 2013-06-05
Category : Business & Economics
ISBN : 1118533879

GET BOOK

Score your highest in econometrics? Easy. Econometrics can prove challenging for many students unfamiliar with the terms and concepts discussed in a typical econometrics course. Econometrics For Dummies eliminates that confusion with easy-to-understand explanations of important topics in the study of economics. Econometrics For Dummies breaks down this complex subject and provides you with an easy-to-follow course supplement to further refine your understanding of how econometrics works and how it can be applied in real-world situations. An excellent resource for anyone participating in a college or graduate level econometrics course Provides you with an easy-to-follow introduction to the techniques and applications of econometrics Helps you score high on exam day If you're seeking a degree in economics and looking for a plain-English guide to this often-intimidating course, Econometrics For Dummies has you covered.

Author : Michael H. Kutner
Publisher : McGraw-Hill/Irwin
Page : 1396 pages
File Size : 37,31 MB
Release : 2005
Category : Mathematics
ISBN : 9780072386882

GET BOOK

Linear regression with one predictor variable; Inferences in regression and correlation analysis; Diagnosticis and remedial measures; Simultaneous inferences and other topics in regression analysis; Matrix approach to simple linear regression analysis; Multiple linear regression; Nonlinear regression; Design and analysis of single-factor studies; Multi-factor studies; Specialized study designs.

Author : William Dale Berry
Publisher : SAGE
Page : 100 pages
File Size : 48,45 MB
Release : 1985-05
Category : Mathematics
ISBN : 9780803920545

GET BOOK

The authors provide a systematic treatment of the major problems involved in using regression analysis. They clearly and concisely discuss the consequences of violating the assumptions of the regression model, procedures for detecting violations, and strategies for dealing with these problems.

Author : Richard F. Gunst
Publisher : Routledge
Page : 426 pages
File Size : 16,28 MB
Release : 2018-04-27
Category : Mathematics
ISBN : 1351419293

GET BOOK

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.