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Regression Analysis By Example

Author: Samprit Chatterjee
Publisher: John Wiley & Sons
ISBN: 0470055456
Size: 76.66 MB
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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.

Sensitivity Analysis In Linear Regression

Author: Samprit Chatterjee
Publisher: John Wiley & Sons
ISBN: 9780471822165
Size: 43.95 MB
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Prediction matrix; Role of variables in a regression equation; Effects of an observation on a regression equation; Assessing the effects of multiple observations; Joint impact of a variable and an observation; Assessing the effects of errors of measurement; Study of model sensitivity by the generalized linear model approach; Computational considerations.

Regression Analysis

Author: Ashish Sen
Publisher: Springer Science & Business Media
ISBN: 1461244706
Size: 32.99 MB
Format: PDF, Kindle
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An up-to-date, rigorous, and lucid treatment of the theory, methods, and applications of regression analysis, and thus ideally suited for those interested in the theory as well as those whose interests lie primarily with applications. It is further enhanced through real-life examples drawn from many disciplines, showing the difficulties typically encountered in the practice of regression analysis. Consequently, this book provides a sound foundation in the theory of this important subject.

Categorical Data Analysis By Example

Author: Graham J. G. Upton
Publisher: John Wiley & Sons
ISBN: 1119307937
Size: 56.25 MB
Format: PDF
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Introduces the key concepts in the analysis of categoricaldata with illustrative examples and accompanying R code This book is aimed at all those who wish to discover how to analyze categorical data without getting immersed in complicated mathematics and without needing to wade through a large amount of prose. It is aimed at researchers with their own data ready to be analyzed and at students who would like an approachable alternative view of the subject. Each new topic in categorical data analysis is illustrated with an example that readers can apply to their own sets of data. In many cases, R code is given and excerpts from the resulting output are presented. In the context of log-linear models for cross-tabulations, two specialties of the house have been included: the use of cobweb diagrams to get visual information concerning significant interactions, and a procedure for detecting outlier category combinations. The R code used for these is available and may be freely adapted. In addition, this book: Uses an example to illustrate each new topic in categorical data Provides a clear explanation of an important subject Is understandable to most readers with minimal statistical and mathematical backgrounds Contains examples that are accompanied by R code and resulting output Includes starred sections that provide more background details for interested readers Categorical Data Analysis by Example is a reference for students in statistics and researchers in other disciplines, especially the social sciences, who use categorical data. This book is also a reference for practitioners in market research, medicine, and other fields.

Spatial Data Analysis By Example Categorical And Directional Data

Author: Graham J. G. Upton
Publisher: Wiley
ISBN: 9780471920861
Size: 80.52 MB
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Volume 1 describes recent advances in analytical methods of point pattern data and surveys regression methods for analysis of quantitative spatial data. Volume 2 confronts the problems presented by categorical and directional data, including measurements taken in situ, and the study of the movements of people and animals. Emphasis is on application of the techniques, which are illustrated through numerous examples, tables and figures. Heavily referenced.

Formulas Useful For Linear Regression Analysis And Related Matrix Theory

Author: Simo Puntanen
Publisher: Springer Science & Business Media
ISBN: 3642329314
Size: 50.72 MB
Format: PDF, Mobi
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​This is an unusual book because it contains a great deal of formulas. Hence it is a blend of monograph, textbook, and handbook.It is intended for students and researchers who need quick access to useful formulas appearing in the linear regression model and related matrix theory. This is not a regular textbook - this is supporting material for courses given in linear statistical models. Such courses are extremely common at universities with quantitative statistical analysis programs.

Regression Analysis For The Social Sciences

Author: Rachel A. Gordon
Publisher: Routledge
ISBN: 1136307745
Size: 33.20 MB
Format: PDF, Kindle
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The book provides graduate students in the social sciences with the basic skills that they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set.

Robust Diagnostic Regression Analysis

Author: Anthony Atkinson
Publisher: Springer Science & Business Media
ISBN: 9780387950174
Size: 62.51 MB
Format: PDF, ePub
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"The four main chapters cover regression, transformations of data in regression, nonlinear least squares, and generalized linear models. As well as illustrating their new procedures the authors develop the theory of the models used, particularly for generalized linear models. Exercises with solutions are given for these chapters. The book could thus be used as a text for a second course in regression as well as provide statisticians and scientists with a new set of tools for data analysis."--Jacket.