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 ...

Author: Samprit Chatterjee

Publisher: John Wiley & Sons

ISBN: 9780470055458

Category: Mathematics

Page: 416

View: 660

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.

Praise for the Fourth Edition: "This book is . . . an excellent source of examples for regression analysis.

Author: Samprit Chatterjee

Publisher: John Wiley & Sons

ISBN: 9781119122739

Category: Mathematics

Page: 424

View: 471

Praise for the Fourth Edition: "This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable." —Journal of the American Statistical Association 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 judgment. Regression Analysis by Example, Fifth 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. The book now includes a new chapter on the detection and correction of multicollinearity, while also showcasing the use of the discussed methods on newly added data sets from the fields of engineering, medicine, and business. The Fifth Edition also explores additional topics, including: Surrogate ridge regression Fitting nonlinear models Errors in variables ANOVA for designed experiments 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, the required assumptions, and the evaluated success of each technique. Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. Regression Analysis by Example, Fifth Edition is suitable for anyone with an understanding of elementary statistics.

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 ...

Author: Samprit Chatterjee

Publisher:

ISBN: OCLC:1121224636

Category:

Page: 408

View: 114

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.

R code and data for all of the analyses described in the book are available via an author-maintained website. "I enjoyed the presentation of the Handbook, and I would be happy to recommend this nice handy book as a reference to my students.

Author: Samprit Chatterjee

Publisher: John Wiley & Sons

ISBN: 9781118532836

Category: Mathematics

Page: 252

View: 181

A Comprehensive Account for Data Analysts of the Methods and Applications of Regression Analysis. Written by two established experts in the field, the purpose of the Handbook of Regression Analysis is to provide a practical, one-stop reference on regression analysis. The focus is on the tools that both practitioners and researchers use in real life. It is intended to be a comprehensive collection of the theory, methods, and applications of regression methods, but it has been deliberately written at an accessible level. The handbook provides a quick and convenient reference or “refresher” on ideas and methods that are useful for the effective analysis of data and its resulting interpretations. Students can use the book as an introduction to and/or summary of key concepts in regression and related course work (including linear, binary logistic, multinomial logistic, count, and nonlinear regression models). Theory underlying the methodology is presented when it advances conceptual understanding and is always supplemented by hands-on examples. References are supplied for readers wanting more detailed material on the topics discussed in the book. R code and data for all of the analyses described in the book are available via an author-maintained website. "I enjoyed the presentation of the Handbook, and I would be happy to recommend this nice handy book as a reference to my students. The clarity of the writing and proper choices of examples allows the presentations ofmany statisticalmethods shine. The quality of the examples at the end of each chapter is a strength. They entail explanations of the resulting R outputs and successfully guide readers to interpret them." American Statistician

The book further pays particular attention to methods that have become prominent in the last few decades as increasingly large data sets have made new techniques and applications possible.

Author: Samprit Chatterjee

Publisher: John Wiley & Sons

ISBN: 9781119392477

Category: Mathematics

Page: 384

View: 690

Handbook and reference guide for students and practitioners of statistical regression-based analyses in R Handbook of Regression Analysis with Applications in R, Second Edition is a comprehensive and up-to-date guide to conducting complex regressions in the R statistical programming language. The authors’ thorough treatment of “classical” regression analysis in the first edition is complemented here by their discussion of more advanced topics including time-to-event survival data and longitudinal and clustered data. The book further pays particular attention to methods that have become prominent in the last few decades as increasingly large data sets have made new techniques and applications possible. These include: Regularization methods Smoothing methods Tree-based methods In the new edition of the Handbook, the data analyst’s toolkit is explored and expanded. Examples are drawn from a wide variety of real-life applications and data sets. All the utilized R code and data are available via an author-maintained website. Of interest to undergraduate and graduate students taking courses in statistics and regression, the Handbook of Regression Analysis will also be invaluable to practicing data scientists and statisticians.

... still be able to do linear regression by transforming (i.e., using functions of) the
independent and/or the dependent variables. While we shall look at this subject
in detail in the other sections of this chapter, let us consider two examples now.

Author: Ashish Sen

Publisher: Springer Science & Business Media

ISBN: 9781461244707

Category: Psychology

Page: 348

View: 627

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.

Author: Michael Patrick AllenPublish On: 2007-11-23

For example, we might be interested in examining the effect of income on
whether an individual voted for a particular political candidate. At first glance, it
might seem that such an analysis could be accomplished using an ordinary regression ...

Author: Michael Patrick Allen

Publisher: Springer Science & Business Media

ISBN: 9780585256573

Category: Social Science

Page: 216

View: 704

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.

Conventional books on this topic frighten readers by presenting unidentified data to them indiscriminately. This book provides a new path to statistical concepts and practical skills in a readily accessible manner.

Author: Kunio Takezawa

Publisher: Springer Science & Business Media

ISBN: 9784431543213

Category: Mathematics

Page: 300

View: 724

The standard approach of most introductory books for practical statistics is that readers first learn the minimum mathematical basics of statistics and rudimentary concepts of statistical methodology. They then are given examples of analyses of data obtained from natural and social phenomena so that they can grasp practical definitions of statistical methods. Finally they go on to acquaint themselves with statistical software for the PC and analyze similar data to expand and deepen their understanding of statistical methods. This book, however, takes a slightly different approach, using simulation data instead of actual data to illustrate the functions of statistical methods. Also, R programs listed in the book help readers realize clearly how these methods work to bring intrinsic values of data to the surface. R is free software enabling users to handle vectors, matrices, data frames, and so on. For example, when a statistical theory indicates that an event happens with a 5 % probability, readers can confirm the fact using R programs that this event actually occurs with roughly that probability, by handling data generated by pseudo-random numbers. Simulation gives readers populations with known backgrounds and the nature of the population can be adjusted easily. This feature of the simulation data helps provide a clear picture of statistical methods painlessly. Most readers of introductory books of statistics for practical purposes do not like complex mathematical formulae, but they do not mind using a PC to produce various numbers and graphs by handling a huge variety of numbers. If they know the characteristics of these numbers beforehand, they treat them with ease. Struggling with actual data should come later. Conventional books on this topic frighten readers by presenting unidentified data to them indiscriminately. This book provides a new path to statistical concepts and practical skills in a readily accessible manner.

Author: Douglas C. MontgomeryPublish On: 2013-06-06

Praise for the Fourth Edition "As with previous editions, the authors have produced a leading textbook on regression." —Journal of the American Statistical Association A comprehensive and up-to-date introduction to the fundamentals of ...

Author: Douglas C. Montgomery

Publisher: John Wiley & Sons

ISBN: 9781118627365

Category: Mathematics

Page: 672

View: 281

Praise for the Fourth Edition "As with previous editions, the authors have produced a leadingtextbook on regression." —Journal of the American Statistical Association A comprehensive and up-to-date introduction to thefundamentals of regression analysis Introduction to Linear Regression Analysis, Fifth Editioncontinues to present both the conventional and less common uses oflinear regression in today’s cutting-edge scientificresearch. The authors blend both theory and application to equipreaders with an understanding of the basic principles needed toapply regression model-building techniques in various fields ofstudy, including engineering, management, and the healthsciences. Following a general introduction to regression modeling,including typical applications, a host of technical tools areoutlined such as basic inference procedures, introductory aspectsof model adequacy checking, and polynomial regression models andtheir variations. The book then discusses how transformations andweighted least squares can be used to resolve problems of modelinadequacy and also how to deal with influential observations. TheFifth Edition features numerous newly added topics,including: A chapter on regression analysis of time series data thatpresents the Durbin-Watson test and other techniques for detectingautocorrelation as well as parameter estimation in time seriesregression models Regression models with random effects in addition to adiscussion on subsampling and the importance of the mixedmodel Tests on individual regression coefficients and subsets ofcoefficients Examples of current uses of simple linear regression models andthe use of multiple regression models for understanding patientsatisfaction data. In addition to Minitab, SAS, and S-PLUS, the authors haveincorporated JMP and the freely available R software to illustratethe discussed techniques and procedures in this new edition.Numerous exercises have been added throughout, allowing readers totest their understanding of the material. Introduction to Linear Regression Analysis, Fifth Editionis an excellent book for statistics and engineering courses onregression at the upper-undergraduate and graduate levels. The bookalso serves as a valuable, robust resource for professionals in thefields of engineering, life and biological sciences, and the socialsciences.

Some further books are, for example, by Abadir & Magnus (2005) and Bernstein (
2009). Quick visits to matrices in linear models and multivariate analysis appear
in Puntanen, Seber & Styan (2013) and in Puntanen & Styan (2013). We do not ...

Author: Simo Puntanen

Publisher: Springer Science & Business Media

ISBN: 9783642329319

Category: Mathematics

Page: 125

View: 828

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.

1.1 Influence and Outliers Regression analysis is the most widely used technique
for fitting models to data. This book is not confined to regression, but we use three examples of regression to introduce our general ideas. When a regression ...

Author: Anthony Atkinson

Publisher: Springer Science & Business Media

ISBN: 0387950176

Category: Mathematics

Page: 327

View: 658

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.

This book guides how to interpret regression analysis outputs properly If you want to champion your knowledge on regression analysis, use this book

Author: Anusha Illukkumbura

Publisher: Independently Published

ISBN: 9798649727648

Category:

Page: 122

View: 487

This book covers basic and major topics related to Simple Linear Regression Non Linear RegressionMulti Linear Regression in simple language with simple examples, so that even a beginner can easily comprehend without much effort. Most importantly complex calculations are presented step by step in an uncomplicated manner. The examples are solved using manual calculations and statistical software such as Minitab and R (RStudio Version 4.0.0). Necessary commands are explicitly presented. Furthermore concepts such as parameter testing, residual testing, ANOVA table, exponential regression models, quadratic regression models, partial F test, multi collinearity, best subsets regression and stepwise regression are discussed with examples in this book.This book can be used as a self-study material and also a text book of regression analysis.

Linear Regression Analysis, Second Edition, revises and expands this standard text, providing extensive coverage of state-of-the-art theory and applications of linear regression analysis.

Author: George A. F. Seber

Publisher: John Wiley & Sons

ISBN: 9781118274422

Category: Mathematics

Page: 582

View: 221

Concise, mathematically clear, and comprehensive treatment of thesubject. * Expanded coverage of diagnostics and methods of modelfitting. * Requires no specialized knowledge beyond a good grasp of matrixalgebra and some acquaintance with straight-line regression andsimple analysis of variance models. * More than 200 problems throughout the book plus outline solutionsfor the exercises. * This revision has been extensively class-tested.

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.

Author: Richard F. Gunst

Publisher: CRC Press

ISBN: 0824769937

Category: Mathematics

Page: 424

View: 577

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.

... Edition BIRKES and DODGE · Alternative Methods of Regression BLISCHKE
and MURTHY · (editors) Case Studies in ... Regression Analysis by Example,
Fourth Edition CHATTERJEE and HADI · Sensitivity Analysis in Linear
Regression ...

Author: Natalia Markovich

Publisher: John Wiley & Sons

ISBN: 0470723599

Category: Mathematics

Page: 336

View: 822

Heavy-tailed distributions are typical for phenomena in complex multi-component systems such as biometry, economics, ecological systems, sociology, web access statistics, internet traffic, biblio-metrics, finance and business. The analysis of such distributions requires special methods of estimation due to their specific features. These are not only the slow decay to zero of the tail, but also the violation of Cramer’s condition, possible non-existence of some moments, and sparse observations in the tail of the distribution. The book focuses on the methods of statistical analysis of heavy-tailed independent identically distributed random variables by empirical samples of moderate sizes. It provides a detailed survey of classical results and recent developments in the theory of nonparametric estimation of the probability density function, the tail index, the hazard rate and the renewal function. Both asymptotical results, for example convergence rates of the estimates, and results for the samples of moderate sizes supported by Monte-Carlo investigation, are considered. The text is illustrated by the application of the considered methodologies to real data of web traffic measurements.

Matrix algebra; Probability abd distribution theory; Statistical inference; Computation and optimization; The classical multiple linear regression model - specification and estimation; Inference and prediction; Functional form, nonlinearity ...

Author: William H. Greene

Publisher: PRENTICE HALL

ISBN: 0023466022

Category: Business & Economics

Page: 1075

View: 606

Matrix algebra; Probability abd distribution theory; Statistical inference; Computation and optimization; The classical multiple linear regression model - specification and estimation; Inference and prediction; Functional form, nonlinearity, and specification; Data problems; Nonlinear regression models; Nonspherical disturbances; generalized regression, and GMM estimation; Autocorrelated disturbances; Models for panel data; Systems of regression equations; Regressions with lagged variables; Time-series models; Models with discrete dependent variables; Limited dependent variable and duration models.

Among texts on regression analysis, Pedhazur (1982) gives perhaps the best
understanding of thepitfalls. Mosteller andTukey (1977) is excellent though more
advanced. Regression analysis by example (Chatterjee & Price, 1991) is ahelpful
...

Author: Norman H. Anderson

Publisher: Psychology Press

ISBN: 9781135643386

Category: Psychology

Page: 880

View: 468

The goal of Norman H. Anderson's new book is to help students develop skills of scientific inference. To accomplish this he organized the book around the "Experimental Pyramid"--six levels that represent a hierarchy of considerations in empirical investigation--conceptual framework, phenomena, behavior, measurement, design, and statistical inference. To facilitate conceptual and empirical understanding, Anderson de-emphasizes computational formulas and null hypothesis testing. Other features include: *emphasis on visual inspection as a basic skill in experimental analysis to help students develop an intuitive appreciation of data patterns; *exercises that emphasize development of conceptual and empirical application of methods of design and analysis and de-emphasize formulas and calculations; and *heavier emphasis on confidence intervals than significance tests. The book is intended for use in graduate-level experimental design/research methods or statistics courses in psychology, education, and other applied social sciences, as well as a professional resource for active researchers. The first 12 chapters present the core concepts graduate students must understand. The next nine chapters serve as a reference handbook by focusing on specialized topics with a minimum of technicalities.

... BIRKES and DODGE ' Alternative Methods of Regression BLISCHKE AND
MURTHY - Reliability: Modeling, Prediction, ... and HADI ~ Sensitivity Analysis in
Linear Regression CHATTERJEE and PRICE ' Regression Analysis by Example,
...

Author: James R. Thompson

Publisher: John Wiley & Sons

ISBN: 9780470317938

Category: Mathematics

Page: 408

View: 186

* Considers neoclassical models in light of results that can go wrong with them to bring about better models. * Questions the assumption that markets clear quickly. * Offers a timely examination of the LTCM collapse. * Written by a group of well-respected and highly qualified authors.