Applied Regression Analysis, Linear Models, and Related Methods

Author: John Fox

Publisher: SAGE

ISBN: 9780803945401

Category: Social Science

Page: 597

View: 8417

An accessible, detailed, and up-to-date treatment of regression analysis, linear models, and closely related methods is provided in this book. Incorporating nearly 200 graphs and numerous examples and exercises that employ real data from the social sciences, the book begins with a consideration of the role of statistical data analysis in social research. It then moves on to cover the following topics: graphical methods for examining and transforming data; linear least-squares regression; dummy-variables regression; analysis of variance; diagnostic methods for discovering whether a linear model fit to data adequately represents the data; extensions to linear least squares, including logit and probit models, time-series regression, nonlinear
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Applied Regression Analysis and Generalized Linear Models

Author: John Fox

Publisher: SAGE Publications

ISBN: 1483321312

Category: Social Science

Page: 816

View: 1239

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book.
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An R Companion to Applied Regression

Author: John Fox,Sanford Weisberg

Publisher: SAGE Publications

ISBN: 141297514X

Category: Social Science

Page: 449

View: 3112

This book aims to provide a broad introduction to the R statistical environment in the context of applied regression analysis, which is typically studied by social scientists and others in a second course in applied statistics.
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Regressionsanalyse: Theorie, Technik und Anwendung

Author: Dieter Urban,Jochen Mayerl

Publisher: Springer-Verlag

ISBN: 3531931148

Category: Social Science

Page: 368

View: 8469

Das Skript erläutert die Durchführung und Interpretation von klassischen Regressionsanalysen (nach der OLS-Methode). Zudem gibt das Skript eine Einführung in die logistische Regressionsanalyse mit Maximum-Likelihood-Schätzverfahren (ML-Methode). In der Darstellung wird insbesondere auf die Überprüfung der Anwendungsvoraussetzungen von Regressionsschätzungen eingegangen. Auch werden typische Fehlschlüsse und häufig anzutreffende Fehlinterpretationen verdeutlicht (u.a. bei Determinationskoeffizienten und standardisierten Regressionskoeffizienten).
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Angewandte Regressionsanalyse: Theorie, Technik und Praxis

Author: Dieter Urban,Jochen Mayerl

Publisher: Springer-Verlag

ISBN: 3658019158

Category: Social Science

Page: 517

View: 903

Das Lehrbuch erläutert die Durchführung und Interpretation von klassischen Regressionsanalysen (nach der OLS-Methode) und von logistischen Regressionsanalysen (nach der ML-Methode). Im Text wird insbesondere auf die Überprüfung der Anwendungsvoraussetzungen von Regressionsschätzungen eingegangen. Auch werden typische Fehlschlüsse und häufig anzutreffende Fehlinterpretationen behandelt (u.a. bei Determinationskoeffizienten, bei standardisierten Regressionskoeffizienten und bei zentrierten Effekten). Zudem werden erweiterte Verfahren wie z.B. Teststärkeanalysen, Regressionen mit Dummy-Variablen und Modellschätzungen mit Moderator- und Mediatorvariablen erläutert. Die Form der Darstellung ist praxisorientiert. Alle Verfahren werden an Beispielen erläutert (inkl. der für die Praxis erforderlichen SPSS-Anweisungen). Neu in der fünften Auflage sind (u.a.): Regressionsanalyse bei fehlenden Werten Bootstrapping in der Regressionsanalyse Berechnung durchschnittlicher marginaler Effekte (AME) mit SPSS. Die Autoren Dr. Dieter Urban ist Professor für Soziologie am Institut für Sozialwissenschaften der Universität Stuttgart. Dr. Jochen Mayerl ist Juniorprofessor am Fachbereich Sozialwissenschaften der TU Kaiserslautern.
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Applied Regression Including Computing and Graphics

Author: R. Dennis Cook,Sanford Weisberg

Publisher: John Wiley & Sons

ISBN: 0470317787

Category: Mathematics

Page: 632

View: 2283

A step-by-step guide to computing and graphics in regression analysis In this unique book, leading statisticians Dennis Cook and Sanford Weisberg expertly blend regression fundamentals and cutting-edge graphical techniques. They combine and up- date most of the material from their widely used earlier work, An Introduction to Regression Graphics, and Weisberg's Applied Linear Regression; incorporate the latest in statistical graphics, computing, and regression models; and wind up with a modern, fully integrated approach to one of the most important tools of data analysis. In 23 concise, easy-to-digest chapters, the authors present:? A wealth of simple 2D and 3D graphical techniques, helping visualize results through graphs * An improved version of the user-friendly Arc software, which lets readers promptly implement new ideas * Complete coverage of regression models, including logistic regression and generalized linear models * More than 300 figures, easily reproducible on the computer * Numerous examples and problems based on real data * A companion Web site featuring free software and advice, available at www.wiley.com/mathem atics Accessible, self-contained, and fully referenced, Applied Regression Including Computing and Graphics assumes only a first course in basic statistical methods and provides a bona fide user manual for the Arc software. It is an invaluable resource for anyone interested in learning how to analyze regression problems with confidence and depth.
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Biostatistics for Animal Science

Author: Miroslav Kaps,William R. Lamberson

Publisher: CABI

ISBN: 1845935403

Category: Technology & Engineering

Page: 504

View: 9179

Designed to cover techniques for analysis of data in the animal sciences, this textbook provides an overview of the basic principles of statistics enabling the subsequent applications to be carried out with familiarity and understanding, followed by more complex applications and detailed procedures commonly used in animal sciences. Each chapter begins by introducing a problem with practical questions, followed by a brief theoretical background, and is supplemented with an abundance of examples in SAS from animal sciences and related fields. Key features: - New larger format and updated throughout - Covers both basic techniques and more complex procedures - Contains exercises for readers to work through
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Generalized Linear Models

with Applications in Engineering and the Sciences

Author: Raymond H. Myers,Douglas C. Montgomery,G. Geoffrey Vining,Timothy J. Robinson

Publisher: John Wiley & Sons

ISBN: 0470556978

Category: Mathematics

Page: 544

View: 2144

Praise for the First Edition "The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities." —Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Maintaining the same nontechnical approach as its predecessor, this update has been thoroughly extended to include the latest developments, relevant computational approaches, and modern examples from the fields of engineering and physical sciences. This new edition maintains its accessible approach to the topic by reviewing the various types of problems that support the use of GLMs and providing an overview of the basic, related concepts such as multiple linear regression, nonlinear regression, least squares, and the maximum likelihood estimation procedure. Incorporating the latest developments, new features of this Second Edition include: A new chapter on random effects and designs for GLMs A thoroughly revised chapter on logistic and Poisson regression, now with additional results on goodness of fit testing, nominal and ordinal responses, and overdispersion A new emphasis on GLM design, with added sections on designs for regression models and optimal designs for nonlinear regression models Expanded discussion of weighted least squares, including examples that illustrate how to estimate the weights Illustrations of R code to perform GLM analysis The authors demonstrate the diverse applications of GLMs through numerous examples, from classical applications in the fields of biology and biopharmaceuticals to more modern examples related to engineering and quality assurance. The Second Edition has been designed to demonstrate the growing computational nature of GLMs, as SAS®, Minitab®, JMP®, and R software packages are used throughout the book to demonstrate fitting and analysis of generalized linear models, perform inference, and conduct diagnostic checking. Numerous figures and screen shots illustrating computer output are provided, and a related FTP site houses supplementary material, including computer commands and additional data sets. Generalized Linear Models, Second Edition is an excellent book for courses on regression analysis and regression modeling at the upper-undergraduate and graduate level. It also serves as a valuable reference for engineers, scientists, and statisticians who must understand and apply GLMs in their work.
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Applied Longitudinal Data Analysis

Modeling Change and Event Occurrence

Author: Judith D. Singer,John B. Willett

Publisher: Oxford University Press

ISBN: 0199882401

Category: Psychology

Page: 644

View: 9438

Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models. Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods. Visit http://www.ats.ucla.edu/stat/examples/alda.htm for: · Downloadable data sets · Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more · Additional material for data analysis
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Applied Regression Analysis

A Research Tool

Author: John O. Rawlings,Sastry G. Pantula,David A. Dickey

Publisher: Springer Science & Business Media

ISBN: 0387227539

Category: Mathematics

Page: 660

View: 8365

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.
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Linear Models in Statistics

Author: Alvin C. Rencher,G. Bruce Schaalje

Publisher: John Wiley & Sons

ISBN: 0470192607

Category: Mathematics

Page: 688

View: 3162

The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.
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Applied Regression Modeling

Author: Iain Pardoe

Publisher: John Wiley & Sons

ISBN: 1118345045

Category: Mathematics

Page: 346

View: 1026

Praise for the First Edition "The attention to detail is impressive. The book is very wellwritten and the author is extremely careful with his descriptions .. . the examples are wonderful." —The AmericanStatistician Fully revised to reflect the latest methodologies and emergingapplications, Applied Regression Modeling, Second Editioncontinues to highlight the benefits of statistical methods,specifically regression analysis and modeling, for understanding,analyzing, and interpreting multivariate data in business, science,and social science applications. The author utilizes a bounty of real-life examples, casestudies, illustrations, and graphics to introduce readers to theworld of regression analysis using various software packages,including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear andcareful writing style, the book introduces modeling extensions thatillustrate more advanced regression techniques, including logisticregression, Poisson regression, discrete choice models, multilevelmodels, and Bayesian modeling. In addition, the Second Edition features clarificationand expansion of challenging topics, such as: Transformations, indicator variables, and interaction Testing model assumptions Nonconstant variance Autocorrelation Variable selection methods Model building and graphical interpretation Throughout the book, datasets and examples have been updated andadditional problems are included at the end of each chapter,allowing readers to test their comprehension of the presentedmaterial. In addition, a related website features the book'sdatasets, presentation slides, detailed statistical softwareinstructions, and learning resources including additional problemsand instructional videos. With an intuitive approach that is not heavy on mathematicaldetail, Applied Regression Modeling, Second Edition is anexcellent book for courses on statistical regression analysis atthe upper-undergraduate and graduate level. The book also serves asa valuable resource for professionals and researchers who utilizestatistical methods for decision-making in their everyday work.
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Ökonometrie für Dummies

Author: Roberto Pedace

Publisher: John Wiley & Sons

ISBN: 3527801529

Category: Business & Economics

Page: 388

View: 7513

Theorien verstehen und Techniken anwenden Was haben die Gehälter von Spitzensportlern und der Mindestlohn gemeinsam? Richtig, man kann sie mit Ökonometrie erforschen. Im Buch steht, wie es geht. Und nicht nur dafür, sondern für viele weitere Gebiete lohnt es sich, der zunächst etwas trocken und sperrig anmutenden Materie eine Chance zu geben. Lernen Sie von den Autoren, wie Sie spannende Fragen formulieren, passende Variablen festlegen, treffsichere Modelle entwerfen und Ihre Aussagen auf Herz und Nieren prüfen. Werden Sie sicher im Umgang mit Hypothesentests, Regressionsmodellen, Logit- & Probit-Modellen und allen weiteren gängigen Methoden der Ökonometrie. So begleitet Ökonometrie für Dummies Sie Schritt für Schritt und mit vielen Beispielen samt R Output durch dieses spannende Thema.
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Modern methods of data analysis

Author: John Fox,J. Scott Long

Publisher: Sage Publications, Inc

ISBN: N.A

Category: Mathematics

Page: 446

View: 3063

This volume seeks to move the standard of statistical analysis and presentation in the social sciences towards an accurate and sensitive representation of data. This is accomplished by focusing on four themes: an emphasis on graphical data analysis; the use of computers for doing intensive computations; regression analysis; and sampling characteristics of data.
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Statistical computing environments for social research

Author: Robert A. Stine,John Fox

Publisher: Sage Publications, Inc

ISBN: 9780761902690

Category: Social Science

Page: 250

View: 7039

The nature of statistics has changed from classical notions of hypothesis testing, towards graphical and exploratory data analysis which exploits the flexibility of interactive computing and graphical displays. This book describes seven statistical computing environments - APL2STAT, GAUSS, Lisp-Stat, Mathematica, S, SAS//IML, and Stata - which can be used effectively in graphical and exploratory modeling. These statistical computing environments, in contrast to standard statistical packages, provide programming tools for building other statistical applications. Programmability, flexible data structures, and - in the case of some of the computing environments - graphical interfaces and object-oriented programming, permit res
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Generalized Linear Models

An Applied Approach

Author: John Patrick Hoffmann

Publisher: Pearson College Division

ISBN: N.A

Category: Mathematics

Page: 204

View: 2166

This brief and economical text shows students with relatively little mathematical background how to understand and apply sophisticated linear regression models in their research areas within the social, behavioral, and medical sciences, as well as marketing, and business. Less theoretical than competing texts, Hoffman includes numerous exercises and worked-out examples and sample programs and data sets for three popular statistical software programs: SPSS, SAS, and Stata.
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An R and S-Plus Companion to Applied Regression

Author: John Fox

Publisher: SAGE

ISBN: 9780761922803

Category: Mathematics

Page: 312

View: 7546

"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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