If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul Allison's Logistic Regression Using SAS: Theory and Application, Second Edition, is for you! Informal and nontechnical, this book both explains the theory behind logistic regression, and looks at all the practical details involved in its implementation using SAS. Several real-world examples are included in full detail. This book also explains the differences and similarities among the many generalizations of the logistic regression model. The following topics are covered: binary logistic regression, logit analysis of contingency tables, multinomial logit analysis, ordered logit analysis, discrete-choice analysis, and Poisson regression. Other highlights include discussions on how to use the GENMOD procedure to do loglinear analysis and GEE estimation for longitudinal binary data. Only basic knowledge of the SAS DATA step is assumed. The second edition describes many new features of PROC LOGISTIC, including conditional logistic regression, exact logistic regression, generalized logit models, ROC curves, the ODDSRATIO statement (for analyzing interactions), and the EFFECTPLOT statement (for graphing nonlinear effects). Also new is coverage of PROC SURVEYLOGISTIC (for complex samples), PROC GLIMMIX (for generalized linear mixed models), PROC QLIM (for selection models and heterogeneous logit models), and PROC MDC (for advanced discrete choice models). This book is part of the SAS Press program.
Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul D. Allison, is an accessible, data-based introduction to methods of survival analysis. Researchers who want to analyze survival data with SAS will find just what they need with this fully updated new edition that incorporates the many enhancements in SAS procedures for survival analysis in SAS 9. Although the book assumes only a minimal knowledge of SAS, more experienced users will learn new techniques of data input and manipulation. Numerous examples of SAS code and output make this an eminently practical book, ensuring that even the uninitiated become sophisticated users of survival analysis. The main topics presented include censoring, survival curves, Kaplan-Meier estimation, accelerated failure time models, Cox regression models, and discrete-time analysis. Also included are topics not usually covered in survival analysis books, such as time-dependent covariates, competing risks, and repeated events. Survival Analysis Using SAS: A Practical Guide, Second Edition, has been thoroughly updated for SAS 9, and all figures are presented using ODS Graphics. This new edition also documents major enhancements to the STRATA statement in the LIFETEST procedure; includes a section on the PROBPLOT command, which offers graphical methods to evaluate the fit of each parametric regression model; introduces the new BAYES statement for both parametric and Cox models, which allows the user to do a Bayesian analysis using MCMC methods; demonstrates the use of the counting process syntax as an alternative method for handling time-dependent covariates; contains a section on cumulative incidence functions; and describes the use of the new GLIMMIX procedure to estimate random-effects models for discrete-time data. This book is part of the SAS Press program.
Agresti, A. (2013) Categorical Data Analysis, Third Edition. John Wiley & Sons,
Inc., Hoboken, New Jersey. Allison, P. D. (2012) Logistic Regression Using the SAS System: Theory and Application, Second Edition. SAS Institute Inc., Cary,
Author: Miroslav Kaps
Designed to cover techniques for analysis of data in the animal sciences, this popular textbook provides an overview of the basic principles of statistics enabling the subsequent applications to be carried out with familiarity and understanding. Each chapter begins by introducing a problem with practical questions, followed by a brief theoretical background. Most topics are followed up with numerical examples to illustrate the methods described using data-sets from animal sciences and related fields. The same examples are then solved using the SAS software package. Written primarily for students and researchers in animal sciences, the text is also useful for those studying agricultural, biological, and veterinary sciences.
Allen, M.P. (1977) Understanding Regression Analysis. Plenum Press, New York
and London. Allison, P. D. (1999) Logistic Regression Using the SAS System: Theory and Application. SAS Institute Inc., Cary, North Carolina. Box, G. E. P. ...
Author: Miroslav Kaps
Category: Technology & Engineering
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
[ 2 ] Allison , Paul D. Logistic Regression Using SAS : Theory and Application
SAS Institute , 2nd edition , 2012 . [ 3 ] Demidenko , Eugene . Mixed Models : Theory and Applications with R , Wiley , 2nd edition , 2013 . [ 4 ] Dobson , Annette
J , and ...
Author: Olga Korosteleva
Publisher: CRC Press
Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book consists of regression models that go beyond linear regression, including models for right-skewed, categorical and hierarchical observations. The book presents the theory as well as fully worked-out numerical examples with complete SAS and R codes for each regression. The emphasis is on model accuracy and the interpretation of results. For each regression, the fitted model is presented along with interpretation of estimated regression coefficients and prediction of response for given values of predictors. Features: Presents the theoretical framework for each regression. Discusses data that are categorical, count, proportions, right-skewed, longitudinal and hierarchical. Uses examples based on real-life consulting projects. Provides complete SAS and R codes for each example. Includes several exercises for every regression. Advanced Regression Models with SAS and R is designed as a text for an upper division undergraduate or a graduate course in regression analysis. Prior exposure to the two software packages is desired but not required. The Author: Olga Korosteleva is a Professor of Statistics at California State University, Long Beach. She teaches a large variety of statistical courses to undergraduate and master’s students. She has published three statistical textbooks. For a number of years, she has held the position of faculty director of the statistical consulting group. Her research interests lie mostly in applications of statistical methodology through collaboration with her clients in health sciences, nursing, kinesiology, and other fields.
The focus in this Second Edition is on logistic regression models for individual level (but aggregate or grouped) data.
Author: Scott Menard
The focus in this Second Edition is again on logistic regression models for individual level data, but aggregate or grouped data are also considered. The book includes detailed discussions of goodness of fit, indices of predictive efficiency, and standardized logistic regression coefficients, and examples using SAS and SPSS are included. More detailed consideration of grouped as opposed to case-wise data throughout the book Updated discussion of the properties and appropriate use of goodness of fit measures, R-square analogues, and indices of predictive efficiency Discussion of the misuse of odds ratios to represent risk ratios, and of over-dispersion and under-dispersion for grouped data Updated coverage of unordered and ordered polytomous logistic regression models.
Advanced Log - Linear Models Using SAS® by Daniel Zelterman Genetic
Analysis of Complex Traits Using SAS® Edited by Arnold M. ... Walter Offen , and
Christy Chuang - Stein A Handbook of Statistical Analyses Using SAS , Second Edition by B.S. Everitt and G. Der ... A Handbook of Tools and Techniques by
Michele M. Burlew Logistic Regression Using the SAS® System : Theory and Application by ...
Author: Thomas Miron
Publisher: SAS Press
This is the ultimate "quick-fix" guide for SAS/GRAPH software users. Have a problem or particular task in mind? Short stand-alone chapters, filled with examples, will guide you through specific functions step-by-step. Organized so you can skip directly to the solutions you need, this book is like a series of flash cards. It is minimal in text, with numerous fully annotated examples. Users of all levels, including those who use SAS/GRAPH infrequently, will find this an inviting and eminently practical aproach to handling their real-world graphics projects. Even if you have no immediate task or problem, you will enjoy browsing through the various topics covered. Book jacket.
All items collapsed onto one component when using principal component
analysis, and achieved high internal consistency (α = .89). 6 Responses to the ...
Allison, P. (2009). Logistic regression using SAS: Theory and application (2nd ed
Author: Jack Quarter
Publisher: University of Toronto Press
Category: Business & Economics
Social Purpose Enterprises: Case Studies for Social Change presents case studies of twelve organizations that operate in a growing niche within the Canadian social economy: market-based entities supported by a nonprofit organization and operated for the benefit of a workforce who lives on the margins of society. Using a variety of research methods, the contributors examine the work of social purpose enterprises in a range of businesses including food services, child care, furniture, courier services, and microfinance. Combining the experience of academics and practitioners, each chapter analyses the economic, social, and policy implications of the case. Building on research published in Researching the Social Economy (2010) and Businesses with a Difference (2013), Social Purpose Enterprises provides a valuable resource for those involved in the growing push to encourage market-based solutions for those on the social margins.
Order No. A55810 Applied Multivariate Statistics with SAS Software , Second Edition by Ravindra Khattree and Dayanand N. Naik . ... A56100 Logistic Regression Using the SAS System : Theory and Application by Paul D. Allison .
P.D.. 1999..Logistic Regression Using the SAS System: Theory and Application..
Cary,.NC:.SAS.Institute. Altman,. D.G.. ... P..and.G..Berry,.1987..Statistical
Methods in Medical Research,.2nd.Edition.. Oxford:.Blackwell. Bartlett,.M.S..
Author: Lakshmi Padgett
Publisher: CRC Press
Practical Statistical Methods: A SAS Programming Approach presents a broad spectrum of statistical methods useful for researchers without an extensive statistical background. In addition to nonparametric methods, it covers methods for discrete and continuous data. Omitting mathematical details and complicated formulae, the text provides SAS programs to carry out the necessary analyses and draw appropriate inferences for common statistical problems. After introducing fundamental statistical concepts, the author describes methods used for quantitative data and continuous data following normal and nonnormal distributions. She then focuses on regression methodology, highlighting simple linear regression, logistic regression, and the proportional hazards model. The final chapter briefly discusses such miscellaneous topics as propensity scores, misclassification errors, interim analysis, conditional power, bootstrap, and jackknife. With SAS code and output integrated throughout, this book shows how to interpret data using SAS and illustrates the many statistical methods available for tackling problems in a range of fields, including the pharmaceutical industry and the social sciences.
ALLISON, P. (1999a) Comparing Logit and Probit Coefficients Across Groups. ... Using the SAS System: Theory and Application. Cary, NC: SAS ... HAMILTON,
L. C. (1992) Regression with Graphics: A Second Course in Applied Statistics.
Author: James Jaccard
This work introduces general strategies for testing interactions in logistic regression as well as providing the tools to interpret and understand the meaning of coefficients in equations with product terms.
Logistic Regression Using SAS: Theory and Application. 2nd ed. Cary, NC: SAS
Press; 2012:54–55, is to report their values as ∞. If a likelihood-based lower
confidence limit for an independent variable with separation is missing in the ...
Author: Stanton A. Glantz
Publisher: McGraw Hill Professional
A textbook on the use of advanced statistical methods in healthcare sciences Primer of Applied Regression & Analysis of Variance is a textbook especially created for medical, public health, and social and environmental science students who need applied (not theoretical) training in the use of statistical methods. The book has been acclaimed for its user-friendly style that makes complicated material understandable to readers who do not have an extensive math background. The text is packed with learning aids that include chapter-ending summaries and end-of-chapter problems that quickly assess mastery of the material. Examples from biological and health sciences are included to clarify and illustrate key points. The techniques discussed apply to a wide range of disciplines, including social and behavioral science as well as health and life sciences. Typical courses that would use this text include those that cover multiple linear regression and ANOVA. Four completely new chapters Completely updated software information and examples
Author: David W. Hosmer, Jr.Publish On: 2013-02-26
This third edition of Applied Logistic Regression comes 12 years after the 2000
publication of the second edition. ... to the underlying theory of the logistic regression model, with a major focus on the application, using real data sets, of
the available ... Other major statistical packages employed at various points
during the preparation of this text include SAS [SAS Institute Inc. (2009)],
OpenBUGS [Lunn et al.
Author: David W. Hosmer, Jr.
Publisher: John Wiley & Sons
A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.
... Jr . , and Lemeshow , S . ( 2000 ) Applied Logistic Regression , 2nd edition ,
New York : John Wiley & Sons , Inc . 14 . Allison , P . D . ( 1999 ) Logistic Regression Using the SAS System : Theory and Application , Cary , NC : SAS
Institute . 15 .
Author: Mukesh Verma
Publisher: Humana Press
Population studies and epidemiology facilitate the discovery of genetic and environmental determinants of cancer and the development of new approaches to cancer control and prevention, therefore they play a central role in the creation of health policies. Cancer Epidemiology compiles areas of research which cover etiological factors or determinants that contribute to the development of cancer and describe the latest technologies in cancer epidemiology. In Volume 1, Host Susceptibility Factors, leading experts provide chapters on cancer incidence, prevalence, mortality and surveillance, methods, technologies and study design in cancer epidemiology as well as host susceptibility factors in cancer epidemiology. Although a non-standard volume of the highly successful Methods in Molecular BiologyTM series, this comprehensive text retains the commitment of the series to collecting the kind of detailed, up-to-date information and implementation advice that is crucial for getting optimal results. Cutting-edge and essential, Cancer Epidemiology allows readers to get the maximum advantage of the methods involved in this exciting and important field.
... meeting and received the Association's Graduate Student “Paper of Distinction”
Award. REFERENCES Allison, P. D. (2012). Logistic regression using SAS: Theory 55 The Second Shift and the Nonstandard Shift Notes Acknowledgments.
Author: Samantha K. Ammons
Publisher: Emerald Group Publishing
Category: Social Science
This volume will focus on innovative research examining how the nature of paid work intersects with family and personal life today. This collection of cutting-edge research will be instrumental in shaping the next wave of work-family scholarship.
Author: American Statistical AssociationPublish On: 2002
95 Methods of Multivariate Analysis , 2nd Edition Sampling , 2nd Edition Alvin C .
Rencher Steven K . Thompson From the ... 95 Logistic Regression Using the SAS
System Theory Generalized Sampling Response Surface and Application ...
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.
Author: Raymond H. Myers
Publisher: John Wiley & Sons
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.
Praise for the Second Edition "A must-have book for anyone expecting to do research and/or applications in categorical data analysis." —Statistics in Medicine "It is a total delight reading this book." —Pharmaceutical Research "If you ...
Author: Alan Agresti
Publisher: John Wiley & Sons
Praise for the Second Edition "A must-have book for anyone expecting to do research and/orapplications in categorical data analysis." —Statistics in Medicine "It is a total delight reading this book." —Pharmaceutical Research "If you do any analysis of categorical data, this is anessential desktop reference." —Technometrics The use of statistical methods for analyzing categorical datahas increased dramatically, particularly in the biomedical, socialsciences, and financial industries. Responding to new developments,this book offers a comprehensive treatment of the most importantmethods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes thelatest methods for univariate and correlated multivariatecategorical responses. Readers will find a unified generalizedlinear models approach that connects logistic regression andPoisson and negative binomial loglinear models for discrete datawith normal regression for continuous data. This edition alsofeatures: An emphasis on logistic and probit regression methods forbinary, ordinal, and nominal responses for independent observationsand for clustered data with marginal models and random effectsmodels Two new chapters on alternative methods for binary responsedata, including smoothing and regularization methods,classification methods such as linear discriminant analysis andclassification trees, and cluster analysis New sections introducing the Bayesian approach for methods inthat chapter More than 100 analyses of data sets and over 600 exercises Notes at the end of each chapter that provide references torecent research and topics not covered in the text, linked to abibliography of more than 1,200 sources A supplementary website showing how to use R and SAS; for allexamples in the text, with information also about SPSS and Stataand with exercise solutions Categorical Data Analysis, Third Edition is an invaluabletool for statisticians and methodologists, such as biostatisticiansand researchers in the social and behavioral sciences, medicine andpublic health, marketing, education, finance, biological andagricultural sciences, and industrial quality control.
A Comparison with HGLM and Logistic Regression DIF Detection Methods
Tianshu Pan. Narayanan ... Hierarchical Linear Models : Applications and Data
Analysis Methods . Thousand Oaks : Sage , second edition . Raudenbush ... Itm :
an R Package for Latent Variable Modeling and Item Response Theory Analyses
, Journal of Statistical Software , 17 ( 5 ) . Rodriguez ... SAS / STAT® 9.2 User's