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Author : SAS Institute
Publisher : Sas Inst
Page : 163 pages
File Size : 48,9 Mb
Release : 1995
Category : Computers
ISBN : 1555446744
Data set examples show how to create a SAS data set for the raw data and print the data set.
Author : Paul D. Allison
Publisher : Unknown
Page : 288 pages
File Size : 50,5 Mb
Release : 2000
Category : Electronic
ISBN : OCLC:901558786
Author : Paul D. Allison,Samprit Chatterjee,Ali S. Hadi
Publisher : Wiley-Interscience
Page : 0 pages
File Size : 44,9 Mb
Release : 2008-03-14
Category : Mathematics
ISBN : 0470388072
This set contains: 9780471221753 Logistic Regression Using the SAS System: Theory and Application by Paul D. Allison and 9780471746966 Regression Analysis by Example, Fourth Edition by Samprit Chatterjee, Ali S. Hadi.
Author : Paul D. Allison
Publisher : SAS Institute
Page : 348 pages
File Size : 40,5 Mb
Release : 2012-03-30
Category : Computers
ISBN : 9781629590189
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. Includes several real-world examples in full detail.
Author : Rudolf Freund,Ramon Littell
Publisher : John Wiley & Sons
Page : 258 pages
File Size : 44,5 Mb
Release : 2000-12-29
Category : Mathematics
ISBN : 9780471416647
SAS® System for Regression Learn to perform a wide variety of regression analyses using SAS® software with this example-driven revised favorite from SAS Publishing. With this Third Edition you will learn the basics of performing regression analyses using a wide variety of models including nonlinear models. Other topics covered include performing linear regression analyses using PROC REG diagnosing and providing remedies for data problems, including outliers and multicollinearity. Examples feature numerous SAS procedures including REG, PLOT, GPLOT, NLIN, RSREG, AUTOREG, PRINCOMP, and others. A helpful discussion of theory is supplied where necessary. Some knowledge of both regression and the SAS System are assumed. New for this edition The Third Edition includes revisions, updated material, and new material. You’ll find new information on using SAS/INSIGHT® software regression with a binary response with emphasis on PROC LOGISTIC nonparametric regression (smoothing) using moving averages and PROC LOESS. Additionally, updated material throughout the book includes high-resolution PROC REG graphics output, using the OUTEST option to produce a data set, and using PROC SCORE to predict another data set.
Author : Paul D. Allison
Publisher : SAS Institute
Page : 349 pages
File Size : 40,9 Mb
Release : 2012-03-30
Category : Computers
ISBN : 9781607649953
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.
Author : Paul D. Allison
Publisher : SAS Institute
Page : 336 pages
File Size : 45,8 Mb
Release : 2010-03-29
Category : Computers
ISBN : 9781629590257
Biomedical and social science researchers who want to analyze survival data with SAS will find just what they need with this easy-to-read and comprehensive guide. Teaches many aspects of data input and manipulation. Numerous examples of SAS code and output make this an eminently practical book, completely updated for SAS 9.
Author : Maura E. Stokes,Charles S. Davis,Gary G. Koch
Publisher : SAS Institute
Page : 590 pages
File Size : 51,5 Mb
Release : 2012-07-31
Category : Computers
ISBN : 9781629590356
Statisticians and researchers will find this book, newly updated for SAS/STAT 12.1, to be a useful discussion of categorical data analysis techniques as well as an invaluable aid in applying these methods with SAS.
Author : Melissa A Hardy,Alan Bryman
Publisher : SAGE
Page : 728 pages
File Size : 41,9 Mb
Release : 2009-06-17
Category : Social Science
ISBN : 9781446203446
Electronic Inspection Copy available for instructors here 'This book provides an excellent reference guide to basic theoretical arguments, practical quantitative techniques and the methodologies that the majority of social science researchers are likely to require for postgraduate study and beyond' - Environment and Planning 'The book provides researchers with guidance in, and examples of, both quantitative and qualitative modes of analysis, written by leading practitioners in the field. The editors give a persuasive account of the commonalities of purpose that exist across both modes, as well as demonstrating a keen awareness of the different things that each offers the practising researcher' - Clive Seale, Brunel University 'With the appearance of this handbook, data analysts no longer have to consult dozens of disparate publications to carry out their work. The essential tools for an intelligent telling of the data story are offered here, in thirty chapters written by recognized experts. ' - Michael Lewis-Beck, F Wendell Miller Distinguished Professor of Political Science, University of Iowa 'This is an excellent guide to current issues in the analysis of social science data. I recommend it to anyone who is looking for authoritative introductions to the state of the art. Each chapter offers a comprehensive review and an extensive bibliography and will be invaluable to researchers wanting to update themselves about modern developments' - Professor Nigel Gilbert, Pro Vice-Chancellor and Professor of Sociology, University of Surrey This is a book that will rapidly be recognized as the bible for social researchers. It provides a first-class, reliable guide to the basic issues in data analysis, such as the construction of variables, the characterization of distributions and the notions of inference. Scholars and students can turn to it for teaching and applied needs with confidence. The book also seeks to enhance debate in the field by tackling more advanced topics such as models of change, causality, panel models and network analysis. Specialists will find much food for thought in these chapters. A distinctive feature of the book is the breadth of coverage. No other book provides a better one-stop survey of the field of data analysis. In 30 specially commissioned chapters the editors aim to encourage readers to develop an appreciation of the range of analytic options available, so they can choose a research problem and then develop a suitable approach to data analysis.
Author : Paul D. Allison
Publisher : Wiley-SAS
Page : 308 pages
File Size : 45,5 Mb
Release : 2001-12-21
Category : Mathematics
ISBN : 0471221759
Written in an informal and non-technical style, this book first explains the theory behind logistic regression and then shows how to implement it using the SAS System. Allison includes several detailed, real-world examples of the social sciences to provide readers with a better understanding of the material. He also explores the differences and similarities among the many generalizations of the logistic regression model.
Author : Xing Liu
Publisher : SAGE Publications
Page : 553 pages
File Size : 43,8 Mb
Release : 2015-09-30
Category : Social Science
ISBN : 9781483319742
The first book to provide a unified framework for both single-level and multilevel modeling of ordinal categorical data, Applied Ordinal Logistic Regression Using Stata by Xing Liu helps readers learn how to conduct analyses, interpret the results from Stata output, and present those results in scholarly writing. Using step-by-step instructions, this non-technical, applied book leads students, applied researchers, and practitioners to a deeper understanding of statistical concepts by closely connecting the underlying theories of models with the application of real-world data using statistical software.
Author : Ken Kleinman,Nicholas J. Horton
Publisher : CRC Press
Page : 473 pages
File Size : 49,6 Mb
Release : 2014-07-17
Category : Mathematics
ISBN : 9781466584495
An Up-to-Date, All-in-One Resource for Using SAS and R to Perform Frequent Tasks The first edition of this popular guide provided a path between SAS and R using an easy-to-understand, dictionary-like approach. Retaining the same accessible format, SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition explains how to easily perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and graphics, along with more complex applications. New to the Second Edition This edition now covers RStudio, a powerful and easy-to-use interface for R. It incorporates a number of additional topics, including using application program interfaces (APIs), accessing data through database management systems, using reproducible analysis tools, and statistical analysis with Markov chain Monte Carlo (MCMC) methods and finite mixture models. It also includes extended examples of simulations and many new examples. Enables Easy Mobility between the Two Systems Through the extensive indexing and cross-referencing, users can directly find and implement the material they need. SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Numerous example analyses demonstrate the code in action and facilitate further exploration. The datasets and code are available for download on the book’s website.
Author : Kattamuri S. Sarma
Publisher : SAS Institute
Page : 574 pages
File Size : 44,8 Mb
Release : 2017-07-20
Category : Computers
ISBN : 9781635260403
« Written for business analysts, data scientists, statisticians, students, predictive modelers, and data miners, this comprehensive text provides examples that will strengthen your understanding of the essential concepts and methods of predictive modeling. »--
Author : Scott Menard
Publisher : SAGE
Page : 393 pages
File Size : 53,5 Mb
Release : 2010
Category : Mathematics
ISBN : 9781412974837
Logistic Regression is designed for readers who have a background in statistics at least up to multiple linear regression, who want to analyze dichotomous, nominal, and ordinal dependent variables cross-sectionally and longitudinally.
Author : SAS Publishing
Publisher : Unknown
Page : 0 pages
File Size : 43,7 Mb
Release : 2011-07
Category : Electronic
ISBN : 1607646331
The REG procedure is a general-purpose procedure for linear regression. This title is also available online.