Author : Paul D. Allison
Publisher : SAS Institute
Page : 348 pages
File Size : 23,44 MB
Release : 2012-03-30
Category : Computers
ISBN : 1629590185
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 : David W. Hosmer, Jr.
Publisher : Wiley-Interscience
Page : 0 pages
File Size : 49,56 MB
Release : 2001-11-13
Category : Mathematics
ISBN : 9780471225898
From the reviews of the First Edition. "An interesting, useful, and well-written book on logistic regression models. . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references.
Author : Patricia Berglund
Publisher : SAS Institute
Page : 328 pages
File Size : 30,30 MB
Release : 2014-07-01
Category : Computers
ISBN : 162959203X
Find guidance on using SAS for multiple imputation and solving common missing data issues. Multiple Imputation of Missing Data Using SAS provides both theoretical background and constructive solutions for those working with incomplete data sets in an engaging example-driven format. It offers practical instruction on the use of SAS for multiple imputation and provides numerous examples that use a variety of public release data sets with applications to survey data. Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. The authors cover the MI and MIANALYZE procedures in detail, along with other procedures used for analysis of complete data sets. They guide analysts through the multiple imputation process, including evaluation of missing data patterns, choice of an imputation method, execution of the process, and interpretation of results. Topics discussed include how to deal with missing data problems in a statistically appropriate manner, how to intelligently select an imputation method, how to incorporate the uncertainty introduced by the imputation process, and how to incorporate the complex sample design (if appropriate) through use of the SAS SURVEY procedures. Discover the theoretical background and see extensive applications of the multiple imputation process in action. This book is part of the SAS Press program.
Author :
Publisher :
Page : 142 pages
File Size : 23,37 MB
Release : 2019-06-21
Category :
ISBN : 9781642954876
This special collection of SAS Global Forum papers demonstrates new and enhanced capabilities and applications of lesser-known SAS/STAT and SAS Viya procedures for regression models. The goal here is to raise awareness of current valuable SAS/STAT content of which the user may not be aware. Also available free as a PDF from sas.com/books.
Author : Christophe Lalanne
Publisher : Elsevier
Page : 176 pages
File Size : 17,35 MB
Release : 2017-06-22
Category : Mathematics
ISBN : 0081011717
This volume of the Biostatistics and Health Sciences Set focuses on statistics applied to clinical research.The use of SAS for data management and statistical modeling is illustrated using various examples. Many aspects of data processing and statistical analysis of cross-sectional and experimental medical data are covered, including regression models commonly found in medical statistics. This practical book is primarily intended for health researchers with a basic knowledge of statistical methodology. Assuming basic concepts, the authors focus on the practice of biostatistical methods essential to clinical research, epidemiology and analysis of biomedical data (including comparison of two groups, analysis of categorical data, ANOVA, linear and logistic regression, and survival analysis). The use of examples from clinical trials and epidemiological studies provide the basis for a series of practical exercises, which provide instruction and familiarize the reader with essential SAS commands. - Presents the use of SAS software in the statistical approach for the management of data modeling - Includes elements of the language and descriptive statistics - Supplies measures of association, comparison of means, and proportions for two or more samples - Explores linear and logistic regression - Provides survival data analysis
Author : Daniel Zelterman
Publisher : Cambridge University Press
Page : 296 pages
File Size : 48,44 MB
Release : 2022-05-12
Category : Medical
ISBN : 1108786545
This textbook for students in the health and social sciences covers the basics of linear model methods with a minimum of mathematics, assuming only a pre-calculus background. Numerous examples drawn from the news and current events with an emphasis on health issues, illustrate the concepts in an immediately accessible way. Methods covered include linear regression models, Poisson regression, logistic regression, proportional hazards regression, survival analysis, and nonparametric regression. The author emphasizes interpretation of computer output in terms of the motivating example. All of the R code is provided and carefully explained, allowing readers to quickly apply the methods to their own data. Plenty of exercises help students think about the issues involved in the analysis and its interpretation. Code and datasets are available for download from the book's website at www.cambridge.org/zelterman
Author : Scott Menard
Publisher : SAGE
Page : 130 pages
File Size : 28,95 MB
Release : 2002
Category : Mathematics
ISBN : 9780761922087
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.
Author : J. Scott Long
Publisher : SAGE
Page : 334 pages
File Size : 28,91 MB
Release : 1997-01-09
Category : Mathematics
ISBN : 9780803973749
Evaluates the most useful models for categorical and limited dependent variables (CLDVs), emphasizing the links among models and applying common methods of derivation, interpretation, and testing. The author also explains how models relate to linear regression models whenever possible. Annotation c.
Author : Sas Institute
Publisher :
Page : 0 pages
File Size : 13,90 MB
Release : 2011-07
Category :
ISBN : 9781607646341
The GLIMMIX procedure fits and analyzes generalized linear mixed models (GLMM), models with random effects for data that can be nonnormally distributed. This title is also available online.
Author : Rudolf Freund
Publisher : John Wiley & Sons
Page : 258 pages
File Size : 22,49 MB
Release : 2000-12-29
Category : Mathematics
ISBN : 0471416649
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.
