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Author : SAS Institute
Publisher : Sas Inst
Page : 163 pages
File Size : 53,8 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,Samprit Chatterjee,Ali S. Hadi
Publisher : Wiley-Interscience
Page : 0 pages
File Size : 45,6 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 : Unknown
Page : 288 pages
File Size : 54,7 Mb
Release : 2000
Category : Electronic
ISBN : OCLC:901558786
Author : Rudolf Freund,Ramon Littell
Publisher : John Wiley & Sons
Page : 258 pages
File Size : 43,5 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 : 348 pages
File Size : 50,9 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 : SAS Publishing
Publisher : Unknown
Page : 0 pages
File Size : 46,5 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.
Author : Ken Kleinman,Nicholas J. Horton
Publisher : CRC Press
Page : 473 pages
File Size : 54,9 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 : Michael Panik
Publisher : CRC Press
Page : 830 pages
File Size : 50,6 Mb
Release : 2009-04-30
Category : Mathematics
ISBN : 9781420091984
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs. The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression, L1 and q-quantile regression, regression in a spatial domain, ridge regression, semiparametric regression, nonlinear least squares, and time-series regression issues. For most of the regression methods, the author includes SAS procedure code, enabling readers to promptly perform their own regression runs. A Comprehensive, Accessible Source on Regression Methodology and Modeling Requiring only basic knowledge of statistics and calculus, this book discusses how to use regression analysis for decision making and problem solving. It shows readers the power and diversity of regression techniques without overwhelming them with calculations.
Author : Mohamed M. Shoukri,Cheryl Cihon
Publisher : CRC Press
Page : 404 pages
File Size : 44,9 Mb
Release : 1998-12-16
Category : Mathematics
ISBN : 1439832439
Building upon material presented in the first edition, Statistical Methods for Health Sciences, Second Edition continues to address the analytical issues related to the modeling and analysis of cluster data, both physical clustering-sampling of communities, families, or herds-and overtime clustering-longitudinal, repeated measures, or time series data. All examples in this new edition are solved using the SAS package, and all SAS programs are provided for understanding material presented. Numerous medical examples make this text especially suitable for applied health scientists and epidemiologists.
Author : Jichuan Wang,Haiyi Xie,James F. Fisher
Publisher : Walter de Gruyter
Page : 275 pages
File Size : 48,7 Mb
Release : 2011-12-23
Category : Mathematics
ISBN : 9783110267709
Interest in multilevel statistical models for social science and public health studies has been aroused dramatically since the mid-1980s. New multilevel modeling techniques are giving researchers tools for analyzing data that have a hierarchical or clustered structure. Multilevel models are now applied to a wide range of studies in sociology, population studies, education studies, psychology, economics, epidemiology, and public health. This book covers a broad range of topics about multilevel modeling. The goal of the authors is to help students and researchers who are interested in analysis of multilevel data to understand the basic concepts, theoretical frameworks and application methods of multilevel modeling. The book is written in non-mathematical terms, focusing on the methods and application of various multilevel models, using the internationally widely used statistical software, the Statistics Analysis System (SAS®). Examples are drawn from analysis of real-world research data. The authors focus on twolevel models in this book because it is most frequently encountered situation in real research. These models can be readily expanded to models with three or more levels when applicable. A wide range of linear and non-linear multilevel models are introduced and demonstrated.
Author : Kattamuri S. Sarma
Publisher : SAS Institute
Page : 574 pages
File Size : 42,6 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 : Paul D. Allison
Publisher : SAS Institute
Page : 336 pages
File Size : 52,9 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 : 46,8 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 : Alan Agresti
Publisher : John Wiley & Sons
Page : 752 pages
File Size : 40,5 Mb
Release : 2013-04-08
Category : Mathematics
ISBN : 9781118710944
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.
Author : Maura Ellen Stokes,Charles S. Davis,Gary Grove Koch
Publisher : Unknown
Page : 524 pages
File Size : 50,6 Mb
Release : 1995
Category : Computers
ISBN : UOM:39015059095235
Discusses hypothesis testing strategies for the assessment of association in contingency tables and sets of contingency tables. Also discusses various modeling strategies available for describing the nature of the association between a categorical outcome measure and a set of explanatory variables.