The Little SAS Book

Author: Lora D. Delwiche
Publisher: SAS Institute
ISBN: 1599948753
Format: PDF, Docs
Download Now
This easy-to-read classic introduces the most commonly used features of the SAS language. Topics include such basic SAS concepts as the DATA and PROC steps, inputting data, modifying and combining data sets, and debugging SAS programs.

The Little SAS Book for Enterprise Guide 4 2

Author: Susan J. Slaughter
Publisher: SAS Press
ISBN: 9781599947266
Format: PDF, ePub, Mobi
Download Now
The authors help programmers quickly become familiar with the SAS Enterprise Guide point-and-click environment. A series of carefully designed tutorials helps readers master the basics of the tasks they will want to do most frequently.

Datenanalyse mit SAS

Author: Walter Krämer
Publisher: Springer-Verlag
ISBN: 3662577992
Format: PDF, ePub, Docs
Download Now
Das Programmpaket SAS hat sich im Lauf der Jahre als Standardprogramm zur statistischen Datenanalyse etabliert. Der souveräne Umgang mit statistischen Methoden und deren praktischer Umsetzung in SAS bietet somit einen unschätzbaren Vorteil für die tägliche Arbeit des Datenanalytikers. Im vorliegenden Buch erlernt der Leser zunächst die Grundlagen der Programmierung. Anschließend wird eine große Auswahl statistischer Verfahren und deren Umsetzung als SAS-Programm vorgestellt. Dabei wird großes Augenmerk auf die grafischen Aspekte der statistischen Datenanalyse gelegt. Ein zusätzlicher Teil über Programmierung mit IML und Makros sowie hilfreiche Assistenten in SAS runden die Darstellung ab. Kommentierte Beispiele und Übungsaufgaben mit Lösungshinweisen ermöglichen das Selbststudium. Mit seiner umfassenden Themenauswahl ist das Buch als Einführung, aber auch als Nachschlagewerk für den fortgeschritteneren Leser geeignet. Für die 4. Auflage wurden Kapitel zur Epidemiologie und Biometrie ergänzt.

SAS System for Regression

Author: Rudolf J. Freud, Ph.D.
Publisher: SAS Institute
ISBN: 9781599941417
Format: PDF, ePub, Mobi
Download Now
Learn to perform a wide variety of regression analyses using SAS software with this example-driven favorite from SAS Publishing. With SAS System for Regression, Third Edition, you will learn the basics of performing regression analyses using a wide variety of models including nonlinear models. Other topics include performing linear regression analyses using PROC REG and 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. Authors Rudolf Freund and Ramon Littell supply a helpful discussion of theory where necessary. Some knowledge of both regression and SAS are assumed. The updated third edition includes revisions, updated material, and new material. You'll find information on using SAS/INSIGHT software, regression with a binary response with emphasis on PROC LOGISTIC, and nonparametric regression (smoothing) using moving averages and PROC LOESS. Additionally, updated material throughout the book includes high-resolution PROC REG graphics output, data sets by the OUTEST option described and illustrated, and using PROC SCORE to predict another data set.

SAS for Monte Carlo Studies

Author: Xitao Fan
Publisher: SAS Institute
ISBN: 9781590474921
Format: PDF, Docs
Download Now
With the advance of computing technology, Monte Carlo simulation research has become increasingly popular among quantitative researchers in a variety of disciplines. More and more, statistical methods are being subjected to rigorous empirical scrutiny in the form of statistical simulation so that their limitations and strengths can be understood. With the combination of powerful built-in statistical procedures and versatile programming capabilities, SAS is ideal for conducting Monte Carlo simulation research! Xitao Fan, Akos Felsovalyi, Stephen Sivo, and Sean Keenan's SAS for Monte Carlo Studies: A Guide for Quantitative Researchers provides detailed and practical guidance for conducting Monte Carlo studies using SAS. Quantitative researchers will find this book attractive for its practicality and for its many hands-on application examples of Monte Carlo research.

SAS for Linear Models Fourth Edition

Author: Ramon C. Littell, Ph.D.
Publisher: SAS Institute
ISBN: 9781599941424
Format: PDF, Mobi
Download Now
This clear and comprehensive guide provides everything you need for powerful linear model analysis. Using a tutorial approach and plenty of examples, authors Ramon Littell, Walter Stroup, and Rudolf Freund lead you through methods related to analysis of variance with fixed and random effects. You will learn to use the appropriate SAS procedure for most experiment designs (including completely random, randomized blocks, and split plot) as well as factorial treatment designs and repeated measures. SAS for Linear Models, Fourth Edition, also includes analysis of covariance, multivariate linear models, and generalized linear models for non-normal data. Find inside: regression models; balanced ANOVA with both fixed- and random-effects models; unbalanced data with both fixed- and random-effects models; covariance models; generalized linear models; multivariate models; and repeated measures. New in this edition: MIXED and GENMOD procedures, updated examples, new software-related features, and other new material. This book is part of the SAS Press program.

Longitudinal Data and SAS

Author: Ron Cody
Publisher: SAS Institute
ISBN: 9781590474297
Format: PDF, ePub, Mobi
Download Now
Working with longitudinal data introduces a unique set of challenges. Once you've mastered the art of performing calculations within a single observation of a data set, you're faced with the task of performing calculations or making comparisons between observations. It's easy to look backward in data sets, but how do you look forward and across observations? Ron Cody provides straightforward answers to these and other questions. Longitudinal Data and SAS details useful techniques for conducting operations between observations in a SAS data set. For quick reference, the book is conveniently organized to cover tools-an introduction to powerful SAS programming techniques for longitudinal data; case studies-a variety of illuminating examples that use Ron's techniques; and macros-detailed descriptions of helpful longitudinal data macros. Beginning to intermediate SAS users will appreciate this book's informative, easy-to-comprehend style. And users who frequently process longitudinal data will learn to make the most of their analyses by following Ron's methodologies.