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 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.
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.
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.
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.
Bridging the gap between statistics texts and SAS documentation, Elementary Statistics Using SAS is written for those who want to perform analyses to solve problems. The first section of the book explains the basics of SAS data sets and shows how to use SAS for descriptive statistics and graphs. The second section discusses fundamental statistical concepts, including normality and hypothesis testing. The remaining sections of the book show analyses for comparing two groups, comparing multiple groups, fitting regression equations, and exploring contingency tables. For each analysis, author Sandra Schlotzhauer explains assumptions, statistical approach, and SAS methods and syntax, and makes conclusions from the results. Statistical methods covered include two-sample t-tests, paired-difference t-tests, analysis of variance, multiple comparison techniques, regression, regression diagnostics, and chi-square tests. Elementary Statistics Using SAS is a thoroughly revised and updated edition of Ramon Littell and Sandra Schlotzhauer's SAS System for Elementary Statistical Analysis.
If you've ever stuffed "cheat sheets" into your reference documentation to guide you through specific tasks that the book never describes in detail, you'll appreciate the approach of this how-to book. Self-contained chapters lead you through tasks that SAS/GRAPH software users typically need to perform. The book features many graphical examples and programs and helps you interpret the steps your SAS code follows to produce results. Readers with basic SAS/GRAPH software experience who are looking for tips on particular tasks or computing problems will easily find just the sections they need. The book is also an ideal resource for students who need an example-packed supplement to their computing textbooks.
Available for bundling with Rao's text, this unique companion shows in great detail how to use SAS to do the statistics described in the text. Written specifically to complement and enhance the SAS material in the book, the SAS Companion uses the same examples used in the text, providing instructions and output for all textual examples. The SAS Companion is an essential tool and a handy reference for students as they work through the books' computing assignments.