SAS for Linear Models: Design Methods and Techniques

ISBN-13: 9780471221746 / Angielski / Miękka / 2002 / 496 str.

Ramon C. Littell; Walter Stroup; Rudolf Jakob Freund

Opis

Features and capabilities of the REG, ANOVA, and GLM procedures are included in this introduction to analysing linear models with the SAS System. This guide shows how to apply the appropriate procedure to data analysis problems and understand PROC GLM output. Other helpful guidelines and discussions cover the following significant areas: Multivariate linear models; lack-of-fit analysis; covariance and heterogeneity of slopes; a classification with both crossed and nested effects; and analysis of variance for balanced data. This fourth edition includes updated examples, new software-related features, and new material, including a chapter on generalised linear models. Version 8 of the SAS System was used to run the SAS code examples in the book.
* Provides clear explanations of how to use SAS to analyse linear models
* Includes numerous SAS outputs
* Includes new chapter on generalised linear models
* Uses version 8 of the SAS system
This book assists data analysts who use SAS/STAT software to analyse data using regression analysis and analysis of variance. It assumes familiarity with basic SAS concepts such as creating SAS data sets with the DATA step and manipulating SAS data sets with the procedures in base SAS software.

Recenzje

"The third edition...was published over a decade ago. Thus...the amount of brand–new and updated material in the fourth edition would fully justify its purchase." ( The American Statistician, Vol. 58, No. 1, February 2004)

"...the authors have done an excellent job incorporating the latest analysis methods and latest software updates?an excellent reference, on the I would have enjoyed having as a student...and one that I will certainly use now." (Technometrics, Vol. 45, No. 2, May 2003)

Spis treści

Acknowledgments.

Chapter 1. Introduction.

Chapter 2. Regression.

Chapter 3. Analysis of Variance for Balanced Data.

Chapter 4. Analyzing Data with Random Effects.

Chapter 5. Unbalanced Data Analysis: Basic Methods.

Chapter 6. Understanding Linear Models Concepts.

Chapter 7. Analysis of Covariance.

Chapter 8. Repeated–Measures Analysis.

Chapter 9. Multivariate Linear Models.

Chapter 10. Generalized Linear Models.

Chapter 11. Examples of Special Applications.

References.

Index.