SAS for Linear Models, Fourth Edition
SAS Institute, Mar 22, 2002 - Mathematics - 492 pages
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.
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Chapter 3 Analysis of Variance for Balanced Data
Chapter 4 Analyzing Data with Random Effects
Chapter 6 Understanding Linear Models Concepts
Chapter 7 Analysis of Covariance
Chapter 8 RepeatedMeasures Analysis
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Analysis of Covariance analysis of variance ANOVA appear in Output binomial blocks coefficients compute Contrast DF CONTRAST statement Copyright correlation data set degrees of freedom Dependent Variable deviance DF t Value drug DRUG*HOUR equation estimable functions ESTIMATE statements example expected mean squares F Value Pr F-statistic F-test factor Factorial Experiment FEV1 fixed effects following SAS statements Fourth Edition Freund GLM Procedure hypothesis interaction intercept irrig Least Squares Means levels linear combination Linear Models linear regression Littell logit LSMEANS statement main effect method mixed-model MODEL statement Multiple Comparisons Multivariate option p-value Parameter Estimates PATIENT(DRUG plot probit PROC GENMOD PROC GLM PROC MIXED PROC REG R-Square Ramon random effects RANDOM statement residual response variable Rudolf SAS Institute Output Section Source DF Squares Source DF Type Square F Value SS Mean Square SSCP Matrix standard error Stroup sums of squares Type III SS unbalanced data univariate Var(Error VARIETY Walter