Generalized linear models (GLMs) extend linear regression to models with a non-Gaussian, or even discrete, response. GLM theory is predicated on the exponential family of distributions--a class so rich that it includes the commonly used logit, probit, and Poisson models. Although one can fit these m
SAS for Linear Models, Fourth Edition
โ Scribed by Ramon Littell, Walter Stroup, Rudolf Freund
- Publisher
- SAS Publishing
- Year
- 2002
- Tongue
- English
- Leaves
- 493
- Edition
- 4th
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
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.
โฆ Subjects
ะะธะฑะปะธะพัะตะบะฐ;ะะพะผะฟัััะตัะฝะฐั ะปะธัะตัะฐัััะฐ;SAS / JMP;
๐ SIMILAR VOLUMES
<p><P>Linear models courses are often presented as either theoretical or applied. Consequently, students may find themselves either proving theorems or using high-level procedures like PROC GLM to analyze data. There exists a gap between the derivation of formulas and analyses that hide these formul
<p><P>Linear models courses are often presented as either theoretical or applied. Consequently, students may find themselves either proving theorems or using high-level procedures like PROC GLM to analyze data. There exists a gap between the derivation of formulas and analyses that hide these formul
This textbook for a second course in basic statistics for undergraduates or first-year graduate students introduces linear regression models and describes other linear models including Poisson regression, logistic regression, proportional hazards regression, and nonparametric regression. Numerous ex