PROC GLM
Overview: GLM Procedure. The GLM procedure uses the method of least squares to fit general linear models. Among the statistical methods available in PROC GLM are regression, analysis of variance, analysis of covariance, multivariate analysis of variance, and partial correlation. PROC GLM analyzes data within the framework of general linear models. PROC GLM handles models relating one or several continuous dependent variables to one or several independent variables. The independent variables can be either classification variables, which divide the observations into discrete groups, or continuous variables. Thus, the GLM procedure can be used for many different analyses, including the following: simple regression; multiple regression ...
Keywords for this software
References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
Sorted by year (- Shoukri, Mohamed M.: Analysis of correlated data with SAS and R (2018)
- Shukla, Garima; Kumar, Vinod: Different methods of analyzing multiple samples repeated measures data (2012)
- Gaugler, Trent; Akritas, Michael G.: Testing for interaction in two-way random and mixed effects models: the fully nonparametric approach (2011)
- Perrett, Jamis J.: A SAS/IML companion for linear models. (2010)
- Vallejo, G.; Fernández, M. P.; Livacic-Rojas, P. E.: Analysis of unbalanced factorial designs with heteroscedastic data (2010)
- Malheiros, Euclides Braga: Accuracy of the analysis of repeated measures using SAS methods. (2001)
- Rawlings, John O.; Pantula, Sastry G.; Dickey, David A.: Applied regression analysis. A research tool. (1998)
- Cheng, P. H.; Meng, C. Y. K.: A new formula for tail probabilities of Dunnett’s T with unequal sample sizes (1995)