Regression Using the GLM, CATMOD, LOGISTIC, PROBIT, and LIFEREG Procedures

Introduction to Regression Procedures

Regression Using the GLM, CATMOD, LOGISTIC, PROBIT, and LIFEREG Procedures

The GLM procedure fits general linear models to data, and it can perform regression, analysis of variance, analysis of covariance, and many other analyses. The following features for regression distinguish PROC GLM from other regression procedures:

direct specification of polynomial effects
ease of specifying categorical effects (PROC GLM automatically generates dummy variables for class variables)

Most of the statistics based on predicted and residual values that are available in PROC REG are also available in PROC GLM. However, PROC GLM does not produce collinearity diagnostics, influence diagnostics, or scatter plots. In addition, PROC GLM allows only one model and fits the full model.

See Chapter 4, "Introduction to Analysis-of-Variance Procedures," and Chapter 30, "The GLM Procedure," for more details.

The CATMOD procedure can perform linear regression and logistic regression of response functions for data that can be represented in a contingency table. See Chapter 5, "Introduction to Categorical Data Analysis Procedures," and Chapter 22, "The CATMOD Procedure," for more details.

The LOGISTIC and PROBIT procedures can perform logistic and ordinal logistic regression. See Chapter 5, "Introduction to Categorical Data Analysis Procedures," Chapter 39, "The LOGISTIC Procedure," and Chapter 54, "The PROBIT Procedure," for additional details.

The LIFEREG procedure is useful in fitting equations to data that may be right-censored. See Chapter 10, "Introduction to Survival Analysis Procedures," and Chapter 36, "The LIFEREG Procedure," for more details.

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