The REG Procedure
- Overview
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Getting Started
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Syntax
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Details
Missing Values Input Data Sets Output Data Sets Interactive Analysis Model-Selection Methods Criteria Used in Model-Selection Methods Limitations in Model-Selection Methods Parameter Estimates and Associated Statistics Predicted and Residual Values Models of Less Than Full Rank Collinearity Diagnostics Model Fit and Diagnostic Statistics Influence Statistics Reweighting Observations in an Analysis Testing for Heteroscedasticity Testing for Lack of Fit Multivariate Tests Autocorrelation in Time Series Data Computations for Ridge Regression and IPC Analysis Construction of Q-Q and P-P Plots Computational Methods Computer Resources in Regression Analysis Displayed Output ODS Table Names ODS Graphics
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Examples
- References
| Missing Values |
PROC REG constructs only one crossproducts matrix for the variables in all regressions. If any variable needed for any regression is missing, the observation is excluded from all estimates. If you include variables with missing values in the VAR statement, the corresponding observations are excluded from all analyses, even if you never include the variables in a model. PROC REG assumes that you might want to include these variables after the first RUN statement and deletes observations with missing values.