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
| Computer Resources in Regression Analysis |
The REG procedure is efficient for ordinary regression; however, requests for optional features can greatly increase the amount of time required.
The major computational expense in the regression analysis is the collection of the crossproducts matrix. For 
variables and 
observations, the time required is proportional to 
. For each model run, PROC REG needs time roughly proportional to 
, where 
is the number of regressors in the model. Include an additional 
for the R, CLM, or CLI option and another for the INFLUENCE option.
Most of the memory that PROC REG needs to solve large problems is used for crossproducts matrices. PROC REG requires 
bytes for the main crossproducts matrix plus 
bytes for the largest model. If several output data sets are requested, memory is also needed for buffers.
See the section Input Data Sets for information about how to use TYPE=SSCP data sets to reduce computing time.