The REG Procedure
- Overview
-
Getting Started
-
Syntax
-
DetailsMissing ValuesInput Data SetsOutput Data SetsInteractive AnalysisModel-Selection MethodsCriteria Used in Model-Selection MethodsLimitations in Model-Selection MethodsParameter Estimates and Associated StatisticsPredicted and Residual ValuesModels of Less Than Full RankCollinearity DiagnosticsModel Fit and Diagnostic StatisticsInfluence StatisticsReweighting Observations in an AnalysisTesting for HeteroscedasticityTesting for Lack of FitMultivariate TestsAutocorrelation in Time Series DataComputations for Ridge Regression and IPC AnalysisConstruction of Q-Q and P-P PlotsComputational MethodsComputer Resources in Regression AnalysisDisplayed OutputPlot Options Superseded by ODS GraphicsODS Table NamesODS Graphics
-
Examples
- References
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 p variables and n observations, the time required is proportional to 
. For each model run, PROC REG needs time roughly proportional to 
, where k 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.