MULTREG <options>;
The MULTREG statement performs power and sample size analyses for Type III F tests of sets of predictors in multiple linear regression, assuming either fixed or normally distributed predictors.
Table 89.6 summarizes the options available in the MULTREG statement.
Table 89.6: MULTREG Statement Options
Option 
Description 

Define analysis 

Specifies the statistical analysis 

Specify analysis information 

Specifies the significance level 

Specifies the assumed distribution of the predictors 

Specifies the number of predictors in the full model 

Specifies a nointercept model 

Specifies the number of predictors in the reduced model 

Specifies the number of predictors being tested 

Specify effects 

Specifies the partial correlation 

Specifies the difference in 

Specifies the of the full model 

Specifies the of the reduced model 

Specify sample size 

Enables fractional input and output for sample sizes 

Specifies the sample size 

Specify power 

Specifies the desired power 

Control ordering in output 

Controls the order of parameters 
Table 89.7 summarizes the valid result parameters in the MULTREG statement.
Table 89.7: Summary of Result Parameters in the MULTREG Statement
To specify the number of predictors, use any two of these three options:
the number of predictors in the full model (NFULLPREDICTORS= )
the number of predictors in the reduced model (NREDUCEDPREDICTORS= )
the number of predictors being tested (NTESTPREDICTORS= )
To specify the effect, choose one of the following parameterizations:
partial correlation (by using the PARTIALCORR= option)
for the full and reduced models (by using any two of RSQUAREDIFF= , RSQUAREFULL= , and RSQUAREREDUCED= )
This section summarizes the syntax for the common analyses that are supported in the MULTREG statement.
You can express effects in terms of partial correlation, as in the following statements. Default values of the TEST= , MODEL= , and ALPHA= options specify a Type III F test with a significance level of 0.05, assuming normally distributed predictors.
proc power; multreg model = random nfullpredictors = 7 ntestpredictors = 3 partialcorr = 0.35 ntotal = 100 power = .; run;
You can also express effects in terms of :
proc power; multreg model = fixed nfullpredictors = 7 ntestpredictors = 3 rsquarefull = 0.9 rsquarediff = 0.1 ntotal = . power = 0.9; run;