By SAS Publishing
Presents entire documentation of the bottom SAS statistical systems (CORR, FREQ, and UNIVARIATE), together with introductory examples, syntax, computational info, and complex examples. New for SAS 9.2 is information regarding utilizing ODS Statistical portraits.
Read Online or Download Base SAS 9.2 Procedures Guide: Statistical Procedures PDF
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Additional resources for Base SAS 9.2 Procedures Guide: Statistical Procedures
1 and 2. ods output FisherPearsonCorr=SimCorr; title ’Testing Equality of Population Correlations’; proc corr data=Sim (where=(Batch=1 or Batch=2)) fisher; var X Y; by Batch; run; The ODS OUTPUT statement saves the “FisherPearsonCorr” table into an output data set in the CORR procedure. The output data set SimCorr contains Fisher’s z statistics for both batches. n2 3/, where z1 and z2 are Fisher’s z transformation of the sample correlations r1 and r2 , respectively, and where n1 and n2 are the corresponding sample sizes.
By default, the output data set is a special data set type (TYPE=CORR) that many SAS/STAT procedures recognize, including PROC REG and PROC FACTOR. When you specify the NOCORR option and the COV, CSSCP, or SSCP option, use the TYPE= data set option to change the data set type to COV, CSSCP, or SSCP. The output data set includes the following variables: BY variables, which identify the BY group when using a BY statement _TYPE_ variable, which identifies the type of observation _NAME_ variable, which identifies the variable that corresponds to a given row of the correlation matrix INTERCEPT variable, which identifies variable sums when specifying the SSCP option VAR variables, which identify the variables listed in the VAR statement You can use a combination of the _TYPE_ and _NAME_ variables to identify the contents of an observation.
1 displays univariate statistics for variables in the VAR and WITH statements. 00000 Petal Petal Sepal Sepal Length in mm. Width in mm. Length in mm. Width in mm. When the WITH statement is specified together with the VAR statement, the CORR procedure produces rectangular matrices for statistics such as covariances and correlations. The matrix rows correspond to the WITH variables (PetalLength and PetalWidth), while the matrix columns correspond to the VAR variables (SepalLength and SepalWidth).