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SAS/STAT Topics

SAS/STAT Software

Standardization

When performing multivariate analysis, having variables that are measured at different scales can influence the numerical stability and precision of the estimators. Standardizing the data prior to performing statistical analysis can often prevent this problem. The STDIZE procedure in SAS/STAT software standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure. A variety of location and scale measures are provided, including estimates that are resistant to outliers and clustering. Some of the well-known standardization methods such as mean, median, standard deviation, range, Huber’s estimate, Tukey’s biweight estimate, and Andrew’s wave estimate are available in the STDIZE procedure.

The SAS/STAT standardization procedures include the following:

STDIZE Procedure


The STDIZE procedure standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure. The following are highlights of the STDIZE procedure's features:

  • provides 18 standardization methods
  • enables you to multiply each standardized value by a constant and add a constant
  • finds quantiles in one pass of the data
  • performs BY group processing, which enables you to obtain separate analyses on grouped observations
  • performs weighted standardization
For further details, see STDIZE Procedure