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 wellknown 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:
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:

