@PaigeMiller If I'm understanding you correctly, the STD method in PROC STDIZE sets the cutoff for outliers at > mean + 3(std) or < mean – 3(std) and provides measures of location and scale that are "standardized" and thus resistant to outliers (Using whatever definition is associated w/ a given method).
When I use the data and syntax in the documentation, however, the mean and std is the same when using PROC UNIVARIATE (only the Extreme Observations table is provided in the documentation) and PROC STDIZE (Figure 109.3).
The extreme value in the example is 64 (Obs 23). 153.3 (mean w/ outlier) - 3(30.0667678)(std w/ outlier) = 61. So observation 23 does NOT meet the definition using the STD method, thus the mean is the same when using PROC UNIVARIATE and PROC STDIZE.
So I changed the value from 64 to 24, well below 61. When I do this, the standardized mean and std produced by PROC STDIZE are different from the unstandardized mean and std produced by PROC UNIVARIATE. This approach seems similar to trimming and winsorizing means, no?
The extreme values in PROC UNIVARIATE are helpful, but I don't know if they meet the definition of an outlier (as defined by me) unless I perform some calculations (e.g.,153.3 (mean w/ outlier) - 3(30.0667678)(std w/ outlier) = 63.1). I'm looking for an approach that tells me if certain values are outliers based on a definition I choose (similar to standardization methods in PROC STDIZE), so that I can evaluate those observations to determine what to do with them. It sounds like you think PROC STDIZE does this, but I don't see it in the documentation.
Thanks for your help.
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