Explore Outliers - Updated in JMP 16

[#1]

See how to use the Explore Outliers utility, with emphasis on finding missing values and working with the Nines report and High Nines (missing) values. JMP 16 updates include:

• New Robust PCA Outliers utility (which replaces Multivariate Robust Outliers) is included in the Explore Outliers platform 
• Multivariate Robust Outliers continue to be supported by JSL only and cannot be accessed through the menus.
• Robust PCA identifies cells, continues to respect the multivariate structure of the data, allows missing values, and is much faster than the Huber Mahalanobis feature used in previous JMP versions.
• Robust PCA supports creating a cleaned set of new columns, with imputed values for missing, and variously truncated values for the outliers.

Resources

• Ledi Trutna's @ledi_trutna 's JMP On Air session on exploring outliers
•  Examples of all the Explore Outliers options

[wendytseng]

Updated link for Ledi's JMP On Air session: Exploring Outliers in Your Data 

[PatrickGiuliano]

Thanks for the link share @wendytseng  and for the intro video @ryandewitt.  

Is there some rationale that we can point to for why the default values for Tail Quantile and Q, are 0.1, and 3, respectively? 

From the JMP 16 Online Help Documentation: https://www.jmp.com/support/help/en/16.2/#page/jmp/quantile-range-outliers.shtml

Classically we know that values that fall beyond Q = 1.5 times the interquartile range past the Tail Quantile (or 1 minus the Tail Quantile) are identified as outliers, where the Tail Quantile is = 0.25 on the upper bound and 1 - 0.25 = 0.75 on the lower bound.  This is the basis for the Outlier Box Plot: