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Comment: Published by Scroll Versions from space DEV and version r0811

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Before you begin performing analytics on a dataset, it is important to identify and recognize outlier data patterns and values. Unusual values or patterns in the data can be sources for the following:

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  • Let them be. If the data is valid, do not remove it unless you have an explicit reason for doing so. 


  • Convert to more meaningful values. You can use the set transform to change outlier values to values that are valid for purposes of analysis.  

    Info

    NOTE: Please be aware that changing of values may impact the validity of your statistical analysis.

    Example of overwriting values where values in the col_numbers column that are below 25 are set to the average value for the column. Otherwise, use the current value:

    D trans
    Typestep
    p01NameColumns
    p01Valuecol_numbers
    p02NameFormula
    p02ValueIF((col_numbers < 25), AVERAGE(col_numbers), col_numbers)
    SearchTermEdit column with formula


 

D s also
inCQLtrue
label(label = "discovery_tasks")