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jhimelic's avatar
jhimelic
Frequent Visitor
6 years ago
Solved

norm.inv

i have 8760 data for multiple units for multiple variables extending across multiple years. 

 

I am trying to calculate the p50, p5 and the p95 for a particular unit (e.g. unit_1), a particular variable (e.g. renewable production), and particular time period. In this case i am interested in hourly averages by month for a particular year (i.e. a 12x24 grid). 

 

i filter the data appropriately to isolate the unit and variable of interest. i then assign a data hierarchy and i can get the YMH layout as desired. i then create two measures, one for the average (Avg_RE) and one for the standard deviation (STDEV_RE), and activate them in the table and I get the following table. so far, so good. 

 

however, when i try to calculate the p5 and add this as a column alongside the other data streams, i am running into problems.

 

when i enter in: norm.inv(.05,avg_re, stdev_re)

 

rather than getting the P5 value for that particular hour and month in that year, i get a single result back. how can i write this function to get the P5 to show as a column across the hours, months, and years just like it shows for the other measures? I would take the same approach for the P95 calculation (adjusting the norm.inv inputs as appropriate). 

 

thank you. 

  • Hi jhimelic ,

     

    You may try to create "norm.inv(.05,avg_re, stdev_re) "  using a measure instead of calculated column.

     

    Best Regards,

    Amy 

     

    Community Support Team _ Amy

    If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

1 Reply

  • v-xicai's avatar
    v-xicai
    Community Support

    Hi jhimelic ,

     

    You may try to create "norm.inv(.05,avg_re, stdev_re) "  using a measure instead of calculated column.

     

    Best Regards,

    Amy 

     

    Community Support Team _ Amy

    If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.