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Anonymous's avatar
Anonymous
Not applicable
2 years ago

Dynamically select column based on slicer for regression/correlation calculation

Hi, I have approx 400 columns & thousand of rows. All column have numerical value.  My Requirement is that, Two slicers which select dynamically two different column that will represent as x & y-ax...
multiple column
Regression
Slicer
Anonymous's avatar
Anonymous
Not applicable
2 years ago

Hi tamerj1,
Great solution that you provided.
Also I need some suggesions. I am explaining what I did.
I used header as x-axis & y-axis. Then created measures using SWITCH DAX for column selection at both axis. Now, Simply I used craydec regression chart which is working fine, showing equation perfect but in pictorial view. So that finally we have to use LINEST/LINESTX dax.
So, can we create LINEST/LINESTX DAX using measures, otherway what you did.

 

  

tamerj1's avatar
tamerj1
Icon for Community Champion rankCommunity Champion
2 years ago

Anonymous 

Yes probably you can but

1. I cannot 100% confirm without having the sample file. You share a download link of it.

2. Not sure if having a switch statement of 400 columns is practical. What happens if you add or delete columns? The code would break. 

  • Anonymous's avatar
    Anonymous
    Not applicable
    2 years ago

    Hi tamerj1 ,
    One more small thing needs to know, is it option in field parameterto to modify/add more field in case of addition more field in data base. Also can we select all column of table at a time  or have to select one by one.

    • tamerj1's avatar
      tamerj1
      Icon for Community Champion rankCommunity Champion
      2 years ago

      Anonymous 

      That needs to be done manually with field parameters. The advantage of field parameters is that they are simple to create and and are aboe to dynamicaly change the tye display name as per the selected column or measure. However, I believe other solution that utilizes DAX to create something similar to a parameter table could be possible but way more complicated.