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I have a student project in which I would like to analyze application usage data specifically between three user groups. I have log data gathered including unique User IDs, actions and the identifier for the user group they belong to. Ex:

UserID   Action   Group  
  0001        A       1  
  0002        B       1  
  0003        A       2  
  0001        A       1  
  0004        B       1  

My problem now lies within analyzing and visualizing this data ideally in a manner that takes into account the amount of unique users within the user group. In this example if I were to do try and find a correlation between user group and likelihood of performing a specific action I would only get the output that a user in group 1 is twice as likely to perform action A than a user in group 2. Of course that is not correct, since group 2 has less users than group 1.

I would like to use jmp to do this analysis and the closest I could get was a contingency analysis which output a mosaic graph. Unfortunately the graph cannot take into account how many users are within each group.

I'm certain there must be some simple way of doing it, but I was unable to come up with a solution so far.

Any help would be much appreciated.

Thanks!

Edit: removed the timestamps as they weren't relevant.

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  • $\begingroup$ How many possible actions & how many groups are there? Are you trying to incorporate the tome variable into the analysis? If so, how? $\endgroup$ – gung Jul 13 '15 at 14:35
  • $\begingroup$ Hi, actually time doesn't factor in yet (I need that for a later analysis), sorry for the confusion. There are 7 actions and 3 groups. $\endgroup$ – Grinner Jul 14 '15 at 12:33
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JMP has a "Freq" role option in each analysis launcher, and for contingency the frequency is accounted for in the mosaic plot. If that's not it and you want JMP-specific help, you can try the JMP Community forums.

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