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SUBMIT YOUR IDEAMy Solution:
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Learnt a lot from this one. Can deffo see myself using this at work in the near future.
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Hi,
This one was definitely tougher for me (mostly because it took me a few minutes to understand the task).
My solution is similar to many other replies (I did use the data cleansing tool in addition). I am sure there is a quicker way to get the result but this is what I got.
V
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Confounded me - Till I saw the setting on the multi-row formula to make rows values closest to existing for those that do not exist. I would have expected averages to work out no matter what pervious rows were.
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Challenge #3 - Done!
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Didn't impute null values like the official solution so it didn't have the same ramp up, ramp down values which I think gives a better result.
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Hi,
Please see attached for my solution workflow. One comment - I did some testing to compare my output with the suggested output, and it seems like the one provided includes some incorrect values. For example, if you look at RecordID #73 in the r6mo_d_CAGI field, then compare that to the supporting data, something seems off. Please let me know if that makes sense or if I can provide more details.
Lastly, is there a way I can check which challenges I've been given credit for?
Thanks!
Johannes
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Hello,
Can anyone share how to derive the calculation for the below?
Month: Value: Running 3 mo. Average:
1 218 218
2 200 212
3 272 233
I am confused because shouldn't it be the following?
Month: Value: Running 3 mo. Average:
1 218 218 (218/1=218)
2 200 212 (218+200/2=209)
3 272 233 (218+200+272/3=230)
In the output of the answer, I also see below which is confusing.
Month: Value: Running 3 mo. Average (r3mo_u_CAGI):
1 218 218
2 200 212
3 272 230
Please help to understand the calculation. Thanks.