Don't miss your chance to take the Fabric Data Engineer (DP-700) exam on us!
Learn moreWe've captured the moments from FabCon & SQLCon that everyone is talking about, and we are bringing them to the community, live and on-demand. Starts on April 14th. Register now
| Name | Marks |
| A | 10 |
| A | 20 |
| A | 30 |
| A | 40 |
| A | 50 |
| A | 60 |
PERCENTILE.INC(Table[Marks],0.25) =22.5
PERCENTILE.EXC(Table[Marks],0.25) =17.5
I would like to determine Q1 from dataset. Q1 should be 20.
Why do the results produced by DAX above not equivalent to 20?
Hi,
Even in MS Excel, the answer is 22.5. So that answer seems correct.
Excel and Power BI share same function. Needless to say, both yield the same output.
If we were going to determine Q1 using conventional method, we will be sorting the group of data in ascending order (in this case, 10,20,30,40,50,60).
Q1 is deemed the half of half of dataset. Therefore, the first half is "10,20,30.". Subsequently, we further dive into second half between 10 and 30, yielding 20 as Q1 value.
Hi @Nigel99,
I'd like to suggest you take a look at the following link about PERCENTILE functions if it help you to understand these functions and usages:
Regards,
XIaoxin Sheng
Unfortunately, the video does not seem to explain the function in details.
Excel and Power BI share same function. Needless to say, both yield the same output.
If we were going to determine Q1 using conventional method, we will be sorting the group of data in ascending order (in this case, 10,20,30,40,50,60).
Q1 is deemed the half of half of dataset. Therefore, the first half is "10,20,30.". Subsequently, we further dive into second half between 10 and 30, yielding 20 as Q1 value. This baffles me as both outputs are contradicting to each other.
If you have recently started exploring Fabric, we'd love to hear how it's going. Your feedback can help with product improvements.
A new Power BI DataViz World Championship is coming this June! Don't miss out on submitting your entry.
Share feedback directly with Fabric product managers, participate in targeted research studies and influence the Fabric roadmap.
| User | Count |
|---|---|
| 55 | |
| 34 | |
| 32 | |
| 19 | |
| 17 |
| User | Count |
|---|---|
| 75 | |
| 72 | |
| 38 | |
| 35 | |
| 25 |