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I am trying to calculate the correlation coefficient using a quick measure. However I am kind of puzzled by the fields is requires.
I come from a R background and here we only send in 2 column to calculate the pearson coefficient. However the quick measure asks me for a third one, the category.
What is this category?
Solved! Go to Solution.
@Anonymous
You can use the Quick Measure after adding an Index column to your dataset if you do not have a column with unique values. This is required by the Quick Measure to calculate the correlation coefficient over a category.
If you really need to calculate with only two columns then find below the modified measure. I have also attached the PBIX file. Do verify the result from your model.
Age and Gluco correlation =
VAR __CORRELATION_TABLE = 'Table'
VAR __COUNT =
COUNTX(
KEEPFILTERS(__CORRELATION_TABLE),
CALCULATE(SUM('Table'[Age]) * SUM('Table'[Gluco]))
)
VAR __SUM_X = SUMX(KEEPFILTERS(__CORRELATION_TABLE), CALCULATE(SUM('Table'[Age])))
VAR __SUM_Y = SUMX(KEEPFILTERS(__CORRELATION_TABLE), CALCULATE(SUM('Table'[Gluco])))
VAR __SUM_XY =
SUMX(
KEEPFILTERS(__CORRELATION_TABLE),
CALCULATE(SUM('Table'[Age]) * SUM('Table'[Gluco]) * 1.)
)
VAR __SUM_X2 =
SUMX(
KEEPFILTERS(__CORRELATION_TABLE),
CALCULATE(SUM('Table'[Age]) ^ 2)
)
VAR __SUM_Y2 =
SUMX(
KEEPFILTERS(__CORRELATION_TABLE),
CALCULATE(SUM('Table'[Gluco]) ^ 2)
)
RETURN
DIVIDE(
__COUNT * __SUM_XY - __SUM_X * __SUM_Y * 1.,
SQRT(
(__COUNT * __SUM_X2 - __SUM_X ^ 2)
* (__COUNT * __SUM_Y2 - __SUM_Y ^ 2)
)
)
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Hi all, I have used the quick measure to calculate the correltion (r value), however, it has returned a value greater then 1. Has anyone come acorss this error in their calculations? Obviously they must be an error as an R vlaue can only be between -1 and 1.
Thanks
Rich
@Anonymous
You can use the Quick Measure after adding an Index column to your dataset if you do not have a column with unique values. This is required by the Quick Measure to calculate the correlation coefficient over a category.
If you really need to calculate with only two columns then find below the modified measure. I have also attached the PBIX file. Do verify the result from your model.
Age and Gluco correlation =
VAR __CORRELATION_TABLE = 'Table'
VAR __COUNT =
COUNTX(
KEEPFILTERS(__CORRELATION_TABLE),
CALCULATE(SUM('Table'[Age]) * SUM('Table'[Gluco]))
)
VAR __SUM_X = SUMX(KEEPFILTERS(__CORRELATION_TABLE), CALCULATE(SUM('Table'[Age])))
VAR __SUM_Y = SUMX(KEEPFILTERS(__CORRELATION_TABLE), CALCULATE(SUM('Table'[Gluco])))
VAR __SUM_XY =
SUMX(
KEEPFILTERS(__CORRELATION_TABLE),
CALCULATE(SUM('Table'[Age]) * SUM('Table'[Gluco]) * 1.)
)
VAR __SUM_X2 =
SUMX(
KEEPFILTERS(__CORRELATION_TABLE),
CALCULATE(SUM('Table'[Age]) ^ 2)
)
VAR __SUM_Y2 =
SUMX(
KEEPFILTERS(__CORRELATION_TABLE),
CALCULATE(SUM('Table'[Gluco]) ^ 2)
)
RETURN
DIVIDE(
__COUNT * __SUM_XY - __SUM_X * __SUM_Y * 1.,
SQRT(
(__COUNT * __SUM_X2 - __SUM_X ^ 2)
* (__COUNT * __SUM_Y2 - __SUM_Y ^ 2)
)
)
⭕ Subscribe and learn Power BI from these videos
⚪ Website ⚪ LinkedIn ⚪ PBI User Group
Hi @Anonymous ,
You can check this explanation here:
https://community.powerbi.com/t5/Quick-Measures-Gallery/Correlation-coefficient/td-p/196274
https://xxlbi.com/blog/pearson-correlation-coefficient-in-dax/
Thanks,
Pragati
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