Correlation coefficient
The quick measure calculates the Pearson correlation coefficient between two measures within the category.
NAME:
Correlation coefficient
DESCRIPTION:
Calculate the Pearson correlation coefficient between two measures within the category
PARAMETERS:
Name: Category
Tooltip: The category in which you want to calculate the correlation coefficient
Type: Categorical field
Name: Measure X
Tooltip: The first measure in a correlation pair
Type: Numerical field / measure
Name: Measure Y
Tooltip: The second measure in a correlation pair
Type: Numerical field / measure
DAX:
Correlation Coefficient :=
VAR Correlation_Table =
FILTER (
ADDCOLUMNS (
VALUES ( {Category} ),
"Value_X", CALCULATE ( {Measure X} ),
"Value_Y", CALCULATE ( {Measure Y} )
),
AND (
NOT ( ISBLANK ( [Value_X] ) ),
NOT ( ISBLANK ( [Value_Y] ) )
)
)
VAR Count_Items =
COUNTROWS ( Correlation_Table )
VAR Sum_X =
SUMX ( Correlation_Table, [Value_X] )
VAR Sum_X2 =
SUMX ( Correlation_Table, [Value_X] ^ 2 )
VAR Sum_Y =
SUMX ( Correlation_Table, [Value_Y] )
VAR Sum_Y2 =
SUMX ( Correlation_Table, [Value_Y] ^ 2 )
VAR Sum_XY =
SUMX ( Correlation_Table, [Value_X] * [Value_Y] )
VAR Pearson_Numerator =
Count_Items * Sum_XY - Sum_X * Sum_Y
VAR Pearson_Denominator_X =
Count_Items * Sum_X2 - Sum_X ^ 2
VAR Pearson_Denominator_Y =
Count_Items * Sum_Y2 - Sum_Y ^ 2
VAR Pearson_Denominator =
SQRT ( Pearson_Denominator_X * Pearson_Denominator_Y )
RETURN
DIVIDE ( Pearson_Numerator, Pearson_Denominator )
18 Replies
Hello Daniil,
Thanks for this, this a great solution for correlation calculations on DAX.
I have tried the calculation with different measures and I will add the following improvement. In the following line, you could get a negative number.
VAR Pearson_Denominator = SQRT ( Pearson_Denominator_X * Pearson_Denominator_Y )I suggest this slight modification to run in all scenarios.
VAR Pearson_Denominator = SQRT(ABS( Pearson_Denominator_X * Pearson_Denominator_Y ))Regards,
Hi acanepa
Good programming practice would suggest avoiding a division by zero, however you need to think like a statistician - which can often be counter intuitive!
You actually want the Pearson Coefficient to "fail" when you divide by zero.
See this post for more info https://stackoverflow.com/questions/38548343/pearson-correlation-fails-for-perfectly-correlated-sets
From a DAX point of view the divide function will tolerate a division by zero.
Regards
Graeme
Very handy addition.
Are there, however, plans to add a measure/some other output feature that will also report on the uncertainty of the Correlation Coefficient calculated for a given series pair (i.e. implementing Fisher's z-transformation and evaluating the confidence interval at difference levels that the user chooses, or just a standard set of levels like 80%, 90 % and 95%)
The risk is that people could state (and frequently do state) correlation coefficients for insufficiently sized samples and derive insights that are actually attributable to noise etc.
Thanks for the awesome work! :smileyvery-happy:Thanks for the formula Daniil. I'm trying to use it to calculate the correlation coefficient on some data I have but I have hit an issue - I only have two columns of data, one showing the month name and one showing the volume (a measure).
I.e.
Month Volume
Jan-07 1,000
Feb-07 1,613
Mar-07 1,128
etc.
I don't have two measures as per your data nor do I have a column showing a 'category'. How would I be able to carry out the calculation for my data please? Do I need to create a new measure column based on the month name, coverting it into a numerical value? (just a guess). Then I would classify the month column as the 'category'?
Hope this is clear (still classing myself as a Power BI newbie!)
Regards,
Lee
PowerBI_77 please have a look at the second example in my Simple Linear Regression blog -- you can employ a similar technique here.
Also, in case people still read this -- acanepa said in a private message there "is not an error of the calculation you created but rather an error on my end to feed the formula with wrong numbers", so please ignore his comment about negative numbers.
Hello everyone,
A little bit stuck on this one... Can someone guide me?I have a data set with the following fields...
- Year
- Venue
- Event Length(in days)
- Average Event revenue per day
I want to see if there's a correlation between even length and event revenue per day. I thought I could use the year as the category, but I think this might be wrong. Using the Quick Measure, what Category, and 2 measures should I use to establish the hypotheses that "the longer the event, the more/less menoy per day is made"
Thanks!
This is wonderfully useful thanks Daniil
I've been trying to modify your DAX to use in a scenario where the variables to be correlated are pivoted and the categories are contained in an attribute column (example below). Sadly I'm not having much luck, does anyone have any suggestions on how to generate a correlation matrix with this data structure?Item Attribute Value A Value X 2 B Value X 3 C Value X 5 D Value X 7 A Value Y 0 B Value Y 1 C Value Y 1 D Value Y 2 Statistics only work with numbers. You need to add numerical key columns for your text columns before you can calculate the correlation.
Apologies for reviving an old question however I want to check your response to systemnova if you wouldn't mind.
Say I wanted to check the correlation between day of the week and temperature and had collected the below data:Index Category Temp 1 Monday 20 2 Monday 24 3 Monday 19 4 Wednesday 15 5 Wednesday 28 6 Wednesday 12 7 Wednesday 20 8 Friday 20 9 Friday 10 Are you saying I can swap the days of the week for a numerical key such as Monday = 1, Wednesday = 2 and Friday = 3 to give the below and then use this quick measure to produce meaningful results? Unfortunately I am still very new to stats and Power BI. Any direction would be greatly appreciated. Thanks.
Index Category Temp 1 1 20 2 1 24 3 1 19 4 2 15 5 2 28 6 2 12 7 2 20 8 3 20 9 3 10
Thank you so much for this! Helped out a lot and saved me plenty of time.
Sorry to revive an old thread, but I was wondering what the differences were in the DAX Linest and Linestx in calculating the coefficient of detemination. I was using the square for the Pearson coefficient to get R squared. It appears that I am getting different coefficient values between the two functions. I checked in excel and also was getting the same values as the Linest function. Would you happen to know if there are approximations in the Linest function vs your Pearson coefficient?
Can't see a reason for that, but is likely a question for jeffrey_wang
Linest is just a shortcut for LinestX with the <table> argument derived from the column references. The internal algorithm is the same.
