Skip to main content
cancel
Showing results for 
Search instead for 
Did you mean: 

Enhance your career with this limited time 50% discount on Fabric and Power BI exams. Ends August 31st. Request your voucher.

Reply
ChPetru
Helper I
Helper I

Find the Power Law (or log) relationship between two variables

Hello,

 

I am trying to find a constant (equation) that describes the non-linear proportionality between two variables (below).

I need this constant because I am multiplying the second (Y) variable with a number, and want to see the corresponding X values.

 

Example: Y is 84,91% and X is 0.03. What would X be if Y would be 86%?

I can calculate this in excel by making a power law graphical trendline and use the trendline the equation as multiplier with Y values to get aproximate X.

 

Second question, how can I make a power law or log trendline in Powerbi? I can only see the linear trendline..

 

         X                            Y

ChPetru_0-1647248627604.png

 

1 ACCEPTED SOLUTION
PiEye
Resolver II
Resolver II

Hi, there isn't a default equation in DAX or setting in the line chart that will help you do that. There is, however a lot of guidance online that can tell you how to calculate a straight line gradient / regression. See this great example here: https://xxlbi.com/blog/simple-linear-regression-in-dax/.

This can be applied to the logs.

 

The logs of your X / Y values have a linear relationship which looks like this:

PiEye_0-1647257471660.png

 

Using the guidance in the blog, I can apply the linear logic to those logs and calculate a straight line relationship:

PiEye_1-1647257541488.png

 

 

Applying I can then reverse this estimated log of Y back into a predicted value for Y and fit a curve on the XY chart:

PiEye_2-1647257615792.png

 

 

My expressions are as follows:

EstY = 10^('Table'[EstLogY])

EstLogY = 
VAR Known =
    FILTER (
        SELECTCOLUMNS (
            ALLSELECTED ( 'Table' ),
            "Known[X]", 'Table'[logX],
            "Known[Y]", 'Table'[logY]
        ),
        AND (
            NOT ( ISBLANK ( Known[X] ) ),
            NOT ( ISBLANK ( Known[Y] ) )
        )
    )
VAR Count_Items =
    COUNTROWS ( Known )
VAR Sum_X =
    SUMX ( Known, Known[X] )
VAR Sum_X2 =
    SUMX ( Known, Known[X] ^ 2 )
VAR Sum_Y =
    SUMX ( Known, Known[Y] )
VAR Sum_XY =
    SUMX ( Known, Known[X] * Known[Y] )
VAR Average_X =
    AVERAGEX ( Known, Known[X] )
VAR Average_Y =
    AVERAGEX ( Known, Known[Y] )
VAR Slope =
    DIVIDE (
        Count_Items * Sum_XY - Sum_X * Sum_Y,
        Count_Items * Sum_X2 - Sum_X ^ 2
    )
VAR Intercept =
    Average_Y - Slope * Average_X
RETURN 
   SUMX (
        DISTINCT ( 'Table'[Index]),
        Intercept + Slope * 'Table'[lnX]    )



 

Hope this helps!

 

Pi Eye 🙂

View solution in original post

4 REPLIES 4
PiEye
Resolver II
Resolver II

Hi, there isn't a default equation in DAX or setting in the line chart that will help you do that. There is, however a lot of guidance online that can tell you how to calculate a straight line gradient / regression. See this great example here: https://xxlbi.com/blog/simple-linear-regression-in-dax/.

This can be applied to the logs.

 

The logs of your X / Y values have a linear relationship which looks like this:

PiEye_0-1647257471660.png

 

Using the guidance in the blog, I can apply the linear logic to those logs and calculate a straight line relationship:

PiEye_1-1647257541488.png

 

 

Applying I can then reverse this estimated log of Y back into a predicted value for Y and fit a curve on the XY chart:

PiEye_2-1647257615792.png

 

 

My expressions are as follows:

EstY = 10^('Table'[EstLogY])

EstLogY = 
VAR Known =
    FILTER (
        SELECTCOLUMNS (
            ALLSELECTED ( 'Table' ),
            "Known[X]", 'Table'[logX],
            "Known[Y]", 'Table'[logY]
        ),
        AND (
            NOT ( ISBLANK ( Known[X] ) ),
            NOT ( ISBLANK ( Known[Y] ) )
        )
    )
VAR Count_Items =
    COUNTROWS ( Known )
VAR Sum_X =
    SUMX ( Known, Known[X] )
VAR Sum_X2 =
    SUMX ( Known, Known[X] ^ 2 )
VAR Sum_Y =
    SUMX ( Known, Known[Y] )
VAR Sum_XY =
    SUMX ( Known, Known[X] * Known[Y] )
VAR Average_X =
    AVERAGEX ( Known, Known[X] )
VAR Average_Y =
    AVERAGEX ( Known, Known[Y] )
VAR Slope =
    DIVIDE (
        Count_Items * Sum_XY - Sum_X * Sum_Y,
        Count_Items * Sum_X2 - Sum_X ^ 2
    )
VAR Intercept =
    Average_Y - Slope * Average_X
RETURN 
   SUMX (
        DISTINCT ( 'Table'[Index]),
        Intercept + Slope * 'Table'[lnX]    )



 

Hope this helps!

 

Pi Eye 🙂

Thank you!!

Welcome!

Greg_Deckler
Community Champion
Community Champion

@ChPetru Well, I believe the formula for Power Law is:

n = (logT2 - logT1) / (logM2 - logM1) is that what you are looking for? Here is a good paper on the math involved. 

LR05/PowerLaw (pomona.edu)

 

You also might consider Runge-Kutta for finding unknown values: 

https://community.powerbi.com/t5/Quick-Measures-Gallery/Runge-Kutta/m-p/411280#M149

 



Follow on LinkedIn
@ me in replies or I'll lose your thread!!!
Instead of a Kudo, please vote for this idea
Become an expert!: Enterprise DNA
External Tools: MSHGQM
YouTube Channel!: Microsoft Hates Greg
Latest book!:
DAX For Humans

DAX is easy, CALCULATE makes DAX hard...

Helpful resources

Announcements
July PBI25 Carousel

Power BI Monthly Update - July 2025

Check out the July 2025 Power BI update to learn about new features.

Join our Fabric User Panel

Join our Fabric User Panel

This is your chance to engage directly with the engineering team behind Fabric and Power BI. Share your experiences and shape the future.

June 2025 community update carousel

Fabric Community Update - June 2025

Find out what's new and trending in the Fabric community.