Trend analysis using linear regression gradient per category

Hi PowerBIUserX 

You can refer to the following solution.

Sample data 

I create a sample measure

Value_measure = SUM('Table'[Value]) 

Then create a measure

Lines =
VAR _minxy =
    MINX (
        FILTER ( ALLSELECTED ( 'Table' ), [Category] IN VALUES ( 'Table'[Category] ) ),
        [Value_measure]
    )
VAR _maxy =
    MAXX (
        FILTER ( ALLSELECTED ( 'Table' ), [Category] IN VALUES ( 'Table'[Category] ) ),
        [Value_measure]
    )
VAR x_minxy =
    MINX (
        FILTER (
            ALLSELECTED ( 'Table' ),
            [Category]
                IN VALUES ( 'Table'[Category] )
                    && [Value_measure] = _minxy
        ),
        MONTH ( [Date] )
    )
VAR x_maxy =
    MINX (
        FILTER (
            ALLSELECTED ( 'Table' ),
            [Category]
                IN VALUES ( 'Table'[Category] )
                    && [Value_measure] = _maxy
        ),
        MONTH ( [Date] )
    )
RETURN
    DIVIDE ( _maxy - _minxy, x_maxy - x_minxy )

Best Regards!

Yolo Zhu

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Hi Anonymous ,

Thank you for your reply. While it works on the limited dataset I initially provided, I do not think the solution works in all cases.
If I understand correctly, the measures you suggested calculate the slope [ (y2-y1)/(x2-x1) ] of the maximum point and minimum point in the dataset, but not the slope of the regression. In the dataset it works because they are already linear functions.

On the following dataset (non-linear function), the slope I am expected is -3.5%, but your measure returns -45%.

Do you have an idea on how to achieve the same but with the slope of the linear regression?