Linear Regression to predict values

Hello, 

I am trying to predict future values based on historical data. The new values are displayed in a matrix visual with future dates as the rows. Here is my code: 

linReg = 
VAR Known =
    FILTER (
        SELECTCOLUMNS (
            ALLSELECTED (' Claims This Year'[ Date] ),
            "Known[X]", 'Claims This Year'[ Date],
            "Known[Y]", [totalAmount]
        ),
        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 Intercept + Slope * [monthNumber]

I have a feeling it has something to do with the ALL SELECTED part because the historical dates are not in the visual but I am not sure. 

Also, please advise on how to counter blanks in the historical data matrix (Dates = rows, type = columns, claimTotal= values) being counted as nulls instead of 0. This is changing my slope. 

Thank you!!