What most people gloss over is that standard correlation / regression can't be used for measurement analysis. in standard regression the 'cause' or input factor (x-axis) is taken to be a true value and the variation is all in the output Y value. so the standard deviation of interest is the variation only in the Y value (along the axis). In measurement comparisons both values are variable, so the standard deviation of interest (the measurement error) is on a vector that is perpendicular to a 45 degree line drawn thru the origin. (if there is no measurement error, the points will fall on the 45 degree 1:1 line)

You can use

Deming regression, (in Minitab, its called Orthogonal Regression) which takes the variation in the input factor into account, but you still have a problem with interpretation.

In the ideal situation, the equation

*should be* Y = X. That is, the intercept should be zero and the slope should be 1. In reality, you get an intercept that is non-zero and a slope not equal to 1. How do you determine whether this is acceptable or not?

Bottom line, the methods discussed above are better analytical and decision tools than regression.

Yes, this is necromancy of an old thread, but someone thanked my post and made me re-read the thread.