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Suppose one has a set of numbers. To help understand my question, suppose that these numbers are from two different temperature sensors. In this first example, both sensors are placed in the same environment and should read the same temp

Col 1     Col 2 10        10 20        19 30        29 20        20 20        19 30        30 20        19 10        9 20        20 30        28 

Since the sensors are in the same environment, they should read the same, but they don't so I need to correct for their offset. To calculate a correction factor between these two sets of numbers, so that column 2 is as equal to col 1 as possible, I do a regression analysis. For a linear regression the equation would be:

y=0.8041x + 3.7143 

or

Col 2= 0.8041 * Col 1 + 3.7143 

Now suppose I have a second set of numbers. In this second example the numbers represent the same sensors, but this time they are placed in different environments. So I expect them to read differently, but I also expect them to retain the same error I calculated above

Col 3     Col 4 11        10 21        19 30        27 20        20 21        19 30        25 20        18 11        15 20        20 30        25 

My question is- is there a way to apply the same correction factor calculated from the first set of numbers to the second set? To be more specific, I am not looking to do this:

Col 4= =0.8041* Col 3 + 3.7143 

and get this

Col 3     Col 4 (new based on regression) 11      12.5 21      20.6 30      27.8 20      19.7 21      20.6 30      27.8 20      19.7 11      12.5 20      19.7 30      27.8 

as I loose all information about the original column 4. I am hoping to find a way to use the correction factor from Col 1 and Col 2 as a "calibration", and apply it to Column 4 in a way that retains the original information in that column but adjusts it to reflect the calibration equation.

If I assume Col 3 is correct and Col 4 is off, I was thinking the equation would look something like this

Corrected Col 4= Col 4 * (??Correction factor??)

2 Answers 2