Changing sign of data (regression)

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SUMMARY

The discussion centers on the justification for flipping the signs of data points in an experimental dataset modeled by an exponential function. The user successfully transformed their data, achieving an R² value of 0.99971, indicating a strong fit to the exponential model after adjusting the signs of the first six data points. This method is justified when the experimental setup, such as an MRI device, only measures absolute values, necessitating the sign reversal to obtain the true values of magnetization.

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roam
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Homework Statement



I was trying to fit experimental data which are theoretically modeled by an exponential function. They are shown in the first graph below. For the first set (red line), the data does not appear to be exponential. My lecturer once said that I need to flip the signs of the first few data points to get an exponential (see the second plot).

431qBiJ.png


Is there any justification for doing this? Are there any situations where such a thing would be appropriate?

This data relate to the absolute values of magnetisation in an MRI experiment.

Homework Equations


3. The Attempt at a Solution [/B]

When I flipped the signs of the first six data points, I got a curve which could then be modeled with an exponential, just like the other data sets. I got R2 = 0.99971, and the time constant I calculated from this was in agreement with the expected value.

So, why does this method work? Could there be any justification for doing this?

Any help would be greatly appreciated.
 
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Without knowing what your experimental setup is and what exactly is it that you are fitting to, we can't really tell with certainty. However, my suspicion is that your experimental device is only able to measure the absolute value of the quantity (or its square, since I see intensity in your graph there), which is why you have to "reverse" the sign to recover the actual value of the quantity possessed by the system.
 
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