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linéarisation of a quasi-linear function with logarithmic scale and logarithmic axis.

ESAB
10-Marble

linéarisation of a quasi-linear function with logarithmic scale and logarithmic axis.

Hello,

I’m trying to linearize this type of curve using the simplest possible function:

linLogLog.png

 

I tried many kinds of fits, but none of them worked.

Do you have any idea how to do that?

thank you, Emilien

ACCEPTED SOLUTION

Accepted Solutions
Werner_E
25-Diamond I
(To:ESAB)

Are you looking for something like this:

Werner_E_1-1764004193792.png

 

BTW, not sure if it makes any sense, but a sinusoidal seems to make a nice fit (in the range given)

Werner_E_0-1764005618280.png

 

Prime 11 file attached

View solution in original post

3 REPLIES 3
Werner_E
25-Diamond I
(To:ESAB)

Are you looking for something like this:

Werner_E_1-1764004193792.png

 

BTW, not sure if it makes any sense, but a sinusoidal seems to make a nice fit (in the range given)

Werner_E_0-1764005618280.png

 

Prime 11 file attached

ESAB
10-Marble
(To:Werner_E)

Hi Werner,


That's exactly what I wanted — a perfect fit! I've never found this kind of solution before.


Thank you very much.

Werner_E
25-Diamond I
(To:ESAB)

If the graph in the log-log-plot should be a straight line we know that the function must be y=a*x^b.
But its better to fit the logarithmic data to a linear function rather than fitting the original data to a power function. Fitting the original data would mean that the deviation from the first values (order of magnitude 10^6, 10^5) has a much greater impact on the calculation of the total error than the deviation from the last values (10, 1). Therefore, the solution (see the file I posted) is almost exclusively just the connection of the first two data points and the rest is virtually ignored.

 

EDIT:

Here is a way to get a pretty good fit using the original data and a power function. The method is to minimize the relative error.

The outcome is (function f5) is a bit different from the result of the linear regression of the log data (Function f2) and on optical inspection I would say that f2 still is the better fit. But that may depend on the specific needs you have.

Werner_E_0-1764094165096.png

 

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