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### Weird resulting values with an exponential regression

I have this model:

var('a,b') R = [[100, 0.0489], [110, 0.0633], [120, 0.1213],[130, 0.1244], [140, 0.1569], [150, 0.1693], [160, 0.3154], [170, 0.6146], [180, 0.9118], [190, 01.7478], [200, 2.4523], [210, 4.7945], [230, 17.9766], [240, 29.3237], [250, 52.4374], [260, 94.6463], [270, 173.3447], [280, 396.0443], [290, 538.6976], [300, 1118.9984], [310, 1442.3694], [320, 4151.9089], [330, 6940.7322]] model(x) = aexp(bx) find_fit(R,model)

(which is the results of factorization time of an algorithm for people interested)

But I get weird values, obviously not in accordance with the points, when plotting all this. I was extremely surprised. That's what I get: [a=(2.3863359829×10−15),b=1.0000000003]

Then I had the idea to try to get a formula with LibreOffice calc, just to have something to compare (I wasn't about to do this work on libreoffice initially), the result is different, but still so weird. In both cases, if I apply the formula given, it returns unthinkable values (very high).

Seems to be a problem with these values, no? Impossible to compute? Does sb have a solution? Thank you.

### Weird resulting values with an exponential regression

I have this model:

var('a,b') R = [[100, 0.0489], [110, 0.0633], [120, 0.1213],[130, 0.1244], [140, 0.1569], [150, 0.1693], [160, 0.3154], [170, 0.6146], [180, 0.9118], [190, 01.7478], [200, 2.4523], [210, 4.7945], [230, 17.9766], [240, 29.3237], [250, 52.4374], [260, 94.6463], [270, 173.3447], [280, 396.0443], [290, 538.6976], [300, 1118.9984], [310, 1442.3694], [320, 4151.9089], [330, 6940.7322]] 6940.7322]]

model(x) = aexp(bx) x)

find_fit(R,model)

(which is the results of factorization time of an algorithm for people interested)

But I get weird values, obviously not in accordance with the points, when plotting all this. I was extremely surprised. That's what I get: [a=(2.3863359829×10−15),b=1.0000000003]

Then I had the idea to try to get a formula with LibreOffice calc, just to have something to compare (I wasn't about to do this work on libreoffice initially), the result is different, but still so weird. In both cases, if I apply the formula given, it returns unthinkable values (very high).

Seems to be a problem with these values, no? Impossible to compute? Does sb have a solution? Thank you.

### Weird resulting values with an exponential regression

I have this model:

var('a,b') R = [[100, 0.0489], [110, 0.0633], [120, 0.1213],[130, 0.1244], [140, 0.1569], [150, 0.1693], [160, 0.3154], [170, 0.6146], [180, 0.9118], [190, 01.7478], [200, 2.4523], [210, 4.7945], [230, 17.9766], [240, 29.3237], [250, 52.4374], [260, 94.6463], [270, 173.3447], [280, 396.0443], [290, 538.6976], [300, 1118.9984], [310, 1442.3694], [320, 4151.9089], [330, 6940.7322]]

model(x) = aexp(bx)

find_fit(R,model)

(which is the results of factorization time of an algorithm for people interested)

But I get weird values, obviously not in accordance with the points, when plotting all this. I was extremely surprised. That's what I get: [a=(2.3863359829×10−15),b=1.0000000003][a=(2.3863359829×10^−15),b=1.0000000003]

Then I had the idea to try to get a formula with LibreOffice calc, just to have something to compare (I wasn't about to do this work on libreoffice initially), the result is different, but still so weird. In both cases, if I apply the formula given, it returns unthinkable values (very high).

Seems to be a problem with these values, no? Impossible to compute? Does sb have a solution? Thank you.

 4 None John Palmieri 6582 ●18 ●57 ●146 http://www.math.washin...

### Weird resulting values with an exponential regression

I have this model:

var('a,b')
R = [[100, 0.0489], [110, 0.0633], [120, 0.1213],[130, 0.1244], [140, 0.1569], [150, 0.1693], [160, 0.3154], [170, 0.6146], [180, 0.9118], [190, 01.7478], [200, 2.4523], [210, 4.7945], [230, 17.9766], [240, 29.3237], [250, 52.4374], [260, 94.6463], [270, 173.3447], [280, 396.0443], [290, 538.6976], [300, 1118.9984], [310, 1442.3694], [320, 4151.9089], [330, 6940.7322]] 6940.7322]]

model(x) = aexp(bx) find_fit(R,model)a*exp(b*x)

find_fit(R,model)


(which is the results of factorization time of an algorithm for people interested)

But I get weird values, obviously not in accordance with the points, when plotting all this. I was extremely surprised. That's what I get: [a=(2.3863359829×10^−15),b=1.0000000003]

get:

[a=(2.3863359829×10^−15),b=1.0000000003]


Then I had the idea to try to get a formula with LibreOffice calc, just to have something to compare (I wasn't about to do this work on libreoffice initially), the result is different, but still so weird. In both cases, if I apply the formula given, it returns unthinkable values (very high).

Seems to be a problem with these values, no? Impossible to compute? Does sb have a solution? Thank you.