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I have two datasets:

dataset1 = {{3514147200, 5.83}, {3514233600, 7.48}, {3514320000, 7.86}, {3514406400, 6.74}}
dataset2 = {{2407708800, 131.3}, {2407795200, 131.7}, {2407881600, 130.9}, {2407968000, 131}}

By down sampling to get the datasets to be the same "length", I know that there is a correlation of approximately 0.95 between the two datasets.

I have created a very large polynomial from dataset2 to approximate dataset1.

polynomial[x_] := -1.19937*10^11 - 16.2653 x - 9.10704*10^-10 x^2 + 4.74474*10^-19 x^3 + ...

When polynomial and dataset1 are plotted together, I can see that the polynomial is close to the data. I am looking for a way to measure the "distance" between the dataset and the polynomial. I tried using KolmogorovSmirnovTest but it wasn't much help, any ideas about how to do this?

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    $\begingroup$ Can you clarify what kind of data you have? "Dataset" in itself is not descriptive. $\endgroup$
    – Szabolcs
    Commented Jul 14, 2014 at 16:39
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    $\begingroup$ Another thing you might want to clarify is what you mean by "distance". If you are asking about what is a good distance measure, it probably depends on the applications you have in mind, and it's not a question about Mathematica. stats.stackexchange.com is likely a better place to ask. If you already know what distance measure you need, then asking about how to compute it is appropriate here. $\endgroup$
    – Szabolcs
    Commented Jul 14, 2014 at 16:47
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    $\begingroup$ So you have $(x_i,y_i)$ pairs that could be considered to approximate a function. If the domain of the two curves is the same, you could build an interpolation function from them (Interpolation) and NIntegrate the square of their difference. $\endgroup$
    – Szabolcs
    Commented Jul 14, 2014 at 16:52
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    $\begingroup$ just fyi a high order polynomial is usually a terrible way to approximate data. You will tend to hit the points well and deviate wildly in between. $\endgroup$
    – george2079
    Commented Jul 14, 2014 at 18:06
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    $\begingroup$ Have you considered functions like Fit and NonlinearModelFit? $\endgroup$
    – Michael E2
    Commented Jul 14, 2014 at 19:37

1 Answer 1

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dataset1 = Table[{x1[n], y1[n]}, {n, 5}];

poly2[x_] = a*x^2 + b*x + c;

rms1 = Norm[(poly2 /@ dataset1[[All, 1]]) - dataset1[[All, 2]]]/
   Sqrt[Length[dataset1]];

rms2 = RootMeanSquare[(poly2 /@ dataset1[[All, 1]]) - 
    dataset1[[All, 2]]];

(rms1 // ComplexExpand) == rms2

True

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