## Goodness of fits: one model, different datasets [General Statistics]

Hi all,

I have some different datasets that are not large at all, and I am doing some model fits on them and from that I am extracting a correlation measure like r squared etc. For the sake of simplicity let us just assume there in a single model like a linear regression without weights.

I would like to compare apples and pears, or at least the fits of three different datasets A, B and C. These datasets do not have the same number of data points, so I cannot fairly compare e.g. r squared of A vs B vs C directly.

Akaike and Schwarz are presumably not the way to go, I think, as I am not varying the model but the dataset, so to say. Kolmogorov-Smirnoff would potentially be useful if I had a boatload of points, which I don't anyway. I am very poor at explaining what I think I am looking for but I would call it a "fit likelihood" or "correlation statistic that is sample size corrected" . Google and Wikipedia aren't my friends in this regard (although on all other matters, including politics, religion, science and baking recipes G. and W. are always providing the right answers).

Does anyone here know of a handy statistic that allows a fair comparison of goodness of fits across datasets with unequal sizes, given a

Muchas gracias.

I have some different datasets that are not large at all, and I am doing some model fits on them and from that I am extracting a correlation measure like r squared etc. For the sake of simplicity let us just assume there in a single model like a linear regression without weights.

I would like to compare apples and pears, or at least the fits of three different datasets A, B and C. These datasets do not have the same number of data points, so I cannot fairly compare e.g. r squared of A vs B vs C directly.

Akaike and Schwarz are presumably not the way to go, I think, as I am not varying the model but the dataset, so to say. Kolmogorov-Smirnoff would potentially be useful if I had a boatload of points, which I don't anyway. I am very poor at explaining what I think I am looking for but I would call it a "fit likelihood" or "correlation statistic that is sample size corrected" . Google and Wikipedia aren't my friends in this regard (although on all other matters, including politics, religion, science and baking recipes G. and W. are always providing the right answers).

Does anyone here know of a handy statistic that allows a fair comparison of goodness of fits across datasets with unequal sizes, given a

*single*model??Muchas gracias.

—

Best regards,

ElMaestro

"(...) targeted cancer therapies will benefit fewer than 2 percent of the cancer patients they’re aimed at. That reality is often lost on consumers, who are being fed a steady diet of winning anecdotes about miracle cures." New York Times (ed.), June 9, 2018.

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Best regards,

ElMaestro

"(...) targeted cancer therapies will benefit fewer than 2 percent of the cancer patients they’re aimed at. That reality is often lost on consumers, who are being fed a steady diet of winning anecdotes about miracle cures." New York Times (ed.), June 9, 2018.

### Complete thread:

- Goodness of fits: one model, different datasets - ElMaestro, 2017-10-06 23:01 [General Statistics]
- Goodness of fits: one model, different datasets - nobody, 2017-10-07 16:03
- Experimental setup, details - ElMaestro, 2017-10-07 18:06
- Visualization - ElMaestro, 2017-10-07 19:07
- multiple regression? - Helmut, 2017-10-08 17:17
- just y=ax+b - ElMaestro, 2017-10-08 17:30
- just y=ax+b - Helmut, 2017-10-08 17:35
- just y=ax+b - ElMaestro, 2017-10-08 17:50
- just y=ax+b - nobody, 2017-10-08 20:26
- ANCOVA with R? - yjlee168, 2017-10-08 21:28
- just y=ax+b - DavidManteigas, 2017-10-09 10:34
- just y=ax+b - nobody, 2017-10-09 10:45

- just y=ax+b - Helmut, 2017-10-10 18:15

- just y=ax+b - ElMaestro, 2017-10-08 17:50

- just y=ax+b - Helmut, 2017-10-08 17:35

- just y=ax+b - ElMaestro, 2017-10-08 17:30

- Experimental setup, details - ElMaestro, 2017-10-07 18:06

- Goodness of fits: one model, different datasets - nobody, 2017-10-07 16:03