How to test how well data points fit a curve

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I have generated a 1x100 prediction based on a set of observations. The prediction is in the form of a curve. I would like to quantify how well the prediction fits a seperate set of observations (different than the one I generated the predictions from). Here's a visual representation (two curves are shown):
What might be the best way to quantify the predictive accuracy of each model with respect to the observed data (black diamonds)?
x = Contrast is a 1 x 100 vector
y1 = Linear Summation is a 1x100 vector
y2 = Binocular data is a 4x1 vector

Answers (1)

Image Analyst
Image Analyst on 9 Oct 2020
How about the mean or median of absolute differences? Or mean square error, or sum of residuals, or RMSE, or correlation coefficient. What metric would you LIKE to use to describe the difference?
  2 Comments
Image Analyst
Image Analyst on 9 Oct 2020
Not sure what you're going to do with the result. Maybe it doesn't matter and RMSE is just as good as any of them. Just go with that and see if it works for you.

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