Any idea of what curve you want to fit it to? Like a polynomial, or an exponential decay (demo attached), or something else?
That said, the formula you get, whatever it is, will be virtually worthless in it's predicting ability of points not in your training set. I mean with so few and so noisy data, whatever parameters you come up with could be vastly different with a different training set. You need to get a lot more points. For example if I put in 3.5, I could get almost anything between -15 and -15.6 depending on the formula. In other words, you train with that set and you might get -15, but then you take some more measurements that are nominally the same but since there's a high amount of noise you'd get a different formula and now you might get -15.3 or -15.6. You couldn't really trust the prediction. Again, get more points!
Test4.m is the attached demo with your data plugged in, and it gives this: