Sammy,

depending on your data (!) you alternatively can try with InterpolatingPolynomial[], e.g. like so:

inputs = {16, 22, 114, 118, 120, 121, 122, 126, 128, 131, 141, 161, 177, 213, 225, 237};
outputs = {1.6, 2.2, 10.9, 11.3, 11.5, 11.6, 11.7, 12, 12.2, 12.5, 13.4, 15.2, 16.6, 19.7, 20.7, 21.8};
table = Transpose[{inputs, outputs}];

ipolpol = Simplify[Chop[InterpolatingPolynomial[table, x]]]

(*  Out:   -0.03252010729072907`+0.10366058109484573` x - 0.0001088717878462003` x^2 + 4.586421356854481`*^-7 x^3 - 8.63811125839574`*^-10 x^4  *)

Hey David, thanks for chiming in. However, this data is nonlinear. The first few points fit directly to y = 0.1x, but once x is over 100, we see that we've dropped slightly below that. By the time we're in the 200s, increasing from 213 to 225 gives an increase of only 1.0, rather than 1.2.

The example you gave of (* 0.438595 + 0.0911061 x *) would mean f(16) ~ 1.9, which is significantly off the observed result of 1.6.

However, whether or not this specific dataset does end up being linear, I have some others that definitely aren't. Do you know how I could get best curve fit that checks for potential logarithmic curves, polynomial curves, and/or sigmoids?