Before describing the regressions on the complete feature set, it is worthwhile to examine the state-of-the-art in predicting propagation rate coefficients. Two influences are known with fairly high accuracy, that is that the propagation rate coefficient increases with the length of the ester side chain in (meth)acrylates; and acrylates propagate up to a factor 100 faster than methacrylates. For all other monomers, no clear correlation has to date been quantified. Thus, in principle, for acrylates and methacrylates individually linear regressions with molecular weight can be carried out. Based on literature assumptions, this should yield some predictivity. Indeed, when plotting experimental kp as a function of molecular weight, a slight tendency towards increasing molecular weight can be observed. The overall correlation is, however, less than satisfactory. This is due to the list of monomers containing examples that have branched side chains, or that are associated with H-bonding. Nonetheless, this simplistic model can be used to derive a more general visualization of data. Using the LOOCV method, individual kp was predicted for each linear acrylate and methacrylate. For this, we fitted each group of monomers individually as would classically be done. This results in a predicted value for each of the monomers. This residual, predicted value is then plotted against its measured value. Ideally, if the experiment was error free and if the prediction was 100% accurate, a linear plot with the slope of 1 should be observed. The sum of squares of this line can hence be used to quantify the predictivity of the underlying model used. The outcome of this data procedure is shown in Figure . It should be noted that for all predictions, the logarithm of kp was used rather than its actual value; this prevents the model from predicting non-physical negative values. As can be seen from the figure, the linear regression representing the state of the art basically predicts two plateaus, one for methacrylates and one for acrylates. Rather than predicting an increasing kp with molecular weight, it thus results in not more than a rough average per monomer group. This shows that the perceived correlation between ester side chain length and kp of each monomer is not statistically robust (while not necessarily Figure . The predicted versus experimental values of the ln(kp) at 25˚C for a dataset n=29 using the leave one out cross validation on a linear regression. Predictions are determined using a separation between acrylates (blue) and methacrylates (red) and the molecular weight. (r 2 = 0.980) wrong). It cannot be used to predict any unknown propagation rate coefficient from a statistical point of view. This is, taking the large scatter of data into account, not really surprising.