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\\   To select the best fit model we used the Akaike Information Criterion corrected for finite sample sizes. This measure describes how much information is gained by extra parameters when fitting models. Therefore, a better AICc means a balance between the ability of a model to predict the data and the minimization of number of parameters. This measure has an advantage over similar equations, because the main limiting factor is the number of parameters. It is especially useful when compared to the Bayesian Information Criterion that operates under the assumption that the number of data points is much greater than the number of parameters, which is an issue when many sources for tumor growth data do not include enough data points to satisfy this condition. (Citation?) (Also a weirdly worded sentence, I'll fix that). The AICc does require a certain number of data points, but is explicit in that if not enough data is used, the equation will render a zero in the denominator returning an error message. This is helpful in that it prevents conclusions from being drawn from unsubstantial data sets.  \subsection{Statistical Analysis}