Whilst the regression coefficient plots help to provide a possible interpretation of the relationship between the different model components and the predicted technology substitution classifications, it is also necessary to check the 'goodness-of-fit' measures associated with these results. These common statistical measures examine the amount of variability that is explained by the current model, as well as testing the likelihood that the same result could be obtained by chance. As such, R-Squared, adjusted R-Squared, and F-ratio statistics are calculated (see section 9.4.1 and 9.4.2 of \cite{Ramsay_2009}) to assess the overall fit of the high-dimensional functional linear regression model, and are summarised in Table \ref{table:results_high_dimensional_model}.