% Given the following variables: 
% ‘super_mos’ holds the super-set’s DMOS scores. 
% ‘super_std’ holds the super-set’s standard deviation of DMOS scores. 
% ‘super_viewers’ holds the number of viewers used to compute each data 
% point in super_mos and super_std. 
% ‘yhat’ holds one model VM fitted to this super-set 
% ‘len_minus_df’ is the number of data points minus the degrees of freedom 
% of the fit, adjusted by any averaging performed (e.g., HRC averaging). 
% If computed on a per-clip basis, then this is simply the 
% number of clips (e.g., 152 for one VQEG MM experiment) minus 4 (three degrees 
% of freedom for the fit variables, plus one for the constant term). 
 
% compute correlation, and place that into variable ‘corr’ 
temp = corrcoef(yhat, super_mos); 
corr = temp(1,2); 
 
% compute RMSE, and place that into variable ‘rmse’ 
rmse = sqrt(sum((yhat – super_mos).^2) / len_minus_df ); 
 
% compute Outlier Ratio, and place that into variable ‘outratio’ 
temp = 2.069 * super_std ./ sqrt(super_veiwers); 
outratio = length(find(abs(yhat – super_hat) > temp)) / length(yhat); 
 
% When computing on a per-HRC basis, the above equations remain the same but 
% the definitions of the variables change slightly. ‘len_minus_df’ must be 
% divided by the number of SRC averaged (e.g., 8 for VQEG MM). 
% Also, the number of viewers increases by the number of SRC averaged. This 
% also changes the 2.069 multiplier constant to 1.96 in the equation for temp. 
