Assuming columns with 1's will only have 1's, and columns with 2's and 3's will never have 1's, then it is as simple as taking the minimium along the first dimension (the 1 in the thrid argument of min())
sex = min( data,[],1);
The second part depends on how Resident and MC_participant_Mean are structured. The easiest approach is to issue three scatter plot commands, logically indexed based on sex.
hold on
scatter (Resident(idx1) , MC_participant_Mean(idx1),[],'k') % note the [] is the size of the scatter
scatter (Resident(idx2) , MC_participant_Mean(idx2),[],'r')
scatter (Resident(idx3) , MC_participant_Mean(idx3),[],'b')
Where idx1/idx2/idx3 are obtained by logical operations.
So, if your first matrix has the same number of columns as the length of Resident and MC_participant_Mean, then you would define them (before plotting of course) by
idx1 = sex==1;
idx2 = sex==2;
idx3 = sex==3;
Of course, you could just drop in these equalities into the plot line commands.
This won't work if Resident or MC_participant_Mean are multidimensional, or don't correspond to the columns in the first matrix.
