If your data don't have any outliers, then the results of a robust regression will be very similar to a plain linear regression.
For example, here is some code comparing the two:
x = rand(20,1);
y = x + 0.3*rand(20,1);
y(1) = 40;
figure
hold on
[a,b]= robustfit(x,y);
plot(x,a(1)+a(2).*x,'g');
[a1, b1]=regress(y, [ones(size(x)) x]) ;
plot(x,a1(1)+a1(2).* x,'r','LineWidth',2);
Notice that I deliberately added an outlier [y(1)= 40], and plotted the lines on the same plot to compare. If you comment out that line y(1) = 40, then those lines will be (nearly) on top of each other.
In the latter case, you may need to use
>> format long
to see that a and a1 are actually a little different from each other.
