FDM by centred difference gives a gradient of -0.5 instead of -2

Okay, maybe I'm just stupid, but the title pretty much says it all. The code I made is as follows,

n=[10:10:90 100:100:900 1000:1000:9000 10000:10000:90000];

for i=1:length(n),

      h(i)=1/(n(i));

      E=sparse(1:n(i)+1,1:n(i)+1,2*ones(1,n(i)+1),n(i)+1,n(i)+1);
      F=sparse(2:n(i)+1,1:n(i),-1*ones(1,n(i)),n(i)+1,n(i)+1);
      A=F+E+F';
      A(1,1)=1;
      A(1,2)=0;
      A(n(i)+1,n(i))=-1;
      A(n(i)+1,n(i)+1)=1;

      x=0:h(i):1;

      B(1,1:1+n(i))=((h(i))^2)*10;
      B(1,1)=0;
      B(1,1+n(i))=0;

      u=A\B';

      y=-5*x.^2+10*x; %Analytical solution

      e(i)=norm(u-y');
  end

figure

loglog(h.^-2,e,'r-+')

grid on

title('Graph of partition width to the power of -2, h^-^2 versus error, e')

xlabel('partition width to the power of -2, h^-^2')

ylabel('error, e')

I have been hammering away at this all night but can't seem to figure out why the gradient is -0.5. Any help?