% generate correlated data:
n=200;
a=0.2;
x = randn(n, 1);
y = randn(n, 1) + a*x;

% correlation coefficient:
rd = corr(x, y);
fprintf('correlation coefficient of data r = %.2f\n', rd);

% distribution of null hypothesis by permutation:
nperm = 1000;
rs = zeros(nperm,1);
for i=1:nperm
    xr=x(randperm(length(x)));  % shuffle x
    yr=y(randperm(length(y)));  % shuffle y
    rs(i) = corr(xr, yr);
end
[h,b] = hist(rs, 20);
h = h/sum(h)/(b(2)-b(1));       % normalization

% significance:
rq = quantile(rs, 0.95);
fprintf('correlation coefficient of null hypothesis at 5%% significance = %.2f\n', rq);
if rd >= rq
    fprintf('--> correlation r=%.2f is significant\n', rd);
else
    fprintf('--> r=%.2f is not a significant correlation\n', rd);
end

% plot:
bar(b, h, 'facecolor', 'b');
hold on;
bar(b(b>=rq), h(b>=rq), 'facecolor', 'r');
plot([rd rd], [0 4], 'r', 'linewidth', 2);
xlabel('Correlation coefficient');
ylabel('Probability density of H0');
hold off;