n = 200;
corrs = [ 1.0, 0.6, 0.0, -0.9 ];
for k = [1:length(corrs)]
    r = corrs(k);
    x = randn(n, 1);
    y = r*x; % linear dependence of y on x
    % add noise to destroy perfect correlations:
    y = y + sqrt(1.0-r*r)*randn(n, 1);
    % compute correlation coefficient of data:
    rho = corr(x, y);
    subplot(2, 2, k)
    scatter( x, y )
    text( -2, 2.5, sprintf('r=%.1f', rho) )
    xlabel('x')
    ylabel('y')
    xlim([-3.0, 3.0])
    ylim([-3.0, 3.0])
end