View difference between Paste ID: mbapsz4c and 6748SLib

 %% clear workspace
 close all
 clear all
 clc
 
 %% parameters
 n_boot = 1e2;
 n_data = 20;
 sigma = 0.5;
 p0 = 5;
 p1 = 2;
 
 %% generate data
 t = (1:n_data)';
 x_true = p0 + p1 * t;
 
 %% generate noise
 x_noise =  sigma * randn(n_data,1);
 x = x_true + x_noise;
 
 %% draw bootstrap samples
 x_noise_boot = x_noise( randi( n_data, n_data, n_boot ) );
 x_boot = bsxfun( @plus, x_noise_boot, x_true );
 
 %% fit bootstrap samples
 x_all = [x, x_boot];
 p_est = zeros(2, n_boot);
 for i = 1:n_boot
-    p_est(:, i) = polyfit( t, x_all(:, i), 1 );
+    p_est(:, i) = polyfit( t, x_all(:, i), 1 ); % the actual objective function would be minimizing the
 						% sum of squares with x_true, but this is hard to incorporate
 						% in this example since my actual application isn't a fit per se,
 						% but a model that sort of de-noises x (or x_boot).
 end
 
 %% plot
 figure;
 subplot(2,2,1:2)
 hold on
 for i = 1:n_boot
     plot( t, polyval( p_est(:, i), t ), 'r-')
 end
 plot(t, x, 'k.')
 hold off
 title('original noisy data and bootstrapped fit')
 subplot(2,2,3)
 hist( p_est(2, :) )
 title('p0')
 subplot(2,2,4)
 hist( p_est(1, :) )
 title('p1')