diff --git a/step2_effective_sample_size.m b/step2_effective_sample_size.m
new file mode 100644
index 0000000..e6308ca
--- /dev/null
+++ b/step2_effective_sample_size.m
@@ -0,0 +1,68 @@
+clear all;
+close all;
+clc;
+
+n_cohort=11;
+n_sample=randi([250 270],1,n_cohort);
+n_sample_t=sum(n_sample);
+index_cohort=cumsum(n_sample);
+
+mu=0;
+sigma=10;
+x_t=normrnd(mu,sigma,[n_sample_t,1]);
+beta=0.06;
+eps=rand(n_sample_t,1);
+y_t=x_t*beta+eps;
+rat=var(x_t*beta)/var(y_t);
+
+cov=0:500:index_cohort(end);% number of covariates
+n_cov=size(cov,2)% loop for different number of covariates
+
+for i=1:n_cohort 
+    x_i=x_t(1:index_cohort(i));
+    y_i=y_t(1:index_cohort(i));
+    y_i=y_i/norm(y_i);% normalize
+    for j=1:n_cov
+        mc=normrnd(mu,sigma,[index_cohort(i),cov(j)]);
+        % generate effect size of covariates
+        r=linspace(0.1,0.2,cov(j))';
+        beta_es=mc\y_i;%(y-eps);
+        y_res=y_i-mc*beta_es; % residulize
+        
+        [coef, t(i,j)] = nancorr(y_res, x_i);
+    end
+end
+z2=t.*t;
+  
+figure
+for j1=1:n_cov
+    plot(index_cohort,z2(:,j1),'LineWidth',2)
+    %plot(index_cohort,z2(:,j1),'LineWidth',2)
+    legendInfo{j1} = ['No.cov= ' num2str(cov(j1))]; 
+    hold on
+end
+xlabel('Sample size')
+ylabel('Z^2')
+xlim([-index_cohort(end) index_cohort(end)])
+xlim([index_cohort(1) index_cohort(end)])
+legend(legendInfo,'Position',[0.7 0.2 0.1 0.2])
+
+%z_eff=z2/coe;
+z_eff=index_cohort(end)*z2./z2(:,1)
+
+
+%% effective sample size
+figure
+X = cov
+
+Y = floor(z_eff(n_cohort,:))
+
+plot(X,Y,'k-s')
+xlim([0 cov(end)*1.1])
+ylim([0 Y(1)*1.1])
+strValues = strtrim(cellstr(num2str([X(:) Y(:)],'(%d,%d)')));
+text(X,Y,strValues,'VerticalAlignment','bottom');
+xlabel('Number of covariates')
+ylabel('Effective sample size')
+
+
diff --git a/step2_effective_sample_size_alex.m b/step2_effective_sample_size_alex.m
new file mode 100644
index 0000000..905576e
--- /dev/null
+++ b/step2_effective_sample_size_alex.m
@@ -0,0 +1,84 @@
+
+mu = 0;
+sigma = 1;
+beta = 0.4;
+
+n_sample_t = 5002;   % _t means 'total'
+n_cov_t = 300;
+cov_size=0:100:n_cov_t;              % number of covariates
+cohort_size=2:1000:n_sample_t;
+
+x_t = normrnd(mu, sigma, [n_sample_t, 1]);
+c_t = normrnd(mu, sigma, [n_sample_t, n_cov_t]);
+
+eps = rand(n_sample_t, 1);
+y_t = x_t*beta + eps;
+rat = var(x_t*beta)/var(y_t);fprintf('variance: %f\n',rat)
+y_t=y_t/norm(y_t);                   % normalize
+
+t_preres = nan(length(cohort_size), length(cov_size));
+t_multreg = nan(length(cohort_size), length(cov_size));
+
+effN_preres = nan(length(cohort_size), length(cov_size));
+effN_multreg = nan(length(cohort_size), length(cov_size));
+
+%rand('seed', 123)
+
+for i=1:length(cohort_size) 
+    fprintf('subjects: %f\n',cohort_size(i))
+    x = x_t(1:cohort_size(i));
+    y = y_t(1:cohort_size(i));
+    for j = 1:length(cov_size)
+        fprintf('covariates: %f\n',cov_size(j))
+        c = c_t(1:cohort_size(i), 1:cov_size(j));
+        y_res   = y - c*(c\y); % pre-residulize
+        [coef, t_preres(i,j)] = nancorr(y_res, x);
+    
+        if (cov_size(j) ~= 0) && (cov_size(j)+2 >= cohort_size(i)), continue; end
+       
+        % try multiple regression (instead of pre-residualization)
+        % https://stats.stackexchange.com/questions/27916/standard-errors-for-multiple-regression-coefficients
+        X = [x c];
+        hat_beta = X \ y;
+        s2=var(y - X * hat_beta);
+        %Sigma = s2 * inv(X' * X); se = sqrt(Sigma(1,1)); 
+        b1 = zeros(size(X, 2), 1); b1(1)=1; a=(X' * X \ b1); se = sqrt(s2 * a(1)); 
+        t_multreg(i, j) = hat_beta(1) / se;
+    end
+end
+z2_preres=t_preres.*t_preres;
+z2_multreg=t_multreg.*t_multreg;
+
+for j = 1:length(cov_size)
+    effN_multreg(:, j) =  z2_multreg(:, j) ./ z2_multreg(:, 1) .* cohort_size';
+    effN_preres(:, j) =  z2_preres(:, j) ./ z2_preres(:, 1) .* cohort_size';
+end
+
+figure(1); clf; hold on;legendInfo={};
+for j=1:length(cov_size)
+    plot(cohort_size, z2_preres(:,j), 'LineWidth', 2)
+    legendInfo{j} = ['No.cov= ' num2str(cov_size(j))]; 
+end
+set(gca,'ColorOrderIndex',1)
+for j=1:length(cov_size)
+    plot(cohort_size, z2_multreg(:,j), '--', 'LineWidth', 2)
+end
+xlabel('Sample size')
+ylabel('Z^2')
+legend(legendInfo,'Position',[0.7 0.2 0.1 0.2])
+title({'solid lines: pre-regularization' 'dashed lines: multiple regression'})
+
+figure(2); clf; hold on; legendInfo={};
+for i=1:length(cohort_size)
+    plot(cov_size, effN_preres(i, 1) - effN_preres(i,:), 'LineWidth', 2)
+    legendInfo{i} = ['No.subj= ' num2str(cohort_size(i))]; 
+end
+set(gca,'ColorOrderIndex',1)
+for i=1:length(cohort_size)
+    plot(cov_size, effN_multreg(i,1) - effN_multreg(i,:), '--', 'LineWidth', 2)
+end
+xlabel('No.cov')
+ylabel('totalN - effN (delta)')
+legend(legendInfo, 'location', 'northwest')
+
+