Reorganized statistic exercises and added soultions

statistics/code/bootstraptymus.m

@@ -1,24 +0,0 @@
-resample = 500
-
-load( 'thymusglandweights.dat' );
-x = thymusglandweights;
-nsamples = length( x );
-sem = std(x)/sqrt(nsamples);
-
-mu = zeros( resample, 1 );
-for i = 1:resample
-  % resample:
-  xr = x(randi(nsamples, nsamples, 1));
-  % compute statistics on sample:
-  mu(i) = mean(xr);
-end
-bootsem = std( mu );
-
-hold on
-hist( x, 20 );
-hist( mu, 20 );
-hold off
-
-disp(['bootstrap standard error: ', num2str(bootsem)]);
-disp(['standard error: ', num2str(sem)]);
-

statistics/code/randomwalk.m

@@ -1,8 +0,0 @@
-function x = randomwalk(n,p)
-% returns a random wolk with n steps and 
-% probability p for positive steps.
-    r = rand(n,1);
-    r(r<p) = -1.0;
-    r(r>=p) = +1.0;
-    x = cumsum(r);
-end

statistics/code/randomwalkstatistics.m

@@ -1,25 +0,0 @@
-p = 0.5;
-nsteps = 100;
-nwalks = 1000;
-
-y = zeros( nwalks, nsteps/10 );
-for k = 1:length( y )
-    x = randomwalk( nsteps, p );
-    for j = 1:nsteps/10
-        y(k,j) = x((j-1)*10+1);
-    end
-    %plot( x )
-    %pause( 1 )
-    if rem(k,100) == 0
-        %[h1,b1] = hist( y(1:k,1), [-50:2:50] );
-        %[h2,b2] = hist( y(1:k,2), [-50:2:50] );
-        %bar( b1, h1, 1.0, 'b' );
-        %hold on;
-        %bar( b2, h2, 'FaceColor', 'r' );
-        %hold off;
-        sdev = var( y(1:k,:), 1 );
-        plot( sdev )
-        pause( 1.0 );
-    end
-end
-

statistics/code/tdistribution.m

@@ -1,32 +0,0 @@
-n = 100000
-x=randn(n, 1);
-
-nsamples = 3;
-nmeans = 10000;
-means = zeros( nmeans, 1 );
-sdevs = zeros( nmeans, 1 );
-students = zeros( nmeans, 1 );
-for i=1:nmeans
-  sample = x(randi(n, nsamples, 1));
-  means(i) = mean(sample);
-  sdevs(i) = std(sample);
-  students(i) = mean(sample)/std(sample)*sqrt(nsamples);
-end
-sdev = 1.0
-msdev = std(means)
-
-%  scatter( means, sdevs )
-
-hold on;
-dxg=0.01;
-xmax = 10.0
-xmin = -xmax
-xg = [xmin:dxg:xmax];
-pg = exp(-0.5*(xg/sdev).^2)/sqrt(2.0*pi)/sdev;
-hold on
-plot(xg, pg, 'r', 'linewidth', 4)
-
-bins = xmin:0.1:xmax;
-hist(means, bins, 1.0/(bins(2)-bins(1)) );
-hold off;
-