/*
**	Monte Carlo Program for z, x-square, t and f distribution
*/

@ Data generation under Classical Linear Regression Assumptions @

new; 
seed   = 1;
iter   = 10000; @ # of sets of different data points @

z = zeros(iter,1);
t = zeros(iter,1);
x = zeros(iter,1);
f = zeros(iter,1);

i = 1; do while i <= iter;

z[i,1] = rndns(1,1,seed);
t[i,1] = rndns(1,1,seed)./sqrt( sumc(rndns(4,1,seed)^2)/4 );
x[i,1] = sumc(rndns(2,1,seed)^2);
f[i,1] = ( sumc( rndns(2,1,seed)^2 )/2 )./ (sumc( rndns(10,1,seed)^2 )/10) ;

i = i + 1; endo ;

@ Histograms @

library pgraph;
graphset;
ytics(0,6,0.1,0) ;
v = seqa(-8,0.1,220); 
@ {a1,a2,a3}=histp(z,v); @
@ {b1,b2,b3}=histp(t,v); @

library pgraph;
graphset;
ytics(0,10,0.1,0); 
w = seqa(0, 0.1, 330);
@ {c1,c2,c3} = histp(x,w); @
{d1,d2,d3} = histp(f,w);