This example shows a regression module that calculates statistics that are associated with a linear regression.

   /*         Regression Routine               */
   /* Given X and Y, this fits Y = X B + E    */
   /* by least squares.                        */
proc iml;
start reg;
   n = nrow(x);                    /* number of observations */
   k = ncol(x);                       /* number of variables */
   xpx = x`*x;                              /* crossproducts */
   xpy = x`*y;    
   xpxi = inv(xpx);                 /* inverse crossproducts */
   b = xpxi*xpy;                      /* parameter estimates */
   yhat = x*b;                           /* predicted values */
   resid = y-yhat;                              /* residuals */
   sse = resid`*resid;              /* sum of squared errors */
   dfe = n-k;                    /* degrees of freedom error */
   mse = sse/dfe;                      /* mean squared error */
   rmse = sqrt(mse);              /* root mean squared error */
   covb = xpxi#mse;               /* covariance of estimates */
   stdb = sqrt(vecdiag(covb));            /* standard errors */
   t = b/stdb;                      /* ttest for estimates=0 */
   probt = 1-probf(t#t,1,dfe);   /* significance probability */
   print name b stdb t probt;  
   s = diag(1/stdb);           
   corrb = s*covb*s;             /* correlation of estimates */
   print ,"Covariance of Estimates", covb[r=name c=name] ,
          "Correlation of Estimates",corrb[r=name c=name] ;

   if nrow(tval)=0 then return;   /* is a t value specified? */
   projx = x*xpxi*x`;                          /* hat matrix */
   vresid = (i(n)-projx)*mse;     /* covariance of residuals */
   vpred = projx#mse;     /* covariance of predicted values  */
   h = vecdiag(projx);                /* hat leverage values */
   lowerm = yhat-tval#sqrt(h*mse); /* low. conf lim for mean */
   upperm = yhat+tval#sqrt(h*mse);    /* upper lim. for mean */
   lower = yhat-tval#sqrt(h*mse+mse); /* lower lim. for indiv*/
   upper = yhat+tval#sqrt(h*mse+mse);/* upper lim. for indiv */
   print ,,"Predicted Values, Residuals, and Limits" ,,
   y yhat resid h lowerm upperm lower upper;
finish reg;

   /* Routine to test a linear combination of the estimates  */
   /* given L, this routine tests hypothesis that LB = 0.    */

start test;
   dfn=nrow(L);
   Lb=L*b;
   vLb=L*xpxi*L`;
   q=Lb`*inv(vLb)*Lb /dfn;
   f=q/mse;
   prob=1-probf(f,dfn,dfe);
   print ,f dfn dfe prob;
finish test;

  /* Run it on population of U.S. for decades beginning 1790 */

x= { 1 1 1,
     1 2 4,
     1 3 9,
     1 4 16,
     1 5 25,
     1 6 36,
     1 7 49,
     1 8 64 };

y= {3.929,5.308,7.239,9.638,12.866,17.069,23.191,31.443};
name={"Intercept", "Decade", "Decade**2" };
tval=2.57;  /* for 5 df at 0.025 level to get 95% conf. int. */
reset fw=7;
run reg;
do;
   print ,"TEST Coef for Linear";
   L={0 1 0 };
   run test;
   print ,"TEST Coef for Linear,Quad";
   L={0 1 0,0 0 1};
   run test;
   print ,"TEST Linear+Quad = 0";
   L={0 1 1 };
   run test;
end;

The results are shown in Output 9.3.1.