data a1;
  infile 'U:\.www\datasets512\Ch10ta01.dat';
  input age diast;
proc print data=a1;
run;

*Weighted regression analysis.  Used to adjust for
   nonconstant variance.  This is very obvious in the
   scatterplot.  First fit the model as usual and 
   save the residuals; 
symbol1 v=circle i=sm70;
proc sort data=a1; by age;
proc gplot data=a1;
  plot diast*age/frame;
run;

proc reg data=a1;
  model diast=age;
  output out=a2 r=resid;
run;

*Compute the absolute and squared residuals to 
  find pattern with age;
data a2; 
  set a2;
  absr=abs(resid);
  sqrr=resid*resid;

proc gplot data=a2;
   plot (resid absr sqrr)*age;
run;

*From scatterplot, model absolute residual as a linear 
   function of age.  Fit model and predict the standard
   deviation for each observation;
proc reg data=a2; 
   model absr=age;
   output out=a3 p=shat;

*Compute weights;
data a3; 
   set a3;
   wt=1/(shat*shat);

*Run wieghted analysis using weight statement;
proc reg data=a3;
   model diast=age / clb;
   weight wt; 
run;