MODEL:
! Linear Regression with one independent variable;
! Linear regression is a forecasting method that 
  models the relationship between a dependent 
  variable to one or more independent variable. For
  this model we wish to predict Y with the equation:
     Y(i) = CONS + SLOPE * X(i);

SETS:
    ! The OBS set contains the data points for 
      X and Y;
     OBS/1..11/:
      Y, ! The dependent variable (annual road 
           casualties);
      X; ! The independent or explanatory variable 
           (annual licensed vehicles;
    ! The OUT set contains the output of the model.;
     OUT/ CONS, SLOPE, RSQRU, RSQRA/: R;
 ENDSETS

 ! Our data on yearly road casualties vs. licensed
   vehicles, was taken from Johnston, Econometric 
   Methods;
 DATA:
  Y = 166 153 177 201 216 208 227 238 268 268 274;
  X = 352 373 411 441 462 490 529 577 641 692 743;
 ENDDATA

SETS:
    ! The derived set OBS contains the mean shifted
      values of the independent and dependent 
      variables;
     OBSN( OBS): XS, YS;
 ENDSETS

! Number of observations;
 NK = @SIZE( OBS);

! Compute means;
 XBAR = @SUM( OBS: X)/ NK;
 YBAR = @SUM( OBS: Y)/ NK;

! Shift the observations by their means;
 @FOR( OBS( I):
 XS( I) = X( I) - XBAR;
 YS( I) = Y( I) - YBAR);

! Compute various sums of squares;
 XYBAR = @SUM( OBSN: XS * YS);
 XXBAR = @SUM( OBSN: XS * XS);
 YYBAR = @SUM( OBSN: YS * YS);

! Finally, the regression equation;
 R( @INDEX( SLOPE)) = XYBAR/ XXBAR;
 R( @INDEX( CONS)) = YBAR - R( @INDEX( SLOPE))
  * XBAR;
 RESID = @SUM( OBSN: ( YS - R( @INDEX( SLOPE))
  * XS)^2);
! A measure of how well X can be used to predict Y -
  the unadjusted (RSQRU) and adjusted (RSQRA) 
  fractions of variance explained;
 R( @INDEX( RSQRU)) = 1 - RESID/ YYBAR;
 R( @INDEX( RSQRA)) = 1 - ( RESID/ YYBAR) * 
  ( NK - 1)/( NK - 2);

! XS and YS may take on negative values;
 @FOR( OBSN: @FREE( XS); @FREE( YS));

END