 ```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 ```