alpar@1: /*Curve fitting problem by Least Squares alpar@1: Nigel_Galloway@operamail.com alpar@1: October 1st., 2007 alpar@1: */ alpar@1: set Sample; alpar@1: param Sx {z in Sample}; alpar@1: param Sy {z in Sample}; alpar@1: alpar@1: var X; alpar@1: var Y; alpar@1: var Ex{z in Sample}; alpar@1: var Ey{z in Sample}; alpar@1: alpar@1: /* sum of variances is zero for Sx*/ alpar@1: variencesX{z in Sample}: X + Ex[z] = Sx[z]; alpar@1: zumVariancesX: sum{z in Sample} Ex[z] = 0; alpar@1: /* sum of variances is zero for Sy*/ alpar@1: variencesY{z in Sample}: Y + Ey[z] = Sy[z]; alpar@1: zumVariancesY: sum{z in Sample} Ey[z] = 0; alpar@1: alpar@1: solve; alpar@1: alpar@1: param b1 := (sum{z in Sample} Ex[z]*Ey[z])/(sum{z in Sample} Ex[z]*Ex[z]); alpar@1: printf "\nbest linear fit is:\n\ty = %f %s %fx\n\n", Y-b1*X, if b1 < 0 then "-" else "+", abs(b1); alpar@1: alpar@1: data; alpar@1: alpar@1: param: alpar@1: Sample: Sx Sy := alpar@1: 1 0 1 alpar@1: 2 0.5 0.9 alpar@1: 3 1 0.7 alpar@1: 4 1.5 1.5 alpar@1: 5 1.9 2 alpar@1: 6 2.5 2.4 alpar@1: 7 3 3.2 alpar@1: 8 3.5 2 alpar@1: 9 4 2.7 alpar@1: 10 4.5 3.5 alpar@1: 11 5 1 alpar@1: 12 5.5 4 alpar@1: 13 6 3.6 alpar@1: 14 6.6 2.7 alpar@1: 15 7 5.7 alpar@1: 16 7.6 4.6 alpar@1: 17 8.5 6 alpar@1: 18 9 6.8 alpar@1: 19 10 7.3 alpar@1: ; alpar@1: alpar@1: end;