Tue Oct 19 11:57:22 2021 llsq_test(): Python version: 2.7.17 Test llsq(). llsq_test01(): Python version: 2.7.17 LLSQ can compute the formula for a line of the form y = A * x + B which minimizes the RMS error to a set of N data values. Estimated relationship is y = 61.2722 * x + -39.062 Expected value is y = 61.272 * x - 39.062 I X Y B+A*X |error| 0 1.470000 52.210000 51.008158 -1.201842 1 1.500000 53.120000 52.846324 -0.273676 2 1.520000 54.480000 54.071768 -0.408232 3 1.550000 55.840000 55.909933 0.069933 4 1.570000 57.200000 57.135377 -0.064623 5 1.600000 58.570000 58.973543 0.403543 6 1.630000 59.930000 60.811708 0.881708 7 1.650000 61.290000 62.037152 0.747152 8 1.680000 63.110000 63.875317 0.765317 9 1.700000 64.470000 65.100761 0.630761 10 1.730000 66.280000 66.938927 0.658927 11 1.750000 68.100000 68.164371 0.064371 12 1.780000 69.920000 70.002536 0.082536 13 1.800000 72.190000 71.227980 -0.962020 14 1.830000 74.460000 73.066145 -1.393855 RMS error = 0.706662 llsq_test01(): Normal end of execution. llsq_test02(): Python version: 2.7.17 LLSQ can compute the formula for a line of the form y = A * x which minimizes the RMS error to a set of N data values. Estimated relationship is y = 0.641657 * x I X Y A*X |error| 0 0.000000 0.000000 0.000000 0.000000 1 0.100000 0.086500 0.064166 -0.022334 2 0.150000 0.101500 0.096249 -0.005251 3 0.200000 0.110600 0.128331 0.017731 4 0.250000 0.127900 0.160414 0.032514 5 0.300000 0.189200 0.192497 0.003297 6 0.350000 0.269500 0.224580 -0.044920 7 0.400000 0.288800 0.256663 -0.032137 8 0.450000 0.242500 0.288746 0.046246 9 0.500000 0.346500 0.320828 -0.025672 10 0.550000 0.322500 0.352911 0.030411 11 0.600000 0.376400 0.384994 0.008594 12 0.650000 0.426300 0.417077 -0.009223 13 0.700000 0.456200 0.449160 -0.007040 RMS error = 0.0251999 llsq_test02(): Normal end of execution. llsq_test() Normal end of execution. Tue Oct 19 11:57:22 2021