22 January 2020 08:20:47 AM LLSQ_TEST C++ version Test the LLSQ library. TEST01 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 1.47 52.21 51.0082 -1.20184 1 1.5 53.12 52.8463 -0.273676 2 1.52 54.48 54.0718 -0.408232 3 1.55 55.84 55.9099 0.0699332 4 1.57 57.2 57.1354 -0.064623 5 1.6 58.57 58.9735 0.403543 6 1.63 59.93 60.8117 0.881708 7 1.65 61.29 62.0372 0.747152 8 1.68 63.11 63.8753 0.765317 9 1.7 64.47 65.1008 0.630761 10 1.73 66.28 66.9389 0.658927 11 1.75 68.1 68.1644 0.0643705 12 1.78 69.92 70.0025 0.0825361 13 1.8 72.19 71.228 -0.96202 14 1.83 74.46 73.0661 -1.39385 RMS error = 0.706662 TEST02 LLSQ0 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 0 0 0 1 0.1 0.0865 0.0641657 -0.0223343 2 0.15 0.1015 0.0962485 -0.00525148 3 0.2 0.1106 0.128331 0.0177314 4 0.25 0.1279 0.160414 0.0325142 5 0.3 0.1892 0.192497 0.00329704 6 0.35 0.2695 0.22458 -0.0449201 7 0.4 0.2888 0.256663 -0.0321373 8 0.45 0.2425 0.288746 0.0462456 9 0.5 0.3465 0.320828 -0.0256716 10 0.55 0.3225 0.352911 0.0304112 11 0.6 0.3764 0.384994 0.00859408 12 0.65 0.4263 0.417077 -0.00922308 13 0.7 0.4562 0.44916 -0.00704024 RMS error = 0.0251999 LLSQ_TEST Normal end of execution. 22 January 2020 08:20:47 AM