/* NIST/ITL StRD Dataset Name: Bennett5 (Bennett5.dat) File Format: ASCII Starting Values (lines 41 to 43) Certified Values (lines 41 to 48) Data (lines 61 to 214) Procedure: Nonlinear Least Squares Regression Description: These data are the result of a NIST study involving superconductivity magnetization modeling. The response variable is magnetism, and the predictor variable is the log of time in minutes. Reference: Bennett, L., L. Swartzendruber, and H. Brown, NIST (1994). Superconductivity Magnetization Modeling. Data: 1 Response Variable (y = magnetism) 1 Predictor Variable (x = log[time]) 154 Observations Higher Level of Difficulty Observed Data Model: Miscellaneous Class 3 Parameters (b1 to b3) y = b1 * (b2+x)**(-1/b3) + e Starting values Certified Values Start 1 Start 2 Parameter Standard Deviation b1 = -2000 -1500 -2.5235058043E+03 2.9715175411E+02 b2 = 50 45 4.6736564644E+01 1.2448871856E+00 b3 = 0.8 0.85 9.3218483193E-01 2.0272299378E-02 Residual Sum of Squares: 5.2404744073E-04 Residual Standard Deviation: 1.8629312528E-03 Degrees of Freedom: 151 Number of Observations: 154 */ clear scalar N = 154 scalar df_r = 151 scalar df_m = 3 scalar rss = 5.2404744073E-04 scalar rmse = 1.8629312528E-03 scalar b1 = -2.5235058043E+03 scalar seb1 = 2.9715175411E+02 scalar b2 = 4.6736564644E+01 scalar seb2 = 1.2448871856E+00 scalar b3 = 9.3218483193E-01 scalar seb3 = 2.0272299378E-02 qui input double(y x) -34.834702E0 7.447168E0 -34.393200E0 8.102586E0 -34.152901E0 8.452547E0 -33.979099E0 8.711278E0 -33.845901E0 8.916774E0 -33.732899E0 9.087155E0 -33.640301E0 9.232590E0 -33.559200E0 9.359535E0 -33.486801E0 9.472166E0 -33.423100E0 9.573384E0 -33.365101E0 9.665293E0 -33.313000E0 9.749461E0 -33.260899E0 9.827092E0 -33.217400E0 9.899128E0 -33.176899E0 9.966321E0 -33.139198E0 10.029280E0 -33.101601E0 10.088510E0 -33.066799E0 10.144430E0 -33.035000E0 10.197380E0 -33.003101E0 10.247670E0 -32.971298E0 10.295560E0 -32.942299E0 10.341250E0 -32.916302E0 10.384950E0 -32.890202E0 10.426820E0 -32.864101E0 10.467000E0 -32.841000E0 10.505640E0 -32.817799E0 10.542830E0 -32.797501E0 10.578690E0 -32.774300E0 10.613310E0 -32.757000E0 10.646780E0 -32.733799E0 10.679150E0 -32.716400E0 10.710520E0 -32.699100E0 10.740920E0 -32.678799E0 10.770440E0 -32.661400E0 10.799100E0 -32.644001E0 10.826970E0 -32.626701E0 10.854080E0 -32.612202E0 10.880470E0 -32.597698E0 10.906190E0 -32.583199E0 10.931260E0 -32.568699E0 10.955720E0 -32.554298E0 10.979590E0 -32.539799E0 11.002910E0 -32.525299E0 11.025700E0 -32.510799E0 11.047980E0 -32.499199E0 11.069770E0 -32.487598E0 11.091100E0 -32.473202E0 11.111980E0 -32.461601E0 11.132440E0 -32.435501E0 11.152480E0 -32.435501E0 11.172130E0 -32.426800E0 11.191410E0 -32.412300E0 11.210310E0 -32.400799E0 11.228870E0 -32.392101E0 11.247090E0 -32.380501E0 11.264980E0 -32.366001E0 11.282560E0 -32.357300E0 11.299840E0 -32.348598E0 11.316820E0 -32.339901E0 11.333520E0 -32.328400E0 11.349940E0 -32.319698E0 11.366100E0 -32.311001E0 11.382000E0 -32.299400E0 11.397660E0 -32.290699E0 11.413070E0 -32.282001E0 11.428240E0 -32.273300E0 11.443200E0 -32.264599E0 11.457930E0 -32.256001E0 11.472440E0 -32.247299E0 11.486750E0 -32.238602E0 11.500860E0 -32.229900E0 11.514770E0 -32.224098E0 11.528490E0 -32.215401E0 11.542020E0 -32.203800E0 11.555380E0 -32.198002E0 11.568550E0 -32.189400E0 11.581560E0 -32.183601E0 11.594420E0 -32.174900E0 11.607121E0 -32.169102E0 11.619640E0 -32.163300E0 11.632000E0 -32.154598E0 11.644210E0 -32.145901E0 11.656280E0 -32.140099E0 11.668200E0 -32.131401E0 11.679980E0 -32.125599E0 11.691620E0 -32.119801E0 11.703130E0 -32.111198E0 11.714510E0 -32.105400E0 11.725760E0 -32.096699E0 11.736880E0 -32.090900E0 11.747890E0 -32.088001E0 11.758780E0 -32.079300E0 11.769550E0 -32.073502E0 11.780200E0 -32.067699E0 11.790730E0 -32.061901E0 11.801160E0 -32.056099E0 11.811480E0 -32.050301E0 11.821700E0 -32.044498E0 11.831810E0 -32.038799E0 11.841820E0 -32.033001E0 11.851730E0 -32.027199E0 11.861550E0 -32.024300E0 11.871270E0 -32.018501E0 11.880890E0 -32.012699E0 11.890420E0 -32.004002E0 11.899870E0 -32.001099E0 11.909220E0 -31.995300E0 11.918490E0 -31.989500E0 11.927680E0 -31.983700E0 11.936780E0 -31.977900E0 11.945790E0 -31.972099E0 11.954730E0 -31.969299E0 11.963590E0 -31.963501E0 11.972370E0 -31.957701E0 11.981070E0 -31.951900E0 11.989700E0 -31.946100E0 11.998260E0 -31.940300E0 12.006740E0 -31.937401E0 12.015150E0 -31.931601E0 12.023490E0 -31.925800E0 12.031760E0 -31.922899E0 12.039970E0 -31.917101E0 12.048100E0 -31.911301E0 12.056170E0 -31.908400E0 12.064180E0 -31.902599E0 12.072120E0 -31.896900E0 12.080010E0 -31.893999E0 12.087820E0 -31.888201E0 12.095580E0 -31.885300E0 12.103280E0 -31.882401E0 12.110920E0 -31.876600E0 12.118500E0 -31.873699E0 12.126030E0 -31.867901E0 12.133500E0 -31.862101E0 12.140910E0 -31.859200E0 12.148270E0 -31.856300E0 12.155570E0 -31.850500E0 12.162830E0 -31.844700E0 12.170030E0 -31.841801E0 12.177170E0 -31.838900E0 12.184270E0 -31.833099E0 12.191320E0 -31.830200E0 12.198320E0 -31.827299E0 12.205270E0 -31.821600E0 12.212170E0 -31.818701E0 12.219030E0 -31.812901E0 12.225840E0 -31.809999E0 12.232600E0 -31.807100E0 12.239320E0 -31.801300E0 12.245990E0 -31.798401E0 12.252620E0 -31.795500E0 12.259200E0 -31.789700E0 12.265750E0 -31.786800E0 12.272240E0 end nl ( y = {b1} * ({b2}+x)^(-1/{b3}) ), init(b1 -2000 b2 50 b3 0.8) eps(1e-10) assert N == e(N) assert df_r == e(df_r) assert df_m == e(df_m) lrecomp [b1]_b[_cons] b1 [b2]_b[_cons] b2 [b3]_b[_cons] b3 () /* */ [b1]_se[_cons] seb1 [b2]_se[_cons] seb2 [b3]_se[_cons] seb3 () /* */ e(rmse) rmse e(rss) rss