Jurgen Van Gael
17 years ago
committed by
Christoph Ruegg
Christoph Ruegg
57 changed files with 16008 additions and 0 deletions
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NIST/ITL StRD |
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Dataset Name: AtmWtAg (AtmWtAgt.dat) |
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File Format: ASCII |
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Certified Values (lines 41 to 47) |
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Data (lines 61 to 84) |
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Procedure: Analysis of Variance |
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Reference: Powell, L.J., Murphy, T.J. and Gramlich, J.W. (1982). |
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"The Absolute Isotopic Abundance & Atomic Weight |
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of a Reference Sample of Silver". |
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NBS Journal of Research, 87, pp. 9-19. |
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Data: 1 Factor |
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2 Treatments |
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24 Replicates/Cell |
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48 Observations |
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7 Constant Leading Digits |
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Average Level of Difficulty |
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Observed Data |
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Model: 3 Parameters (mu, tau_1, tau_2) |
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y_{ij} = mu + tau_i + epsilon_{ij} |
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Certified Values: |
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Source of Sums of Mean |
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Variation df Squares Squares F Statistic |
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Between Instrument 1 3.63834187500000E-09 3.63834187500000E-09 1.59467335677930E+01 |
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Within Instrument 46 1.04951729166667E-08 2.28155932971014E-10 |
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Certified R-Squared 2.57426544538321E-01 |
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Certified Residual |
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Standard Deviation 1.51048314446410E-05 |
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Data: |
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Instrument |
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1 2 |
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107.8681568 107.8681079 |
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107.8681465 107.8681344 |
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107.8681572 107.8681513 |
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107.8681785 107.8681197 |
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107.8681446 107.8681604 |
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107.8681903 107.8681385 |
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107.8681526 107.8681642 |
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107.8681494 107.8681365 |
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107.8681616 107.8681151 |
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107.8681587 107.8681082 |
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107.8681519 107.8681517 |
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107.8681486 107.8681448 |
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107.8681419 107.8681198 |
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107.8681569 107.8681482 |
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107.8681508 107.8681334 |
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107.8681672 107.8681609 |
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107.8681385 107.8681101 |
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107.8681518 107.8681512 |
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107.8681662 107.8681469 |
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107.8681424 107.8681360 |
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107.8681360 107.8681254 |
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107.8681333 107.8681261 |
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107.8681610 107.8681450 |
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107.8681477 107.8681368 |
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NIST/ITL StRD |
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Dataset Name: Bennett5 (Bennett5.dat) |
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File Format: ASCII |
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Starting Values (lines 41 to 43) |
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Certified Values (lines 41 to 48) |
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Data (lines 61 to 214) |
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Procedure: Nonlinear Least Squares Regression |
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Description: These data are the result of a NIST study involving |
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superconductivity magnetization modeling. The |
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response variable is magnetism, and the predictor |
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variable is the log of time in minutes. |
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Reference: Bennett, L., L. Swartzendruber, and H. Brown, |
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NIST (1994). |
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Superconductivity Magnetization Modeling. |
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Data: 1 Response Variable (y = magnetism) |
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1 Predictor Variable (x = log[time]) |
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154 Observations |
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Higher Level of Difficulty |
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Observed Data |
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Model: Miscellaneous Class |
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3 Parameters (b1 to b3) |
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y = b1 * (b2+x)**(-1/b3) + e |
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Starting values Certified Values |
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Start 1 Start 2 Parameter Standard Deviation |
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b1 = -2000 -1500 -2.5235058043E+03 2.9715175411E+02 |
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b2 = 50 45 4.6736564644E+01 1.2448871856E+00 |
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b3 = 0.8 0.85 9.3218483193E-01 2.0272299378E-02 |
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Residual Sum of Squares: 5.2404744073E-04 |
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Residual Standard Deviation: 1.8629312528E-03 |
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Degrees of Freedom: 151 |
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Number of Observations: 154 |
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Data: y x |
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-34.834702E0 7.447168E0 |
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-34.393200E0 8.102586E0 |
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-34.152901E0 8.452547E0 |
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-33.979099E0 8.711278E0 |
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-33.845901E0 8.916774E0 |
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-33.732899E0 9.087155E0 |
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-33.640301E0 9.232590E0 |
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-33.559200E0 9.359535E0 |
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-33.486801E0 9.472166E0 |
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-33.423100E0 9.573384E0 |
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-33.365101E0 9.665293E0 |
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-33.313000E0 9.749461E0 |
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-33.260899E0 9.827092E0 |
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-33.217400E0 9.899128E0 |
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-33.176899E0 9.966321E0 |
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-33.139198E0 10.029280E0 |
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-33.101601E0 10.088510E0 |
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-33.066799E0 10.144430E0 |
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-33.035000E0 10.197380E0 |
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-33.003101E0 10.247670E0 |
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-32.971298E0 10.295560E0 |
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-32.942299E0 10.341250E0 |
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-32.916302E0 10.384950E0 |
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-32.890202E0 10.426820E0 |
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-32.864101E0 10.467000E0 |
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-32.841000E0 10.505640E0 |
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-32.817799E0 10.542830E0 |
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-32.797501E0 10.578690E0 |
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-32.774300E0 10.613310E0 |
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-32.757000E0 10.646780E0 |
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-32.733799E0 10.679150E0 |
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-32.716400E0 10.710520E0 |
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-32.699100E0 10.740920E0 |
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-32.678799E0 10.770440E0 |
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-32.661400E0 10.799100E0 |
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-32.644001E0 10.826970E0 |
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-32.626701E0 10.854080E0 |
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-32.612202E0 10.880470E0 |
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-32.597698E0 10.906190E0 |
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-32.583199E0 10.931260E0 |
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-32.568699E0 10.955720E0 |
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-32.554298E0 10.979590E0 |
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-32.539799E0 11.002910E0 |
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-32.525299E0 11.025700E0 |
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-32.510799E0 11.047980E0 |
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-32.499199E0 11.069770E0 |
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-32.487598E0 11.091100E0 |
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-32.473202E0 11.111980E0 |
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-32.461601E0 11.132440E0 |
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-32.435501E0 11.152480E0 |
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-32.435501E0 11.172130E0 |
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-32.426800E0 11.191410E0 |
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-32.412300E0 11.210310E0 |
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-32.400799E0 11.228870E0 |
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-32.392101E0 11.247090E0 |
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-32.380501E0 11.264980E0 |
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-32.366001E0 11.282560E0 |
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-32.357300E0 11.299840E0 |
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-32.348598E0 11.316820E0 |
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-32.339901E0 11.333520E0 |
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-32.328400E0 11.349940E0 |
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-32.319698E0 11.366100E0 |
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-32.311001E0 11.382000E0 |
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-32.299400E0 11.397660E0 |
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-32.290699E0 11.413070E0 |
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-32.282001E0 11.428240E0 |
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-32.273300E0 11.443200E0 |
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-32.264599E0 11.457930E0 |
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-32.256001E0 11.472440E0 |
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-32.247299E0 11.486750E0 |
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-32.238602E0 11.500860E0 |
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-32.229900E0 11.514770E0 |
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-32.224098E0 11.528490E0 |
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-32.215401E0 11.542020E0 |
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-32.203800E0 11.555380E0 |
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-32.198002E0 11.568550E0 |
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-32.189400E0 11.581560E0 |
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-32.183601E0 11.594420E0 |
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-32.174900E0 11.607121E0 |
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-32.169102E0 11.619640E0 |
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-32.163300E0 11.632000E0 |
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-32.154598E0 11.644210E0 |
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-32.145901E0 11.656280E0 |
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-32.140099E0 11.668200E0 |
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-32.131401E0 11.679980E0 |
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-32.125599E0 11.691620E0 |
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-32.119801E0 11.703130E0 |
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-32.111198E0 11.714510E0 |
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-32.105400E0 11.725760E0 |
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-32.096699E0 11.736880E0 |
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-32.090900E0 11.747890E0 |
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-32.088001E0 11.758780E0 |
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-32.079300E0 11.769550E0 |
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-32.073502E0 11.780200E0 |
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-32.067699E0 11.790730E0 |
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-32.061901E0 11.801160E0 |
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-32.056099E0 11.811480E0 |
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-32.050301E0 11.821700E0 |
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-32.044498E0 11.831810E0 |
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-32.038799E0 11.841820E0 |
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-32.033001E0 11.851730E0 |
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-32.027199E0 11.861550E0 |
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-32.024300E0 11.871270E0 |
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-32.018501E0 11.880890E0 |
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-32.012699E0 11.890420E0 |
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-32.004002E0 11.899870E0 |
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-32.001099E0 11.909220E0 |
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-31.995300E0 11.918490E0 |
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-31.989500E0 11.927680E0 |
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-31.983700E0 11.936780E0 |
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-31.977900E0 11.945790E0 |
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-31.972099E0 11.954730E0 |
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-31.969299E0 11.963590E0 |
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-31.963501E0 11.972370E0 |
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-31.957701E0 11.981070E0 |
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-31.951900E0 11.989700E0 |
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-31.946100E0 11.998260E0 |
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-31.940300E0 12.006740E0 |
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-31.937401E0 12.015150E0 |
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-31.931601E0 12.023490E0 |
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-31.925800E0 12.031760E0 |
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-31.922899E0 12.039970E0 |
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-31.917101E0 12.048100E0 |
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-31.911301E0 12.056170E0 |
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-31.908400E0 12.064180E0 |
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-31.902599E0 12.072120E0 |
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-31.896900E0 12.080010E0 |
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-31.893999E0 12.087820E0 |
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-31.888201E0 12.095580E0 |
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-31.885300E0 12.103280E0 |
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-31.882401E0 12.110920E0 |
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-31.876600E0 12.118500E0 |
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-31.873699E0 12.126030E0 |
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-31.867901E0 12.133500E0 |
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-31.862101E0 12.140910E0 |
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-31.859200E0 12.148270E0 |
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-31.856300E0 12.155570E0 |
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-31.850500E0 12.162830E0 |
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-31.844700E0 12.170030E0 |
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-31.841801E0 12.177170E0 |
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-31.838900E0 12.184270E0 |
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-31.833099E0 12.191320E0 |
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-31.830200E0 12.198320E0 |
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-31.827299E0 12.205270E0 |
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-31.821600E0 12.212170E0 |
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-31.818701E0 12.219030E0 |
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-31.812901E0 12.225840E0 |
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-31.809999E0 12.232600E0 |
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-31.807100E0 12.239320E0 |
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-31.801300E0 12.245990E0 |
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-31.798401E0 12.252620E0 |
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-31.795500E0 12.259200E0 |
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-31.789700E0 12.265750E0 |
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-31.786800E0 12.272240E0 |
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NIST/ITL StRD |
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Dataset Name: BoxBOD (BoxBOD.dat) |
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File Format: ASCII |
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Starting Values (lines 41 to 42) |
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Certified Values (lines 41 to 47) |
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Data (lines 61 to 66) |
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Procedure: Nonlinear Least Squares Regression |
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Description: These data are described in detail in Box, Hunter and |
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Hunter (1978). The response variable is biochemical |
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oxygen demand (BOD) in mg/l, and the predictor |
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variable is incubation time in days. |
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Reference: Box, G. P., W. G. Hunter, and J. S. Hunter (1978). |
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Statistics for Experimenters. |
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New York, NY: Wiley, pp. 483-487. |
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Data: 1 Response (y = biochemical oxygen demand) |
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1 Predictor (x = incubation time) |
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6 Observations |
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Higher Level of Difficulty |
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Observed Data |
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Model: Exponential Class |
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2 Parameters (b1 and b2) |
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y = b1*(1-exp[-b2*x]) + e |
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Starting values Certified Values |
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Start 1 Start 2 Parameter Standard Deviation |
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b1 = 1 100 2.1380940889E+02 1.2354515176E+01 |
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b2 = 1 0.75 5.4723748542E-01 1.0455993237E-01 |
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Residual Sum of Squares: 1.1680088766E+03 |
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Residual Standard Deviation: 1.7088072423E+01 |
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Degrees of Freedom: 4 |
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Number of Observations: 6 |
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Data: y x |
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109 1 |
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149 2 |
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149 3 |
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191 5 |
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213 7 |
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224 10 |
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NIST/ITL StRD |
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Dataset Name: Chwirut1 (Chwirut1.dat) |
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File Format: ASCII |
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Starting Values (lines 41 to 43) |
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Certified Values (lines 41 to 48) |
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Data (lines 61 to 274) |
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Procedure: Nonlinear Least Squares Regression |
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Description: These data are the result of a NIST study involving |
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ultrasonic calibration. The response variable is |
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ultrasonic response, and the predictor variable is |
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metal distance. |
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Reference: Chwirut, D., NIST (197?). |
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Ultrasonic Reference Block Study. |
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Data: 1 Response Variable (y = ultrasonic response) |
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1 Predictor Variable (x = metal distance) |
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214 Observations |
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Lower Level of Difficulty |
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Observed Data |
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Model: Exponential Class |
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3 Parameters (b1 to b3) |
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y = exp[-b1*x]/(b2+b3*x) + e |
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Starting values Certified Values |
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Start 1 Start 2 Parameter Standard Deviation |
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b1 = 0.1 0.15 1.9027818370E-01 2.1938557035E-02 |
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b2 = 0.01 0.008 6.1314004477E-03 3.4500025051E-04 |
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b3 = 0.02 0.010 1.0530908399E-02 7.9281847748E-04 |
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Residual Sum of Squares: 2.3844771393E+03 |
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Residual Standard Deviation: 3.3616721320E+00 |
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Degrees of Freedom: 211 |
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Number of Observations: 214 |
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Data: y x |
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92.9000E0 0.5000E0 |
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78.7000E0 0.6250E0 |
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64.2000E0 0.7500E0 |
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64.9000E0 0.8750E0 |
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57.1000E0 1.0000E0 |
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43.3000E0 1.2500E0 |
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31.1000E0 1.7500E0 |
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23.6000E0 2.2500E0 |
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31.0500E0 1.7500E0 |
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23.7750E0 2.2500E0 |
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17.7375E0 2.7500E0 |
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13.8000E0 3.2500E0 |
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11.5875E0 3.7500E0 |
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9.4125E0 4.2500E0 |
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7.7250E0 4.7500E0 |
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7.3500E0 5.2500E0 |
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8.0250E0 5.7500E0 |
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90.6000E0 0.5000E0 |
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76.9000E0 0.6250E0 |
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71.6000E0 0.7500E0 |
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63.6000E0 0.8750E0 |
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54.0000E0 1.0000E0 |
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39.2000E0 1.2500E0 |
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29.3000E0 1.7500E0 |
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21.4000E0 2.2500E0 |
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29.1750E0 1.7500E0 |
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22.1250E0 2.2500E0 |
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17.5125E0 2.7500E0 |
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14.2500E0 3.2500E0 |
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9.4500E0 3.7500E0 |
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9.1500E0 4.2500E0 |
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7.9125E0 4.7500E0 |
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8.4750E0 5.2500E0 |
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6.1125E0 5.7500E0 |
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80.0000E0 0.5000E0 |
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79.0000E0 0.6250E0 |
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63.8000E0 0.7500E0 |
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57.2000E0 0.8750E0 |
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53.2000E0 1.0000E0 |
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42.5000E0 1.2500E0 |
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26.8000E0 1.7500E0 |
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20.4000E0 2.2500E0 |
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26.8500E0 1.7500E0 |
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21.0000E0 2.2500E0 |
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16.4625E0 2.7500E0 |
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12.5250E0 3.2500E0 |
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10.5375E0 3.7500E0 |
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8.5875E0 4.2500E0 |
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7.1250E0 4.7500E0 |
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6.1125E0 5.2500E0 |
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5.9625E0 5.7500E0 |
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74.1000E0 0.5000E0 |
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67.3000E0 0.6250E0 |
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60.8000E0 0.7500E0 |
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55.5000E0 0.8750E0 |
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50.3000E0 1.0000E0 |
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41.0000E0 1.2500E0 |
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29.4000E0 1.7500E0 |
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20.4000E0 2.2500E0 |
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29.3625E0 1.7500E0 |
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21.1500E0 2.2500E0 |
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16.7625E0 2.7500E0 |
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13.2000E0 3.2500E0 |
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10.8750E0 3.7500E0 |
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8.1750E0 4.2500E0 |
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7.3500E0 4.7500E0 |
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5.9625E0 5.2500E0 |
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5.6250E0 5.7500E0 |
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81.5000E0 .5000E0 |
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62.4000E0 .7500E0 |
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32.5000E0 1.5000E0 |
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12.4100E0 3.0000E0 |
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13.1200E0 3.0000E0 |
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15.5600E0 3.0000E0 |
|||
5.6300E0 6.0000E0 |
|||
78.0000E0 .5000E0 |
|||
59.9000E0 .7500E0 |
|||
33.2000E0 1.5000E0 |
|||
13.8400E0 3.0000E0 |
|||
12.7500E0 3.0000E0 |
|||
14.6200E0 3.0000E0 |
|||
3.9400E0 6.0000E0 |
|||
76.8000E0 .5000E0 |
|||
61.0000E0 .7500E0 |
|||
32.9000E0 1.5000E0 |
|||
13.8700E0 3.0000E0 |
|||
11.8100E0 3.0000E0 |
|||
13.3100E0 3.0000E0 |
|||
5.4400E0 6.0000E0 |
|||
78.0000E0 .5000E0 |
|||
63.5000E0 .7500E0 |
|||
33.8000E0 1.5000E0 |
|||
12.5600E0 3.0000E0 |
|||
5.6300E0 6.0000E0 |
|||
12.7500E0 3.0000E0 |
|||
13.1200E0 3.0000E0 |
|||
5.4400E0 6.0000E0 |
|||
76.8000E0 .5000E0 |
|||
60.0000E0 .7500E0 |
|||
47.8000E0 1.0000E0 |
|||
32.0000E0 1.5000E0 |
|||
22.2000E0 2.0000E0 |
|||
22.5700E0 2.0000E0 |
|||
18.8200E0 2.5000E0 |
|||
13.9500E0 3.0000E0 |
|||
11.2500E0 4.0000E0 |
|||
9.0000E0 5.0000E0 |
|||
6.6700E0 6.0000E0 |
|||
75.8000E0 .5000E0 |
|||
62.0000E0 .7500E0 |
|||
48.8000E0 1.0000E0 |
|||
35.2000E0 1.5000E0 |
|||
20.0000E0 2.0000E0 |
|||
20.3200E0 2.0000E0 |
|||
19.3100E0 2.5000E0 |
|||
12.7500E0 3.0000E0 |
|||
10.4200E0 4.0000E0 |
|||
7.3100E0 5.0000E0 |
|||
7.4200E0 6.0000E0 |
|||
70.5000E0 .5000E0 |
|||
59.5000E0 .7500E0 |
|||
48.5000E0 1.0000E0 |
|||
35.8000E0 1.5000E0 |
|||
21.0000E0 2.0000E0 |
|||
21.6700E0 2.0000E0 |
|||
21.0000E0 2.5000E0 |
|||
15.6400E0 3.0000E0 |
|||
8.1700E0 4.0000E0 |
|||
8.5500E0 5.0000E0 |
|||
10.1200E0 6.0000E0 |
|||
78.0000E0 .5000E0 |
|||
66.0000E0 .6250E0 |
|||
62.0000E0 .7500E0 |
|||
58.0000E0 .8750E0 |
|||
47.7000E0 1.0000E0 |
|||
37.8000E0 1.2500E0 |
|||
20.2000E0 2.2500E0 |
|||
21.0700E0 2.2500E0 |
|||
13.8700E0 2.7500E0 |
|||
9.6700E0 3.2500E0 |
|||
7.7600E0 3.7500E0 |
|||
5.4400E0 4.2500E0 |
|||
4.8700E0 4.7500E0 |
|||
4.0100E0 5.2500E0 |
|||
3.7500E0 5.7500E0 |
|||
24.1900E0 3.0000E0 |
|||
25.7600E0 3.0000E0 |
|||
18.0700E0 3.0000E0 |
|||
11.8100E0 3.0000E0 |
|||
12.0700E0 3.0000E0 |
|||
16.1200E0 3.0000E0 |
|||
70.8000E0 .5000E0 |
|||
54.7000E0 .7500E0 |
|||
48.0000E0 1.0000E0 |
|||
39.8000E0 1.5000E0 |
|||
29.8000E0 2.0000E0 |
|||
23.7000E0 2.5000E0 |
|||
29.6200E0 2.0000E0 |
|||
23.8100E0 2.5000E0 |
|||
17.7000E0 3.0000E0 |
|||
11.5500E0 4.0000E0 |
|||
12.0700E0 5.0000E0 |
|||
8.7400E0 6.0000E0 |
|||
80.7000E0 .5000E0 |
|||
61.3000E0 .7500E0 |
|||
47.5000E0 1.0000E0 |
|||
29.0000E0 1.5000E0 |
|||
24.0000E0 2.0000E0 |
|||
17.7000E0 2.5000E0 |
|||
24.5600E0 2.0000E0 |
|||
18.6700E0 2.5000E0 |
|||
16.2400E0 3.0000E0 |
|||
8.7400E0 4.0000E0 |
|||
7.8700E0 5.0000E0 |
|||
8.5100E0 6.0000E0 |
|||
66.7000E0 .5000E0 |
|||
59.2000E0 .7500E0 |
|||
40.8000E0 1.0000E0 |
|||
30.7000E0 1.5000E0 |
|||
25.7000E0 2.0000E0 |
|||
16.3000E0 2.5000E0 |
|||
25.9900E0 2.0000E0 |
|||
16.9500E0 2.5000E0 |
|||
13.3500E0 3.0000E0 |
|||
8.6200E0 4.0000E0 |
|||
7.2000E0 5.0000E0 |
|||
6.6400E0 6.0000E0 |
|||
13.6900E0 3.0000E0 |
|||
81.0000E0 .5000E0 |
|||
64.5000E0 .7500E0 |
|||
35.5000E0 1.5000E0 |
|||
13.3100E0 3.0000E0 |
|||
4.8700E0 6.0000E0 |
|||
12.9400E0 3.0000E0 |
|||
5.0600E0 6.0000E0 |
|||
15.1900E0 3.0000E0 |
|||
14.6200E0 3.0000E0 |
|||
15.6400E0 3.0000E0 |
|||
25.5000E0 1.7500E0 |
|||
25.9500E0 1.7500E0 |
|||
81.7000E0 .5000E0 |
|||
61.6000E0 .7500E0 |
|||
29.8000E0 1.7500E0 |
|||
29.8100E0 1.7500E0 |
|||
17.1700E0 2.7500E0 |
|||
10.3900E0 3.7500E0 |
|||
28.4000E0 1.7500E0 |
|||
28.6900E0 1.7500E0 |
|||
81.3000E0 .5000E0 |
|||
60.9000E0 .7500E0 |
|||
16.6500E0 2.7500E0 |
|||
10.0500E0 3.7500E0 |
|||
28.9000E0 1.7500E0 |
|||
28.9500E0 1.7500E0 |
|||
@ -0,0 +1,114 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Chwirut2 (Chwirut2.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 43) |
|||
Certified Values (lines 41 to 48) |
|||
Data (lines 61 to 114) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: These data are the result of a NIST study involving |
|||
ultrasonic calibration. The response variable is |
|||
ultrasonic response, and the predictor variable is |
|||
metal distance. |
|||
|
|||
|
|||
|
|||
Reference: Chwirut, D., NIST (197?). |
|||
Ultrasonic Reference Block Study. |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response (y = ultrasonic response) |
|||
1 Predictor (x = metal distance) |
|||
54 Observations |
|||
Lower Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Exponential Class |
|||
3 Parameters (b1 to b3) |
|||
|
|||
y = exp(-b1*x)/(b2+b3*x) + e |
|||
|
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 0.1 0.15 1.6657666537E-01 3.8303286810E-02 |
|||
b2 = 0.01 0.008 5.1653291286E-03 6.6621605126E-04 |
|||
b3 = 0.02 0.010 1.2150007096E-02 1.5304234767E-03 |
|||
|
|||
Residual Sum of Squares: 5.1304802941E+02 |
|||
Residual Standard Deviation: 3.1717133040E+00 |
|||
Degrees of Freedom: 51 |
|||
Number of Observations: 54 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
92.9000E0 0.500E0 |
|||
57.1000E0 1.000E0 |
|||
31.0500E0 1.750E0 |
|||
11.5875E0 3.750E0 |
|||
8.0250E0 5.750E0 |
|||
63.6000E0 0.875E0 |
|||
21.4000E0 2.250E0 |
|||
14.2500E0 3.250E0 |
|||
8.4750E0 5.250E0 |
|||
63.8000E0 0.750E0 |
|||
26.8000E0 1.750E0 |
|||
16.4625E0 2.750E0 |
|||
7.1250E0 4.750E0 |
|||
67.3000E0 0.625E0 |
|||
41.0000E0 1.250E0 |
|||
21.1500E0 2.250E0 |
|||
8.1750E0 4.250E0 |
|||
81.5000E0 .500E0 |
|||
13.1200E0 3.000E0 |
|||
59.9000E0 .750E0 |
|||
14.6200E0 3.000E0 |
|||
32.9000E0 1.500E0 |
|||
5.4400E0 6.000E0 |
|||
12.5600E0 3.000E0 |
|||
5.4400E0 6.000E0 |
|||
32.0000E0 1.500E0 |
|||
13.9500E0 3.000E0 |
|||
75.8000E0 .500E0 |
|||
20.0000E0 2.000E0 |
|||
10.4200E0 4.000E0 |
|||
59.5000E0 .750E0 |
|||
21.6700E0 2.000E0 |
|||
8.5500E0 5.000E0 |
|||
62.0000E0 .750E0 |
|||
20.2000E0 2.250E0 |
|||
7.7600E0 3.750E0 |
|||
3.7500E0 5.750E0 |
|||
11.8100E0 3.000E0 |
|||
54.7000E0 .750E0 |
|||
23.7000E0 2.500E0 |
|||
11.5500E0 4.000E0 |
|||
61.3000E0 .750E0 |
|||
17.7000E0 2.500E0 |
|||
8.7400E0 4.000E0 |
|||
59.2000E0 .750E0 |
|||
16.3000E0 2.500E0 |
|||
8.6200E0 4.000E0 |
|||
81.0000E0 .500E0 |
|||
4.8700E0 6.000E0 |
|||
14.6200E0 3.000E0 |
|||
81.7000E0 .500E0 |
|||
17.1700E0 2.750E0 |
|||
81.3000E0 .500E0 |
|||
28.9000E0 1.750E0 |
|||
@ -0,0 +1,66 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: DanWood (DanWood.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 42) |
|||
Certified Values (lines 41 to 47) |
|||
Data (lines 61 to 66) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: These data and model are described in Daniel and Wood |
|||
(1980), and originally published in E.S.Keeping, |
|||
"Introduction to Statistical Inference," Van Nostrand |
|||
Company, Princeton, NJ, 1962, p. 354. The response |
|||
variable is energy radieted from a carbon filament |
|||
lamp per cm**2 per second, and the predictor variable |
|||
is the absolute temperature of the filament in 1000 |
|||
degrees Kelvin. |
|||
|
|||
Reference: Daniel, C. and F. S. Wood (1980). |
|||
Fitting Equations to Data, Second Edition. |
|||
New York, NY: John Wiley and Sons, pp. 428-431. |
|||
|
|||
|
|||
Data: 1 Response Variable (y = energy) |
|||
1 Predictor Variable (x = temperature) |
|||
6 Observations |
|||
Lower Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Miscellaneous Class |
|||
2 Parameters (b1 and b2) |
|||
|
|||
y = b1*x**b2 + e |
|||
|
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 1 0.7 7.6886226176E-01 1.8281973860E-02 |
|||
b2 = 5 4 3.8604055871E+00 5.1726610913E-02 |
|||
|
|||
Residual Sum of Squares: 4.3173084083E-03 |
|||
Residual Standard Deviation: 3.2853114039E-02 |
|||
Degrees of Freedom: 4 |
|||
Number of Observations: 6 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
2.138E0 1.309E0 |
|||
3.421E0 1.471E0 |
|||
3.597E0 1.490E0 |
|||
4.340E0 1.565E0 |
|||
4.882E0 1.611E0 |
|||
5.660E0 1.680E0 |
|||
@ -0,0 +1,228 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: ENSO (ENSO.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 49) |
|||
Certified Values (lines 41 to 54) |
|||
Data (lines 61 to 228) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: The data are monthly averaged atmospheric pressure |
|||
differences between Easter Island and Darwin, |
|||
Australia. This difference drives the trade winds in |
|||
the southern hemisphere. Fourier analysis of the data |
|||
reveals 3 significant cycles. The annual cycle is the |
|||
strongest, but cycles with periods of approximately 44 |
|||
and 26 months are also present. These cycles |
|||
correspond to the El Nino and the Southern Oscillation. |
|||
Arguments to the SIN and COS functions are in radians. |
|||
|
|||
Reference: Kahaner, D., C. Moler, and S. Nash, (1989). |
|||
Numerical Methods and Software. |
|||
Englewood Cliffs, NJ: Prentice Hall, pp. 441-445. |
|||
|
|||
Data: 1 Response (y = atmospheric pressure) |
|||
1 Predictor (x = time) |
|||
168 Observations |
|||
Average Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Miscellaneous Class |
|||
9 Parameters (b1 to b9) |
|||
|
|||
y = b1 + b2*cos( 2*pi*x/12 ) + b3*sin( 2*pi*x/12 ) |
|||
+ b5*cos( 2*pi*x/b4 ) + b6*sin( 2*pi*x/b4 ) |
|||
+ b8*cos( 2*pi*x/b7 ) + b9*sin( 2*pi*x/b7 ) + e |
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 11.0 10.0 1.0510749193E+01 1.7488832467E-01 |
|||
b2 = 3.0 3.0 3.0762128085E+00 2.4310052139E-01 |
|||
b3 = 0.5 0.5 5.3280138227E-01 2.4354686618E-01 |
|||
b4 = 40.0 44.0 4.4311088700E+01 9.4408025976E-01 |
|||
b5 = -0.7 -1.5 -1.6231428586E+00 2.8078369611E-01 |
|||
b6 = -1.3 0.5 5.2554493756E-01 4.8073701119E-01 |
|||
b7 = 25.0 26.0 2.6887614440E+01 4.1612939130E-01 |
|||
b8 = -0.3 -0.1 2.1232288488E-01 5.1460022911E-01 |
|||
b9 = 1.4 1.5 1.4966870418E+00 2.5434468893E-01 |
|||
|
|||
Residual Sum of Squares: 7.8853978668E+02 |
|||
Residual Standard Deviation: 2.2269642403E+00 |
|||
Degrees of Freedom: 159 |
|||
Number of Observations: 168 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
12.90000 1.000000 |
|||
11.30000 2.000000 |
|||
10.60000 3.000000 |
|||
11.20000 4.000000 |
|||
10.90000 5.000000 |
|||
7.500000 6.000000 |
|||
7.700000 7.000000 |
|||
11.70000 8.000000 |
|||
12.90000 9.000000 |
|||
14.30000 10.000000 |
|||
10.90000 11.00000 |
|||
13.70000 12.00000 |
|||
17.10000 13.00000 |
|||
14.00000 14.00000 |
|||
15.30000 15.00000 |
|||
8.500000 16.00000 |
|||
5.700000 17.00000 |
|||
5.500000 18.00000 |
|||
7.600000 19.00000 |
|||
8.600000 20.00000 |
|||
7.300000 21.00000 |
|||
7.600000 22.00000 |
|||
12.70000 23.00000 |
|||
11.00000 24.00000 |
|||
12.70000 25.00000 |
|||
12.90000 26.00000 |
|||
13.00000 27.00000 |
|||
10.90000 28.00000 |
|||
10.400000 29.00000 |
|||
10.200000 30.00000 |
|||
8.000000 31.00000 |
|||
10.90000 32.00000 |
|||
13.60000 33.00000 |
|||
10.500000 34.00000 |
|||
9.200000 35.00000 |
|||
12.40000 36.00000 |
|||
12.70000 37.00000 |
|||
13.30000 38.00000 |
|||
10.100000 39.00000 |
|||
7.800000 40.00000 |
|||
4.800000 41.00000 |
|||
3.000000 42.00000 |
|||
2.500000 43.00000 |
|||
6.300000 44.00000 |
|||
9.700000 45.00000 |
|||
11.60000 46.00000 |
|||
8.600000 47.00000 |
|||
12.40000 48.00000 |
|||
10.500000 49.00000 |
|||
13.30000 50.00000 |
|||
10.400000 51.00000 |
|||
8.100000 52.00000 |
|||
3.700000 53.00000 |
|||
10.70000 54.00000 |
|||
5.100000 55.00000 |
|||
10.400000 56.00000 |
|||
10.90000 57.00000 |
|||
11.70000 58.00000 |
|||
11.40000 59.00000 |
|||
13.70000 60.00000 |
|||
14.10000 61.00000 |
|||
14.00000 62.00000 |
|||
12.50000 63.00000 |
|||
6.300000 64.00000 |
|||
9.600000 65.00000 |
|||
11.70000 66.00000 |
|||
5.000000 67.00000 |
|||
10.80000 68.00000 |
|||
12.70000 69.00000 |
|||
10.80000 70.00000 |
|||
11.80000 71.00000 |
|||
12.60000 72.00000 |
|||
15.70000 73.00000 |
|||
12.60000 74.00000 |
|||
14.80000 75.00000 |
|||
7.800000 76.00000 |
|||
7.100000 77.00000 |
|||
11.20000 78.00000 |
|||
8.100000 79.00000 |
|||
6.400000 80.00000 |
|||
5.200000 81.00000 |
|||
12.00000 82.00000 |
|||
10.200000 83.00000 |
|||
12.70000 84.00000 |
|||
10.200000 85.00000 |
|||
14.70000 86.00000 |
|||
12.20000 87.00000 |
|||
7.100000 88.00000 |
|||
5.700000 89.00000 |
|||
6.700000 90.00000 |
|||
3.900000 91.00000 |
|||
8.500000 92.00000 |
|||
8.300000 93.00000 |
|||
10.80000 94.00000 |
|||
16.70000 95.00000 |
|||
12.60000 96.00000 |
|||
12.50000 97.00000 |
|||
12.50000 98.00000 |
|||
9.800000 99.00000 |
|||
7.200000 100.00000 |
|||
4.100000 101.00000 |
|||
10.60000 102.00000 |
|||
10.100000 103.00000 |
|||
10.100000 104.00000 |
|||
11.90000 105.00000 |
|||
13.60000 106.0000 |
|||
16.30000 107.0000 |
|||
17.60000 108.0000 |
|||
15.50000 109.0000 |
|||
16.00000 110.0000 |
|||
15.20000 111.0000 |
|||
11.20000 112.0000 |
|||
14.30000 113.0000 |
|||
14.50000 114.0000 |
|||
8.500000 115.0000 |
|||
12.00000 116.0000 |
|||
12.70000 117.0000 |
|||
11.30000 118.0000 |
|||
14.50000 119.0000 |
|||
15.10000 120.0000 |
|||
10.400000 121.0000 |
|||
11.50000 122.0000 |
|||
13.40000 123.0000 |
|||
7.500000 124.0000 |
|||
0.6000000 125.0000 |
|||
0.3000000 126.0000 |
|||
5.500000 127.0000 |
|||
5.000000 128.0000 |
|||
4.600000 129.0000 |
|||
8.200000 130.0000 |
|||
9.900000 131.0000 |
|||
9.200000 132.0000 |
|||
12.50000 133.0000 |
|||
10.90000 134.0000 |
|||
9.900000 135.0000 |
|||
8.900000 136.0000 |
|||
7.600000 137.0000 |
|||
9.500000 138.0000 |
|||
8.400000 139.0000 |
|||
10.70000 140.0000 |
|||
13.60000 141.0000 |
|||
13.70000 142.0000 |
|||
13.70000 143.0000 |
|||
16.50000 144.0000 |
|||
16.80000 145.0000 |
|||
17.10000 146.0000 |
|||
15.40000 147.0000 |
|||
9.500000 148.0000 |
|||
6.100000 149.0000 |
|||
10.100000 150.0000 |
|||
9.300000 151.0000 |
|||
5.300000 152.0000 |
|||
11.20000 153.0000 |
|||
16.60000 154.0000 |
|||
15.60000 155.0000 |
|||
12.00000 156.0000 |
|||
11.50000 157.0000 |
|||
8.600000 158.0000 |
|||
13.80000 159.0000 |
|||
8.700000 160.0000 |
|||
8.600000 161.0000 |
|||
8.600000 162.0000 |
|||
8.700000 163.0000 |
|||
12.80000 164.0000 |
|||
13.20000 165.0000 |
|||
14.00000 166.0000 |
|||
13.40000 167.0000 |
|||
14.80000 168.0000 |
|||
@ -0,0 +1,95 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Eckerle4 (Eckerle4.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 43) |
|||
Certified Values (lines 41 to 48) |
|||
Data (lines 61 to 95) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: These data are the result of a NIST study involving |
|||
circular interference transmittance. The response |
|||
variable is transmittance, and the predictor variable |
|||
is wavelength. |
|||
|
|||
|
|||
Reference: Eckerle, K., NIST (197?). |
|||
Circular Interference Transmittance Study. |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response Variable (y = transmittance) |
|||
1 Predictor Variable (x = wavelength) |
|||
35 Observations |
|||
Higher Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Exponential Class |
|||
3 Parameters (b1 to b3) |
|||
|
|||
y = (b1/b2) * exp[-0.5*((x-b3)/b2)**2] + e |
|||
|
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 1 1.5 1.5543827178E+00 1.5408051163E-02 |
|||
b2 = 10 5 4.0888321754E+00 4.6803020753E-02 |
|||
b3 = 500 450 4.5154121844E+02 4.6800518816E-02 |
|||
|
|||
Residual Sum of Squares: 1.4635887487E-03 |
|||
Residual Standard Deviation: 6.7629245447E-03 |
|||
Degrees of Freedom: 32 |
|||
Number of Observations: 35 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
0.0001575E0 400.000000E0 |
|||
0.0001699E0 405.000000E0 |
|||
0.0002350E0 410.000000E0 |
|||
0.0003102E0 415.000000E0 |
|||
0.0004917E0 420.000000E0 |
|||
0.0008710E0 425.000000E0 |
|||
0.0017418E0 430.000000E0 |
|||
0.0046400E0 435.000000E0 |
|||
0.0065895E0 436.500000E0 |
|||
0.0097302E0 438.000000E0 |
|||
0.0149002E0 439.500000E0 |
|||
0.0237310E0 441.000000E0 |
|||
0.0401683E0 442.500000E0 |
|||
0.0712559E0 444.000000E0 |
|||
0.1264458E0 445.500000E0 |
|||
0.2073413E0 447.000000E0 |
|||
0.2902366E0 448.500000E0 |
|||
0.3445623E0 450.000000E0 |
|||
0.3698049E0 451.500000E0 |
|||
0.3668534E0 453.000000E0 |
|||
0.3106727E0 454.500000E0 |
|||
0.2078154E0 456.000000E0 |
|||
0.1164354E0 457.500000E0 |
|||
0.0616764E0 459.000000E0 |
|||
0.0337200E0 460.500000E0 |
|||
0.0194023E0 462.000000E0 |
|||
0.0117831E0 463.500000E0 |
|||
0.0074357E0 465.000000E0 |
|||
0.0022732E0 470.000000E0 |
|||
0.0008800E0 475.000000E0 |
|||
0.0004579E0 480.000000E0 |
|||
0.0002345E0 485.000000E0 |
|||
0.0001586E0 490.000000E0 |
|||
0.0001143E0 495.000000E0 |
|||
0.0000710E0 500.000000E0 |
|||
@ -0,0 +1,142 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Filip (Filip.dat) |
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 31 to 55) |
|||
Data (lines 61 to 142) |
|||
|
|||
Procedure: Linear Least Squares Regression |
|||
|
|||
Reference: Filippelli, A., NIST. |
|||
|
|||
Data: 1 Response Variable (y) |
|||
1 Predictor Variable (x) |
|||
82 Observations |
|||
Higher Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Polynomial Class |
|||
11 Parameters (B0,B1,...,B10) |
|||
|
|||
y = B0 + B1*x + B2*(x**2) + ... + B9*(x**9) + B10*(x**10) + e |
|||
|
|||
|
|||
|
|||
|
|||
Certified Regression Statistics |
|||
|
|||
Standard Deviation |
|||
Parameter Estimate of Estimate |
|||
|
|||
B0 -1467.48961422980 298.084530995537 |
|||
B1 -2772.17959193342 559.779865474950 |
|||
B2 -2316.37108160893 466.477572127796 |
|||
B3 -1127.97394098372 227.204274477751 |
|||
B4 -354.478233703349 71.6478660875927 |
|||
B5 -75.1242017393757 15.2897178747400 |
|||
B6 -10.8753180355343 2.23691159816033 |
|||
B7 -1.06221498588947 0.221624321934227 |
|||
B8 -0.670191154593408E-01 0.142363763154724E-01 |
|||
B9 -0.246781078275479E-02 0.535617408889821E-03 |
|||
B10 -0.402962525080404E-04 0.896632837373868E-05 |
|||
|
|||
Residual |
|||
Standard Deviation 0.334801051324544E-02 |
|||
|
|||
R-Squared 0.996727416185620 |
|||
|
|||
|
|||
Certified Analysis of Variance Table |
|||
|
|||
Source of Degrees of Sums of Mean |
|||
Variation Freedom Squares Squares F Statistic |
|||
|
|||
Regression 10 0.242391619837339 0.242391619837339E-01 2162.43954511489 |
|||
Residual 71 0.795851382172941E-03 0.112091743968020E-04 |
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
0.8116 -6.860120914 |
|||
0.9072 -4.324130045 |
|||
0.9052 -4.358625055 |
|||
0.9039 -4.358426747 |
|||
0.8053 -6.955852379 |
|||
0.8377 -6.661145254 |
|||
0.8667 -6.355462942 |
|||
0.8809 -6.118102026 |
|||
0.7975 -7.115148017 |
|||
0.8162 -6.815308569 |
|||
0.8515 -6.519993057 |
|||
0.8766 -6.204119983 |
|||
0.8885 -5.853871964 |
|||
0.8859 -6.109523091 |
|||
0.8959 -5.79832982 |
|||
0.8913 -5.482672118 |
|||
0.8959 -5.171791386 |
|||
0.8971 -4.851705903 |
|||
0.9021 -4.517126416 |
|||
0.909 -4.143573228 |
|||
0.9139 -3.709075441 |
|||
0.9199 -3.499489089 |
|||
0.8692 -6.300769497 |
|||
0.8872 -5.953504836 |
|||
0.89 -5.642065153 |
|||
0.891 -5.031376979 |
|||
0.8977 -4.680685696 |
|||
0.9035 -4.329846955 |
|||
0.9078 -3.928486195 |
|||
0.7675 -8.56735134 |
|||
0.7705 -8.363211311 |
|||
0.7713 -8.107682739 |
|||
0.7736 -7.823908741 |
|||
0.7775 -7.522878745 |
|||
0.7841 -7.218819279 |
|||
0.7971 -6.920818754 |
|||
0.8329 -6.628932138 |
|||
0.8641 -6.323946875 |
|||
0.8804 -5.991399828 |
|||
0.7668 -8.781464495 |
|||
0.7633 -8.663140179 |
|||
0.7678 -8.473531488 |
|||
0.7697 -8.247337057 |
|||
0.77 -7.971428747 |
|||
0.7749 -7.676129393 |
|||
0.7796 -7.352812702 |
|||
0.7897 -7.072065318 |
|||
0.8131 -6.774174009 |
|||
0.8498 -6.478861916 |
|||
0.8741 -6.159517513 |
|||
0.8061 -6.835647144 |
|||
0.846 -6.53165267 |
|||
0.8751 -6.224098421 |
|||
0.8856 -5.910094889 |
|||
0.8919 -5.598599459 |
|||
0.8934 -5.290645224 |
|||
0.894 -4.974284616 |
|||
0.8957 -4.64454848 |
|||
0.9047 -4.290560426 |
|||
0.9129 -3.885055584 |
|||
0.9209 -3.408378962 |
|||
0.9219 -3.13200249 |
|||
0.7739 -8.726767166 |
|||
0.7681 -8.66695597 |
|||
0.7665 -8.511026475 |
|||
0.7703 -8.165388579 |
|||
0.7702 -7.886056648 |
|||
0.7761 -7.588043762 |
|||
0.7809 -7.283412422 |
|||
0.7961 -6.995678626 |
|||
0.8253 -6.691862621 |
|||
0.8602 -6.392544977 |
|||
0.8809 -6.067374056 |
|||
0.8301 -6.684029655 |
|||
0.8664 -6.378719832 |
|||
0.8834 -6.065855188 |
|||
0.8898 -5.752272167 |
|||
0.8964 -5.132414673 |
|||
0.8963 -4.811352704 |
|||
0.9074 -4.098269308 |
|||
0.9119 -3.66174277 |
|||
0.9228 -3.2644011 |
|||
@ -0,0 +1,310 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Gauss1 (Gauss1.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 48) |
|||
Certified Values (lines 41 to 53) |
|||
Data (lines 61 to 310) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: The data are two well-separated Gaussians on a |
|||
decaying exponential baseline plus normally |
|||
distributed zero-mean noise with variance = 6.25. |
|||
|
|||
Reference: Rust, B., NIST (1996). |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response (y) |
|||
1 Predictor (x) |
|||
250 Observations |
|||
Lower Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Exponential Class |
|||
8 Parameters (b1 to b8) |
|||
|
|||
y = b1*exp( -b2*x ) + b3*exp( -(x-b4)**2 / b5**2 ) |
|||
+ b6*exp( -(x-b7)**2 / b8**2 ) + e |
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 97.0 94.0 9.8778210871E+01 5.7527312730E-01 |
|||
b2 = 0.009 0.0105 1.0497276517E-02 1.1406289017E-04 |
|||
b3 = 100.0 99.0 1.0048990633E+02 5.8831775752E-01 |
|||
b4 = 65.0 63.0 6.7481111276E+01 1.0460593412E-01 |
|||
b5 = 20.0 25.0 2.3129773360E+01 1.7439951146E-01 |
|||
b6 = 70.0 71.0 7.1994503004E+01 6.2622793913E-01 |
|||
b7 = 178.0 180.0 1.7899805021E+02 1.2436988217E-01 |
|||
b8 = 16.5 20.0 1.8389389025E+01 2.0134312832E-01 |
|||
|
|||
Residual Sum of Squares: 1.3158222432E+03 |
|||
Residual Standard Deviation: 2.3317980180E+00 |
|||
Degrees of Freedom: 242 |
|||
Number of Observations: 250 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
97.62227 1.000000 |
|||
97.80724 2.000000 |
|||
96.62247 3.000000 |
|||
92.59022 4.000000 |
|||
91.23869 5.000000 |
|||
95.32704 6.000000 |
|||
90.35040 7.000000 |
|||
89.46235 8.000000 |
|||
91.72520 9.000000 |
|||
89.86916 10.000000 |
|||
86.88076 11.00000 |
|||
85.94360 12.00000 |
|||
87.60686 13.00000 |
|||
86.25839 14.00000 |
|||
80.74976 15.00000 |
|||
83.03551 16.00000 |
|||
88.25837 17.00000 |
|||
82.01316 18.00000 |
|||
82.74098 19.00000 |
|||
83.30034 20.00000 |
|||
81.27850 21.00000 |
|||
81.85506 22.00000 |
|||
80.75195 23.00000 |
|||
80.09573 24.00000 |
|||
81.07633 25.00000 |
|||
78.81542 26.00000 |
|||
78.38596 27.00000 |
|||
79.93386 28.00000 |
|||
79.48474 29.00000 |
|||
79.95942 30.00000 |
|||
76.10691 31.00000 |
|||
78.39830 32.00000 |
|||
81.43060 33.00000 |
|||
82.48867 34.00000 |
|||
81.65462 35.00000 |
|||
80.84323 36.00000 |
|||
88.68663 37.00000 |
|||
84.74438 38.00000 |
|||
86.83934 39.00000 |
|||
85.97739 40.00000 |
|||
91.28509 41.00000 |
|||
97.22411 42.00000 |
|||
93.51733 43.00000 |
|||
94.10159 44.00000 |
|||
101.91760 45.00000 |
|||
98.43134 46.00000 |
|||
110.4214 47.00000 |
|||
107.6628 48.00000 |
|||
111.7288 49.00000 |
|||
116.5115 50.00000 |
|||
120.7609 51.00000 |
|||
123.9553 52.00000 |
|||
124.2437 53.00000 |
|||
130.7996 54.00000 |
|||
133.2960 55.00000 |
|||
130.7788 56.00000 |
|||
132.0565 57.00000 |
|||
138.6584 58.00000 |
|||
142.9252 59.00000 |
|||
142.7215 60.00000 |
|||
144.1249 61.00000 |
|||
147.4377 62.00000 |
|||
148.2647 63.00000 |
|||
152.0519 64.00000 |
|||
147.3863 65.00000 |
|||
149.2074 66.00000 |
|||
148.9537 67.00000 |
|||
144.5876 68.00000 |
|||
148.1226 69.00000 |
|||
148.0144 70.00000 |
|||
143.8893 71.00000 |
|||
140.9088 72.00000 |
|||
143.4434 73.00000 |
|||
139.3938 74.00000 |
|||
135.9878 75.00000 |
|||
136.3927 76.00000 |
|||
126.7262 77.00000 |
|||
124.4487 78.00000 |
|||
122.8647 79.00000 |
|||
113.8557 80.00000 |
|||
113.7037 81.00000 |
|||
106.8407 82.00000 |
|||
107.0034 83.00000 |
|||
102.46290 84.00000 |
|||
96.09296 85.00000 |
|||
94.57555 86.00000 |
|||
86.98824 87.00000 |
|||
84.90154 88.00000 |
|||
81.18023 89.00000 |
|||
76.40117 90.00000 |
|||
67.09200 91.00000 |
|||
72.67155 92.00000 |
|||
68.10848 93.00000 |
|||
67.99088 94.00000 |
|||
63.34094 95.00000 |
|||
60.55253 96.00000 |
|||
56.18687 97.00000 |
|||
53.64482 98.00000 |
|||
53.70307 99.00000 |
|||
48.07893 100.00000 |
|||
42.21258 101.00000 |
|||
45.65181 102.00000 |
|||
41.69728 103.00000 |
|||
41.24946 104.00000 |
|||
39.21349 105.00000 |
|||
37.71696 106.0000 |
|||
36.68395 107.0000 |
|||
37.30393 108.0000 |
|||
37.43277 109.0000 |
|||
37.45012 110.0000 |
|||
32.64648 111.0000 |
|||
31.84347 112.0000 |
|||
31.39951 113.0000 |
|||
26.68912 114.0000 |
|||
32.25323 115.0000 |
|||
27.61008 116.0000 |
|||
33.58649 117.0000 |
|||
28.10714 118.0000 |
|||
30.26428 119.0000 |
|||
28.01648 120.0000 |
|||
29.11021 121.0000 |
|||
23.02099 122.0000 |
|||
25.65091 123.0000 |
|||
28.50295 124.0000 |
|||
25.23701 125.0000 |
|||
26.13828 126.0000 |
|||
33.53260 127.0000 |
|||
29.25195 128.0000 |
|||
27.09847 129.0000 |
|||
26.52999 130.0000 |
|||
25.52401 131.0000 |
|||
26.69218 132.0000 |
|||
24.55269 133.0000 |
|||
27.71763 134.0000 |
|||
25.20297 135.0000 |
|||
25.61483 136.0000 |
|||
25.06893 137.0000 |
|||
27.63930 138.0000 |
|||
24.94851 139.0000 |
|||
25.86806 140.0000 |
|||
22.48183 141.0000 |
|||
26.90045 142.0000 |
|||
25.39919 143.0000 |
|||
17.90614 144.0000 |
|||
23.76039 145.0000 |
|||
25.89689 146.0000 |
|||
27.64231 147.0000 |
|||
22.86101 148.0000 |
|||
26.47003 149.0000 |
|||
23.72888 150.0000 |
|||
27.54334 151.0000 |
|||
30.52683 152.0000 |
|||
28.07261 153.0000 |
|||
34.92815 154.0000 |
|||
28.29194 155.0000 |
|||
34.19161 156.0000 |
|||
35.41207 157.0000 |
|||
37.09336 158.0000 |
|||
40.98330 159.0000 |
|||
39.53923 160.0000 |
|||
47.80123 161.0000 |
|||
47.46305 162.0000 |
|||
51.04166 163.0000 |
|||
54.58065 164.0000 |
|||
57.53001 165.0000 |
|||
61.42089 166.0000 |
|||
62.79032 167.0000 |
|||
68.51455 168.0000 |
|||
70.23053 169.0000 |
|||
74.42776 170.0000 |
|||
76.59911 171.0000 |
|||
81.62053 172.0000 |
|||
83.42208 173.0000 |
|||
79.17451 174.0000 |
|||
88.56985 175.0000 |
|||
85.66525 176.0000 |
|||
86.55502 177.0000 |
|||
90.65907 178.0000 |
|||
84.27290 179.0000 |
|||
85.72220 180.0000 |
|||
83.10702 181.0000 |
|||
82.16884 182.0000 |
|||
80.42568 183.0000 |
|||
78.15692 184.0000 |
|||
79.79691 185.0000 |
|||
77.84378 186.0000 |
|||
74.50327 187.0000 |
|||
71.57289 188.0000 |
|||
65.88031 189.0000 |
|||
65.01385 190.0000 |
|||
60.19582 191.0000 |
|||
59.66726 192.0000 |
|||
52.95478 193.0000 |
|||
53.87792 194.0000 |
|||
44.91274 195.0000 |
|||
41.09909 196.0000 |
|||
41.68018 197.0000 |
|||
34.53379 198.0000 |
|||
34.86419 199.0000 |
|||
33.14787 200.0000 |
|||
29.58864 201.0000 |
|||
27.29462 202.0000 |
|||
21.91439 203.0000 |
|||
19.08159 204.0000 |
|||
24.90290 205.0000 |
|||
19.82341 206.0000 |
|||
16.75551 207.0000 |
|||
18.24558 208.0000 |
|||
17.23549 209.0000 |
|||
16.34934 210.0000 |
|||
13.71285 211.0000 |
|||
14.75676 212.0000 |
|||
13.97169 213.0000 |
|||
12.42867 214.0000 |
|||
14.35519 215.0000 |
|||
7.703309 216.0000 |
|||
10.234410 217.0000 |
|||
11.78315 218.0000 |
|||
13.87768 219.0000 |
|||
4.535700 220.0000 |
|||
10.059280 221.0000 |
|||
8.424824 222.0000 |
|||
10.533120 223.0000 |
|||
9.602255 224.0000 |
|||
7.877514 225.0000 |
|||
6.258121 226.0000 |
|||
8.899865 227.0000 |
|||
7.877754 228.0000 |
|||
12.51191 229.0000 |
|||
10.66205 230.0000 |
|||
6.035400 231.0000 |
|||
6.790655 232.0000 |
|||
8.783535 233.0000 |
|||
4.600288 234.0000 |
|||
8.400915 235.0000 |
|||
7.216561 236.0000 |
|||
10.017410 237.0000 |
|||
7.331278 238.0000 |
|||
6.527863 239.0000 |
|||
2.842001 240.0000 |
|||
10.325070 241.0000 |
|||
4.790995 242.0000 |
|||
8.377101 243.0000 |
|||
6.264445 244.0000 |
|||
2.706213 245.0000 |
|||
8.362329 246.0000 |
|||
8.983658 247.0000 |
|||
3.362571 248.0000 |
|||
1.182746 249.0000 |
|||
4.875359 250.0000 |
|||
@ -0,0 +1,310 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Gauss2 (Gauss2.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 48) |
|||
Certified Values (lines 41 to 53) |
|||
Data (lines 61 to 310) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: The data are two slightly-blended Gaussians on a |
|||
decaying exponential baseline plus normally |
|||
distributed zero-mean noise with variance = 6.25. |
|||
|
|||
Reference: Rust, B., NIST (1996). |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response (y) |
|||
1 Predictor (x) |
|||
250 Observations |
|||
Lower Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Exponential Class |
|||
8 Parameters (b1 to b8) |
|||
|
|||
y = b1*exp( -b2*x ) + b3*exp( -(x-b4)**2 / b5**2 ) |
|||
+ b6*exp( -(x-b7)**2 / b8**2 ) + e |
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 96.0 98.0 9.9018328406E+01 5.3748766879E-01 |
|||
b2 = 0.009 0.0105 1.0994945399E-02 1.3335306766E-04 |
|||
b3 = 103.0 103.0 1.0188022528E+02 5.9217315772E-01 |
|||
b4 = 106.0 105.0 1.0703095519E+02 1.5006798316E-01 |
|||
b5 = 18.0 20.0 2.3578584029E+01 2.2695595067E-01 |
|||
b6 = 72.0 73.0 7.2045589471E+01 6.1721965884E-01 |
|||
b7 = 151.0 150.0 1.5327010194E+02 1.9466674341E-01 |
|||
b8 = 18.0 20.0 1.9525972636E+01 2.6416549393E-01 |
|||
|
|||
Residual Sum of Squares: 1.2475282092E+03 |
|||
Residual Standard Deviation: 2.2704790782E+00 |
|||
Degrees of Freedom: 242 |
|||
Number of Observations: 250 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
97.58776 1.000000 |
|||
97.76344 2.000000 |
|||
96.56705 3.000000 |
|||
92.52037 4.000000 |
|||
91.15097 5.000000 |
|||
95.21728 6.000000 |
|||
90.21355 7.000000 |
|||
89.29235 8.000000 |
|||
91.51479 9.000000 |
|||
89.60966 10.000000 |
|||
86.56187 11.00000 |
|||
85.55316 12.00000 |
|||
87.13054 13.00000 |
|||
85.67940 14.00000 |
|||
80.04851 15.00000 |
|||
82.18925 16.00000 |
|||
87.24081 17.00000 |
|||
80.79407 18.00000 |
|||
81.28570 19.00000 |
|||
81.56940 20.00000 |
|||
79.22715 21.00000 |
|||
79.43275 22.00000 |
|||
77.90195 23.00000 |
|||
76.75468 24.00000 |
|||
77.17377 25.00000 |
|||
74.27348 26.00000 |
|||
73.11900 27.00000 |
|||
73.84826 28.00000 |
|||
72.47870 29.00000 |
|||
71.92292 30.00000 |
|||
66.92176 31.00000 |
|||
67.93835 32.00000 |
|||
69.56207 33.00000 |
|||
69.07066 34.00000 |
|||
66.53983 35.00000 |
|||
63.87883 36.00000 |
|||
69.71537 37.00000 |
|||
63.60588 38.00000 |
|||
63.37154 39.00000 |
|||
60.01835 40.00000 |
|||
62.67481 41.00000 |
|||
65.80666 42.00000 |
|||
59.14304 43.00000 |
|||
56.62951 44.00000 |
|||
61.21785 45.00000 |
|||
54.38790 46.00000 |
|||
62.93443 47.00000 |
|||
56.65144 48.00000 |
|||
57.13362 49.00000 |
|||
58.29689 50.00000 |
|||
58.91744 51.00000 |
|||
58.50172 52.00000 |
|||
55.22885 53.00000 |
|||
58.30375 54.00000 |
|||
57.43237 55.00000 |
|||
51.69407 56.00000 |
|||
49.93132 57.00000 |
|||
53.70760 58.00000 |
|||
55.39712 59.00000 |
|||
52.89709 60.00000 |
|||
52.31649 61.00000 |
|||
53.98720 62.00000 |
|||
53.54158 63.00000 |
|||
56.45046 64.00000 |
|||
51.32276 65.00000 |
|||
53.11676 66.00000 |
|||
53.28631 67.00000 |
|||
49.80555 68.00000 |
|||
54.69564 69.00000 |
|||
56.41627 70.00000 |
|||
54.59362 71.00000 |
|||
54.38520 72.00000 |
|||
60.15354 73.00000 |
|||
59.78773 74.00000 |
|||
60.49995 75.00000 |
|||
65.43885 76.00000 |
|||
60.70001 77.00000 |
|||
63.71865 78.00000 |
|||
67.77139 79.00000 |
|||
64.70934 80.00000 |
|||
70.78193 81.00000 |
|||
70.38651 82.00000 |
|||
77.22359 83.00000 |
|||
79.52665 84.00000 |
|||
80.13077 85.00000 |
|||
85.67823 86.00000 |
|||
85.20647 87.00000 |
|||
90.24548 88.00000 |
|||
93.61953 89.00000 |
|||
95.86509 90.00000 |
|||
93.46992 91.00000 |
|||
105.8137 92.00000 |
|||
107.8269 93.00000 |
|||
114.0607 94.00000 |
|||
115.5019 95.00000 |
|||
118.5110 96.00000 |
|||
119.6177 97.00000 |
|||
122.1940 98.00000 |
|||
126.9903 99.00000 |
|||
125.7005 100.00000 |
|||
123.7447 101.00000 |
|||
130.6543 102.00000 |
|||
129.7168 103.00000 |
|||
131.8240 104.00000 |
|||
131.8759 105.00000 |
|||
131.9994 106.0000 |
|||
132.1221 107.0000 |
|||
133.4414 108.0000 |
|||
133.8252 109.0000 |
|||
133.6695 110.0000 |
|||
128.2851 111.0000 |
|||
126.5182 112.0000 |
|||
124.7550 113.0000 |
|||
118.4016 114.0000 |
|||
122.0334 115.0000 |
|||
115.2059 116.0000 |
|||
118.7856 117.0000 |
|||
110.7387 118.0000 |
|||
110.2003 119.0000 |
|||
105.17290 120.0000 |
|||
103.44720 121.0000 |
|||
94.54280 122.0000 |
|||
94.40526 123.0000 |
|||
94.57964 124.0000 |
|||
88.76605 125.0000 |
|||
87.28747 126.0000 |
|||
92.50443 127.0000 |
|||
86.27997 128.0000 |
|||
82.44307 129.0000 |
|||
80.47367 130.0000 |
|||
78.36608 131.0000 |
|||
78.74307 132.0000 |
|||
76.12786 133.0000 |
|||
79.13108 134.0000 |
|||
76.76062 135.0000 |
|||
77.60769 136.0000 |
|||
77.76633 137.0000 |
|||
81.28220 138.0000 |
|||
79.74307 139.0000 |
|||
81.97964 140.0000 |
|||
80.02952 141.0000 |
|||
85.95232 142.0000 |
|||
85.96838 143.0000 |
|||
79.94789 144.0000 |
|||
87.17023 145.0000 |
|||
90.50992 146.0000 |
|||
93.23373 147.0000 |
|||
89.14803 148.0000 |
|||
93.11492 149.0000 |
|||
90.34337 150.0000 |
|||
93.69421 151.0000 |
|||
95.74256 152.0000 |
|||
91.85105 153.0000 |
|||
96.74503 154.0000 |
|||
87.60996 155.0000 |
|||
90.47012 156.0000 |
|||
88.11690 157.0000 |
|||
85.70673 158.0000 |
|||
85.01361 159.0000 |
|||
78.53040 160.0000 |
|||
81.34148 161.0000 |
|||
75.19295 162.0000 |
|||
72.66115 163.0000 |
|||
69.85504 164.0000 |
|||
66.29476 165.0000 |
|||
63.58502 166.0000 |
|||
58.33847 167.0000 |
|||
57.50766 168.0000 |
|||
52.80498 169.0000 |
|||
50.79319 170.0000 |
|||
47.03490 171.0000 |
|||
46.47090 172.0000 |
|||
43.09016 173.0000 |
|||
34.11531 174.0000 |
|||
39.28235 175.0000 |
|||
32.68386 176.0000 |
|||
30.44056 177.0000 |
|||
31.98932 178.0000 |
|||
23.63330 179.0000 |
|||
23.69643 180.0000 |
|||
20.26812 181.0000 |
|||
19.07074 182.0000 |
|||
17.59544 183.0000 |
|||
16.08785 184.0000 |
|||
18.94267 185.0000 |
|||
18.61354 186.0000 |
|||
17.25800 187.0000 |
|||
16.62285 188.0000 |
|||
13.48367 189.0000 |
|||
15.37647 190.0000 |
|||
13.47208 191.0000 |
|||
15.96188 192.0000 |
|||
12.32547 193.0000 |
|||
16.33880 194.0000 |
|||
10.438330 195.0000 |
|||
9.628715 196.0000 |
|||
13.12268 197.0000 |
|||
8.772417 198.0000 |
|||
11.76143 199.0000 |
|||
12.55020 200.0000 |
|||
11.33108 201.0000 |
|||
11.20493 202.0000 |
|||
7.816916 203.0000 |
|||
6.800675 204.0000 |
|||
14.26581 205.0000 |
|||
10.66285 206.0000 |
|||
8.911574 207.0000 |
|||
11.56733 208.0000 |
|||
11.58207 209.0000 |
|||
11.59071 210.0000 |
|||
9.730134 211.0000 |
|||
11.44237 212.0000 |
|||
11.22912 213.0000 |
|||
10.172130 214.0000 |
|||
12.50905 215.0000 |
|||
6.201493 216.0000 |
|||
9.019605 217.0000 |
|||
10.80607 218.0000 |
|||
13.09625 219.0000 |
|||
3.914271 220.0000 |
|||
9.567886 221.0000 |
|||
8.038448 222.0000 |
|||
10.231040 223.0000 |
|||
9.367410 224.0000 |
|||
7.695971 225.0000 |
|||
6.118575 226.0000 |
|||
8.793207 227.0000 |
|||
7.796692 228.0000 |
|||
12.45065 229.0000 |
|||
10.61601 230.0000 |
|||
6.001003 231.0000 |
|||
6.765098 232.0000 |
|||
8.764653 233.0000 |
|||
4.586418 234.0000 |
|||
8.390783 235.0000 |
|||
7.209202 236.0000 |
|||
10.012090 237.0000 |
|||
7.327461 238.0000 |
|||
6.525136 239.0000 |
|||
2.840065 240.0000 |
|||
10.323710 241.0000 |
|||
4.790035 242.0000 |
|||
8.376431 243.0000 |
|||
6.263980 244.0000 |
|||
2.705892 245.0000 |
|||
8.362109 246.0000 |
|||
8.983507 247.0000 |
|||
3.362469 248.0000 |
|||
1.182678 249.0000 |
|||
4.875312 250.0000 |
|||
@ -0,0 +1,310 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Gauss3 (Gauss3.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 48) |
|||
Certified Values (lines 41 to 53) |
|||
Data (lines 61 to 310) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: The data are two strongly-blended Gaussians on a |
|||
decaying exponential baseline plus normally |
|||
distributed zero-mean noise with variance = 6.25. |
|||
|
|||
Reference: Rust, B., NIST (1996). |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response (y) |
|||
1 Predictor (x) |
|||
250 Observations |
|||
Average Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Exponential Class |
|||
8 Parameters (b1 to b8) |
|||
|
|||
y = b1*exp( -b2*x ) + b3*exp( -(x-b4)**2 / b5**2 ) |
|||
+ b6*exp( -(x-b7)**2 / b8**2 ) + e |
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 94.9 96.0 9.8940368970E+01 5.3005192833E-01 |
|||
b2 = 0.009 0.0096 1.0945879335E-02 1.2554058911E-04 |
|||
b3 = 90.1 80.0 1.0069553078E+02 8.1256587317E-01 |
|||
b4 = 113.0 110.0 1.1163619459E+02 3.5317859757E-01 |
|||
b5 = 20.0 25.0 2.3300500029E+01 3.6584783023E-01 |
|||
b6 = 73.8 74.0 7.3705031418E+01 1.2091239082E+00 |
|||
b7 = 140.0 139.0 1.4776164251E+02 4.0488183351E-01 |
|||
b8 = 20.0 25.0 1.9668221230E+01 3.7806634336E-01 |
|||
|
|||
Residual Sum of Squares: 1.2444846360E+03 |
|||
Residual Standard Deviation: 2.2677077625E+00 |
|||
Degrees of Freedom: 242 |
|||
Number of Observations: 250 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
97.58776 1.000000 |
|||
97.76344 2.000000 |
|||
96.56705 3.000000 |
|||
92.52037 4.000000 |
|||
91.15097 5.000000 |
|||
95.21728 6.000000 |
|||
90.21355 7.000000 |
|||
89.29235 8.000000 |
|||
91.51479 9.000000 |
|||
89.60965 10.000000 |
|||
86.56187 11.00000 |
|||
85.55315 12.00000 |
|||
87.13053 13.00000 |
|||
85.67938 14.00000 |
|||
80.04849 15.00000 |
|||
82.18922 16.00000 |
|||
87.24078 17.00000 |
|||
80.79401 18.00000 |
|||
81.28564 19.00000 |
|||
81.56932 20.00000 |
|||
79.22703 21.00000 |
|||
79.43259 22.00000 |
|||
77.90174 23.00000 |
|||
76.75438 24.00000 |
|||
77.17338 25.00000 |
|||
74.27296 26.00000 |
|||
73.11830 27.00000 |
|||
73.84732 28.00000 |
|||
72.47746 29.00000 |
|||
71.92128 30.00000 |
|||
66.91962 31.00000 |
|||
67.93554 32.00000 |
|||
69.55841 33.00000 |
|||
69.06592 34.00000 |
|||
66.53371 35.00000 |
|||
63.87094 36.00000 |
|||
69.70526 37.00000 |
|||
63.59295 38.00000 |
|||
63.35509 39.00000 |
|||
59.99747 40.00000 |
|||
62.64843 41.00000 |
|||
65.77345 42.00000 |
|||
59.10141 43.00000 |
|||
56.57750 44.00000 |
|||
61.15313 45.00000 |
|||
54.30767 46.00000 |
|||
62.83535 47.00000 |
|||
56.52957 48.00000 |
|||
56.98427 49.00000 |
|||
58.11459 50.00000 |
|||
58.69576 51.00000 |
|||
58.23322 52.00000 |
|||
54.90490 53.00000 |
|||
57.91442 54.00000 |
|||
56.96629 55.00000 |
|||
51.13831 56.00000 |
|||
49.27123 57.00000 |
|||
52.92668 58.00000 |
|||
54.47693 59.00000 |
|||
51.81710 60.00000 |
|||
51.05401 61.00000 |
|||
52.51731 62.00000 |
|||
51.83710 63.00000 |
|||
54.48196 64.00000 |
|||
49.05859 65.00000 |
|||
50.52315 66.00000 |
|||
50.32755 67.00000 |
|||
46.44419 68.00000 |
|||
50.89281 69.00000 |
|||
52.13203 70.00000 |
|||
49.78741 71.00000 |
|||
49.01637 72.00000 |
|||
54.18198 73.00000 |
|||
53.17456 74.00000 |
|||
53.20827 75.00000 |
|||
57.43459 76.00000 |
|||
51.95282 77.00000 |
|||
54.20282 78.00000 |
|||
57.46687 79.00000 |
|||
53.60268 80.00000 |
|||
58.86728 81.00000 |
|||
57.66652 82.00000 |
|||
63.71034 83.00000 |
|||
65.24244 84.00000 |
|||
65.10878 85.00000 |
|||
69.96313 86.00000 |
|||
68.85475 87.00000 |
|||
73.32574 88.00000 |
|||
76.21241 89.00000 |
|||
78.06311 90.00000 |
|||
75.37701 91.00000 |
|||
87.54449 92.00000 |
|||
89.50588 93.00000 |
|||
95.82098 94.00000 |
|||
97.48390 95.00000 |
|||
100.86070 96.00000 |
|||
102.48510 97.00000 |
|||
105.7311 98.00000 |
|||
111.3489 99.00000 |
|||
111.0305 100.00000 |
|||
110.1920 101.00000 |
|||
118.3581 102.00000 |
|||
118.8086 103.00000 |
|||
122.4249 104.00000 |
|||
124.0953 105.00000 |
|||
125.9337 106.0000 |
|||
127.8533 107.0000 |
|||
131.0361 108.0000 |
|||
133.3343 109.0000 |
|||
135.1278 110.0000 |
|||
131.7113 111.0000 |
|||
131.9151 112.0000 |
|||
132.1107 113.0000 |
|||
127.6898 114.0000 |
|||
133.2148 115.0000 |
|||
128.2296 116.0000 |
|||
133.5902 117.0000 |
|||
127.2539 118.0000 |
|||
128.3482 119.0000 |
|||
124.8694 120.0000 |
|||
124.6031 121.0000 |
|||
117.0648 122.0000 |
|||
118.1966 123.0000 |
|||
119.5408 124.0000 |
|||
114.7946 125.0000 |
|||
114.2780 126.0000 |
|||
120.3484 127.0000 |
|||
114.8647 128.0000 |
|||
111.6514 129.0000 |
|||
110.1826 130.0000 |
|||
108.4461 131.0000 |
|||
109.0571 132.0000 |
|||
106.5308 133.0000 |
|||
109.4691 134.0000 |
|||
106.8709 135.0000 |
|||
107.3192 136.0000 |
|||
106.9000 137.0000 |
|||
109.6526 138.0000 |
|||
107.1602 139.0000 |
|||
108.2509 140.0000 |
|||
104.96310 141.0000 |
|||
109.3601 142.0000 |
|||
107.6696 143.0000 |
|||
99.77286 144.0000 |
|||
104.96440 145.0000 |
|||
106.1376 146.0000 |
|||
106.5816 147.0000 |
|||
100.12860 148.0000 |
|||
101.66910 149.0000 |
|||
96.44254 150.0000 |
|||
97.34169 151.0000 |
|||
96.97412 152.0000 |
|||
90.73460 153.0000 |
|||
93.37949 154.0000 |
|||
82.12331 155.0000 |
|||
83.01657 156.0000 |
|||
78.87360 157.0000 |
|||
74.86971 158.0000 |
|||
72.79341 159.0000 |
|||
65.14744 160.0000 |
|||
67.02127 161.0000 |
|||
60.16136 162.0000 |
|||
57.13996 163.0000 |
|||
54.05769 164.0000 |
|||
50.42265 165.0000 |
|||
47.82430 166.0000 |
|||
42.85748 167.0000 |
|||
42.45495 168.0000 |
|||
38.30808 169.0000 |
|||
36.95794 170.0000 |
|||
33.94543 171.0000 |
|||
34.19017 172.0000 |
|||
31.66097 173.0000 |
|||
23.56172 174.0000 |
|||
29.61143 175.0000 |
|||
23.88765 176.0000 |
|||
22.49812 177.0000 |
|||
24.86901 178.0000 |
|||
17.29481 179.0000 |
|||
18.09291 180.0000 |
|||
15.34813 181.0000 |
|||
14.77997 182.0000 |
|||
13.87832 183.0000 |
|||
12.88891 184.0000 |
|||
16.20763 185.0000 |
|||
16.29024 186.0000 |
|||
15.29712 187.0000 |
|||
14.97839 188.0000 |
|||
12.11330 189.0000 |
|||
14.24168 190.0000 |
|||
12.53824 191.0000 |
|||
15.19818 192.0000 |
|||
11.70478 193.0000 |
|||
15.83745 194.0000 |
|||
10.035850 195.0000 |
|||
9.307574 196.0000 |
|||
12.86800 197.0000 |
|||
8.571671 198.0000 |
|||
11.60415 199.0000 |
|||
12.42772 200.0000 |
|||
11.23627 201.0000 |
|||
11.13198 202.0000 |
|||
7.761117 203.0000 |
|||
6.758250 204.0000 |
|||
14.23375 205.0000 |
|||
10.63876 206.0000 |
|||
8.893581 207.0000 |
|||
11.55398 208.0000 |
|||
11.57221 209.0000 |
|||
11.58347 210.0000 |
|||
9.724857 211.0000 |
|||
11.43854 212.0000 |
|||
11.22636 213.0000 |
|||
10.170150 214.0000 |
|||
12.50765 215.0000 |
|||
6.200494 216.0000 |
|||
9.018902 217.0000 |
|||
10.80557 218.0000 |
|||
13.09591 219.0000 |
|||
3.914033 220.0000 |
|||
9.567723 221.0000 |
|||
8.038338 222.0000 |
|||
10.230960 223.0000 |
|||
9.367358 224.0000 |
|||
7.695937 225.0000 |
|||
6.118552 226.0000 |
|||
8.793192 227.0000 |
|||
7.796682 228.0000 |
|||
12.45064 229.0000 |
|||
10.61601 230.0000 |
|||
6.001000 231.0000 |
|||
6.765096 232.0000 |
|||
8.764652 233.0000 |
|||
4.586417 234.0000 |
|||
8.390782 235.0000 |
|||
7.209201 236.0000 |
|||
10.012090 237.0000 |
|||
7.327461 238.0000 |
|||
6.525136 239.0000 |
|||
2.840065 240.0000 |
|||
10.323710 241.0000 |
|||
4.790035 242.0000 |
|||
8.376431 243.0000 |
|||
6.263980 244.0000 |
|||
2.705892 245.0000 |
|||
8.362109 246.0000 |
|||
8.983507 247.0000 |
|||
3.362469 248.0000 |
|||
1.182678 249.0000 |
|||
4.875312 250.0000 |
|||
@ -0,0 +1,296 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Hahn1 (Hahn1.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 47) |
|||
Certified Values (lines 41 to 52) |
|||
Data (lines 61 to 296) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: These data are the result of a NIST study involving |
|||
the thermal expansion of copper. The response |
|||
variable is the coefficient of thermal expansion, and |
|||
the predictor variable is temperature in degrees |
|||
kelvin. |
|||
|
|||
|
|||
Reference: Hahn, T., NIST (197?). |
|||
Copper Thermal Expansion Study. |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response (y = coefficient of thermal expansion) |
|||
1 Predictor (x = temperature, degrees kelvin) |
|||
236 Observations |
|||
Average Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Rational Class (cubic/cubic) |
|||
7 Parameters (b1 to b7) |
|||
|
|||
y = (b1+b2*x+b3*x**2+b4*x**3) / |
|||
(1+b5*x+b6*x**2+b7*x**3) + e |
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 10 1 1.0776351733E+00 1.7070154742E-01 |
|||
b2 = -1 -0.1 -1.2269296921E-01 1.2000289189E-02 |
|||
b3 = 0.05 0.005 4.0863750610E-03 2.2508314937E-04 |
|||
b4 = -0.00001 -0.000001 -1.4262662514E-06 2.7578037666E-07 |
|||
b5 = -0.05 -0.005 -5.7609940901E-03 2.4712888219E-04 |
|||
b6 = 0.001 0.0001 2.4053735503E-04 1.0449373768E-05 |
|||
b7 = -0.000001 -0.0000001 -1.2314450199E-07 1.3027335327E-08 |
|||
|
|||
Residual Sum of Squares: 1.5324382854E+00 |
|||
Residual Standard Deviation: 8.1803852243E-02 |
|||
Degrees of Freedom: 229 |
|||
Number of Observations: 236 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
.591E0 24.41E0 |
|||
1.547E0 34.82E0 |
|||
2.902E0 44.09E0 |
|||
2.894E0 45.07E0 |
|||
4.703E0 54.98E0 |
|||
6.307E0 65.51E0 |
|||
7.03E0 70.53E0 |
|||
7.898E0 75.70E0 |
|||
9.470E0 89.57E0 |
|||
9.484E0 91.14E0 |
|||
10.072E0 96.40E0 |
|||
10.163E0 97.19E0 |
|||
11.615E0 114.26E0 |
|||
12.005E0 120.25E0 |
|||
12.478E0 127.08E0 |
|||
12.982E0 133.55E0 |
|||
12.970E0 133.61E0 |
|||
13.926E0 158.67E0 |
|||
14.452E0 172.74E0 |
|||
14.404E0 171.31E0 |
|||
15.190E0 202.14E0 |
|||
15.550E0 220.55E0 |
|||
15.528E0 221.05E0 |
|||
15.499E0 221.39E0 |
|||
16.131E0 250.99E0 |
|||
16.438E0 268.99E0 |
|||
16.387E0 271.80E0 |
|||
16.549E0 271.97E0 |
|||
16.872E0 321.31E0 |
|||
16.830E0 321.69E0 |
|||
16.926E0 330.14E0 |
|||
16.907E0 333.03E0 |
|||
16.966E0 333.47E0 |
|||
17.060E0 340.77E0 |
|||
17.122E0 345.65E0 |
|||
17.311E0 373.11E0 |
|||
17.355E0 373.79E0 |
|||
17.668E0 411.82E0 |
|||
17.767E0 419.51E0 |
|||
17.803E0 421.59E0 |
|||
17.765E0 422.02E0 |
|||
17.768E0 422.47E0 |
|||
17.736E0 422.61E0 |
|||
17.858E0 441.75E0 |
|||
17.877E0 447.41E0 |
|||
17.912E0 448.7E0 |
|||
18.046E0 472.89E0 |
|||
18.085E0 476.69E0 |
|||
18.291E0 522.47E0 |
|||
18.357E0 522.62E0 |
|||
18.426E0 524.43E0 |
|||
18.584E0 546.75E0 |
|||
18.610E0 549.53E0 |
|||
18.870E0 575.29E0 |
|||
18.795E0 576.00E0 |
|||
19.111E0 625.55E0 |
|||
.367E0 20.15E0 |
|||
.796E0 28.78E0 |
|||
0.892E0 29.57E0 |
|||
1.903E0 37.41E0 |
|||
2.150E0 39.12E0 |
|||
3.697E0 50.24E0 |
|||
5.870E0 61.38E0 |
|||
6.421E0 66.25E0 |
|||
7.422E0 73.42E0 |
|||
9.944E0 95.52E0 |
|||
11.023E0 107.32E0 |
|||
11.87E0 122.04E0 |
|||
12.786E0 134.03E0 |
|||
14.067E0 163.19E0 |
|||
13.974E0 163.48E0 |
|||
14.462E0 175.70E0 |
|||
14.464E0 179.86E0 |
|||
15.381E0 211.27E0 |
|||
15.483E0 217.78E0 |
|||
15.59E0 219.14E0 |
|||
16.075E0 262.52E0 |
|||
16.347E0 268.01E0 |
|||
16.181E0 268.62E0 |
|||
16.915E0 336.25E0 |
|||
17.003E0 337.23E0 |
|||
16.978E0 339.33E0 |
|||
17.756E0 427.38E0 |
|||
17.808E0 428.58E0 |
|||
17.868E0 432.68E0 |
|||
18.481E0 528.99E0 |
|||
18.486E0 531.08E0 |
|||
19.090E0 628.34E0 |
|||
16.062E0 253.24E0 |
|||
16.337E0 273.13E0 |
|||
16.345E0 273.66E0 |
|||
16.388E0 282.10E0 |
|||
17.159E0 346.62E0 |
|||
17.116E0 347.19E0 |
|||
17.164E0 348.78E0 |
|||
17.123E0 351.18E0 |
|||
17.979E0 450.10E0 |
|||
17.974E0 450.35E0 |
|||
18.007E0 451.92E0 |
|||
17.993E0 455.56E0 |
|||
18.523E0 552.22E0 |
|||
18.669E0 553.56E0 |
|||
18.617E0 555.74E0 |
|||
19.371E0 652.59E0 |
|||
19.330E0 656.20E0 |
|||
0.080E0 14.13E0 |
|||
0.248E0 20.41E0 |
|||
1.089E0 31.30E0 |
|||
1.418E0 33.84E0 |
|||
2.278E0 39.70E0 |
|||
3.624E0 48.83E0 |
|||
4.574E0 54.50E0 |
|||
5.556E0 60.41E0 |
|||
7.267E0 72.77E0 |
|||
7.695E0 75.25E0 |
|||
9.136E0 86.84E0 |
|||
9.959E0 94.88E0 |
|||
9.957E0 96.40E0 |
|||
11.600E0 117.37E0 |
|||
13.138E0 139.08E0 |
|||
13.564E0 147.73E0 |
|||
13.871E0 158.63E0 |
|||
13.994E0 161.84E0 |
|||
14.947E0 192.11E0 |
|||
15.473E0 206.76E0 |
|||
15.379E0 209.07E0 |
|||
15.455E0 213.32E0 |
|||
15.908E0 226.44E0 |
|||
16.114E0 237.12E0 |
|||
17.071E0 330.90E0 |
|||
17.135E0 358.72E0 |
|||
17.282E0 370.77E0 |
|||
17.368E0 372.72E0 |
|||
17.483E0 396.24E0 |
|||
17.764E0 416.59E0 |
|||
18.185E0 484.02E0 |
|||
18.271E0 495.47E0 |
|||
18.236E0 514.78E0 |
|||
18.237E0 515.65E0 |
|||
18.523E0 519.47E0 |
|||
18.627E0 544.47E0 |
|||
18.665E0 560.11E0 |
|||
19.086E0 620.77E0 |
|||
0.214E0 18.97E0 |
|||
0.943E0 28.93E0 |
|||
1.429E0 33.91E0 |
|||
2.241E0 40.03E0 |
|||
2.951E0 44.66E0 |
|||
3.782E0 49.87E0 |
|||
4.757E0 55.16E0 |
|||
5.602E0 60.90E0 |
|||
7.169E0 72.08E0 |
|||
8.920E0 85.15E0 |
|||
10.055E0 97.06E0 |
|||
12.035E0 119.63E0 |
|||
12.861E0 133.27E0 |
|||
13.436E0 143.84E0 |
|||
14.167E0 161.91E0 |
|||
14.755E0 180.67E0 |
|||
15.168E0 198.44E0 |
|||
15.651E0 226.86E0 |
|||
15.746E0 229.65E0 |
|||
16.216E0 258.27E0 |
|||
16.445E0 273.77E0 |
|||
16.965E0 339.15E0 |
|||
17.121E0 350.13E0 |
|||
17.206E0 362.75E0 |
|||
17.250E0 371.03E0 |
|||
17.339E0 393.32E0 |
|||
17.793E0 448.53E0 |
|||
18.123E0 473.78E0 |
|||
18.49E0 511.12E0 |
|||
18.566E0 524.70E0 |
|||
18.645E0 548.75E0 |
|||
18.706E0 551.64E0 |
|||
18.924E0 574.02E0 |
|||
19.1E0 623.86E0 |
|||
0.375E0 21.46E0 |
|||
0.471E0 24.33E0 |
|||
1.504E0 33.43E0 |
|||
2.204E0 39.22E0 |
|||
2.813E0 44.18E0 |
|||
4.765E0 55.02E0 |
|||
9.835E0 94.33E0 |
|||
10.040E0 96.44E0 |
|||
11.946E0 118.82E0 |
|||
12.596E0 128.48E0 |
|||
13.303E0 141.94E0 |
|||
13.922E0 156.92E0 |
|||
14.440E0 171.65E0 |
|||
14.951E0 190.00E0 |
|||
15.627E0 223.26E0 |
|||
15.639E0 223.88E0 |
|||
15.814E0 231.50E0 |
|||
16.315E0 265.05E0 |
|||
16.334E0 269.44E0 |
|||
16.430E0 271.78E0 |
|||
16.423E0 273.46E0 |
|||
17.024E0 334.61E0 |
|||
17.009E0 339.79E0 |
|||
17.165E0 349.52E0 |
|||
17.134E0 358.18E0 |
|||
17.349E0 377.98E0 |
|||
17.576E0 394.77E0 |
|||
17.848E0 429.66E0 |
|||
18.090E0 468.22E0 |
|||
18.276E0 487.27E0 |
|||
18.404E0 519.54E0 |
|||
18.519E0 523.03E0 |
|||
19.133E0 612.99E0 |
|||
19.074E0 638.59E0 |
|||
19.239E0 641.36E0 |
|||
19.280E0 622.05E0 |
|||
19.101E0 631.50E0 |
|||
19.398E0 663.97E0 |
|||
19.252E0 646.9E0 |
|||
19.89E0 748.29E0 |
|||
20.007E0 749.21E0 |
|||
19.929E0 750.14E0 |
|||
19.268E0 647.04E0 |
|||
19.324E0 646.89E0 |
|||
20.049E0 746.9E0 |
|||
20.107E0 748.43E0 |
|||
20.062E0 747.35E0 |
|||
20.065E0 749.27E0 |
|||
19.286E0 647.61E0 |
|||
19.972E0 747.78E0 |
|||
20.088E0 750.51E0 |
|||
20.743E0 851.37E0 |
|||
20.83E0 845.97E0 |
|||
20.935E0 847.54E0 |
|||
21.035E0 849.93E0 |
|||
20.93E0 851.61E0 |
|||
21.074E0 849.75E0 |
|||
21.085E0 850.98E0 |
|||
20.935E0 848.23E0 |
|||
@ -0,0 +1,211 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Kirby2 (Kirby2.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 45) |
|||
Certified Values (lines 41 to 50) |
|||
Data (lines 61 to 211) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: These data are the result of a NIST study involving |
|||
scanning electron microscope line with standards. |
|||
|
|||
|
|||
Reference: Kirby, R., NIST (197?). |
|||
Scanning electron microscope line width standards. |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response (y) |
|||
1 Predictor (x) |
|||
151 Observations |
|||
Average Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Rational Class (quadratic/quadratic) |
|||
5 Parameters (b1 to b5) |
|||
|
|||
y = (b1 + b2*x + b3*x**2) / |
|||
(1 + b4*x + b5*x**2) + e |
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 2 1.5 1.6745063063E+00 8.7989634338E-02 |
|||
b2 = -0.1 -0.15 -1.3927397867E-01 4.1182041386E-03 |
|||
b3 = 0.003 0.0025 2.5961181191E-03 4.1856520458E-05 |
|||
b4 = -0.001 -0.0015 -1.7241811870E-03 5.8931897355E-05 |
|||
b5 = 0.00001 0.00002 2.1664802578E-05 2.0129761919E-07 |
|||
|
|||
Residual Sum of Squares: 3.9050739624E+00 |
|||
Residual Standard Deviation: 1.6354535131E-01 |
|||
Degrees of Freedom: 146 |
|||
Number of Observations: 151 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
0.0082E0 9.65E0 |
|||
0.0112E0 10.74E0 |
|||
0.0149E0 11.81E0 |
|||
0.0198E0 12.88E0 |
|||
0.0248E0 14.06E0 |
|||
0.0324E0 15.28E0 |
|||
0.0420E0 16.63E0 |
|||
0.0549E0 18.19E0 |
|||
0.0719E0 19.88E0 |
|||
0.0963E0 21.84E0 |
|||
0.1291E0 24.00E0 |
|||
0.1710E0 26.25E0 |
|||
0.2314E0 28.86E0 |
|||
0.3227E0 31.85E0 |
|||
0.4809E0 35.79E0 |
|||
0.7084E0 40.18E0 |
|||
1.0220E0 44.74E0 |
|||
1.4580E0 49.53E0 |
|||
1.9520E0 53.94E0 |
|||
2.5410E0 58.29E0 |
|||
3.2230E0 62.63E0 |
|||
3.9990E0 67.03E0 |
|||
4.8520E0 71.25E0 |
|||
5.7320E0 75.22E0 |
|||
6.7270E0 79.33E0 |
|||
7.8350E0 83.56E0 |
|||
9.0250E0 87.75E0 |
|||
10.2670E0 91.93E0 |
|||
11.5780E0 96.10E0 |
|||
12.9440E0 100.28E0 |
|||
14.3770E0 104.46E0 |
|||
15.8560E0 108.66E0 |
|||
17.3310E0 112.71E0 |
|||
18.8850E0 116.88E0 |
|||
20.5750E0 121.33E0 |
|||
22.3200E0 125.79E0 |
|||
22.3030E0 125.79E0 |
|||
23.4600E0 128.74E0 |
|||
24.0600E0 130.27E0 |
|||
25.2720E0 133.33E0 |
|||
25.8530E0 134.79E0 |
|||
27.1100E0 137.93E0 |
|||
27.6580E0 139.33E0 |
|||
28.9240E0 142.46E0 |
|||
29.5110E0 143.90E0 |
|||
30.7100E0 146.91E0 |
|||
31.3500E0 148.51E0 |
|||
32.5200E0 151.41E0 |
|||
33.2300E0 153.17E0 |
|||
34.3300E0 155.97E0 |
|||
35.0600E0 157.76E0 |
|||
36.1700E0 160.56E0 |
|||
36.8400E0 162.30E0 |
|||
38.0100E0 165.21E0 |
|||
38.6700E0 166.90E0 |
|||
39.8700E0 169.92E0 |
|||
40.0300E0 170.32E0 |
|||
40.5000E0 171.54E0 |
|||
41.3700E0 173.79E0 |
|||
41.6700E0 174.57E0 |
|||
42.3100E0 176.25E0 |
|||
42.7300E0 177.34E0 |
|||
43.4600E0 179.19E0 |
|||
44.1400E0 181.02E0 |
|||
44.5500E0 182.08E0 |
|||
45.2200E0 183.88E0 |
|||
45.9200E0 185.75E0 |
|||
46.3000E0 186.80E0 |
|||
47.0000E0 188.63E0 |
|||
47.6800E0 190.45E0 |
|||
48.0600E0 191.48E0 |
|||
48.7400E0 193.35E0 |
|||
49.4100E0 195.22E0 |
|||
49.7600E0 196.23E0 |
|||
50.4300E0 198.05E0 |
|||
51.1100E0 199.97E0 |
|||
51.5000E0 201.06E0 |
|||
52.1200E0 202.83E0 |
|||
52.7600E0 204.69E0 |
|||
53.1800E0 205.86E0 |
|||
53.7800E0 207.58E0 |
|||
54.4600E0 209.50E0 |
|||
54.8300E0 210.65E0 |
|||
55.4000E0 212.33E0 |
|||
56.4300E0 215.43E0 |
|||
57.0300E0 217.16E0 |
|||
58.0000E0 220.21E0 |
|||
58.6100E0 221.98E0 |
|||
59.5800E0 225.06E0 |
|||
60.1100E0 226.79E0 |
|||
61.1000E0 229.92E0 |
|||
61.6500E0 231.69E0 |
|||
62.5900E0 234.77E0 |
|||
63.1200E0 236.60E0 |
|||
64.0300E0 239.63E0 |
|||
64.6200E0 241.50E0 |
|||
65.4900E0 244.48E0 |
|||
66.0300E0 246.40E0 |
|||
66.8900E0 249.35E0 |
|||
67.4200E0 251.32E0 |
|||
68.2300E0 254.22E0 |
|||
68.7700E0 256.24E0 |
|||
69.5900E0 259.11E0 |
|||
70.1100E0 261.18E0 |
|||
70.8600E0 264.02E0 |
|||
71.4300E0 266.13E0 |
|||
72.1600E0 268.94E0 |
|||
72.7000E0 271.09E0 |
|||
73.4000E0 273.87E0 |
|||
73.9300E0 276.08E0 |
|||
74.6000E0 278.83E0 |
|||
75.1600E0 281.08E0 |
|||
75.8200E0 283.81E0 |
|||
76.3400E0 286.11E0 |
|||
76.9800E0 288.81E0 |
|||
77.4800E0 291.08E0 |
|||
78.0800E0 293.75E0 |
|||
78.6000E0 295.99E0 |
|||
79.1700E0 298.64E0 |
|||
79.6200E0 300.84E0 |
|||
79.8800E0 302.02E0 |
|||
80.1900E0 303.48E0 |
|||
80.6600E0 305.65E0 |
|||
81.2200E0 308.27E0 |
|||
81.6600E0 310.41E0 |
|||
82.1600E0 313.01E0 |
|||
82.5900E0 315.12E0 |
|||
83.1400E0 317.71E0 |
|||
83.5000E0 319.79E0 |
|||
84.0000E0 322.36E0 |
|||
84.4000E0 324.42E0 |
|||
84.8900E0 326.98E0 |
|||
85.2600E0 329.01E0 |
|||
85.7400E0 331.56E0 |
|||
86.0700E0 333.56E0 |
|||
86.5400E0 336.10E0 |
|||
86.8900E0 338.08E0 |
|||
87.3200E0 340.60E0 |
|||
87.6500E0 342.57E0 |
|||
88.1000E0 345.08E0 |
|||
88.4300E0 347.02E0 |
|||
88.8300E0 349.52E0 |
|||
89.1200E0 351.44E0 |
|||
89.5400E0 353.93E0 |
|||
89.8500E0 355.83E0 |
|||
90.2500E0 358.32E0 |
|||
90.5500E0 360.20E0 |
|||
90.9300E0 362.67E0 |
|||
91.2000E0 364.53E0 |
|||
91.5500E0 367.00E0 |
|||
92.2000E0 371.30E0 |
|||
@ -0,0 +1,84 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Lanczos1 (Lanczos1.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 46) |
|||
Certified Values (lines 41 to 51) |
|||
Data (lines 61 to 84) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: These data are taken from an example discussed in |
|||
Lanczos (1956). The data were generated to 14-digits |
|||
of accuracy using |
|||
f(x) = 0.0951*exp(-x) + 0.8607*exp(-3*x) |
|||
+ 1.5576*exp(-5*x). |
|||
|
|||
|
|||
Reference: Lanczos, C. (1956). |
|||
Applied Analysis. |
|||
Englewood Cliffs, NJ: Prentice Hall, pp. 272-280. |
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response (y) |
|||
1 Predictor (x) |
|||
24 Observations |
|||
Average Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Exponential Class |
|||
6 Parameters (b1 to b6) |
|||
|
|||
y = b1*exp(-b2*x) + b3*exp(-b4*x) + b5*exp(-b6*x) + e |
|||
|
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 1.2 0.5 9.5100000027E-02 5.3347304234E-11 |
|||
b2 = 0.3 0.7 1.0000000001E+00 2.7473038179E-10 |
|||
b3 = 5.6 3.6 8.6070000013E-01 1.3576062225E-10 |
|||
b4 = 5.5 4.2 3.0000000002E+00 3.3308253069E-10 |
|||
b5 = 6.5 4 1.5575999998E+00 1.8815731448E-10 |
|||
b6 = 7.6 6.3 5.0000000001E+00 1.1057500538E-10 |
|||
|
|||
Residual Sum of Squares: 1.4307867721E-25 |
|||
Residual Standard Deviation: 8.9156129349E-14 |
|||
Degrees of Freedom: 18 |
|||
Number of Observations: 24 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
2.513400000000E+00 0.000000000000E+00 |
|||
2.044333373291E+00 5.000000000000E-02 |
|||
1.668404436564E+00 1.000000000000E-01 |
|||
1.366418021208E+00 1.500000000000E-01 |
|||
1.123232487372E+00 2.000000000000E-01 |
|||
9.268897180037E-01 2.500000000000E-01 |
|||
7.679338563728E-01 3.000000000000E-01 |
|||
6.388775523106E-01 3.500000000000E-01 |
|||
5.337835317402E-01 4.000000000000E-01 |
|||
4.479363617347E-01 4.500000000000E-01 |
|||
3.775847884350E-01 5.000000000000E-01 |
|||
3.197393199326E-01 5.500000000000E-01 |
|||
2.720130773746E-01 6.000000000000E-01 |
|||
2.324965529032E-01 6.500000000000E-01 |
|||
1.996589546065E-01 7.000000000000E-01 |
|||
1.722704126914E-01 7.500000000000E-01 |
|||
1.493405660168E-01 8.000000000000E-01 |
|||
1.300700206922E-01 8.500000000000E-01 |
|||
1.138119324644E-01 9.000000000000E-01 |
|||
1.000415587559E-01 9.500000000000E-01 |
|||
8.833209084540E-02 1.000000000000E+00 |
|||
7.833544019350E-02 1.050000000000E+00 |
|||
6.976693743449E-02 1.100000000000E+00 |
|||
6.239312536719E-02 1.150000000000E+00 |
|||
@ -0,0 +1,84 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Lanczos2 (Lanczos2.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 46) |
|||
Certified Values (lines 41 to 51) |
|||
Data (lines 61 to 84) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: These data are taken from an example discussed in |
|||
Lanczos (1956). The data were generated to 6-digits |
|||
of accuracy using |
|||
f(x) = 0.0951*exp(-x) + 0.8607*exp(-3*x) |
|||
+ 1.5576*exp(-5*x). |
|||
|
|||
|
|||
Reference: Lanczos, C. (1956). |
|||
Applied Analysis. |
|||
Englewood Cliffs, NJ: Prentice Hall, pp. 272-280. |
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response (y) |
|||
1 Predictor (x) |
|||
24 Observations |
|||
Average Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Exponential Class |
|||
6 Parameters (b1 to b6) |
|||
|
|||
y = b1*exp(-b2*x) + b3*exp(-b4*x) + b5*exp(-b6*x) + e |
|||
|
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 1.2 0.5 9.6251029939E-02 6.6770575477E-04 |
|||
b2 = 0.3 0.7 1.0057332849E+00 3.3989646176E-03 |
|||
b3 = 5.6 3.6 8.6424689056E-01 1.7185846685E-03 |
|||
b4 = 5.5 4.2 3.0078283915E+00 4.1707005856E-03 |
|||
b5 = 6.5 4 1.5529016879E+00 2.3744381417E-03 |
|||
b6 = 7.6 6.3 5.0028798100E+00 1.3958787284E-03 |
|||
|
|||
Residual Sum of Squares: 2.2299428125E-11 |
|||
Residual Standard Deviation: 1.1130395851E-06 |
|||
Degrees of Freedom: 18 |
|||
Number of Observations: 24 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
2.51340E+00 0.00000E+00 |
|||
2.04433E+00 5.00000E-02 |
|||
1.66840E+00 1.00000E-01 |
|||
1.36642E+00 1.50000E-01 |
|||
1.12323E+00 2.00000E-01 |
|||
9.26890E-01 2.50000E-01 |
|||
7.67934E-01 3.00000E-01 |
|||
6.38878E-01 3.50000E-01 |
|||
5.33784E-01 4.00000E-01 |
|||
4.47936E-01 4.50000E-01 |
|||
3.77585E-01 5.00000E-01 |
|||
3.19739E-01 5.50000E-01 |
|||
2.72013E-01 6.00000E-01 |
|||
2.32497E-01 6.50000E-01 |
|||
1.99659E-01 7.00000E-01 |
|||
1.72270E-01 7.50000E-01 |
|||
1.49341E-01 8.00000E-01 |
|||
1.30070E-01 8.50000E-01 |
|||
1.13812E-01 9.00000E-01 |
|||
1.00042E-01 9.50000E-01 |
|||
8.83321E-02 1.00000E+00 |
|||
7.83354E-02 1.05000E+00 |
|||
6.97669E-02 1.10000E+00 |
|||
6.23931E-02 1.15000E+00 |
|||
@ -0,0 +1,84 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Lanczos3 (Lanczos3.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 46) |
|||
Certified Values (lines 41 to 51) |
|||
Data (lines 61 to 84) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: These data are taken from an example discussed in |
|||
Lanczos (1956). The data were generated to 5-digits |
|||
of accuracy using |
|||
f(x) = 0.0951*exp(-x) + 0.8607*exp(-3*x) |
|||
+ 1.5576*exp(-5*x). |
|||
|
|||
|
|||
Reference: Lanczos, C. (1956). |
|||
Applied Analysis. |
|||
Englewood Cliffs, NJ: Prentice Hall, pp. 272-280. |
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response (y) |
|||
1 Predictor (x) |
|||
24 Observations |
|||
Lower Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Exponential Class |
|||
6 Parameters (b1 to b6) |
|||
|
|||
y = b1*exp(-b2*x) + b3*exp(-b4*x) + b5*exp(-b6*x) + e |
|||
|
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 1.2 0.5 8.6816414977E-02 1.7197908859E-02 |
|||
b2 = 0.3 0.7 9.5498101505E-01 9.7041624475E-02 |
|||
b3 = 5.6 3.6 8.4400777463E-01 4.1488663282E-02 |
|||
b4 = 5.5 4.2 2.9515951832E+00 1.0766312506E-01 |
|||
b5 = 6.5 4 1.5825685901E+00 5.8371576281E-02 |
|||
b6 = 7.6 6.3 4.9863565084E+00 3.4436403035E-02 |
|||
|
|||
Residual Sum of Squares: 1.6117193594E-08 |
|||
Residual Standard Deviation: 2.9923229172E-05 |
|||
Degrees of Freedom: 18 |
|||
Number of Observations: 24 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
2.5134E+00 0.00000E+00 |
|||
2.0443E+00 5.00000E-02 |
|||
1.6684E+00 1.00000E-01 |
|||
1.3664E+00 1.50000E-01 |
|||
1.1232E+00 2.00000E-01 |
|||
0.9269E+00 2.50000E-01 |
|||
0.7679E+00 3.00000E-01 |
|||
0.6389E+00 3.50000E-01 |
|||
0.5338E+00 4.00000E-01 |
|||
0.4479E+00 4.50000E-01 |
|||
0.3776E+00 5.00000E-01 |
|||
0.3197E+00 5.50000E-01 |
|||
0.2720E+00 6.00000E-01 |
|||
0.2325E+00 6.50000E-01 |
|||
0.1997E+00 7.00000E-01 |
|||
0.1723E+00 7.50000E-01 |
|||
0.1493E+00 8.00000E-01 |
|||
0.1301E+00 8.50000E-01 |
|||
0.1138E+00 9.00000E-01 |
|||
0.1000E+00 9.50000E-01 |
|||
0.0883E+00 1.00000E+00 |
|||
0.0783E+00 1.05000E+00 |
|||
0.0698E+00 1.10000E+00 |
|||
0.0624E+00 1.15000E+00 |
|||
@ -0,0 +1,239 @@ |
|||
File Name: Lew.dat |
|||
|
|||
Dataset Name: Lew (Beam Deflection Data) |
|||
|
|||
Description: This is an observed/"real world" data set |
|||
consisting of 200 deflections of a steel-concrete |
|||
beam while subjected to periodic pressure. |
|||
The experimenter was H. S. Lew of the |
|||
Center for Building Technology at NIST. |
|||
We here use this data to test accuracy |
|||
in summary statistics calculations. |
|||
|
|||
Stat Category: Univariate: Summary Statistics |
|||
|
|||
Reference: None |
|||
|
|||
Data: "Real World" |
|||
1 Response : y = beam deflection |
|||
0 Predictors |
|||
200 Observations |
|||
|
|||
Model: Lower Level of Difficulty |
|||
2 Parameters : mu, sigma |
|||
1 Response Variable : y |
|||
0 Predictor Variables |
|||
|
|||
y = mu + e |
|||
|
|||
|
|||
Certified Values |
|||
Sample Mean ybar: -177.435000000000 |
|||
Sample Standard Deviation (denom. = n-1) s: 277.332168044316 |
|||
Sample Autocorrelation Coefficient (lag 1) r(1): -0.307304800605679 |
|||
|
|||
Number of Observations: 200 |
|||
|
|||
|
|||
Data: Y |
|||
---------- |
|||
-213 |
|||
-564 |
|||
-35 |
|||
-15 |
|||
141 |
|||
115 |
|||
-420 |
|||
-360 |
|||
203 |
|||
-338 |
|||
-431 |
|||
194 |
|||
-220 |
|||
-513 |
|||
154 |
|||
-125 |
|||
-559 |
|||
92 |
|||
-21 |
|||
-579 |
|||
-52 |
|||
99 |
|||
-543 |
|||
-175 |
|||
162 |
|||
-457 |
|||
-346 |
|||
204 |
|||
-300 |
|||
-474 |
|||
164 |
|||
-107 |
|||
-572 |
|||
-8 |
|||
83 |
|||
-541 |
|||
-224 |
|||
180 |
|||
-420 |
|||
-374 |
|||
201 |
|||
-236 |
|||
-531 |
|||
83 |
|||
27 |
|||
-564 |
|||
-112 |
|||
131 |
|||
-507 |
|||
-254 |
|||
199 |
|||
-311 |
|||
-495 |
|||
143 |
|||
-46 |
|||
-579 |
|||
-90 |
|||
136 |
|||
-472 |
|||
-338 |
|||
202 |
|||
-287 |
|||
-477 |
|||
169 |
|||
-124 |
|||
-568 |
|||
17 |
|||
48 |
|||
-568 |
|||
-135 |
|||
162 |
|||
-430 |
|||
-422 |
|||
172 |
|||
-74 |
|||
-577 |
|||
-13 |
|||
92 |
|||
-534 |
|||
-243 |
|||
194 |
|||
-355 |
|||
-465 |
|||
156 |
|||
-81 |
|||
-578 |
|||
-64 |
|||
139 |
|||
-449 |
|||
-384 |
|||
193 |
|||
-198 |
|||
-538 |
|||
110 |
|||
-44 |
|||
-577 |
|||
-6 |
|||
66 |
|||
-552 |
|||
-164 |
|||
161 |
|||
-460 |
|||
-344 |
|||
205 |
|||
-281 |
|||
-504 |
|||
134 |
|||
-28 |
|||
-576 |
|||
-118 |
|||
156 |
|||
-437 |
|||
-381 |
|||
200 |
|||
-220 |
|||
-540 |
|||
83 |
|||
11 |
|||
-568 |
|||
-160 |
|||
172 |
|||
-414 |
|||
-408 |
|||
188 |
|||
-125 |
|||
-572 |
|||
-32 |
|||
139 |
|||
-492 |
|||
-321 |
|||
205 |
|||
-262 |
|||
-504 |
|||
142 |
|||
-83 |
|||
-574 |
|||
0 |
|||
48 |
|||
-571 |
|||
-106 |
|||
137 |
|||
-501 |
|||
-266 |
|||
190 |
|||
-391 |
|||
-406 |
|||
194 |
|||
-186 |
|||
-553 |
|||
83 |
|||
-13 |
|||
-577 |
|||
-49 |
|||
103 |
|||
-515 |
|||
-280 |
|||
201 |
|||
300 |
|||
-506 |
|||
131 |
|||
-45 |
|||
-578 |
|||
-80 |
|||
138 |
|||
-462 |
|||
-361 |
|||
201 |
|||
-211 |
|||
-554 |
|||
32 |
|||
74 |
|||
-533 |
|||
-235 |
|||
187 |
|||
-372 |
|||
-442 |
|||
182 |
|||
-147 |
|||
-566 |
|||
25 |
|||
68 |
|||
-535 |
|||
-244 |
|||
194 |
|||
-351 |
|||
-463 |
|||
174 |
|||
-125 |
|||
-570 |
|||
15 |
|||
72 |
|||
-550 |
|||
-190 |
|||
172 |
|||
-424 |
|||
-385 |
|||
198 |
|||
-218 |
|||
-536 |
|||
96 |
|||
@ -0,0 +1,76 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Longley (Longley.dat) |
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 31 to 51) |
|||
Data (lines 61 to 76) |
|||
|
|||
Procedure: Linear Least Squares Regression |
|||
|
|||
Reference: Longley, J. W. (1967). |
|||
An Appraisal of Least Squares Programs for the |
|||
Electronic Computer from the Viewpoint of the User. |
|||
Journal of the American Statistical Association, 62, pp. 819-841. |
|||
|
|||
Data: 1 Response Variable (y) |
|||
6 Predictor Variable (x) |
|||
16 Observations |
|||
Higher Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Polynomial Class |
|||
7 Parameters (B0,B1,...,B7) |
|||
|
|||
y = B0 + B1*x1 + B2*x2 + B3*x3 + B4*x4 + B5*x5 + B6*x6 + e |
|||
|
|||
Certified Regression Statistics |
|||
|
|||
Standard Deviation |
|||
Parameter Estimate of Estimate |
|||
|
|||
B0 -3482258.63459582 890420.383607373 |
|||
B1 15.0618722713733 84.9149257747669 |
|||
B2 -0.358191792925910E-01 0.334910077722432E-01 |
|||
B3 -2.02022980381683 0.488399681651699 |
|||
B4 -1.03322686717359 0.214274163161675 |
|||
B5 -0.511041056535807E-01 0.226073200069370 |
|||
B6 1829.15146461355 455.478499142212 |
|||
|
|||
Residual |
|||
Standard Deviation 304.854073561965 |
|||
|
|||
R-Squared 0.995479004577296 |
|||
|
|||
|
|||
Certified Analysis of Variance Table |
|||
|
|||
Source of Degrees of Sums of Mean |
|||
Variation Freedom Squares Squares F Statistic |
|||
|
|||
Regression 6 184172401.944494 30695400.3240823 330.285339234588 |
|||
Residual 9 836424.055505915 92936.0061673238 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x1 x2 x3 x4 x5 x6 |
|||
60323 83.0 234289 2356 1590 107608 1947 |
|||
61122 88.5 259426 2325 1456 108632 1948 |
|||
60171 88.2 258054 3682 1616 109773 1949 |
|||
61187 89.5 284599 3351 1650 110929 1950 |
|||
63221 96.2 328975 2099 3099 112075 1951 |
|||
63639 98.1 346999 1932 3594 113270 1952 |
|||
64989 99.0 365385 1870 3547 115094 1953 |
|||
63761 100.0 363112 3578 3350 116219 1954 |
|||
66019 101.2 397469 2904 3048 117388 1955 |
|||
67857 104.6 419180 2822 2857 118734 1956 |
|||
68169 108.4 442769 2936 2798 120445 1957 |
|||
66513 110.8 444546 4681 2637 121950 1958 |
|||
68655 112.6 482704 3813 2552 123366 1959 |
|||
69564 114.2 502601 3931 2514 125368 1960 |
|||
69331 115.7 518173 4806 2572 127852 1961 |
|||
70551 116.9 554894 4007 2827 130081 1962 |
|||
@ -0,0 +1,258 @@ |
|||
File Name: Lottery.dat |
|||
|
|||
Dataset Name: Lottery |
|||
|
|||
Description: This is an observed/"real world" data set |
|||
consisting of 218 lottery values |
|||
from September 3, 1989 to April 14, 1990 (32 weeks). |
|||
One 3-digit random number (from 000 to 999) |
|||
is drawn per day, 7 days per week for most |
|||
weeks, but fewer days per week for some weeks. |
|||
We here use this data to test accuracy |
|||
in summary statistics calculations. |
|||
|
|||
Stat Category: Univariate: Summary Statistics |
|||
|
|||
Reference: None |
|||
|
|||
Data: "Real World" |
|||
1 Response : y = 3-digit random number |
|||
0 Predictors |
|||
218 Observations |
|||
|
|||
Model: Lower Level of Difficulty |
|||
2 Parameters : mu, sigma |
|||
1 Response Variable : y |
|||
0 Predictor Variables |
|||
|
|||
y = mu + e |
|||
|
|||
|
|||
Certified Values |
|||
Sample Mean ybar: 518.958715596330 |
|||
Sample Standard Deviation (denom. = n-1) s: 291.699727470969 |
|||
Sample Autocorrelation Coefficient (lag 1) r(1): -0.120948622967393 |
|||
|
|||
Number of Observations: 218 |
|||
|
|||
|
|||
Data: Y |
|||
---------- |
|||
162 |
|||
671 |
|||
933 |
|||
414 |
|||
788 |
|||
730 |
|||
817 |
|||
33 |
|||
536 |
|||
875 |
|||
670 |
|||
236 |
|||
473 |
|||
167 |
|||
877 |
|||
980 |
|||
316 |
|||
950 |
|||
456 |
|||
92 |
|||
517 |
|||
557 |
|||
956 |
|||
954 |
|||
104 |
|||
178 |
|||
794 |
|||
278 |
|||
147 |
|||
773 |
|||
437 |
|||
435 |
|||
502 |
|||
610 |
|||
582 |
|||
780 |
|||
689 |
|||
562 |
|||
964 |
|||
791 |
|||
28 |
|||
97 |
|||
848 |
|||
281 |
|||
858 |
|||
538 |
|||
660 |
|||
972 |
|||
671 |
|||
613 |
|||
867 |
|||
448 |
|||
738 |
|||
966 |
|||
139 |
|||
636 |
|||
847 |
|||
659 |
|||
754 |
|||
243 |
|||
122 |
|||
455 |
|||
195 |
|||
968 |
|||
793 |
|||
59 |
|||
730 |
|||
361 |
|||
574 |
|||
522 |
|||
97 |
|||
762 |
|||
431 |
|||
158 |
|||
429 |
|||
414 |
|||
22 |
|||
629 |
|||
788 |
|||
999 |
|||
187 |
|||
215 |
|||
810 |
|||
782 |
|||
47 |
|||
34 |
|||
108 |
|||
986 |
|||
25 |
|||
644 |
|||
829 |
|||
630 |
|||
315 |
|||
567 |
|||
919 |
|||
331 |
|||
207 |
|||
412 |
|||
242 |
|||
607 |
|||
668 |
|||
944 |
|||
749 |
|||
168 |
|||
864 |
|||
442 |
|||
533 |
|||
805 |
|||
372 |
|||
63 |
|||
458 |
|||
777 |
|||
416 |
|||
340 |
|||
436 |
|||
140 |
|||
919 |
|||
350 |
|||
510 |
|||
572 |
|||
905 |
|||
900 |
|||
85 |
|||
389 |
|||
473 |
|||
758 |
|||
444 |
|||
169 |
|||
625 |
|||
692 |
|||
140 |
|||
897 |
|||
672 |
|||
288 |
|||
312 |
|||
860 |
|||
724 |
|||
226 |
|||
884 |
|||
508 |
|||
976 |
|||
741 |
|||
476 |
|||
417 |
|||
831 |
|||
15 |
|||
318 |
|||
432 |
|||
241 |
|||
114 |
|||
799 |
|||
955 |
|||
833 |
|||
358 |
|||
935 |
|||
146 |
|||
630 |
|||
830 |
|||
440 |
|||
642 |
|||
356 |
|||
373 |
|||
271 |
|||
715 |
|||
367 |
|||
393 |
|||
190 |
|||
669 |
|||
8 |
|||
861 |
|||
108 |
|||
795 |
|||
269 |
|||
590 |
|||
326 |
|||
866 |
|||
64 |
|||
523 |
|||
862 |
|||
840 |
|||
219 |
|||
382 |
|||
998 |
|||
4 |
|||
628 |
|||
305 |
|||
747 |
|||
247 |
|||
34 |
|||
747 |
|||
729 |
|||
645 |
|||
856 |
|||
974 |
|||
24 |
|||
568 |
|||
24 |
|||
694 |
|||
608 |
|||
480 |
|||
410 |
|||
729 |
|||
947 |
|||
293 |
|||
53 |
|||
930 |
|||
223 |
|||
203 |
|||
677 |
|||
227 |
|||
62 |
|||
455 |
|||
387 |
|||
318 |
|||
562 |
|||
242 |
|||
428 |
|||
968 |
|||
@ -0,0 +1,71 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: MGH09 (MGH09.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 44) |
|||
Certified Values (lines 41 to 49) |
|||
Data (lines 61 to 71) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: This problem was found to be difficult for some very |
|||
good algorithms. There is a local minimum at (+inf, |
|||
-14.07..., -inf, -inf) with final sum of squares |
|||
0.00102734.... |
|||
|
|||
See More, J. J., Garbow, B. S., and Hillstrom, K. E. |
|||
(1981). Testing unconstrained optimization software. |
|||
ACM Transactions on Mathematical Software. 7(1): |
|||
pp. 17-41. |
|||
|
|||
Reference: Kowalik, J.S., and M. R. Osborne, (1978). |
|||
Methods for Unconstrained Optimization Problems. |
|||
New York, NY: Elsevier North-Holland. |
|||
|
|||
Data: 1 Response (y) |
|||
1 Predictor (x) |
|||
11 Observations |
|||
Higher Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Rational Class (linear/quadratic) |
|||
4 Parameters (b1 to b4) |
|||
|
|||
y = b1*(x**2+x*b2) / (x**2+x*b3+b4) + e |
|||
|
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 25 0.25 1.9280693458E-01 1.1435312227E-02 |
|||
b2 = 39 0.39 1.9128232873E-01 1.9633220911E-01 |
|||
b3 = 41.5 0.415 1.2305650693E-01 8.0842031232E-02 |
|||
b4 = 39 0.39 1.3606233068E-01 9.0025542308E-02 |
|||
|
|||
Residual Sum of Squares: 3.0750560385E-04 |
|||
Residual Standard Deviation: 6.6279236551E-03 |
|||
Degrees of Freedom: 7 |
|||
Number of Observations: 11 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
1.957000E-01 4.000000E+00 |
|||
1.947000E-01 2.000000E+00 |
|||
1.735000E-01 1.000000E+00 |
|||
1.600000E-01 5.000000E-01 |
|||
8.440000E-02 2.500000E-01 |
|||
6.270000E-02 1.670000E-01 |
|||
4.560000E-02 1.250000E-01 |
|||
3.420000E-02 1.000000E-01 |
|||
3.230000E-02 8.330000E-02 |
|||
2.350000E-02 7.140000E-02 |
|||
2.460000E-02 6.250000E-02 |
|||
@ -0,0 +1,76 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: MGH10 (MGH10.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 43) |
|||
Certified Values (lines 41 to 48) |
|||
Data (lines 61 to 76) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: This problem was found to be difficult for some very |
|||
good algorithms. |
|||
|
|||
See More, J. J., Garbow, B. S., and Hillstrom, K. E. |
|||
(1981). Testing unconstrained optimization software. |
|||
ACM Transactions on Mathematical Software. 7(1): |
|||
pp. 17-41. |
|||
|
|||
Reference: Meyer, R. R. (1970). |
|||
Theoretical and computational aspects of nonlinear |
|||
regression. In Nonlinear Programming, Rosen, |
|||
Mangasarian and Ritter (Eds). |
|||
New York, NY: Academic Press, pp. 465-486. |
|||
|
|||
Data: 1 Response (y) |
|||
1 Predictor (x) |
|||
16 Observations |
|||
Higher Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Exponential Class |
|||
3 Parameters (b1 to b3) |
|||
|
|||
y = b1 * exp[b2/(x+b3)] + e |
|||
|
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 2 0.02 5.6096364710E-03 1.5687892471E-04 |
|||
b2 = 400000 4000 6.1813463463E+03 2.3309021107E+01 |
|||
b3 = 25000 250 3.4522363462E+02 7.8486103508E-01 |
|||
|
|||
Residual Sum of Squares: 8.7945855171E+01 |
|||
Residual Standard Deviation: 2.6009740065E+00 |
|||
Degrees of Freedom: 13 |
|||
Number of Observations: 16 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
3.478000E+04 5.000000E+01 |
|||
2.861000E+04 5.500000E+01 |
|||
2.365000E+04 6.000000E+01 |
|||
1.963000E+04 6.500000E+01 |
|||
1.637000E+04 7.000000E+01 |
|||
1.372000E+04 7.500000E+01 |
|||
1.154000E+04 8.000000E+01 |
|||
9.744000E+03 8.500000E+01 |
|||
8.261000E+03 9.000000E+01 |
|||
7.030000E+03 9.500000E+01 |
|||
6.005000E+03 1.000000E+02 |
|||
5.147000E+03 1.050000E+02 |
|||
4.427000E+03 1.100000E+02 |
|||
3.820000E+03 1.150000E+02 |
|||
3.307000E+03 1.200000E+02 |
|||
2.872000E+03 1.250000E+02 |
|||
@ -0,0 +1,93 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: MGH17 (MGH17.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 45) |
|||
Certified Values (lines 41 to 50) |
|||
Data (lines 61 to 93) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: This problem was found to be difficult for some very |
|||
good algorithms. |
|||
|
|||
See More, J. J., Garbow, B. S., and Hillstrom, K. E. |
|||
(1981). Testing unconstrained optimization software. |
|||
ACM Transactions on Mathematical Software. 7(1): |
|||
pp. 17-41. |
|||
|
|||
Reference: Osborne, M. R. (1972). |
|||
Some aspects of nonlinear least squares |
|||
calculations. In Numerical Methods for Nonlinear |
|||
Optimization, Lootsma (Ed). |
|||
New York, NY: Academic Press, pp. 171-189. |
|||
|
|||
Data: 1 Response (y) |
|||
1 Predictor (x) |
|||
33 Observations |
|||
Average Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Exponential Class |
|||
5 Parameters (b1 to b5) |
|||
|
|||
y = b1 + b2*exp[-x*b4] + b3*exp[-x*b5] + e |
|||
|
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 50 0.5 3.7541005211E-01 2.0723153551E-03 |
|||
b2 = 150 1.5 1.9358469127E+00 2.2031669222E-01 |
|||
b3 = -100 -1 -1.4646871366E+00 2.2175707739E-01 |
|||
b4 = 1 0.01 1.2867534640E-02 4.4861358114E-04 |
|||
b5 = 2 0.02 2.2122699662E-02 8.9471996575E-04 |
|||
|
|||
Residual Sum of Squares: 5.4648946975E-05 |
|||
Residual Standard Deviation: 1.3970497866E-03 |
|||
Degrees of Freedom: 28 |
|||
Number of Observations: 33 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
8.440000E-01 0.000000E+00 |
|||
9.080000E-01 1.000000E+01 |
|||
9.320000E-01 2.000000E+01 |
|||
9.360000E-01 3.000000E+01 |
|||
9.250000E-01 4.000000E+01 |
|||
9.080000E-01 5.000000E+01 |
|||
8.810000E-01 6.000000E+01 |
|||
8.500000E-01 7.000000E+01 |
|||
8.180000E-01 8.000000E+01 |
|||
7.840000E-01 9.000000E+01 |
|||
7.510000E-01 1.000000E+02 |
|||
7.180000E-01 1.100000E+02 |
|||
6.850000E-01 1.200000E+02 |
|||
6.580000E-01 1.300000E+02 |
|||
6.280000E-01 1.400000E+02 |
|||
6.030000E-01 1.500000E+02 |
|||
5.800000E-01 1.600000E+02 |
|||
5.580000E-01 1.700000E+02 |
|||
5.380000E-01 1.800000E+02 |
|||
5.220000E-01 1.900000E+02 |
|||
5.060000E-01 2.000000E+02 |
|||
4.900000E-01 2.100000E+02 |
|||
4.780000E-01 2.200000E+02 |
|||
4.670000E-01 2.300000E+02 |
|||
4.570000E-01 2.400000E+02 |
|||
4.480000E-01 2.500000E+02 |
|||
4.380000E-01 2.600000E+02 |
|||
4.310000E-01 2.700000E+02 |
|||
4.240000E-01 2.800000E+02 |
|||
4.200000E-01 2.900000E+02 |
|||
4.140000E-01 3.000000E+02 |
|||
4.110000E-01 3.100000E+02 |
|||
4.060000E-01 3.200000E+02 |
|||
@ -0,0 +1,91 @@ |
|||
File Name: Mavro.dat |
|||
|
|||
Dataset Name: Mavro (Filter Transmittance Data) |
|||
|
|||
Description: This is an observed/"real world" data set |
|||
consisting of 50 transmittance measurements |
|||
(at a sampling rate of 10 observations per second) |
|||
from a filter with a nominal value of 2. |
|||
The experimenter was Radu Mavrodineaunu, |
|||
a member of the chemistry staff at NIST. |
|||
We here use this data to test accuracy |
|||
in summary statistics calculations. |
|||
|
|||
Stat Category: Univariate: Summary Statistics |
|||
|
|||
Reference: None |
|||
|
|||
Data: "Real World" |
|||
1 Response : y = transmittance |
|||
0 Predictors |
|||
50 Observations |
|||
|
|||
Model: Lower Level of Difficulty |
|||
2 Parameters : mu, sigma |
|||
1 Response Variable : y |
|||
0 Predictor Variables |
|||
|
|||
y = mu + e |
|||
|
|||
|
|||
|
|||
Certified Values |
|||
Sample Mean ybar: 2.00185600000000 |
|||
Sample Standard Deviation (denom. = n-1) s: 0.000429123454003053 |
|||
Sample Autocorrelation Coefficient (lag 1) r(1): 0.937989183438248 |
|||
|
|||
Number of Observations: 50 |
|||
|
|||
|
|||
Data: Y |
|||
------------- |
|||
2.00180 |
|||
2.00170 |
|||
2.00180 |
|||
2.00190 |
|||
2.00180 |
|||
2.00170 |
|||
2.00150 |
|||
2.00140 |
|||
2.00150 |
|||
2.00150 |
|||
2.00170 |
|||
2.00180 |
|||
2.00180 |
|||
2.00190 |
|||
2.00190 |
|||
2.00210 |
|||
2.00200 |
|||
2.00160 |
|||
2.00140 |
|||
2.00130 |
|||
2.00130 |
|||
2.00150 |
|||
2.00150 |
|||
2.00160 |
|||
2.00150 |
|||
2.00140 |
|||
2.00130 |
|||
2.00140 |
|||
2.00150 |
|||
2.00140 |
|||
2.00150 |
|||
2.00160 |
|||
2.00150 |
|||
2.00160 |
|||
2.00190 |
|||
2.00200 |
|||
2.00200 |
|||
2.00210 |
|||
2.00220 |
|||
2.00230 |
|||
2.00240 |
|||
2.00250 |
|||
2.00270 |
|||
2.00260 |
|||
2.00260 |
|||
2.00260 |
|||
2.00270 |
|||
2.00260 |
|||
2.00250 |
|||
2.00240 |
|||
@ -0,0 +1,130 @@ |
|||
File Name: Michelso.dat |
|||
|
|||
Dataset Name: Michelso (Speed of Light Data, in millions of meters per second) |
|||
|
|||
Description: This is an observed/"real world" data set |
|||
consisting of 100 measurements of the |
|||
speed of light in air. This classic experiment |
|||
was carried out by Michelson is 1879. |
|||
We here use this data to test accuracy |
|||
in summary statistics calculations. |
|||
|
|||
Stat Category: Univariate: Summary Statistics |
|||
|
|||
Reference: Dorsey, Ernest N. (1944). The Velocity of Light. |
|||
Transactions of the American Philiosophical |
|||
Society, Volume 34, Part 1, Pages 1-110, Table 22. |
|||
|
|||
y = mu + e |
|||
|
|||
|
|||
Certified Values |
|||
Sample Mean ybar: 299.852400000000 |
|||
Sample Standard Deviation (denom. = n-1) s: 0.0790105478190518 |
|||
Sample Autocorrelation Coefficient (lag 1) r(1): 0.535199668621283 |
|||
|
|||
Number of Observations: 100 |
|||
|
|||
|
|||
Data: Y |
|||
---------- |
|||
299.85 |
|||
299.74 |
|||
299.90 |
|||
300.07 |
|||
299.93 |
|||
299.85 |
|||
299.95 |
|||
299.98 |
|||
299.98 |
|||
299.88 |
|||
300.00 |
|||
299.98 |
|||
299.93 |
|||
299.65 |
|||
299.76 |
|||
299.81 |
|||
300.00 |
|||
300.00 |
|||
299.96 |
|||
299.96 |
|||
299.96 |
|||
299.94 |
|||
299.96 |
|||
299.94 |
|||
299.88 |
|||
299.80 |
|||
299.85 |
|||
299.88 |
|||
299.90 |
|||
299.84 |
|||
299.83 |
|||
299.79 |
|||
299.81 |
|||
299.88 |
|||
299.88 |
|||
299.83 |
|||
299.80 |
|||
299.79 |
|||
299.76 |
|||
299.80 |
|||
299.88 |
|||
299.88 |
|||
299.88 |
|||
299.86 |
|||
299.72 |
|||
299.72 |
|||
299.62 |
|||
299.86 |
|||
299.97 |
|||
299.95 |
|||
299.88 |
|||
299.91 |
|||
299.85 |
|||
299.87 |
|||
299.84 |
|||
299.84 |
|||
299.85 |
|||
299.84 |
|||
299.84 |
|||
299.84 |
|||
299.89 |
|||
299.81 |
|||
299.81 |
|||
299.82 |
|||
299.80 |
|||
299.77 |
|||
299.76 |
|||
299.74 |
|||
299.75 |
|||
299.76 |
|||
299.91 |
|||
299.92 |
|||
299.89 |
|||
299.86 |
|||
299.88 |
|||
299.72 |
|||
299.84 |
|||
299.85 |
|||
299.85 |
|||
299.78 |
|||
299.89 |
|||
299.84 |
|||
299.78 |
|||
299.81 |
|||
299.76 |
|||
299.81 |
|||
299.79 |
|||
299.81 |
|||
299.82 |
|||
299.85 |
|||
299.87 |
|||
299.87 |
|||
299.81 |
|||
299.74 |
|||
299.81 |
|||
299.94 |
|||
299.95 |
|||
299.80 |
|||
299.81 |
|||
299.87 |
|||
@ -0,0 +1,74 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Misra1a (Misra1a.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 42) |
|||
Certified Values (lines 41 to 47) |
|||
Data (lines 61 to 74) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: These data are the result of a NIST study regarding |
|||
dental research in monomolecular adsorption. The |
|||
response variable is volume, and the predictor |
|||
variable is pressure. |
|||
|
|||
Reference: Misra, D., NIST (1978). |
|||
Dental Research Monomolecular Adsorption Study. |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response Variable (y = volume) |
|||
1 Predictor Variable (x = pressure) |
|||
14 Observations |
|||
Lower Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Exponential Class |
|||
2 Parameters (b1 and b2) |
|||
|
|||
y = b1*(1-exp[-b2*x]) + e |
|||
|
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 500 250 2.3894212918E+02 2.7070075241E+00 |
|||
b2 = 0.0001 0.0005 5.5015643181E-04 7.2668688436E-06 |
|||
|
|||
Residual Sum of Squares: 1.2455138894E-01 |
|||
Residual Standard Deviation: 1.0187876330E-01 |
|||
Degrees of Freedom: 12 |
|||
Number of Observations: 14 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
10.07E0 77.6E0 |
|||
14.73E0 114.9E0 |
|||
17.94E0 141.1E0 |
|||
23.93E0 190.8E0 |
|||
29.61E0 239.9E0 |
|||
35.18E0 289.0E0 |
|||
40.02E0 332.8E0 |
|||
44.82E0 378.4E0 |
|||
50.76E0 434.8E0 |
|||
55.05E0 477.3E0 |
|||
61.01E0 536.8E0 |
|||
66.40E0 593.1E0 |
|||
75.47E0 689.1E0 |
|||
81.78E0 760.0E0 |
|||
@ -0,0 +1,74 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Misra1b (Misra1b.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 42) |
|||
Certified Values (lines 41 to 47) |
|||
Data (lines 61 to 74) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: These data are the result of a NIST study regarding |
|||
dental research in monomolecular adsorption. The |
|||
response variable is volume, and the predictor |
|||
variable is pressure. |
|||
|
|||
Reference: Misra, D., NIST (1978). |
|||
Dental Research Monomolecular Adsorption Study. |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response (y = volume) |
|||
1 Predictor (x = pressure) |
|||
14 Observations |
|||
Lower Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Miscellaneous Class |
|||
2 Parameters (b1 and b2) |
|||
|
|||
y = b1 * (1-(1+b2*x/2)**(-2)) + e |
|||
|
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 500 300 3.3799746163E+02 3.1643950207E+00 |
|||
b2 = 0.0001 0.0002 3.9039091287E-04 4.2547321834E-06 |
|||
|
|||
Residual Sum of Squares: 7.5464681533E-02 |
|||
Residual Standard Deviation: 7.9301471998E-02 |
|||
Degrees of Freedom: 12 |
|||
Number of Observations: 14 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
10.07E0 77.6E0 |
|||
14.73E0 114.9E0 |
|||
17.94E0 141.1E0 |
|||
23.93E0 190.8E0 |
|||
29.61E0 239.9E0 |
|||
35.18E0 289.0E0 |
|||
40.02E0 332.8E0 |
|||
44.82E0 378.4E0 |
|||
50.76E0 434.8E0 |
|||
55.05E0 477.3E0 |
|||
61.01E0 536.8E0 |
|||
66.40E0 593.1E0 |
|||
75.47E0 689.1E0 |
|||
81.78E0 760.0E0 |
|||
@ -0,0 +1,74 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Misra1c (Misra1c.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 42) |
|||
Certified Values (lines 41 to 47) |
|||
Data (lines 61 to 74) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: These data are the result of a NIST study regarding |
|||
dental research in monomolecular adsorption. The |
|||
response variable is volume, and the predictor |
|||
variable is pressure. |
|||
|
|||
Reference: Misra, D., NIST (1978). |
|||
Dental Research Monomolecular Adsorption. |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response (y = volume) |
|||
1 Predictor (x = pressure) |
|||
14 Observations |
|||
Average Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Miscellaneous Class |
|||
2 Parameters (b1 and b2) |
|||
|
|||
y = b1 * (1-(1+2*b2*x)**(-.5)) + e |
|||
|
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 500 600 6.3642725809E+02 4.6638326572E+00 |
|||
b2 = 0.0001 0.0002 2.0813627256E-04 1.7728423155E-06 |
|||
|
|||
Residual Sum of Squares: 4.0966836971E-02 |
|||
Residual Standard Deviation: 5.8428615257E-02 |
|||
Degrees of Freedom: 12 |
|||
Number of Observations: 14 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
10.07E0 77.6E0 |
|||
14.73E0 114.9E0 |
|||
17.94E0 141.1E0 |
|||
23.93E0 190.8E0 |
|||
29.61E0 239.9E0 |
|||
35.18E0 289.0E0 |
|||
40.02E0 332.8E0 |
|||
44.82E0 378.4E0 |
|||
50.76E0 434.8E0 |
|||
55.05E0 477.3E0 |
|||
61.01E0 536.8E0 |
|||
66.40E0 593.1E0 |
|||
75.47E0 689.1E0 |
|||
81.78E0 760.0E0 |
|||
@ -0,0 +1,74 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Misra1d (Misra1d.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 42) |
|||
Certified Values (lines 41 to 47) |
|||
Data (lines 61 to 74) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: These data are the result of a NIST study regarding |
|||
dental research in monomolecular adsorption. The |
|||
response variable is volume, and the predictor |
|||
variable is pressure. |
|||
|
|||
Reference: Misra, D., NIST (1978). |
|||
Dental Research Monomolecular Adsorption Study. |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response (y = volume) |
|||
1 Predictor (x = pressure) |
|||
14 Observations |
|||
Average Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Miscellaneous Class |
|||
2 Parameters (b1 and b2) |
|||
|
|||
y = b1*b2*x*((1+b2*x)**(-1)) + e |
|||
|
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 500 450 4.3736970754E+02 3.6489174345E+00 |
|||
b2 = 0.0001 0.0003 3.0227324449E-04 2.9334354479E-06 |
|||
|
|||
Residual Sum of Squares: 5.6419295283E-02 |
|||
Residual Standard Deviation: 6.8568272111E-02 |
|||
Degrees of Freedom: 12 |
|||
Number of Observations: 14 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
10.07E0 77.6E0 |
|||
14.73E0 114.9E0 |
|||
17.94E0 141.1E0 |
|||
23.93E0 190.8E0 |
|||
29.61E0 239.9E0 |
|||
35.18E0 289.0E0 |
|||
40.02E0 332.8E0 |
|||
44.82E0 378.4E0 |
|||
50.76E0 434.8E0 |
|||
55.05E0 477.3E0 |
|||
61.01E0 536.8E0 |
|||
66.40E0 593.1E0 |
|||
75.47E0 689.1E0 |
|||
81.78E0 760.0E0 |
|||
@ -0,0 +1,188 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Nelson (Nelson.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 43) |
|||
Certified Values (lines 41 to 48) |
|||
Data (lines 61 to 188) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: These data are the result of a study involving |
|||
the analysis of performance degradation data from |
|||
accelerated tests, published in IEEE Transactions |
|||
on Reliability. The response variable is dialectric |
|||
breakdown strength in kilo-volts, and the predictor |
|||
variables are time in weeks and temperature in degrees |
|||
Celcius. |
|||
|
|||
|
|||
Reference: Nelson, W. (1981). |
|||
Analysis of Performance-Degradation Data. |
|||
IEEE Transactions on Reliability. |
|||
Vol. 2, R-30, No. 2, pp. 149-155. |
|||
|
|||
Data: 1 Response ( y = dialectric breakdown strength) |
|||
2 Predictors (x1 = time; x2 = temperature) |
|||
128 Observations |
|||
Average Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Exponential Class |
|||
3 Parameters (b1 to b3) |
|||
|
|||
log[y] = b1 - b2*x1 * exp[-b3*x2] + e |
|||
|
|||
|
|||
|
|||
Starting values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 2 2.5 2.5906836021E+00 1.9149996413E-02 |
|||
b2 = 0.0001 0.000000005 5.6177717026E-09 6.1124096540E-09 |
|||
b3 = -0.01 -0.05 -5.7701013174E-02 3.9572366543E-03 |
|||
|
|||
Residual Sum of Squares: 3.7976833176E+00 |
|||
Residual Standard Deviation: 1.7430280130E-01 |
|||
Degrees of Freedom: 125 |
|||
Number of Observations: 128 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x1 x2 |
|||
15.00E0 1E0 180E0 |
|||
17.00E0 1E0 180E0 |
|||
15.50E0 1E0 180E0 |
|||
16.50E0 1E0 180E0 |
|||
15.50E0 1E0 225E0 |
|||
15.00E0 1E0 225E0 |
|||
16.00E0 1E0 225E0 |
|||
14.50E0 1E0 225E0 |
|||
15.00E0 1E0 250E0 |
|||
14.50E0 1E0 250E0 |
|||
12.50E0 1E0 250E0 |
|||
11.00E0 1E0 250E0 |
|||
14.00E0 1E0 275E0 |
|||
13.00E0 1E0 275E0 |
|||
14.00E0 1E0 275E0 |
|||
11.50E0 1E0 275E0 |
|||
14.00E0 2E0 180E0 |
|||
16.00E0 2E0 180E0 |
|||
13.00E0 2E0 180E0 |
|||
13.50E0 2E0 180E0 |
|||
13.00E0 2E0 225E0 |
|||
13.50E0 2E0 225E0 |
|||
12.50E0 2E0 225E0 |
|||
12.50E0 2E0 225E0 |
|||
12.50E0 2E0 250E0 |
|||
12.00E0 2E0 250E0 |
|||
11.50E0 2E0 250E0 |
|||
12.00E0 2E0 250E0 |
|||
13.00E0 2E0 275E0 |
|||
11.50E0 2E0 275E0 |
|||
13.00E0 2E0 275E0 |
|||
12.50E0 2E0 275E0 |
|||
13.50E0 4E0 180E0 |
|||
17.50E0 4E0 180E0 |
|||
17.50E0 4E0 180E0 |
|||
13.50E0 4E0 180E0 |
|||
12.50E0 4E0 225E0 |
|||
12.50E0 4E0 225E0 |
|||
15.00E0 4E0 225E0 |
|||
13.00E0 4E0 225E0 |
|||
12.00E0 4E0 250E0 |
|||
13.00E0 4E0 250E0 |
|||
12.00E0 4E0 250E0 |
|||
13.50E0 4E0 250E0 |
|||
10.00E0 4E0 275E0 |
|||
11.50E0 4E0 275E0 |
|||
11.00E0 4E0 275E0 |
|||
9.50E0 4E0 275E0 |
|||
15.00E0 8E0 180E0 |
|||
15.00E0 8E0 180E0 |
|||
15.50E0 8E0 180E0 |
|||
16.00E0 8E0 180E0 |
|||
13.00E0 8E0 225E0 |
|||
10.50E0 8E0 225E0 |
|||
13.50E0 8E0 225E0 |
|||
14.00E0 8E0 225E0 |
|||
12.50E0 8E0 250E0 |
|||
12.00E0 8E0 250E0 |
|||
11.50E0 8E0 250E0 |
|||
11.50E0 8E0 250E0 |
|||
6.50E0 8E0 275E0 |
|||
5.50E0 8E0 275E0 |
|||
6.00E0 8E0 275E0 |
|||
6.00E0 8E0 275E0 |
|||
18.50E0 16E0 180E0 |
|||
17.00E0 16E0 180E0 |
|||
15.30E0 16E0 180E0 |
|||
16.00E0 16E0 180E0 |
|||
13.00E0 16E0 225E0 |
|||
14.00E0 16E0 225E0 |
|||
12.50E0 16E0 225E0 |
|||
11.00E0 16E0 225E0 |
|||
12.00E0 16E0 250E0 |
|||
12.00E0 16E0 250E0 |
|||
11.50E0 16E0 250E0 |
|||
12.00E0 16E0 250E0 |
|||
6.00E0 16E0 275E0 |
|||
6.00E0 16E0 275E0 |
|||
5.00E0 16E0 275E0 |
|||
5.50E0 16E0 275E0 |
|||
12.50E0 32E0 180E0 |
|||
13.00E0 32E0 180E0 |
|||
16.00E0 32E0 180E0 |
|||
12.00E0 32E0 180E0 |
|||
11.00E0 32E0 225E0 |
|||
9.50E0 32E0 225E0 |
|||
11.00E0 32E0 225E0 |
|||
11.00E0 32E0 225E0 |
|||
11.00E0 32E0 250E0 |
|||
10.00E0 32E0 250E0 |
|||
10.50E0 32E0 250E0 |
|||
10.50E0 32E0 250E0 |
|||
2.70E0 32E0 275E0 |
|||
2.70E0 32E0 275E0 |
|||
2.50E0 32E0 275E0 |
|||
2.40E0 32E0 275E0 |
|||
13.00E0 48E0 180E0 |
|||
13.50E0 48E0 180E0 |
|||
16.50E0 48E0 180E0 |
|||
13.60E0 48E0 180E0 |
|||
11.50E0 48E0 225E0 |
|||
10.50E0 48E0 225E0 |
|||
13.50E0 48E0 225E0 |
|||
12.00E0 48E0 225E0 |
|||
7.00E0 48E0 250E0 |
|||
6.90E0 48E0 250E0 |
|||
8.80E0 48E0 250E0 |
|||
7.90E0 48E0 250E0 |
|||
1.20E0 48E0 275E0 |
|||
1.50E0 48E0 275E0 |
|||
1.00E0 48E0 275E0 |
|||
1.50E0 48E0 275E0 |
|||
13.00E0 64E0 180E0 |
|||
12.50E0 64E0 180E0 |
|||
16.50E0 64E0 180E0 |
|||
16.00E0 64E0 180E0 |
|||
11.00E0 64E0 225E0 |
|||
11.50E0 64E0 225E0 |
|||
10.50E0 64E0 225E0 |
|||
10.00E0 64E0 225E0 |
|||
7.27E0 64E0 250E0 |
|||
7.50E0 64E0 250E0 |
|||
6.70E0 64E0 250E0 |
|||
7.60E0 64E0 250E0 |
|||
1.50E0 64E0 275E0 |
|||
1.00E0 64E0 275E0 |
|||
1.20E0 64E0 275E0 |
|||
1.20E0 64E0 275E0 |
|||
@ -0,0 +1,71 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: NoInt1 (NoInt1.dat) |
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 31 to 44) |
|||
Data (lines 61 to 71) |
|||
|
|||
Procedure: Linear Least Squares Regression |
|||
|
|||
Reference: Eberhardt, K., NIST. |
|||
|
|||
Data: 1 Response Variable (y) |
|||
1 Predictor Variable (x) |
|||
11 Observations |
|||
Average Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Linear Class |
|||
1 Parameter (B1) |
|||
|
|||
y = B1*x + e |
|||
|
|||
|
|||
|
|||
|
|||
Certified Regression Statistics |
|||
|
|||
Standard Deviation |
|||
Parameter Estimate of Estimate |
|||
|
|||
B1 2.07438016528926 0.165289256198347E-01 |
|||
|
|||
Residual |
|||
Standard Deviation 3.56753034006338 |
|||
|
|||
R-Squared 0.999365492298663 |
|||
|
|||
Certified Analysis of Variance Table |
|||
|
|||
Source of Degrees of Sums of Mean |
|||
Variation Freedom Squares Squares F Statistic |
|||
|
|||
Regression 1 200457.727272727 200457.727272727 15750.2500000000 |
|||
Residual 10 127.272727272727 12.7272727272727 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
130 60 |
|||
131 61 |
|||
132 62 |
|||
133 63 |
|||
134 64 |
|||
135 65 |
|||
136 66 |
|||
137 67 |
|||
138 68 |
|||
139 69 |
|||
140 70 |
|||
@ -0,0 +1,63 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: NoInt2 (NoInt2.dat) |
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 31 to 45) |
|||
Data (lines 61 to 63) |
|||
|
|||
Procedure: Linear Least Squares Regression |
|||
|
|||
Reference: Eberhardt, K., NIST. |
|||
|
|||
Data: 1 Response Variable (y) |
|||
1 Predictor Variable (x) |
|||
3 Observations |
|||
Average Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Linear Class |
|||
1 Parameter (B1) |
|||
|
|||
y = B1*x + e |
|||
|
|||
|
|||
|
|||
|
|||
Certified Regression Statistics |
|||
|
|||
Standard Deviation |
|||
Parameter Estimate of Estimate |
|||
|
|||
B1 0.727272727272727 0.420827318078432E-01 |
|||
|
|||
Residual |
|||
Standard Deviation 0.369274472937998 |
|||
|
|||
R-Squared 0.993348115299335 |
|||
|
|||
|
|||
Certified Analysis of Variance Table |
|||
|
|||
Source of Degrees of Sums of Mean |
|||
Variation Freedom Squares Squares F Statistic |
|||
|
|||
Regression 1 40.7272727272727 40.7272727272727 298.6666666666667 |
|||
Residual 2 0.272727272727273 0.136363636363636 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
3 4 |
|||
4 5 |
|||
4 6 |
|||
@ -0,0 +1,97 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Norris (Norris.dat) |
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 31 to 46) |
|||
Data (lines 61 to 96) |
|||
|
|||
Procedure: Linear Least Squares Regression |
|||
|
|||
Reference: Norris, J., NIST. |
|||
Calibration of Ozone Monitors. |
|||
|
|||
Data: 1 Response Variable (y) |
|||
1 Predictor Variable (x) |
|||
36 Observations |
|||
Lower Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Linear Class |
|||
2 Parameters (B0,B1) |
|||
|
|||
y = B0 + B1*x + e |
|||
|
|||
|
|||
|
|||
Certified Regression Statistics |
|||
|
|||
Standard Deviation |
|||
Parameter Estimate of Estimate |
|||
|
|||
B0 -0.262323073774029 0.232818234301152 |
|||
B1 1.00211681802045 0.429796848199937E-03 |
|||
|
|||
Residual |
|||
Standard Deviation 0.884796396144373 |
|||
|
|||
R-Squared 0.999993745883712 |
|||
|
|||
|
|||
Certified Analysis of Variance Table |
|||
|
|||
Source of Degrees of Sums of Mean |
|||
Variation Freedom Squares Squares F Statistic |
|||
|
|||
Regression 1 4255954.13232369 4255954.13232369 5436385.54079785 |
|||
Residual 34 26.6173985294224 0.782864662630069 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
0.1 0.2 |
|||
338.8 337.4 |
|||
118.1 118.2 |
|||
888.0 884.6 |
|||
9.2 10.1 |
|||
228.1 226.5 |
|||
668.5 666.3 |
|||
998.5 996.3 |
|||
449.1 448.6 |
|||
778.9 777.0 |
|||
559.2 558.2 |
|||
0.3 0.4 |
|||
0.1 0.6 |
|||
778.1 775.5 |
|||
668.8 666.9 |
|||
339.3 338.0 |
|||
448.9 447.5 |
|||
10.8 11.6 |
|||
557.7 556.0 |
|||
228.3 228.1 |
|||
998.0 995.8 |
|||
888.8 887.6 |
|||
119.6 120.2 |
|||
0.3 0.3 |
|||
0.6 0.3 |
|||
557.6 556.8 |
|||
339.3 339.1 |
|||
888.0 887.2 |
|||
998.5 999.0 |
|||
778.9 779.0 |
|||
10.2 11.1 |
|||
117.6 118.3 |
|||
228.9 229.2 |
|||
668.4 669.1 |
|||
449.2 448.9 |
|||
0.2 0.5 |
|||
|
|||
@ -0,0 +1,48 @@ |
|||
File Name: NumAcc1.dat |
|||
|
|||
|
|||
Dataset Name: NumAcc1 |
|||
|
|||
Description: This is a constructed/fabricated data set |
|||
to test accuracy in summary statistic calculations. |
|||
The numbers are large (8-digit integers) and |
|||
differ only in the last decimal place. |
|||
Note--by construction, this data set has |
|||
sample mean = 10000002 (exact) |
|||
sample standard deviation = 1 (exact) |
|||
sample autocorrelation coef. = -0.5 (exact) |
|||
|
|||
Stat Category: Univariate: Summary Statistics |
|||
|
|||
Reference: Simon, Stephen D. and Lesage, James P. (1989). |
|||
Assessing the Accuracy of ANOVA Caluclations |
|||
in Statistical Software", Computational |
|||
Statistics & data Analysis, 8, pp. 325-332. |
|||
|
|||
Data: Constructed |
|||
1 Response : y |
|||
0 Predictors |
|||
3 Observations |
|||
|
|||
Model: Lower Level of Difficulty |
|||
2 Parameters : mu, sigma |
|||
1 Response Variable : y |
|||
0 Predictor Variables |
|||
|
|||
y = mu + e |
|||
|
|||
|
|||
|
|||
Certified Values |
|||
Sample Mean ybar: 10000002 |
|||
Sample Standard Deviation (denom. = n-1) s: 1 |
|||
Sample Autocorrelation Coefficient (lag 1) r(1): -0.5 |
|||
|
|||
Number of Observations: 3 |
|||
|
|||
|
|||
Data: Y |
|||
--------- |
|||
10000001 |
|||
10000003 |
|||
10000002 |
|||
File diff suppressed because it is too large
File diff suppressed because it is too large
File diff suppressed because it is too large
@ -0,0 +1,100 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Pontius |
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 31 to 47) |
|||
Data (lines 61 to 100) |
|||
|
|||
Procedure: Linear Least Squares Regression |
|||
|
|||
Reference: Pontius, P., NIST. |
|||
Load Cell Calibration. |
|||
|
|||
Data: 1 Response Variable (y) |
|||
1 Predictor Variable (x) |
|||
40 Observations |
|||
Lower Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Quadratic Class |
|||
3 Parameters (B0,B1,B2) |
|||
y = B0 + B1*x + B2*(x**2) |
|||
|
|||
|
|||
|
|||
|
|||
Certified Regression Statistics |
|||
|
|||
Standard Deviation |
|||
Parameter Estimate of Estimate |
|||
|
|||
B0 0.673565789473684E-03 0.107938612033077E-03 |
|||
B1 0.732059160401003E-06 0.157817399981659E-09 |
|||
B2 -0.316081871345029E-14 0.486652849992036E-16 |
|||
|
|||
Residual |
|||
Standard Deviation 0.205177424076185E-03 |
|||
|
|||
R-Squared 0.999999900178537 |
|||
|
|||
|
|||
Certified Analysis of Variance Table |
|||
|
|||
Source of Degrees of Sums of Mean |
|||
Variation Freedom Squares Squares F Statistic |
|||
|
|||
Regression 2 15.6040343244198 7.80201716220991 185330865.995752 |
|||
Residual 37 0.155761768796992E-05 0.420977753505385E-07 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
.11019 150000 |
|||
.21956 300000 |
|||
.32949 450000 |
|||
.43899 600000 |
|||
.54803 750000 |
|||
.65694 900000 |
|||
.76562 1050000 |
|||
.87487 1200000 |
|||
.98292 1350000 |
|||
1.09146 1500000 |
|||
1.20001 1650000 |
|||
1.30822 1800000 |
|||
1.41599 1950000 |
|||
1.52399 2100000 |
|||
1.63194 2250000 |
|||
1.73947 2400000 |
|||
1.84646 2550000 |
|||
1.95392 2700000 |
|||
2.06128 2850000 |
|||
2.16844 3000000 |
|||
.11052 150000 |
|||
.22018 300000 |
|||
.32939 450000 |
|||
.43886 600000 |
|||
.54798 750000 |
|||
.65739 900000 |
|||
.76596 1050000 |
|||
.87474 1200000 |
|||
.98300 1350000 |
|||
1.09150 1500000 |
|||
1.20004 1650000 |
|||
1.30818 1800000 |
|||
1.41613 1950000 |
|||
1.52408 2100000 |
|||
1.63159 2250000 |
|||
1.73965 2400000 |
|||
1.84696 2550000 |
|||
1.95445 2700000 |
|||
2.06177 2850000 |
|||
2.16829 3000000 |
|||
@ -0,0 +1,69 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Rat42 (Rat42.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 43) |
|||
Certified Values (lines 41 to 48) |
|||
Data (lines 61 to 69) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: This model and data are an example of fitting |
|||
sigmoidal growth curves taken from Ratkowsky (1983). |
|||
The response variable is pasture yield, and the |
|||
predictor variable is growing time. |
|||
|
|||
|
|||
Reference: Ratkowsky, D.A. (1983). |
|||
Nonlinear Regression Modeling. |
|||
New York, NY: Marcel Dekker, pp. 61 and 88. |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response (y = pasture yield) |
|||
1 Predictor (x = growing time) |
|||
9 Observations |
|||
Higher Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Exponential Class |
|||
3 Parameters (b1 to b3) |
|||
|
|||
y = b1 / (1+exp[b2-b3*x]) + e |
|||
|
|||
|
|||
|
|||
Starting Values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 100 75 7.2462237576E+01 1.7340283401E+00 |
|||
b2 = 1 2.5 2.6180768402E+00 8.8295217536E-02 |
|||
b3 = 0.1 0.07 6.7359200066E-02 3.4465663377E-03 |
|||
|
|||
Residual Sum of Squares: 8.0565229338E+00 |
|||
Residual Standard Deviation: 1.1587725499E+00 |
|||
Degrees of Freedom: 6 |
|||
Number of Observations: 9 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
8.930E0 9.000E0 |
|||
10.800E0 14.000E0 |
|||
18.590E0 21.000E0 |
|||
22.330E0 28.000E0 |
|||
39.350E0 42.000E0 |
|||
56.110E0 57.000E0 |
|||
61.730E0 63.000E0 |
|||
64.620E0 70.000E0 |
|||
67.080E0 79.000E0 |
|||
@ -0,0 +1,75 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Rat43 (Rat43.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 44) |
|||
Certified Values (lines 41 to 49) |
|||
Data (lines 61 to 75) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: This model and data are an example of fitting |
|||
sigmoidal growth curves taken from Ratkowsky (1983). |
|||
The response variable is the dry weight of onion bulbs |
|||
and tops, and the predictor variable is growing time. |
|||
|
|||
|
|||
Reference: Ratkowsky, D.A. (1983). |
|||
Nonlinear Regression Modeling. |
|||
New York, NY: Marcel Dekker, pp. 62 and 88. |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response (y = onion bulb dry weight) |
|||
1 Predictor (x = growing time) |
|||
15 Observations |
|||
Higher Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Exponential Class |
|||
4 Parameters (b1 to b4) |
|||
|
|||
y = b1 / ((1+exp[b2-b3*x])**(1/b4)) + e |
|||
|
|||
|
|||
|
|||
Starting Values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 100 700 6.9964151270E+02 1.6302297817E+01 |
|||
b2 = 10 5 5.2771253025E+00 2.0828735829E+00 |
|||
b3 = 1 0.75 7.5962938329E-01 1.9566123451E-01 |
|||
b4 = 1 1.3 1.2792483859E+00 6.8761936385E-01 |
|||
|
|||
Residual Sum of Squares: 8.7864049080E+03 |
|||
Residual Standard Deviation: 2.8262414662E+01 |
|||
Degrees of Freedom: 9 |
|||
Number of Observations: 15 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
16.08E0 1.0E0 |
|||
33.83E0 2.0E0 |
|||
65.80E0 3.0E0 |
|||
97.20E0 4.0E0 |
|||
191.55E0 5.0E0 |
|||
326.20E0 6.0E0 |
|||
386.87E0 7.0E0 |
|||
520.53E0 8.0E0 |
|||
590.03E0 9.0E0 |
|||
651.92E0 10.0E0 |
|||
724.93E0 11.0E0 |
|||
699.56E0 12.0E0 |
|||
689.96E0 13.0E0 |
|||
637.56E0 14.0E0 |
|||
717.41E0 15.0E0 |
|||
@ -0,0 +1,85 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Roszman1 (Roszman1.dat) |
|||
|
|||
File Format: ASCII |
|||
Starting Values (lines 41 to 44) |
|||
Certified Values (lines 41 to 49) |
|||
Data (lines 61 to 85) |
|||
|
|||
Procedure: Nonlinear Least Squares Regression |
|||
|
|||
Description: These data are the result of a NIST study involving |
|||
quantum defects in iodine atoms. The response |
|||
variable is the number of quantum defects, and the |
|||
predictor variable is the excited energy state. |
|||
The argument to the ARCTAN function is in radians. |
|||
|
|||
Reference: Roszman, L., NIST (19??). |
|||
Quantum Defects for Sulfur I Atom. |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: 1 Response (y = quantum defect) |
|||
1 Predictor (x = excited state energy) |
|||
25 Observations |
|||
Average Level of Difficulty |
|||
Observed Data |
|||
|
|||
Model: Miscellaneous Class |
|||
4 Parameters (b1 to b4) |
|||
|
|||
pi = 3.141592653589793238462643383279E0 |
|||
y = b1 - b2*x - arctan[b3/(x-b4)]/pi + e |
|||
|
|||
|
|||
Starting Values Certified Values |
|||
|
|||
Start 1 Start 2 Parameter Standard Deviation |
|||
b1 = 0.1 0.2 2.0196866396E-01 1.9172666023E-02 |
|||
b2 = -0.00001 -0.000005 -6.1953516256E-06 3.2058931691E-06 |
|||
b3 = 1000 1200 1.2044556708E+03 7.4050983057E+01 |
|||
b4 = -100 -150 -1.8134269537E+02 4.9573513849E+01 |
|||
|
|||
Residual Sum of Squares: 4.9484847331E-04 |
|||
Residual Standard Deviation: 4.8542984060E-03 |
|||
Degrees of Freedom: 21 |
|||
Number of Observations: 25 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
0.252429 -4868.68 |
|||
0.252141 -4868.09 |
|||
0.251809 -4867.41 |
|||
0.297989 -3375.19 |
|||
0.296257 -3373.14 |
|||
0.295319 -3372.03 |
|||
0.339603 -2473.74 |
|||
0.337731 -2472.35 |
|||
0.333820 -2469.45 |
|||
0.389510 -1894.65 |
|||
0.386998 -1893.40 |
|||
0.438864 -1497.24 |
|||
0.434887 -1495.85 |
|||
0.427893 -1493.41 |
|||
0.471568 -1208.68 |
|||
0.461699 -1206.18 |
|||
0.461144 -1206.04 |
|||
0.513532 -997.92 |
|||
0.506641 -996.61 |
|||
0.505062 -996.31 |
|||
0.535648 -834.94 |
|||
0.533726 -834.66 |
|||
0.568064 -710.03 |
|||
0.612886 -530.16 |
|||
0.624169 -464.17 |
|||
@ -0,0 +1,65 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: SiRstv (SiRstvt.dat) |
|||
|
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 41 to 47) |
|||
Data (lines 61 to 65) |
|||
|
|||
|
|||
Procedure: Analysis of Variance |
|||
|
|||
|
|||
Reference: Ehrstein, James and Croarkin, M. Carroll. |
|||
Unpublished NIST dataset. |
|||
|
|||
|
|||
Data: 1 Factor |
|||
5 Treatments |
|||
5 Replicates/Cell |
|||
25 Observations |
|||
3 Constant Leading Digits |
|||
Lower Level of Difficulty |
|||
Observed Data |
|||
|
|||
|
|||
Model: 6 Parameters (mu,tau_1, ... , tau_5) |
|||
y_{ij} = mu + tau_i + epsilon_{ij} |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Certified Values: |
|||
|
|||
Source of Sums of Mean |
|||
Variation df Squares Squares F Statistic |
|||
|
|||
Between Instrument 4 5.11462616000000E-02 1.27865654000000E-02 1.18046237440255E+00 |
|||
Within Instrument 20 2.16636560000000E-01 1.08318280000000E-02 |
|||
|
|||
Certified R-Squared 1.90999039051129E-01 |
|||
|
|||
Certified Residual |
|||
Standard Deviation 1.04076068334656E-01 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: |
|||
Instrument |
|||
|
|||
1 2 3 4 5 |
|||
|
|||
196.3052 196.3042 196.1303 196.2795 196.2119 |
|||
196.1240 196.3825 196.2005 196.1748 196.1051 |
|||
196.1890 196.1669 196.2889 196.1494 196.1850 |
|||
196.2569 196.3257 196.0343 196.1485 196.0052 |
|||
196.3403 196.0422 196.1811 195.9885 196.2090 |
|||
@ -0,0 +1,81 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: SmLs01 (SmLs01t.dat) |
|||
|
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 41 to 47) |
|||
Data (lines 61 to 81) |
|||
|
|||
|
|||
Procedure: Analysis of Variance |
|||
|
|||
|
|||
Reference: Simon, Stephen D. and Lesage, James P. (1989). |
|||
"Assessing the Accuracy of ANOVA Calculations in |
|||
Statistical Software". |
|||
Computational Statistics & Data Analysis, 8, pp. 325-332. |
|||
|
|||
|
|||
Data: 1 Factor |
|||
9 Treatments |
|||
21 Replicates/Cell |
|||
189 Observations |
|||
1 Constant Leading Digit |
|||
Lower Level of Difficulty |
|||
Generated Data |
|||
|
|||
|
|||
Model: 10 Parameters (mu,tau_1, ... , tau_9) |
|||
y_{ij} = mu + tau_i + epsilon_{ij} |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Certified Values: |
|||
|
|||
Source of Sums of Mean |
|||
Variation df Squares Squares F Statistic |
|||
|
|||
Between Treatment 8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01 |
|||
Within Treatment 180 1.80000000000000E+00 1.00000000000000E-02 |
|||
|
|||
Certified R-Squared 4.82758620689655E-01 |
|||
|
|||
Certified Residual |
|||
Standard Deviation 1.00000000000000E-01 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: |
|||
Treatment |
|||
|
|||
1 2 3 4 5 6 7 8 9 |
|||
|
|||
1.4 1.3 1.5 1.3 1.5 1.3 1.5 1.3 1.5 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
@ -0,0 +1,261 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: SmLs02 (SmLs02t.dat) |
|||
|
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 41 to 47) |
|||
Data (lines 61 to 261) |
|||
|
|||
|
|||
Procedure: Analysis of Variance |
|||
|
|||
|
|||
Reference: Simon, Stephen D. and Lesage, James P. (1989). |
|||
"Assessing the Accuracy of ANOVA Calculations in |
|||
Statistical Software". |
|||
Computational Statistics & Data Analysis, 8, pp. 325-332. |
|||
|
|||
|
|||
Data: 1 Factor |
|||
9 Treatments |
|||
201 Replicates/Cell |
|||
1809 Observations |
|||
1 Constant Leading Digit |
|||
Lower Level of Difficulty |
|||
Generated Data |
|||
|
|||
|
|||
Model: 10 Parameters (mu,tau_1, ... , tau_9) |
|||
y_{ij} = mu + tau_i + epsilon_{ij} |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Certified Values: |
|||
|
|||
Source of Sums of Mean |
|||
Variation df Squares Squares F Statistic |
|||
|
|||
Between Treatment 8 1.60800000000000E+01 2.01000000000000E+00 2.01000000000000E+02 |
|||
Within Treatment 1800 1.80000000000000E+01 1.00000000000000E-02 |
|||
|
|||
Certified R-Squared 4.71830985915493E-01 |
|||
|
|||
Certified Residual |
|||
Standard Deviation 1.00000000000000E-01 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: |
|||
Treatment |
|||
|
|||
1 2 3 4 5 6 7 8 9 |
|||
|
|||
1.4 1.3 1.5 1.3 1.5 1.3 1.5 1.3 1.5 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
1.3 1.2 1.4 1.2 1.4 1.2 1.4 1.2 1.4 |
|||
1.5 1.4 1.6 1.4 1.6 1.4 1.6 1.4 1.6 |
|||
File diff suppressed because it is too large
@ -0,0 +1,81 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: SmLs04 (SmLs04t.dat) |
|||
|
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 41 to 47) |
|||
Data (lines 61 to 81) |
|||
|
|||
|
|||
Procedure: Analysis of Variance |
|||
|
|||
|
|||
Reference: Simon, Stephen D. and Lesage, James P. (1989). |
|||
"Assessing the Accuracy of ANOVA Calculations in |
|||
Statistical Software". |
|||
Computational Statistics & Data Analysis, 8, pp. 325-332. |
|||
|
|||
|
|||
Data: 1 Factor |
|||
9 Treatments |
|||
21 Replicates/Cell |
|||
189 Observations |
|||
7 Constant Leading Digits |
|||
Average Level of Difficulty |
|||
Generated Data |
|||
|
|||
|
|||
Model: 10 Parameters (mu,tau_1, ... , tau_9) |
|||
y_{ij} = mu + tau_i + epsilon_{ij} |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Certified Values: |
|||
|
|||
Source of Sums of Mean |
|||
Variation df Squares Squares F Statistic |
|||
|
|||
Between Treatment 8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01 |
|||
Within Treatment 180 1.80000000000000E+00 1.00000000000000E-02 |
|||
|
|||
Certified R-Squared 4.82758620689655E-01 |
|||
|
|||
Certified Residual |
|||
Standard Deviation 1.00000000000000E-01 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: |
|||
Treatment |
|||
|
|||
1 2 3 4 5 6 7 8 9 |
|||
|
|||
1000000.4 1000000.3 1000000.5 1000000.3 1000000.5 1000000.3 1000000.5 1000000.3 1000000.5 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
@ -0,0 +1,261 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: SmLs05 (SmLs05t.dat) |
|||
|
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 41 to 47) |
|||
Data (lines 61 to 261) |
|||
|
|||
|
|||
Procedure: Analysis of Variance |
|||
|
|||
|
|||
Reference: Simon, Stephen D. and Lesage, James P. (1989). |
|||
"Assessing the Accuracy of ANOVA Calculations in |
|||
Statistical Software". |
|||
Computational Statistics & Data Analysis, 8, pp. 325-332. |
|||
|
|||
|
|||
Data: 1 Factor |
|||
9 Treatments |
|||
201 Replicates/Cell |
|||
1809 Observations |
|||
7 Constant Leading Digits |
|||
Average Level of Difficulty |
|||
Generated Data |
|||
|
|||
|
|||
Model: 10 Parameters (mu,tau_1, ... , tau_9) |
|||
y_{ij} = mu + tau_i + epsilon_{ij} |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Certified Values: |
|||
|
|||
Source of Sums of Mean |
|||
Variation df Squares Squares F Statistic |
|||
|
|||
Between Treatment 8 1.60800000000000E+01 2.01000000000000E+00 2.01000000000000E+02 |
|||
Within Treatment 1800 1.80000000000000E+01 1.00000000000000E-02 |
|||
|
|||
Certified R-Squared 4.71830985915493E-01 |
|||
|
|||
Certified Residual |
|||
Standard Deviation 1.00000000000000E-01 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: |
|||
Treatment |
|||
|
|||
1 2 3 4 5 6 7 8 9 |
|||
|
|||
1000000.4 1000000.3 1000000.5 1000000.3 1000000.5 1000000.3 1000000.5 1000000.3 1000000.5 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
1000000.3 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 1000000.2 1000000.4 |
|||
1000000.5 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 1000000.4 1000000.6 |
|||
File diff suppressed because it is too large
@ -0,0 +1,81 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: SmLs07 (SmLs07t.dat) |
|||
|
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 41 to 47) |
|||
Data (lines 61 to 81) |
|||
|
|||
|
|||
Procedure: Analysis of Variance |
|||
|
|||
|
|||
Reference: Simon, Stephen D. and Lesage, James P. (1989). |
|||
"Assessing the Accuracy of ANOVA Calculations in |
|||
Statistical Software". |
|||
Computational Statistics & Data Analysis, 8, pp. 325-332. |
|||
|
|||
|
|||
Data: 1 Factor |
|||
9 Treatments |
|||
21 Replicates/Cell |
|||
189 Observations |
|||
13 Constant Leading Digits |
|||
Higher Level of Difficulty |
|||
Generated Data |
|||
|
|||
|
|||
Model: 10 Parameters (mu,tau_1, ... , tau_9) |
|||
y_{ij} = mu + tau_i + epsilon_{ij} |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Certified Values: |
|||
|
|||
Source of Sums of Mean |
|||
Variation df Squares Squares F Statistic |
|||
|
|||
Between Treatment 8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01 |
|||
Within Treatment 180 1.80000000000000E+00 1.00000000000000E-02 |
|||
|
|||
Certified R-Squared 4.82758620689655E-01 |
|||
|
|||
Certified Residual |
|||
Standard Deviation 1.00000000000000E-01 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: |
|||
Treatment |
|||
|
|||
1 2 3 4 5 6 7 8 9 |
|||
|
|||
1000000000000.4 1000000000000.3 1000000000000.5 1000000000000.3 1000000000000.5 1000000000000.3 1000000000000.5 1000000000000.3 1000000000000.5 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
@ -0,0 +1,261 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: SmLs08 (SmLs08t.dat) |
|||
|
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 41 to 47) |
|||
Data (lines 61 to 261) |
|||
|
|||
|
|||
Procedure: Analysis of Variance |
|||
|
|||
|
|||
Reference: Simon, Stephen D. and Lesage, James P. (1989). |
|||
"Assessing the Accuracy of ANOVA Calculations in |
|||
Statistical Software". |
|||
Computational Statistics & Data Analysis, 8, pp. 325-332. |
|||
|
|||
|
|||
Data: 1 Factor |
|||
9 Treatments |
|||
201 Replicates/Cell |
|||
1809 Observations |
|||
13 Constant Leading Digits |
|||
Higher Level of Difficulty |
|||
Generated Data |
|||
|
|||
|
|||
Model: 10 Parameters (mu,tau_1, ... , tau_9) |
|||
y_{ij} = mu + tau_i + epsilon_{ij} |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Certified Values: |
|||
|
|||
Source of Sums of Mean |
|||
Variation df Squares Squares F Statistic |
|||
|
|||
Between Treatment 8 1.60800000000000E+01 2.01000000000000E+00 2.01000000000000E+02 |
|||
Within Treatment 1800 1.80000000000000E+01 1.00000000000000E-02 |
|||
|
|||
Certified R-Squared 4.71830985915493E-01 |
|||
|
|||
Certified Residual |
|||
Standard Deviation 1.00000000000000E-01 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: |
|||
Treatment |
|||
|
|||
1 2 3 4 5 6 7 8 9 |
|||
|
|||
1000000000000.4 1000000000000.3 1000000000000.5 1000000000000.3 1000000000000.5 1000000000000.3 1000000000000.5 1000000000000.3 1000000000000.5 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
|||
1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
|||
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1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
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1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
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1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
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1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
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1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
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1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
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1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
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1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
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1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
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1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
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1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
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1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
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1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
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1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
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1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
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1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
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1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
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1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
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1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
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1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
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1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
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1000000000000.3 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 1000000000000.2 1000000000000.4 |
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1000000000000.5 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 1000000000000.4 1000000000000.6 |
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File diff suppressed because it is too large
@ -0,0 +1,97 @@ |
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NIST/ITL StRD |
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Dataset Name: Thurber (Thurber.dat) |
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|
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File Format: ASCII |
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Starting Values (lines 41 to 47) |
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Certified Values (lines 41 to 52) |
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Data (lines 61 to 97) |
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|
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Procedure: Nonlinear Least Squares Regression |
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|
|||
Description: These data are the result of a NIST study involving |
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semiconductor electron mobility. The response |
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variable is a measure of electron mobility, and the |
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predictor variable is the natural log of the density. |
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|
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|
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Reference: Thurber, R., NIST (197?). |
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Semiconductor electron mobility modeling. |
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|
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|
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|
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|
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|
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|
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Data: 1 Response Variable (y = electron mobility) |
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1 Predictor Variable (x = log[density]) |
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37 Observations |
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Higher Level of Difficulty |
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Observed Data |
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|
|||
Model: Rational Class (cubic/cubic) |
|||
7 Parameters (b1 to b7) |
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|
|||
y = (b1 + b2*x + b3*x**2 + b4*x**3) / |
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(1 + b5*x + b6*x**2 + b7*x**3) + e |
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|
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|
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Starting Values Certified Values |
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|
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Start 1 Start 2 Parameter Standard Deviation |
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b1 = 1000 1300 1.2881396800E+03 4.6647963344E+00 |
|||
b2 = 1000 1500 1.4910792535E+03 3.9571156086E+01 |
|||
b3 = 400 500 5.8323836877E+02 2.8698696102E+01 |
|||
b4 = 40 75 7.5416644291E+01 5.5675370270E+00 |
|||
b5 = 0.7 1 9.6629502864E-01 3.1333340687E-02 |
|||
b6 = 0.3 0.4 3.9797285797E-01 1.4984928198E-02 |
|||
b7 = 0.03 0.05 4.9727297349E-02 6.5842344623E-03 |
|||
|
|||
Residual Sum of Squares: 5.6427082397E+03 |
|||
Residual Standard Deviation: 1.3714600784E+01 |
|||
Degrees of Freedom: 30 |
|||
Number of Observations: 37 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
80.574E0 -3.067E0 |
|||
84.248E0 -2.981E0 |
|||
87.264E0 -2.921E0 |
|||
87.195E0 -2.912E0 |
|||
89.076E0 -2.840E0 |
|||
89.608E0 -2.797E0 |
|||
89.868E0 -2.702E0 |
|||
90.101E0 -2.699E0 |
|||
92.405E0 -2.633E0 |
|||
95.854E0 -2.481E0 |
|||
100.696E0 -2.363E0 |
|||
101.060E0 -2.322E0 |
|||
401.672E0 -1.501E0 |
|||
390.724E0 -1.460E0 |
|||
567.534E0 -1.274E0 |
|||
635.316E0 -1.212E0 |
|||
733.054E0 -1.100E0 |
|||
759.087E0 -1.046E0 |
|||
894.206E0 -0.915E0 |
|||
990.785E0 -0.714E0 |
|||
1090.109E0 -0.566E0 |
|||
1080.914E0 -0.545E0 |
|||
1122.643E0 -0.400E0 |
|||
1178.351E0 -0.309E0 |
|||
1260.531E0 -0.109E0 |
|||
1273.514E0 -0.103E0 |
|||
1288.339E0 0.010E0 |
|||
1327.543E0 0.119E0 |
|||
1353.863E0 0.377E0 |
|||
1414.509E0 0.790E0 |
|||
1425.208E0 0.963E0 |
|||
1421.384E0 1.006E0 |
|||
1442.962E0 1.115E0 |
|||
1464.350E0 1.572E0 |
|||
1468.705E0 1.841E0 |
|||
1447.894E0 2.047E0 |
|||
1457.628E0 2.200E0 |
|||
@ -0,0 +1,81 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Wampler1 (Wampler1.dat) |
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 31 to 50) |
|||
Data (lines 61 to 81) |
|||
|
|||
Procedure: Linear Least Squares Regression |
|||
|
|||
Reference: Wampler, R. H. (1970). |
|||
A Report of the Accuracy of Some Widely-Used Least |
|||
Squares Computer Programs. |
|||
Journal of the American Statistical Association, 65, pp. 549-565. |
|||
|
|||
Data: 1 Response Variable (y) |
|||
1 Predictor Variable (x) |
|||
21 Observations |
|||
Higher Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Polynomial Class |
|||
6 Parameters (B0,B1,...,B5) |
|||
|
|||
y = B0 + B1*x + B2*(x**2) + B3*(x**3)+ B4*(x**4) + B5*(x**5) |
|||
|
|||
Certified Regression Statistics |
|||
|
|||
Standard Deviation |
|||
Parameter Estimate of Estimate |
|||
|
|||
B0 1.00000000000000 0.000000000000000 |
|||
B1 1.00000000000000 0.000000000000000 |
|||
B2 1.00000000000000 0.000000000000000 |
|||
B3 1.00000000000000 0.000000000000000 |
|||
B4 1.00000000000000 0.000000000000000 |
|||
B5 1.00000000000000 0.000000000000000 |
|||
|
|||
Residual |
|||
Standard Deviation 0.000000000000000 |
|||
|
|||
R-Squared 1.00000000000000 |
|||
|
|||
|
|||
Certified Analysis of Variance Table |
|||
|
|||
Source of Degrees of Sums of Mean |
|||
Variation Freedom Squares Squares F Statistic |
|||
|
|||
Regression 5 18814317208116.7 3762863441623.33 Infinity |
|||
Residual 15 0.000000000000000 0.000000000000000 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
1 0 |
|||
6 1 |
|||
63 2 |
|||
364 3 |
|||
1365 4 |
|||
3906 5 |
|||
9331 6 |
|||
19608 7 |
|||
37449 8 |
|||
66430 9 |
|||
111111 10 |
|||
177156 11 |
|||
271453 12 |
|||
402234 13 |
|||
579195 14 |
|||
813616 15 |
|||
1118481 16 |
|||
1508598 17 |
|||
2000719 18 |
|||
2613660 19 |
|||
3368421 20 |
|||
@ -0,0 +1,81 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Wampler2 (Wampler2.dat) |
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 31 to 50) |
|||
Data (lines 61 to 81) |
|||
|
|||
Procedure: Linear Least Squares Regression |
|||
|
|||
Reference: Wampler, R. H. (1970). |
|||
A Report of the Accuracy of Some Widely-Used Least |
|||
Squares Computer Programs. |
|||
Journal of the American Statistical Association, 65, pp. 549-565. |
|||
|
|||
Data: 1 Response Variable (y) |
|||
1 Predictor Variable (x) |
|||
21 Observations |
|||
Higher Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Polynomial Class |
|||
6 Parameters (B0,B1,...,B5) |
|||
|
|||
y = B0 + B1*x + B2*(x**2) + B3*(x**3)+ B4*(x**4) + B5*(x**5) |
|||
|
|||
Certified Regression Statistics |
|||
|
|||
Standard Deviation |
|||
Parameter Estimate of Estimate |
|||
|
|||
B0 1.00000000000000 0.000000000000000 |
|||
B1 0.100000000000000 0.000000000000000 |
|||
B2 0.100000000000000E-01 0.000000000000000 |
|||
B3 0.100000000000000E-02 0.000000000000000 |
|||
B4 0.100000000000000E-03 0.000000000000000 |
|||
B5 0.100000000000000E-04 0.000000000000000 |
|||
|
|||
Residual |
|||
Standard Deviation 0.000000000000000 |
|||
|
|||
R-Squared 1.00000000000000 |
|||
|
|||
|
|||
Certified Analysis of Variance Table |
|||
|
|||
Source of Degrees of Sums of Mean |
|||
Variation Freedom Squares Squares F Statistic |
|||
|
|||
Regression 5 6602.91858365167 1320.58371673033 Infinity |
|||
Residual 15 0.000000000000000 0.000000000000000 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
1.00000 0 |
|||
1.11111 1 |
|||
1.24992 2 |
|||
1.42753 3 |
|||
1.65984 4 |
|||
1.96875 5 |
|||
2.38336 6 |
|||
2.94117 7 |
|||
3.68928 8 |
|||
4.68559 9 |
|||
6.00000 10 |
|||
7.71561 11 |
|||
9.92992 12 |
|||
12.75603 13 |
|||
16.32384 14 |
|||
20.78125 15 |
|||
26.29536 16 |
|||
33.05367 17 |
|||
41.26528 18 |
|||
51.16209 19 |
|||
63.00000 20 |
|||
@ -0,0 +1,81 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Wampler3 (Wampler3.dat) |
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 31 to 50) |
|||
Data (lines 61 to 81) |
|||
|
|||
Procedure: Linear Least Squares Regression |
|||
|
|||
Reference: Wampler, R. H. (1970). |
|||
A Report of the Accuracy of Some Widely-Used Least |
|||
Squares Computer Programs. |
|||
Journal of the American Statistical Association, 65, pp. 549-565. |
|||
|
|||
Data: 1 Response Variable (y) |
|||
1 Predictor Variable (x) |
|||
21 Observations |
|||
Higher Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Polynomial Class |
|||
6 Parameters (B0,B1,...,B5) |
|||
|
|||
y = B0 + B1*x + B2*(x**2) + B3*(x**3)+ B4*(x**4) + B5*(x**5) |
|||
|
|||
Certified Regression Statistics |
|||
|
|||
Standard Deviation |
|||
Parameter Estimate of Estimate |
|||
|
|||
B0 1.00000000000000 2152.32624678170 |
|||
B1 1.00000000000000 2363.55173469681 |
|||
B2 1.00000000000000 779.343524331583 |
|||
B3 1.00000000000000 101.475507550350 |
|||
B4 1.00000000000000 5.64566512170752 |
|||
B5 1.00000000000000 0.112324854679312 |
|||
|
|||
Residual |
|||
Standard Deviation 2360.14502379268 |
|||
|
|||
R-Squared 0.999995559025820 |
|||
|
|||
|
|||
Certified Analysis of Variance Table |
|||
|
|||
Source of Degrees of Sums of Mean |
|||
Variation Freedom Squares Squares F Statistic |
|||
|
|||
Regression 5 18814317208116.7 3762863441623.33 675524.458240122 |
|||
Residual 15 83554268.0000000 5570284.53333333 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
760. 0 |
|||
-2042. 1 |
|||
2111. 2 |
|||
-1684. 3 |
|||
3888. 4 |
|||
1858. 5 |
|||
11379. 6 |
|||
17560. 7 |
|||
39287. 8 |
|||
64382. 9 |
|||
113159. 10 |
|||
175108. 11 |
|||
273291. 12 |
|||
400186. 13 |
|||
581243. 14 |
|||
811568. 15 |
|||
1121004. 16 |
|||
1506550. 17 |
|||
2002767. 18 |
|||
2611612. 19 |
|||
3369180. 20 |
|||
@ -0,0 +1,81 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Wampler4 (Wampler4.dat) |
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 31 to 50) |
|||
Data (lines 61 to 81) |
|||
|
|||
Procedure: Linear Least Squares Regression |
|||
|
|||
Reference: Wampler, R. H. (1970). |
|||
A Report of the Accuracy of Some Widely-Used Least |
|||
Squares Computer Programs. |
|||
Journal of the American Statistical Association, 65, pp. 549-565. |
|||
|
|||
Data: 1 Response Variable (y) |
|||
1 Predictor Variable (x) |
|||
21 Observations |
|||
Higher Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Polynomial Class |
|||
6 Parameters (B0,B1,...,B5) |
|||
|
|||
y = B0 + B1*x + B2*(x**2) + B3*(x**3)+ B4*(x**4) + B5*(x**5) |
|||
|
|||
Certified Regression Statistics |
|||
|
|||
Standard Deviation |
|||
Parameter Estimate of Estimate |
|||
|
|||
B0 1.00000000000000 215232.624678170 |
|||
B1 1.00000000000000 236355.173469681 |
|||
B2 1.00000000000000 77934.3524331583 |
|||
B3 1.00000000000000 10147.5507550350 |
|||
B4 1.00000000000000 564.566512170752 |
|||
B5 1.00000000000000 11.2324854679312 |
|||
|
|||
Residual |
|||
Standard Deviation 236014.502379268 |
|||
|
|||
R-Squared 0.957478440825662 |
|||
|
|||
|
|||
Certified Analysis of Variance Table |
|||
|
|||
Source of Degrees of Sums of Mean |
|||
Variation Freedom Squares Squares F Statistic |
|||
|
|||
Regression 5 18814317208116.7 3762863441623.33 67.5524458240122 |
|||
Residual 15 835542680000.000 55702845333.3333 |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
75901 0 |
|||
-204794 1 |
|||
204863 2 |
|||
-204436 3 |
|||
253665 4 |
|||
-200894 5 |
|||
214131 6 |
|||
-185192 7 |
|||
221249 8 |
|||
-138370 9 |
|||
315911 10 |
|||
-27644 11 |
|||
455253 12 |
|||
197434 13 |
|||
783995 14 |
|||
608816 15 |
|||
1370781 16 |
|||
1303798 17 |
|||
2205519 18 |
|||
2408860 19 |
|||
3444321 20 |
|||
@ -0,0 +1,81 @@ |
|||
NIST/ITL StRD |
|||
Dataset Name: Wampler5 (Wampler5.dat) |
|||
|
|||
File Format: ASCII |
|||
Certified Values (lines 31 to 50) |
|||
Data (lines 61 to 81) |
|||
|
|||
Procedure: Linear Least Squares Regression |
|||
|
|||
Reference: Wampler, R. H. (1970). |
|||
A Report of the Accuracy of Some Widely-Used Least |
|||
Squares Computer Programs. |
|||
Journal of the American Statistical Association, 65, pp. 549-565. |
|||
|
|||
Data: 1 Response Variable (y) |
|||
1 Predictor Variable (x) |
|||
21 Observations |
|||
Higher Level of Difficulty |
|||
Generated Data |
|||
|
|||
Model: Polynomial Class |
|||
6 Parameters (B0,B1,...,B5) |
|||
|
|||
y = B0 + B1*x + B2*(x**2) + B3*(x**3)+ B4*(x**4) + B5*(x**5) |
|||
|
|||
Certified Regression Statistics |
|||
|
|||
Standard Deviation |
|||
Parameter Estimate of Estimate |
|||
|
|||
B0 1.00000000000000 21523262.4678170 |
|||
B1 1.00000000000000 23635517.3469681 |
|||
B2 1.00000000000000 7793435.24331583 |
|||
B3 1.00000000000000 1014755.07550350 |
|||
B4 1.00000000000000 56456.6512170752 |
|||
B5 1.00000000000000 1123.24854679312 |
|||
|
|||
Residual |
|||
Standard Deviation 23601450.2379268 |
|||
|
|||
R-Squared 0.224668921574940E-02 |
|||
|
|||
|
|||
Certified Analysis of Variance Table |
|||
|
|||
Source of Degrees of Sums of Mean |
|||
Variation Freedom Squares Squares F Statistic |
|||
|
|||
Regression 5 18814317208116.7 3762863441623.33 6.7552445824012241E-03 |
|||
Residual 15 0.835542680000000E+16 557028453333333. |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Data: y x |
|||
7590001 0 |
|||
-20479994 1 |
|||
20480063 2 |
|||
-20479636 3 |
|||
25231365 4 |
|||
-20476094 5 |
|||
20489331 6 |
|||
-20460392 7 |
|||
18417449 8 |
|||
-20413570 9 |
|||
20591111 10 |
|||
-20302844 11 |
|||
18651453 12 |
|||
-20077766 13 |
|||
21059195 14 |
|||
-19666384 15 |
|||
26348481 16 |
|||
-18971402 17 |
|||
22480719 18 |
|||
-17866340 19 |
|||
10958421 20 |
|||
Loading…
Reference in new issue