tsai
/
mathnet-numerics
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

Added statistics test data.

pull/2/head
jvangael 17 years ago
committed by Christoph Ruegg
parent
fef109cdf6
commit
4deb9e90b7
57 changed files with 16008 additions and 0 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 84
      data/NIST/AtmWtAgt.dat
  2. 214
      data/NIST/Bennett5.dat
  3. 66
      data/NIST/BoxBOD.dat
  4. 274
      data/NIST/Chwirut1.dat
  5. 114
      data/NIST/Chwirut2.dat
  6. 66
      data/NIST/DanWood.dat
  7. 228
      data/NIST/ENSO.dat
  8. 95
      data/NIST/Eckerle4.dat
  9. 142
      data/NIST/Filip.dat
  10. 310
      data/NIST/Gauss1.dat
  11. 310
      data/NIST/Gauss2.dat
  12. 310
      data/NIST/Gauss3.dat
  13. 296
      data/NIST/Hahn1.dat
  14. 211
      data/NIST/Kirby2.dat
  15. 84
      data/NIST/Lanczos1.dat
  16. 84
      data/NIST/Lanczos2.dat
  17. 84
      data/NIST/Lanczos3.dat
  18. 239
      data/NIST/Lew.dat
  19. 76
      data/NIST/Longley.dat
  20. 258
      data/NIST/Lottery.dat
  21. 71
      data/NIST/MGH09.dat
  22. 76
      data/NIST/MGH10.dat
  23. 93
      data/NIST/MGH17.dat
  24. 91
      data/NIST/Mavro.dat
  25. 130
      data/NIST/Michelso.dat
  26. 74
      data/NIST/Misra1a.dat
  27. 74
      data/NIST/Misra1b.dat
  28. 74
      data/NIST/Misra1c.dat
  29. 74
      data/NIST/Misra1d.dat
  30. 188
      data/NIST/Nelson.dat
  31. 71
      data/NIST/NoInt1.dat
  32. 63
      data/NIST/NoInt2.dat
  33. 97
      data/NIST/Norris.dat
  34. 48
      data/NIST/NumAcc1.dat
  35. 1045
      data/NIST/NumAcc2.dat
  36. 1044
      data/NIST/NumAcc3.dat
  37. 1045
      data/NIST/NumAcc4.dat
  38. 100
      data/NIST/Pontius.dat
  39. 69
      data/NIST/Rat42.dat
  40. 75
      data/NIST/Rat43.dat
  41. 85
      data/NIST/Roszman1.dat
  42. 65
      data/NIST/SiRstvt.dat
  43. 81
      data/NIST/SmLs01t.dat
  44. 261
      data/NIST/SmLs02t.dat
  45. 2061
      data/NIST/SmLs03t.dat
  46. 81
      data/NIST/SmLs04t.dat
  47. 261
      data/NIST/SmLs05t.dat
  48. 2061
      data/NIST/SmLs06t.dat
  49. 81
      data/NIST/SmLs07t.dat
  50. 261
      data/NIST/SmLs08t.dat
  51. 2061
      data/NIST/SmLs09t.dat
  52. 97
      data/NIST/Thurber.dat
  53. 81
      data/NIST/Wampler1.dat
  54. 81
      data/NIST/Wampler2.dat
  55. 81
      data/NIST/Wampler3.dat
  56. 81
      data/NIST/Wampler4.dat
  57. 81
      data/NIST/Wampler5.dat

84
data/NIST/AtmWtAgt.dat
View File

@ -0,0 +1,84 @@
NIST/ITL StRD
Dataset Name: AtmWtAg (AtmWtAgt.dat)
File Format: ASCII
Certified Values (lines 41 to 47)
Data (lines 61 to 84)
Procedure: Analysis of Variance
Reference: Powell, L.J., Murphy, T.J. and Gramlich, J.W. (1982).
"The Absolute Isotopic Abundance & Atomic Weight
of a Reference Sample of Silver".
NBS Journal of Research, 87, pp. 9-19.
Data: 1 Factor
2 Treatments
24 Replicates/Cell
48 Observations
7 Constant Leading Digits
Average Level of Difficulty
Observed Data
Model: 3 Parameters (mu, tau_1, tau_2)
y_{ij} = mu + tau_i + epsilon_{ij}
Certified Values:
Source of Sums of Mean
Variation df Squares Squares F Statistic
Between Instrument 1 3.63834187500000E-09 3.63834187500000E-09 1.59467335677930E+01
Within Instrument 46 1.04951729166667E-08 2.28155932971014E-10
Certified R-Squared 2.57426544538321E-01
Certified Residual
Standard Deviation 1.51048314446410E-05
Data:
Instrument
1 2
107.8681568 107.8681079
107.8681465 107.8681344
107.8681572 107.8681513
107.8681785 107.8681197
107.8681446 107.8681604
107.8681903 107.8681385
107.8681526 107.8681642
107.8681494 107.8681365
107.8681616 107.8681151
107.8681587 107.8681082
107.8681519 107.8681517
107.8681486 107.8681448
107.8681419 107.8681198
107.8681569 107.8681482
107.8681508 107.8681334
107.8681672 107.8681609
107.8681385 107.8681101
107.8681518 107.8681512
107.8681662 107.8681469
107.8681424 107.8681360
107.8681360 107.8681254
107.8681333 107.8681261
107.8681610 107.8681450
107.8681477 107.8681368

214
data/NIST/Bennett5.dat
View File

@ -0,0 +1,214 @@
NIST/ITL StRD
Dataset Name: Bennett5 (Bennett5.dat)
File Format: ASCII
Starting Values (lines 41 to 43)
Certified Values (lines 41 to 48)
Data (lines 61 to 214)
Procedure: Nonlinear Least Squares Regression
Description: These data are the result of a NIST study involving
superconductivity magnetization modeling. The
response variable is magnetism, and the predictor
variable is the log of time in minutes.
Reference: Bennett, L., L. Swartzendruber, and H. Brown,
NIST (1994).
Superconductivity Magnetization Modeling.
Data: 1 Response Variable (y = magnetism)
1 Predictor Variable (x = log[time])
154 Observations
Higher Level of Difficulty
Observed Data
Model: Miscellaneous Class
3 Parameters (b1 to b3)
y = b1 * (b2+x)**(-1/b3) + e
Starting values Certified Values
Start 1 Start 2 Parameter Standard Deviation
b1 = -2000 -1500 -2.5235058043E+03 2.9715175411E+02
b2 = 50 45 4.6736564644E+01 1.2448871856E+00
b3 = 0.8 0.85 9.3218483193E-01 2.0272299378E-02
Residual Sum of Squares: 5.2404744073E-04
Residual Standard Deviation: 1.8629312528E-03
Degrees of Freedom: 151
Number of Observations: 154
Data: y x
-34.834702E0 7.447168E0
-34.393200E0 8.102586E0
-34.152901E0 8.452547E0
-33.979099E0 8.711278E0
-33.845901E0 8.916774E0
-33.732899E0 9.087155E0
-33.640301E0 9.232590E0
-33.559200E0 9.359535E0
-33.486801E0 9.472166E0
-33.423100E0 9.573384E0
-33.365101E0 9.665293E0
-33.313000E0 9.749461E0
-33.260899E0 9.827092E0
-33.217400E0 9.899128E0
-33.176899E0 9.966321E0
-33.139198E0 10.029280E0
-33.101601E0 10.088510E0
-33.066799E0 10.144430E0
-33.035000E0 10.197380E0
-33.003101E0 10.247670E0
-32.971298E0 10.295560E0
-32.942299E0 10.341250E0
-32.916302E0 10.384950E0
-32.890202E0 10.426820E0
-32.864101E0 10.467000E0
-32.841000E0 10.505640E0
-32.817799E0 10.542830E0
-32.797501E0 10.578690E0
-32.774300E0 10.613310E0
-32.757000E0 10.646780E0
-32.733799E0 10.679150E0
-32.716400E0 10.710520E0
-32.699100E0 10.740920E0
-32.678799E0 10.770440E0
-32.661400E0 10.799100E0
-32.644001E0 10.826970E0
-32.626701E0 10.854080E0
-32.612202E0 10.880470E0
-32.597698E0 10.906190E0
-32.583199E0 10.931260E0
-32.568699E0 10.955720E0
-32.554298E0 10.979590E0
-32.539799E0 11.002910E0
-32.525299E0 11.025700E0
-32.510799E0 11.047980E0
-32.499199E0 11.069770E0
-32.487598E0 11.091100E0
-32.473202E0 11.111980E0
-32.461601E0 11.132440E0
-32.435501E0 11.152480E0
-32.435501E0 11.172130E0
-32.426800E0 11.191410E0
-32.412300E0 11.210310E0
-32.400799E0 11.228870E0
-32.392101E0 11.247090E0
-32.380501E0 11.264980E0
-32.366001E0 11.282560E0
-32.357300E0 11.299840E0
-32.348598E0 11.316820E0
-32.339901E0 11.333520E0
-32.328400E0 11.349940E0
-32.319698E0 11.366100E0
-32.311001E0 11.382000E0
-32.299400E0 11.397660E0
-32.290699E0 11.413070E0
-32.282001E0 11.428240E0
-32.273300E0 11.443200E0
-32.264599E0 11.457930E0
-32.256001E0 11.472440E0
-32.247299E0 11.486750E0
-32.238602E0 11.500860E0
-32.229900E0 11.514770E0
-32.224098E0 11.528490E0
-32.215401E0 11.542020E0
-32.203800E0 11.555380E0
-32.198002E0 11.568550E0
-32.189400E0 11.581560E0
-32.183601E0 11.594420E0
-32.174900E0 11.607121E0
-32.169102E0 11.619640E0
-32.163300E0 11.632000E0
-32.154598E0 11.644210E0
-32.145901E0 11.656280E0
-32.140099E0 11.668200E0
-32.131401E0 11.679980E0
-32.125599E0 11.691620E0
-32.119801E0 11.703130E0
-32.111198E0 11.714510E0
-32.105400E0 11.725760E0
-32.096699E0 11.736880E0
-32.090900E0 11.747890E0
-32.088001E0 11.758780E0
-32.079300E0 11.769550E0
-32.073502E0 11.780200E0
-32.067699E0 11.790730E0
-32.061901E0 11.801160E0
-32.056099E0 11.811480E0
-32.050301E0 11.821700E0
-32.044498E0 11.831810E0
-32.038799E0 11.841820E0
-32.033001E0 11.851730E0
-32.027199E0 11.861550E0
-32.024300E0 11.871270E0
-32.018501E0 11.880890E0
-32.012699E0 11.890420E0
-32.004002E0 11.899870E0
-32.001099E0 11.909220E0
-31.995300E0 11.918490E0
-31.989500E0 11.927680E0
-31.983700E0 11.936780E0
-31.977900E0 11.945790E0
-31.972099E0 11.954730E0
-31.969299E0 11.963590E0
-31.963501E0 11.972370E0
-31.957701E0 11.981070E0
-31.951900E0 11.989700E0
-31.946100E0 11.998260E0
-31.940300E0 12.006740E0
-31.937401E0 12.015150E0
-31.931601E0 12.023490E0
-31.925800E0 12.031760E0
-31.922899E0 12.039970E0
-31.917101E0 12.048100E0
-31.911301E0 12.056170E0
-31.908400E0 12.064180E0
-31.902599E0 12.072120E0
-31.896900E0 12.080010E0
-31.893999E0 12.087820E0
-31.888201E0 12.095580E0
-31.885300E0 12.103280E0
-31.882401E0 12.110920E0
-31.876600E0 12.118500E0
-31.873699E0 12.126030E0
-31.867901E0 12.133500E0
-31.862101E0 12.140910E0
-31.859200E0 12.148270E0
-31.856300E0 12.155570E0
-31.850500E0 12.162830E0
-31.844700E0 12.170030E0
-31.841801E0 12.177170E0
-31.838900E0 12.184270E0
-31.833099E0 12.191320E0
-31.830200E0 12.198320E0
-31.827299E0 12.205270E0
-31.821600E0 12.212170E0
-31.818701E0 12.219030E0
-31.812901E0 12.225840E0
-31.809999E0 12.232600E0
-31.807100E0 12.239320E0
-31.801300E0 12.245990E0
-31.798401E0 12.252620E0
-31.795500E0 12.259200E0
-31.789700E0 12.265750E0
-31.786800E0 12.272240E0

66
data/NIST/BoxBOD.dat
View File

@ -0,0 +1,66 @@
NIST/ITL StRD
Dataset Name: BoxBOD (BoxBOD.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 are described in detail in Box, Hunter and
Hunter (1978). The response variable is biochemical
oxygen demand (BOD) in mg/l, and the predictor
variable is incubation time in days.
Reference: Box, G. P., W. G. Hunter, and J. S. Hunter (1978).
Statistics for Experimenters.
New York, NY: Wiley, pp. 483-487.
Data: 1 Response (y = biochemical oxygen demand)
1 Predictor (x = incubation time)
6 Observations
Higher 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 = 1 100 2.1380940889E+02 1.2354515176E+01
b2 = 1 0.75 5.4723748542E-01 1.0455993237E-01
Residual Sum of Squares: 1.1680088766E+03
Residual Standard Deviation: 1.7088072423E+01
Degrees of Freedom: 4
Number of Observations: 6
Data: y x
109 1
149 2
149 3
191 5
213 7
224 10

274
data/NIST/Chwirut1.dat
View File

@ -0,0 +1,274 @@
NIST/ITL StRD
Dataset Name: Chwirut1 (Chwirut1.dat)
File Format: ASCII
Starting Values (lines 41 to 43)
Certified Values (lines 41 to 48)
Data (lines 61 to 274)
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 Variable (y = ultrasonic response)
1 Predictor Variable (x = metal distance)
214 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 1.9027818370E-01 2.1938557035E-02
b2 = 0.01 0.008 6.1314004477E-03 3.4500025051E-04
b3 = 0.02 0.010 1.0530908399E-02 7.9281847748E-04
Residual Sum of Squares: 2.3844771393E+03
Residual Standard Deviation: 3.3616721320E+00
Degrees of Freedom: 211
Number of Observations: 214
Data: y x
92.9000E0 0.5000E0
78.7000E0 0.6250E0
64.2000E0 0.7500E0
64.9000E0 0.8750E0
57.1000E0 1.0000E0
43.3000E0 1.2500E0
31.1000E0 1.7500E0
23.6000E0 2.2500E0
31.0500E0 1.7500E0
23.7750E0 2.2500E0
17.7375E0 2.7500E0
13.8000E0 3.2500E0
11.5875E0 3.7500E0
9.4125E0 4.2500E0
7.7250E0 4.7500E0
7.3500E0 5.2500E0
8.0250E0 5.7500E0
90.6000E0 0.5000E0
76.9000E0 0.6250E0
71.6000E0 0.7500E0
63.6000E0 0.8750E0
54.0000E0 1.0000E0
39.2000E0 1.2500E0
29.3000E0 1.7500E0
21.4000E0 2.2500E0
29.1750E0 1.7500E0
22.1250E0 2.2500E0
17.5125E0 2.7500E0
14.2500E0 3.2500E0
9.4500E0 3.7500E0
9.1500E0 4.2500E0
7.9125E0 4.7500E0
8.4750E0 5.2500E0
6.1125E0 5.7500E0
80.0000E0 0.5000E0
79.0000E0 0.6250E0
63.8000E0 0.7500E0
57.2000E0 0.8750E0
53.2000E0 1.0000E0
42.5000E0 1.2500E0
26.8000E0 1.7500E0
20.4000E0 2.2500E0
26.8500E0 1.7500E0
21.0000E0 2.2500E0
16.4625E0 2.7500E0
12.5250E0 3.2500E0
10.5375E0 3.7500E0
8.5875E0 4.2500E0
7.1250E0 4.7500E0
6.1125E0 5.2500E0
5.9625E0 5.7500E0
74.1000E0 0.5000E0
67.3000E0 0.6250E0
60.8000E0 0.7500E0
55.5000E0 0.8750E0
50.3000E0 1.0000E0
41.0000E0 1.2500E0
29.4000E0 1.7500E0
20.4000E0 2.2500E0
29.3625E0 1.7500E0
21.1500E0 2.2500E0
16.7625E0 2.7500E0
13.2000E0 3.2500E0
10.8750E0 3.7500E0
8.1750E0 4.2500E0
7.3500E0 4.7500E0
5.9625E0 5.2500E0
5.6250E0 5.7500E0
81.5000E0 .5000E0
62.4000E0 .7500E0
32.5000E0 1.5000E0
12.4100E0 3.0000E0
13.1200E0 3.0000E0
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

114
data/NIST/Chwirut2.dat
View File

@ -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

66
data/NIST/DanWood.dat
View File

@ -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

228
data/NIST/ENSO.dat
View File

@ -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

95
data/NIST/Eckerle4.dat
View File

@ -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

142
data/NIST/Filip.dat
View File

@ -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

310
data/NIST/Gauss1.dat
View File

@ -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

310
data/NIST/Gauss2.dat
View File

@ -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

310
data/NIST/Gauss3.dat
View File

@ -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

296
data/NIST/Hahn1.dat
View File

@ -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

211
data/NIST/Kirby2.dat
View File

@ -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

84
data/NIST/Lanczos1.dat
View File

@ -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

84
data/NIST/Lanczos2.dat
View File

@ -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

84
data/NIST/Lanczos3.dat
View File

@ -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

239
data/NIST/Lew.dat
View File

@ -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

76
data/NIST/Longley.dat
View File

@ -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

258
data/NIST/Lottery.dat
View File

@ -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

71
data/NIST/MGH09.dat
View File

@ -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

76
data/NIST/MGH10.dat
View File

@ -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

93
data/NIST/MGH17.dat
View File

@ -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

91
data/NIST/Mavro.dat
View File

@ -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

130
data/NIST/Michelso.dat
View File

@ -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

74
data/NIST/Misra1a.dat
View File

@ -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

74
data/NIST/Misra1b.dat
View File

@ -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

74
data/NIST/Misra1c.dat
View File

@ -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

74
data/NIST/Misra1d.dat
View File

@ -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

188
data/NIST/Nelson.dat
View File

@ -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

71
data/NIST/NoInt1.dat
View File

@ -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

63
data/NIST/NoInt2.dat
View File

@ -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

97
data/NIST/Norris.dat
View File

@ -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

48
data/NIST/NumAcc1.dat
View File

@ -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

1045
data/NIST/NumAcc2.dat
View File

File diff suppressed because it is too large

1044
data/NIST/NumAcc3.dat
View File

File diff suppressed because it is too large

1045
data/NIST/NumAcc4.dat
View File

File diff suppressed because it is too large

100
data/NIST/Pontius.dat
View File

@ -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

69
data/NIST/Rat42.dat
View File

@ -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

75
data/NIST/Rat43.dat
View File

@ -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

85
data/NIST/Roszman1.dat
View File

@ -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

65
data/NIST/SiRstvt.dat
View File

@ -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

81
data/NIST/SmLs01t.dat
View File

@ -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

261
data/NIST/SmLs02t.dat
View File

@ -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

2061
data/NIST/SmLs03t.dat
View File

File diff suppressed because it is too large

81
data/NIST/SmLs04t.dat
View File

@ -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

261
data/NIST/SmLs05t.dat
View File

@ -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

2061
data/NIST/SmLs06t.dat
View File

File diff suppressed because it is too large

81
data/NIST/SmLs07t.dat
View File

@ -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

261
data/NIST/SmLs08t.dat
View File

@ -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
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
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
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

2061
data/NIST/SmLs09t.dat
View File

File diff suppressed because it is too large

97
data/NIST/Thurber.dat
View File

@ -0,0 +1,97 @@
NIST/ITL StRD
Dataset Name: Thurber (Thurber.dat)
File Format: ASCII
Starting Values (lines 41 to 47)
Certified Values (lines 41 to 52)
Data (lines 61 to 97)
Procedure: Nonlinear Least Squares Regression
Description: These data are the result of a NIST study involving
semiconductor electron mobility. The response
variable is a measure of electron mobility, and the
predictor variable is the natural log of the density.
Reference: Thurber, R., NIST (197?).
Semiconductor electron mobility modeling.
Data: 1 Response Variable (y = electron mobility)
1 Predictor Variable (x = log[density])
37 Observations
Higher 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 = 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

81
data/NIST/Wampler1.dat
View File

@ -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

81
data/NIST/Wampler2.dat
View File

@ -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

81
data/NIST/Wampler3.dat
View File

@ -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

81
data/NIST/Wampler4.dat
View File

@ -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

81
data/NIST/Wampler5.dat
View File

@ -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
Write Preview
Loading…
Cancel
Save