77 lines
2.4 KiB
Plaintext
77 lines
2.4 KiB
Plaintext
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NIST/ITL StRD
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Dataset Name: MGH10 (MGH10.dat)
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File Format: ASCII
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Starting Values (lines 41 to 43)
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Certified Values (lines 41 to 48)
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Data (lines 61 to 76)
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Procedure: Nonlinear Least Squares Regression
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Description: This problem was found to be difficult for some very
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good algorithms.
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See More, J. J., Garbow, B. S., and Hillstrom, K. E.
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(1981). Testing unconstrained optimization software.
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ACM Transactions on Mathematical Software. 7(1):
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pp. 17-41.
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Reference: Meyer, R. R. (1970).
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Theoretical and computational aspects of nonlinear
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regression. In Nonlinear Programming, Rosen,
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Mangasarian and Ritter (Eds).
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New York, NY: Academic Press, pp. 465-486.
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Data: 1 Response (y)
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1 Predictor (x)
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16 Observations
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Higher Level of Difficulty
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Generated Data
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Model: Exponential Class
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3 Parameters (b1 to b3)
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y = b1 * exp[b2/(x+b3)] + e
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Starting values Certified Values
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Start 1 Start 2 Parameter Standard Deviation
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b1 = 2 0.02 5.6096364710E-03 1.5687892471E-04
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b2 = 400000 4000 6.1813463463E+03 2.3309021107E+01
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b3 = 25000 250 3.4522363462E+02 7.8486103508E-01
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Residual Sum of Squares: 8.7945855171E+01
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Residual Standard Deviation: 2.6009740065E+00
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Degrees of Freedom: 13
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Number of Observations: 16
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Data: y x
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3.478000E+04 5.000000E+01
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2.861000E+04 5.500000E+01
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2.365000E+04 6.000000E+01
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1.963000E+04 6.500000E+01
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1.637000E+04 7.000000E+01
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1.372000E+04 7.500000E+01
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1.154000E+04 8.000000E+01
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9.744000E+03 8.500000E+01
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8.261000E+03 9.000000E+01
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7.030000E+03 9.500000E+01
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6.005000E+03 1.000000E+02
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5.147000E+03 1.050000E+02
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4.427000E+03 1.100000E+02
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3.820000E+03 1.150000E+02
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3.307000E+03 1.200000E+02
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2.872000E+03 1.250000E+02
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