2 Dataset Name: Gauss1 (Gauss1.dat)
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5 Starting Values (lines 41 to 48)
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6 Certified Values (lines 41 to 53)
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7 Data (lines 61 to 310)
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9 Procedure: Nonlinear Least Squares Regression
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11 Description: The data are two well-separated Gaussians on a
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12 decaying exponential baseline plus normally
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13 distributed zero-mean noise with variance = 6.25.
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15 Reference: Rust, B., NIST (1996).
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25 Data: 1 Response (y)
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28 Lower Level of Difficulty
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31 Model: Exponential Class
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32 8 Parameters (b1 to b8)
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34 y = b1*exp( -b2*x ) + b3*exp( -(x-b4)**2 / b5**2 )
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35 + b6*exp( -(x-b7)**2 / b8**2 ) + e
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38 Starting values Certified Values
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40 Start 1 Start 2 Parameter Standard Deviation
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41 b1 = 97.0 94.0 9.8778210871E+01 5.7527312730E-01
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42 b2 = 0.009 0.0105 1.0497276517E-02 1.1406289017E-04
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43 b3 = 100.0 99.0 1.0048990633E+02 5.8831775752E-01
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44 b4 = 65.0 63.0 6.7481111276E+01 1.0460593412E-01
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45 b5 = 20.0 25.0 2.3129773360E+01 1.7439951146E-01
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46 b6 = 70.0 71.0 7.1994503004E+01 6.2622793913E-01
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47 b7 = 178.0 180.0 1.7899805021E+02 1.2436988217E-01
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48 b8 = 16.5 20.0 1.8389389025E+01 2.0134312832E-01
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50 Residual Sum of Squares: 1.3158222432E+03
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51 Residual Standard Deviation: 2.3317980180E+00
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52 Degrees of Freedom: 242
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53 Number of Observations: 250
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