2 Dataset Name: Gauss2 (Gauss2.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 slightly-blended 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 = 96.0 98.0 9.9018328406E+01 5.3748766879E-01
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42 b2 = 0.009 0.0105 1.0994945399E-02 1.3335306766E-04
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43 b3 = 103.0 103.0 1.0188022528E+02 5.9217315772E-01
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44 b4 = 106.0 105.0 1.0703095519E+02 1.5006798316E-01
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45 b5 = 18.0 20.0 2.3578584029E+01 2.2695595067E-01
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46 b6 = 72.0 73.0 7.2045589471E+01 6.1721965884E-01
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47 b7 = 151.0 150.0 1.5327010194E+02 1.9466674341E-01
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48 b8 = 18.0 20.0 1.9525972636E+01 2.6416549393E-01
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50 Residual Sum of Squares: 1.2475282092E+03
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51 Residual Standard Deviation: 2.2704790782E+00
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52 Degrees of Freedom: 242
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53 Number of Observations: 250
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