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0001 NIST/ITL StRD
0002 Dataset Name:  Rat42             (Rat42.dat)
0003 
0004 File Format:   ASCII
0005                Starting Values   (lines 41 to 43)
0006                Certified Values  (lines 41 to 48)
0007                Data              (lines 61 to 69)
0008 
0009 Procedure:     Nonlinear Least Squares Regression
0010 
0011 Description:   This model and data are an example of fitting
0012                sigmoidal growth curves taken from Ratkowsky (1983).
0013                The response variable is pasture yield, and the
0014                predictor variable is growing time.
0015 
0016 
0017 Reference:     Ratkowsky, D.A. (1983).  
0018                Nonlinear Regression Modeling.
0019                New York, NY:  Marcel Dekker, pp. 61 and 88.
0020 
0021 
0022 
0023 
0024 
0025 Data:          1 Response  (y = pasture yield)
0026                1 Predictor (x = growing time)
0027                9 Observations
0028                Higher Level of Difficulty
0029                Observed Data
0030 
0031 Model:         Exponential Class
0032                3 Parameters (b1 to b3)
0033 
0034                y = b1 / (1+exp[b2-b3*x])  +  e
0035 
0036 
0037 
0038           Starting Values                  Certified Values
0039 
0040         Start 1     Start 2           Parameter     Standard Deviation
0041   b1 =   100         75            7.2462237576E+01  1.7340283401E+00
0042   b2 =     1          2.5          2.6180768402E+00  8.8295217536E-02
0043   b3 =     0.1        0.07         6.7359200066E-02  3.4465663377E-03
0044 
0045 Residual Sum of Squares:                    8.0565229338E+00
0046 Residual Standard Deviation:                1.1587725499E+00
0047 Degrees of Freedom:                                6
0048 Number of Observations:                            9 
0049 
0050 
0051 
0052 
0053 
0054 
0055 
0056 
0057 
0058 
0059 
0060 Data:   y              x
0061        8.930E0        9.000E0
0062       10.800E0       14.000E0
0063       18.590E0       21.000E0
0064       22.330E0       28.000E0
0065       39.350E0       42.000E0
0066       56.110E0       57.000E0
0067       61.730E0       63.000E0
0068       64.620E0       70.000E0
0069       67.080E0       79.000E0