               NEWS NLREG has been selected as the "Editor"s Pick" by SoftSeek. NLREG is in use at hundreds of universities, laboratories, and government agencies around the world (over 20 countries). For a list of a few organizations using NLREG click here. If you have categorical variables, you may want to use a Decision Tree to model your data. Check out the DTREG Decision Tree Builder. You also should check out the News Rover program that automatically scans Usenet newsgroups, downloads messages of interest to you, decodes binary file attachments, reconstructs files split across multiple messages, and eliminates spam and duplicate files.  ## NIST - Ratkowsky2 Dataset

```   1: /*
2:  * Statistical Reference Datasets  (Nonlinear Regression)
3:  * Statistical Engineering Division
4:  * National Institute of Standards and Technology
5:  * http://www.nist.gov/itl/div898/strd/
6:  *
7:  * Dataset Name:  Ratkowsky2        (Ratkowsky2.dat)
8:  *
9:  * Description:   This model and data are an example of fitting
10:  *                sigmoidal growth curves taken from Ratkowsky (1983).
11:  *                The response variable is pasture yield, and the
12:  *                predictor variable is growing time.
13:  *
14:  * Reference:     Ratkowsky, D.A. (1983).
15:  *                Nonlinear Regression Modeling.
16:  *                New York, NY:  Marcel Dekker, pp. 61 and 88.
17:  *
18:  * Data:          1 Response  (y = pasture yield)
19:  *                1 Predictor (x = growing time)
20:  *                9 Observations
21:  *                Higher Level of Difficulty
22:  *                Observed Data
23:  *
24:  * Model:         Exponential Class
25:  *                3 Parameters (b1 to b3)
26:  *
27:  *                y = b1 / (1+exp[b2-b3*x])  +  e
28:  *
29:  *           Starting Values                  Certified Values
30:  *
31:  *         Start 1     Start 2           Parameter     Standard Deviation
32:  *   b1 =   100         75            7.2462237576E+01  1.7340283401E+00
33:  *   b2 =     1          2.5          2.6180768402E+00  8.8295217536E-02
34:  *   b3 =     0.1        0.07         6.7359200066E-02  3.4465663377E-03
35:  *
36:  * Residual Sum of Squares:                    8.0565229338E+00
37:  * Residual Standard Deviation:                1.1587725499E+00
38:  * Degrees of Freedom:                                6
39:  * Number of Observations:                            9
40:  */
41: Title "Ratkowsky2";
42: Variables y,x;
43: Parameter b1 = 100;
44: Parameter b2 = 1;
45: Parameter b3 = 0.1;
46: Function y = b1 / (1+exp(b2-b3*x));
47: Plot;
48: Data;

Beginning computation...
Stopped due to: Both parameter and relative function convergence.

----  Final Results  ----

NLREG version 4.0
This is a registered copy of NLREG that may not be redistributed.

Ratkowsky2
Number of observations = 9
Maximum allowed number of iterations = 500
Convergence tolerance factor = 1.000000E-010
Stopped due to: Both parameter and relative function convergence.
Number of iterations performed = 8
Final sum of squared deviations = 8.0565229E+000
Final sum of deviations = 3.0233598E-001
Standard error of estimate = 1.15877
Average deviation = 0.779374
Maximum deviation for any observation = 1.8623
Proportion of variance explained (R^2) = 0.9983  (99.83%)
Adjusted coefficient of multiple determination (Ra^2) = 0.9977  (99.77%)
Durbin-Watson test for autocorrelation = 2.572

----  Descriptive Statistics for Variables  ----

Variable    Minimum value   Maximum value    Mean value     Standard dev.
----------  --------------  --------------  --------------  --------------
y            8.93           67.08        38.83778        24.10411
x               9              79        42.55556        25.80267

----  Calculated Parameter Values  ----

Parameter  Initial guess   Final estimate   Standard error      t      Prob(t)
----------  -------------  ----------------  --------------  ---------  -------
b1            100        72.4622376        1.734028      41.79  0.00001
b2              1        2.61807684      0.08829522      29.65  0.00001
b3            0.1      0.0673591999     0.003446566      19.54  0.00001

----  Analysis of Variance  ----

Source     DF   Sum of Squares    Mean Square    F value   Prob(F)
----------  ----  --------------  --------------  ---------  -------
Regression     2        4640.007        2320.003    1727.80  0.00001
Error          6        8.056523        1.342754
Total          8        4648.063
```