Individual Distribution Identification

Data Fitted with 14 Distributions and 2 Transformations - Maximum Likelihood Estimates

  

Maximum likelihood (ML) estimates are calculated by maximizing the likelihood function for each distribution being considered. For each set of distribution parameters, the likelihood function describes the chance that the true distribution has these parameters based on the sample.

Example Output

ML Estimates of Distribution Parameters

 

Distribution             Location      Shape     Scale  Threshold

Normal*                  50.78200              2.76477

Box-Cox Transformation*   0.00000              0.00000

Lognormal*                3.92612              0.05368

3-Parameter Lognormal     1.69295              0.46849   44.74011

Exponential                                   50.78200

2-Parameter Exponential                        4.06326   46.71873

Weibull                             17.82470  52.13681

3-Parameter Weibull                  1.47605   4.53647   46.66579

Smallest Extreme Value   52.22257              2.95894

Largest Extreme Value    49.50370              2.16992

Gamma                              351.04421   0.14466

3-Parameter Gamma                    2.99218   1.63698   45.88376

Logistic                 50.57182              1.59483

Loglogistic               3.92259              0.03121

3-Parameter Loglogistic   1.54860              0.32763   45.46180

Johnson Transformation*   0.02897              0.97293

 

* Scale: Adjusted ML estimate

Interpretation

For the calcium data, the shape, scale, and threshold parameter estimates for the 3-parameter Weibull distribution are 1.47605, 4.53647, and 46.66579.