About Goodness of Fit (GOF) Tests for a Weibull Distribution
A Goodness of Fit test is a statistical test that determines whether the analysis data follows the distribution model.
If the data passes the Goodness of Fit test, it means that it follows the model pattern closely enough that predictions can be made based on that model.
If the data fails the Goodness of Fit test, it means that the data does not follow the model closely enough to confidently make predictions and that the data does not appear to follow a specific pattern.
Weibull results are valid if Goodness of Fit (GOF) tests are satisfied. Goodness of Fit tests for a Weibull distribution include the following types:
R-Squared Linear regression (least squares): An R-Squared test statistic greater than 0.9 is considered a good fit for linear regression.
Kolmogorov-Smirnov: The Meridium Enterprise APM system uses confidence level and P-Value to determine if the data is considered a good fit. If the P-Value is greater than 1 minus the confidence level, the test passes.
Note:The R-Squared test statistic is calculated only for reference. The Meridium Enterprise APM system uses the Kolmogorov-Smirnov test as the Goodness of Fit test.
