Reliability Growth Platform Options

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Reliability and Survival Methods • Reliability Growth • Reliability Growth Platform Options

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Reliability Growth Platform Options

The Reliability Growth red triangle menu has the following options:

If the Time to Event Format, Dates Format, or Concurrent Systems data formats are specified in the launch window, this menu contains options to fit various Non-Homogeneous Poisson Process (NHPP) models, described in detail below. Depending on the choices made in the launch window, the possible options are:

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Crow AMSAA with Modified MLE

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Fixed Parameter Crow AMSAA

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Piecewise Weibull NHPP

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Reinitialized Weibull NHPP

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Piecewise Weibull NHPP Change Point Detection

Fit Parallel System Model

If the Parallel Systems data format is specified in the launch window, this menu contains options to fit various models for multiple-prototype data. The possible options in this menu are dependent on choices made in the launch window.

See Redo Menus and Save Script Menus in the Using JMP book for more information about the following options:

Contains options that enable you to repeat or relaunch the analysis. In platforms that support the feature, the Automatic Recalc option immediately reflects the changes that you make to the data table in the corresponding report window.

Contains options that enable you to save a script that reproduces the report to several destinations.

Save By-Group Script

Contains options that enable you to save a script that reproduces the platform report for all levels of a By variable to several destinations. Available only when a By variable is specified in the launch window.

Once a model is fit, the Model List report appears. This report provides various statistical measures that describe the fit of the model. As additional models are fit, they are added to the Model List, which provides a convenient summary for model comparison. The models are sorted in ascending order based on AICc. The Model List report contains the following statistics:

The number of parameters in the model.

-2Loglikelihood

The likelihood function is a measure of how probable the observed data are, given the estimated model parameters. In a general sense, the higher the likelihood, the better the model fit. It follows that smaller values of twice the negative of the log-likelihood (-2Loglikelihood) indicate better model fits.

The Corrected Akaike’s Information Criterion.

The Bayesian Information Criterion.

See Likelihood, AICc, and BIC in the Fitting Linear Models book for details about -2Loglikelihood, AICc, and BIC.

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Help created on 7/12/2018