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Quality and Reliability Methods - SAS

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328 <strong>Reliability</strong> Growth Chapter 18<br />

<strong>Reliability</strong> Growth Platform Options<br />

Mean Time Between Failures Plot Options<br />

Clicking on the Mean Time Between Failures red triangle <strong>and</strong> then clicking on Options opens a window<br />

that enables you to specify intervals over which to average.<br />

Two types of averaging are offered:<br />

• Equal Interval Average MTBF (Mean Time Between Failures) enables you to specify a common interval<br />

size.<br />

• Customized Average MTBF enables you to specify cut-off points for time intervals.<br />

– Double-click within a table cell to change its value.<br />

– Right-click in the table to open a menu that enables you to add <strong>and</strong> remove rows.<br />

<strong>Reliability</strong> Growth Platform Options<br />

Fit Model<br />

Model List<br />

The <strong>Reliability</strong> Growth red triangle menu has two options: Fit Model <strong>and</strong> Script.<br />

This option fits various non-homogeneous Poisson Process (NHPP) models, described in detail below.<br />

Depending on the choices made in the launch window, the possible options are:<br />

• Crow AMSAA<br />

• Fixed Parameter Crow AMSAA<br />

• Piecewise Weibull NHPP<br />

• Reinitialized Weibull NHPP<br />

• Piecewise Weibull NHPP Change Point Detection<br />

Once a model is fit, a Model List report appears. This report provides various statistical measures that<br />

describe the fit of the model. As additional models are fit, they are added to the Model List, which provides<br />

a convenient summary for model comparison. The models are sorted in ascending order based on AICc.<br />

The statistics provided in the Model List report consist of:<br />

Nparm<br />

The number of parameters in the model.<br />

-2Loglikelihood The likelihood function is a measure of how probable the observed data are, given the<br />

estimated model parameters. In a general sense, the higher the likelihood, the better the model fit. It<br />

follows that smaller values of -2Loglikelihood indicate better model fits.

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