JMP 11 Online Documentation (English)
Discovering JMP
Using JMP
Basic Analysis
Essential Graphing
Profilers
Design of Experiments Guide
Fitting Linear Models
Specialized Models
Multivariate Methods
Quality and Process Methods
Reliability and Survival Methods
Consumer Research
Scripting Guide
JSL Syntax Reference
JMP iPad Help
Capabilities Index
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 two options: Fit Model and Script.
Fit Model
This option fits various Non-Homogeneous Poisson Process (NHPP) models, described in detail below. Depending on the choices made in the launch window, the possible options are:
•
Crow AMSAA
•
Crow AMSAA with Modified MLE
•
Fixed Parameter Crow AMSAA
•
Piecewise Weibull NHPP
•
Reinitialized Weibull NHPP
•
Piecewise Weibull NHPP Change Point Detection
Model List
Once a model is fit, a 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 statistics provided in the Model List report consist of:
Nparm
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 -2Loglikelihood indicate better model fits.
AICc
The Corrected Akaike’s Information Criterion, given by
AICc = -2loglikelihood + 2
k
+ 2
k
(
k
+ 1)/(
n
- k
-1),
where k is the number of parameters in the model and n is the sample size. Smaller values of AICc indicate better model fits. The AICc penalizes the number of parameters, but less strongly than does the BIC.
BIC
The Bayesian Information Criterion, defined by
BIC = -2loglikelihood +
k
ln(
n
),
where k is the number of parameters in the model and n is the sample size. Smaller values of BIC indicate better model fits. The BIC penalizes the number of parameters more strongly than does the AICc.
Script
This provides analysis and scripting options that are available to all platforms. See the
Using JMP
book for more information about the options.